TeReKG: A temporal collaborative knowledge graph framework for software team recommendation

Abstract: Successful software development requires a cohesive team with the right mix of technical skills and the ability to collaborate effectively. However, forming a software team that can execute tasks with precision and efficiency requires a deep understanding of each member’s competence, experience, and cooperation history. Previously, automated software team selection has evaluated technical skills, cohesion, and cooperation history. However, the previous method had some limitations. Particularly, local features directly calculated from team members were subjective to the researchers’ views, and the method ignored the temporal aspect of open-source software development. To overcome these limitations, this paper proposes a knowledge-graph software team recommendation framework called TeReKG. This framework encapsulates temporal collaboration patterns and uses a temporal knowledge graph to encode software collaboration history, technical abilities, task dependencies, and project structure. TeReKG was against state-of-the-art team recommendation algorithms using three popular open-source software projects: Moodle, Apache, and Atlassian. The evaluation results show that TeReKG outperforms the state-of-the-art baselines in both single-role and team recommendation tasks. These findings demonstrate that knowledge graph embedding can be effectively utilized in automated recommendation tasks in software engineering. Additionally, this highlights the potential for knowledge graphs to capture global information that can benefit various software development applications, including impact prediction of software repositories, code clone detection, and source code retrieval.


Informed or outdated consent? An investigation into the media release policies of school districts in the United States

Abstract: School media release policies determine whether, how, and where children's personally identifiable information (PII) is shared, but these key policies and the forms that parents are asked to sign at the start of each year have not yet been considered in light of concerns about the privacy of students and educational institutions' use of social media. In this study, we examined the available media release forms for 120 of the largest school districts in the United States to understand how they communicate and inform their communities of their media release policies. Through the use of qualitative content analysis, we systematically coded 90 available media release forms to evaluate which types, where a child's personally identifiable information might be shared, and whether potential risks of sharing were depicted in the consent process. 95.5% of the available media release forms we analyzed mentioned which PII schools and school districts can share if parents consent and 87.7% mention the various forms of media through which PII can be shared. However, only 13.3% mentioned specific social media platforms, though prior research has suggested that the use of social media by educational institutions is very widespread. Further, just 4.4% of the forms in our sample mentioned any specific risks that may accompany the sharing of PII, despite the articulation of clearly identified risks being an ethical recommendation for consent processes (e.g., the Belmont Report). In conclusion, there exists a significant gap between media release policies in educational institutions and the comprehensive, informed consent that parents should receive, highlighting a need for improved transparency and communication about the risks and avenues of PII sharing.


Homophily modulates double descent generalization in graph convolution networks

Abstract: Graph neural networks (GNNs) excel in modeling relational data such as biological, social, and transportation networks, but the underpinnings of their success are not well understood. Traditional complexity measures from statistical learning theory fail to account for observed phenomena like the double descent or the impact of relational semantics on generalization error. Motivated by experimental observations of "transductive" double descent in key networks and datasets, we use analytical tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model. Our results illuminate the nuances of learning on homophilic versus heterophilic data and predict double descent whose existence in GNNs has been questioned by recent work. We show how risk is shaped by the interplay between the graph noise, feature noise, and the number of training labels. Our findings apply beyond stylized models, capturing qualitative trends in real-world GNNs and datasets. As a case in point, we use our analytic insights to improve performance of state-of-the-art graph convolution networks on heterophilic datasets.


White blood cells classification using multi-fold pre-processing and optimized CNN model

Abstract: White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.


Good-bootstrap: simultaneous confidence intervals for large alphabet distributions

Abstract: Simultaneous confidence intervals (SCI) for multinomial proportions are a corner stone in count data analysis and a key component in many applications. A variety of schemes were introduced over the years, mostly focussing on an asymptotic regime where the sample is large and the alphabet size is relatively small. In this work we introduce a new SCI framework which considers the large alphabet setup. Our proposed framework utilises bootstrap sampling with the Good-Turing probability estimator as a plug-in distribution. We demonstrate the favourable performance of our proposed method in synthetic and real-world experiments. Importantly, we provide an exact analytical expression for the bootstrapped statistic, which replaces the computationally costly sampling procedure. Our proposed framework is publicly available at the first author's Github page.


E-learning in Times of Radical Uncertainty

Abstract: As teachers and teacher educators in the United States, we offer this editorial based on our lived experiences with the political, social, and environmental precarities where we live. Among these precarities are our looming U.S. presidential election after the last one included a plot by bad actors to abscond with our State’s electoral votes, our constant struggle for water in a desert state, and our weak overall internet connectivity. We call our wonderings about how we are living in and somehow surviving these times, <i>radical uncertainties</i>. In this editorial, we canvas radical uncertainties related to e-learning about digital literacies, generative text, datafication, and the camera gaze. Alongside these uncertainties, we offer alternative avenues for collective thinking based on Chicana Feminist epistemologies. We intend for readers to consider how local knowing can and should inform e-learning planning and practice.


Multi-type concept drift detection under a dual-layer variable sliding window in frequent pattern mining with cloud computing

Abstract: Abstract The detection of different types of concept drift has wide applications in the fields of cloud computing and security information detection. Concept drift detection can indeed assist in promptly identifying instances where model performance deteriorates or when there are changes in data distribution. This paper focuses on the problem of concept drift detection in order to conduct frequent pattern mining. To address the limitation of fixed sliding windows in adapting to evolving data streams, we propose a variable sliding window frequent pattern mining algorithm, which dynamically adjusts the window size to adapt to new concept drifts and detect them in a timely manner. Furthermore, considering the challenge of existing concept drift detection algorithms that struggle to adapt to different types of drifting data simultaneously, we introduce an additional dual-layer embedded variable sliding window. This approach helps differentiate types of concept drift and incorporates a decay model for drift adaptation. The proposed algorithm can effectively detect different types of concept drift in data streams, perform targeted drift adaptation, and exhibit efficiency in terms of time complexity and memory consumption. Additionally, the algorithm maintains stable performance, avoiding abrupt changes due to window size variations and ensuring overall robustness.


U.S. and Japanese Consumer Attitudes Toward Tailored and Targeted Communication with Human and Chatbot Agents

Abstract: Consumers in the United States (n = 290) and Japan (n = 221) participated in 2 × 2 experiments designed to test effects of agent type (online human or chatbot) and communication strategy (targeted or tailored) on attitudes toward messages and agents. In both national samples, tailored messages were more effective in positively affecting attitudes. In addition, perceived humanness was found to mediate the relationships between agent type and attitudes such that when human or artificial agents are perceived as more human in text exchanges, perceived humanness in turn increases attitudes toward both the agents and messages. Hypothesized effects based on individualism-collectivism were not found. Theoretical and practical implications for designing interactive message strategy via conversational chatbots in an era of artificial intelligence (AI) and global communication are discussed.


Statistical inference in circular structural model and fitting circles to noisy data

Abstract: It is well known that commonly used algorithms for circle fitting perform poorly when sampling distribution of the points is not symmetric with respect to the circle center, e.g., when the points are sampled from a circle arc. To overcome this difficulty we introduce and study a parametric circular structural model. In this model the points on the circumference are assumed to be sampled according to the von Mises distribution with unknown concentration and mean direction parameters. We develop maximum likelihood and method of moments estimators of the circle center and radius, and study their statistical properties. In particular, we show that the proposed maximum likelihood estimator is asymptotically normal and efficient. We also develop a test of uniformity for the sampling distribution along the circle. Based on the derived theoretical results we propose a numerically stable circle fitting algorithm, investigate its accuracy in a simulation study, and illustrate its behavior in a real data example.


Construction of Knowledge Graph for Emergency Resources

Abstract: Knowledge graphs can effectively organize and represent information related to emergency resources for unforeseen sudden events. In this study, we construct a model layer for the knowledge graph of emergency resources, focused on sudden events, through the classification and analysis of unforeseen disaster measures. This study defines eight interconnected entity types, each characterised by a set of attributes and engaging in one or more relationships with other entity types. Utilizing 121 incident investigation reports from the emergency management departments of various provinces and cities over the past five years, we select five entities with the highest frequency of occurrence along with their corresponding four relationships. We then design an extraction plan for these entities and relationships. Based on the completed knowledge graph data, we formulate 14 questions related to emergency resources for sudden events and construct 19 corresponding question-and-answer templates using a template-based question-answering (QA) approach. We retrieve the corresponding Cypher statement templates through template mapping and obtain the question answers through querying. Finally, we design a knowledge graph question-and-answer system using the Django web framework, which includes entity queries and knowledge QA functions, specifically for emergency resources related to sudden events.


Designing Language Learning Experiences With Generative AI Tools

Abstract: Artificial intelligence (AI), particularly generative AI, can present many opportunities for language learners to practice and improve their language skills, receive timely feedback on their performance, and customize their learning based on their needs and language proficiency. AI's benefits are not limited to second language (L2) learners. Instructors can also benefit from the novel generative AI technologies by using them in curriculum and lesson design, developing new teaching and assessment materials, or addressing diverse learner skills and needs. Despite AI's advantages, the main issue is how to design L2 environments effectively so learners can receive the best benefits from AI while reducing some associated drawbacks. This chapter argues that learning experience design (LXD) presents a road map for L2 instructors as they incorporate generative AI into their instruction. If the learning design is random and left to good intentions, achieving meaningful learning outcomes will also be left to chance. Following proven LXD guidelines may help alleviate the confusion around AI.


Unravelling the Evolution of Generative AI in Communication Education

Abstract: This chapter traces the historical evolution of human-machine communication, highlighting generative AI's transformative role. It begins with the origins of AI and early communication technologies, including Alan Turing's pioneering work and the development of Turing machines, as well as early AI experiments in human-machine interaction. The narrative then transitions to AI's evolution and the emergence of generative models, spotlighting advancements like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, which are fundamental to coherent text generation. The pivotal role of generative AI in language and communication learning is explored, showcasing its impact on writing skills and comprehension. Ethical considerations in AI usage are discussed, including bias, privacy, and ethical communication practices. The chapter concludes by envisioning AI's future in communication pedagogy, emphasizing collaboration between educators and AI developers and recapitulating historical insights for the integration of AI in education.


The Influence of Generative AI on Interpersonal Communication Dynamics

Abstract: In this comprehensive exploration, the interaction between generative AI and interpersonal communication is examined. The initial sections delve into the characteristics and limitations of AI-generated responses, highlighting the challenges of context and non-verbal cues interpretation. The potential for AI-driven interpersonal skill development is presented. The discussion progresses to address the changing dynamics in the classroom, contrasting traditional communication training with AI-augmented methods. The efficacy of AI in group discussions and role-plays is assessed, with a central focus on whether AI augments or diminishes human connections. The final sections explore the potential of generative AI in reshaping our understanding of effective communication and the necessity for educators to uphold the human element while leveraging AI's skill-enhancing capabilities. This comprehensive review offers insights into the evolving landscape of AI and interpersonal communication, shedding light on its opportunities, challenges, and the path forward.


How AI Is Changing the Nature and Landscape of Student Learning and Outcomes Assessment Within Higher Education

Abstract: Technologies have long changed the social lives and practices of users. While some argue that the effects have been positive and others have been a bit more skeptical to take this perspective, there is no question that they have altered the way that we engage in daily behaviors. They have changed the way that we work and the way that we communicate with others. And said technologies are, with little question, here to stay. This chapter introduces readers to the various strategies adopted by both students and assessors (e.g., instructors) regarding the use of ChatGPT within the world of higher education. ChatGPT, in short, is an artificial intelligence chatbot that aids the user in a multitude of tasks and mimics the human mind. Not only will the chapter expose readers to the ways in which students are using ChatGPT (e.g., as artificial tutors, to help with the construction of ideas), but also how instructors are beginning to notice the benefits of this AI tool for the classroom (e.g., lesson plans).


A hybrid combination of CNN Attention with optimized random forest with grey wolf optimizer to discriminate between Arabic hateful, abusive tweets

Abstract: Arabic hateful speech recognition has long been a major area of focus in Natural Language Processing (NLP) research. In light of recent advancements in transformer models and deep learning, researchers are now turning to transfer learning techniques based on existing models such as BERT for Arabic hateful speech recognition. To detect Arabic hateful contexts, using advanced machine learning algorithms and NLP techniques is essential. These techniques can help to detect different forms of hateful contexts in Arabic by analyzing the text for lexical, semantic, and syntactic features. In this research, we proposed a new hybrid approach that combines deep and machine learning models to detect hateful and abusive content in Arabic. The proposed model consists of a combination of convolutional neural networks and attention layers that are trained to differentiate between normal, abusive, and hateful contexts in Arabic. In the first step, we used a pre-trained model to extract features from the hateful Arabic context. After that, we used an optimized random forest combined with particle swarm optimization and wolf optimizer to classify the extracted features. Finally, we evaluated the performance of the model to detect hateful Arabic contexts. To evaluate the proposed method we used 5846 and 6023 tweets with 3 categories of hateful, abusive, and normal Arabic contexts. The experimental result indicates sensitivity have been reported CNN Attention + optimized random forest by the wolf optimizer on 6023 tweets.


A taxonomy for Media Literacy in Portugal

Abstract: Media literacy, which encompasses skills and knowledge considered essential for civic participation in social, political, and professional life, is an area that has been growing steadily. In Portugal, there is a wide range of entities that have developed media literacy initiatives, from public entities, universities, research centers, training centers, media organizations, among others. These actors are adopting different approaches and media literacy is being promoted through training, academic publications, social projects, and learning platforms. These actions, carried out in more or less formal environments, are aimed at different target audiences, from pre-school children to the elderly. Based on the multiplicity of theoretical approaches to the concept of media literacy, this study seeks to understand the extent to which this diversity is reflected in the multiplicity of actors, approaches and target audiences involved in promoting media literacy in Portugal. To achieve this goal, using two analysis grids, one dedicated to the actors and the other to their interventions, a mapping of Portuguese entities dedicated to media literacy was carried out, seeking to identify the types of organizations and the professional areas to which they belong, their target audiences, the incidence of literacy promoted, and the pedagogical tools used. The results are presented in the form of a taxonomy of the actors involved in the operationalization of media literacy initiatives in Portugal, making it possible to characterize the landscape of this literacy promotion and its constituent networks, as well as to identify areas to be explored and the types of target audiences in need of initiatives aimed at their needs. The identification of empirical practices to promote media literacy in Portugal also makes it possible to contribute to the global theoretical-conceptual debate on the subject, particularly in terms of the volatility of the concept, its propensity for change and its readaptation according to the contexts in which it is used.


Exploring emergent syllables in end-to-end automatic speech recognizers through model explainability technique

Abstract: Abstract Automatic speech recognition systems based on end-to-end models (E2E-ASRs) can achieve comparable performance to conventional ASR systems while reproducing all their essential parts automatically, from speech units to the language model. However, they hide the underlying perceptual processes modelled, if any, and they have lower adaptability to multiple application contexts, and, furthermore, they require powerful hardware and an extensive amount of training data. Model-explainability techniques can explore the internal dynamics of these ASR systems and possibly understand and explain the processes conducting to their decisions and outputs. Understanding these processes can help enhance ASR performance and reduce the required training data and hardware significantly. In this paper, we probe the internal dynamics of three E2E-ASRs pre-trained for English by building an acoustic-syllable boundary detector for Italian and Spanish based on the E2E-ASRs’ internal encoding layer outputs. We demonstrate that the shallower E2E-ASR layers spontaneously form a rhythmic component correlated with prominent syllables, central in human speech processing. This finding highlights a parallel between the analysed E2E-ASRs and human speech recognition. Our results contribute to the body of knowledge by providing a human-explainable insight into behaviours encoded in popular E2E-ASR systems.


Generation Z’s Trust in The Chatbot of Mental Health Service

Abstract: Generation Z is the age group that has experienced the most mental health-related issues due to the COVID-19 pandemic in Indonesia. Mental health information system services have become increasingly popular, so patient services are overwhelmed. Chatbot has emerged as one of the solutions to address this problem. However, conversations with them have led to social issues alongside the growing use of chatbots. This study aims to identify the factors influencing Generation Z's trust in patient service chatbots within mental health applications in Indonesia. Using the Interpretative Phenomenological Analysis method, this research analyses qualitative data from observations and interviews with five undergraduate students. Based on the analysis, the study identified seven factors that influence Generation Z's trust in chatbot customer service within mental health applications, with three being novel findings.


Medical Image Description Based on Multimodal Auxiliary Signals and Transformer

Abstract: Medical image description can be applied to clinical medical diagnosis, but the field still faces serious challenges. There is a serious problem of visual and textual data bias in medical datasets, which are the imbalanced distribution of health and disease data. This can greatly affect the learning performance of data-driven neural networks and finally lead to errors in the generated medical image descriptions. To address this problem, we propose a new medical image description network architecture named multimodal data-assisted knowledge fusion network (MDAKF), which introduces multimodal auxiliary signals to guide the Transformer network to generate more accurate medical reports. In detail, audio auxiliary signals provide clear abnormal visual regions to alleviate the visual data bias problem. However, the audio modality signals with similar pronunciation lack recognizability, which may lead to incorrect mapping of audio labels to medical image regions. Therefore, we further fuse the audio with text features as the auxiliary signal to improve the overall performance of the model. Through the experiments on two medical image description datasets, IU-X-ray and COV-CTR, it is found that the proposed model is superior to the previous models in terms of language generation evaluation indicators.


Network Algorithms for Intelligent Evaluation of Composition in Middle School English Cloud Classrooms

Abstract: This study combines link grammar (LG) detector with N-grammar model to analyze and evaluate grammar in compositions. And then the composition level is judged through information entropy. Finally, the composition score is calculated based on the overall composition level and grammar weight. The experimental results show that the combined weight of recall and accuracy of the proposed method in this study is 89.9%, which is 26.6% higher than LG and 9.7% higher than Grammarly. In the performance test of scoring the entire essay, the proportion of error between the proposed method and manual evaluation is 87.29%, with a lower overall mean square error of only 3.08 and a shorter average running time of only 22.69 seconds. The method proposed in this study has a high accuracy and strong applicability in the evaluation of English compositions for middle school students, providing a new approach for teaching English writing for middle school students.


Mapping Ni-Cu-Platinum Group Element-Hosting, Small-Sized, Mafic-Ultramafic Rocks Using WorldView-3 Images and a Spatial-Spectral Transformer Deep Learning Method

Abstract: Abstract Mafic-ultramafic intrusive complexes possess a considerable capacity for hosting Ni-Cu-platinum group element (PGE) sulfide deposits. However, the mapping of small outcrops over large areas by field surveys is time-consuming. In this study, WorldView-3 (WV-3) data with moderate spectral and very high spatial resolution were employed for mapping mafic-ultramafic units using spectral indices and the spatial-spectral transformer (SSTF) deep learning method in the Luotuoshan area of Beishan, Gansu Province, China. Based on representative reflectance signatures extracted from imagery of known locations, false-color composites of three-band ratios and a newly proposed shortwave infrared (SWIR) spectral index provided reasonable delineation of mafic-ultramafic rocks. The SSTF method facilitated mapping the occurrence of small mafic-ultramafic outcrops and defining much clearer boundaries, particularly for tiny units at meter scale. Moreover, the SSTF method is not sensitive to the occurrence of carbonate lenses that may affect the reflectance of outcrops. Field investigation and laboratory sample analyses confirmed the occurrence of mafic and ultramafic rocks with substantial metallic mineral potential in this area. Seven prospects were confirmed to be related to mafic-ultramafic intrusions during field validation, four of which contained metallic minerals such as chalcopyrite, pentlandite, pyrite, and chromite in the samples observed by scanning electron microscopy and energy dispersive spectrometry. This study proves that the spectral indices and SSTF deep learning method applied on WV-3 multispectral data are useful for discriminating small-sized mafic-ultramafic intrusive rocks (&lt;100 m) for prospecting of local mineralization.


Deep learning-based privacy-preserving recommendations in federated learning

Abstract: Privacy preservation in recommendations has been increasingly garnering huge interest from the research community owing to the rapid rise in data security and privacy concerns among users. The computation overhead and attaining high recommendation accuracy remain the key issues in the existing methods. In this research, a course recommendation method using Federated Learning (FL) based on Deep Learning is presented. The course recommendation technique is carried out in the local nodes using multiple phases, like agglomerative matrix generation, course grouping, bi-level matching, retrieval of learner-preferred courses, and course recommendation. Here, course grouping is accomplished using Deep Fuzzy Clustering (DFC), and Deep Convolutional Neural Networks (DCNN) performs recommendation. The DFC-DCNN-FL is examined based on accuracy, False Positive Rate (FPR), loss function, Mean Square Error (MSE), Root MSE (RMSE), and Mean Average Precision (MAP) and is found to have attained values of 0.909, 0.116, 0.126, 0.291, 0.539, and 0.925.


Exploiting multi-scale hierarchical feature representation for visual tracking

Abstract: Abstract Convolutional neural networks (CNNs) have been the dominant architectures for feature extraction tasks, but CNNs do not look for and focus on some specific image features. Correlation operations play an important role in visual tracking. However, the correlation operation reserves a large amount of unfavorable background information. In this paper, we propose an effective feature recognizer including channel and spatial attention modules to focus on important object feature information. Thus, the representation power of the feature extraction network is improved. Further, we design a multi-scale feature fusion network. The fusion network performs feature fusion on template feature and encoded feature branches to establish connections between features at different scales. Experiments on six benchmarks demonstrate that the proposed tracker outperforms the state-of-the-art trackers. In particular, the proposed tracker achieves an 80.4% AUC on TrackingNet and a 68.4% AUC on GOT-10k while running at a real-time speed.


ReLiCADA: Reservoir Computing Using Linear Cellular Automata design algorithm

Abstract: Abstract In this paper, we present a novel algorithm to optimize the design of Reservoir Computing using Cellular Automata models for time series applications. Besides selecting the models’ hyperparameters, the proposed algorithm particularly solves the open problem of Linear Cellular Automaton rule selection. The selection method pre-selects only a few promising candidate rules out of an exponentially growing rule space. When applied to relevant benchmark datasets, the selected rules achieve low errors, with the best rules being among the top 5% of the overall rule space. The algorithm was developed based on mathematical analysis of Linear Cellular Automaton properties and is backed by almost one million experiments, adding up to a computational runtime of nearly one year. Comparisons to other state-of-the-art time series models show that the proposed Reservoir Computing using Cellular Automata models have lower computational complexity and, at the same time, achieve lower errors. Hence, our approach reduces the time needed for training and hyperparameter optimization by up to several orders of magnitude.


A lexicon-based diachronic comparison of emotions and sentiments in literary translation: A case study of five Chinese versions of David Copperfield

Abstract: In the Chinese context, translations have served as a useful conduit for providing access to wider literature authored in other languages. A prominent question has been whether translators' linguistic choices are influenced by factors such as translators' social and cultural background and emotions towards the texts they are translating. When multiple translations of the same text over a span of time are produced, another layer of complexity is introduced, and research such as the present study, must examine how or whether variation in the expression of emotions within translations produced over a period of time is discernible. To this end, the present study made use of Lexicon-based Sentiment Analysis (LBSA), a common natural language processing (NLP) approach, to study people's attitudes, opinions or emotions towards a certain person or thing. LBSA has attracted much attention in the literary works or translated works for analyzing reader response and appraisal of the works themselves. The present study undertook a diachronic comparison of emotions and sentiments in five translations of David Copperfield based on the emotion lexicons. The corpus of the study comprised translations of five books and 3,084,599 tokens. We applied the computational method of emotion and sentiment analysis to the emotion words in the five translations. In addition, we used python and R package to analyze the positive and negative words in five versions. The study revealed that translators as social beings in the target world express unique reactions towards the same emotion in the original text as well as in literary translations. Yet, the modern vernacular Chinese versions also showcase a similarity in the expression of emotions thus demonstrating the decisive role of the overall flow of emotion in the original plays and in translation. The contribution of the study is significant as it is a pioneering investigation given that it undertakes a sentiment and emotion analysis of literary translations in Chinese.


Lightweight high-precision SAR ship detection method based on YOLOv7-LDS

Abstract: The current challenges in Synthetic Aperture Radar (SAR) ship detection tasks revolve around handling significant variations in target sizes and managing high computational expenses, which hinder practical deployment on satellite or mobile airborne platforms. In response to these challenges, this research presents YOLOv7-LDS, a lightweight yet highly accurate SAR ship detection model built upon the YOLOv7 framework. In the core of YOLOv7-LDS's architecture, we introduce a streamlined feature extraction network that strikes a delicate balance between detection precision and computational efficiency. This network is founded on Shufflenetv2 and incorporates Squeeze-and-Excitation (SE) attention mechanisms as its key elements. Additionally, in the Neck section, we introduce the Weighted Efficient Aggregation Network (DCW-ELAN), a fundamental feature extraction module that leverages Coordinate Attention (CA) and Depthwise Convolution (DWConv). This module efficiently aggregates features while preserving the ability to identify small-scale variations, ensuring top-quality feature extraction. Furthermore, we introduce a lightweight Spatial Pyramid Dilated Convolution Cross-Stage Partial Channel (LSPHDCCSPC) module. LSPHDCCSPC is a condensed version of the Spatial Pyramid Pooling Cross-Stage Partial Channel (SPPCSPC) module, incorporating Dilated Convolution (DConv) as a central component for extracting multi-scale information. The experimental results show that YOLOv7-LDS achieves a remarkable Mean Average Precision (mAP) of 99.1% and 95.8% on the SAR Ship Detection Dataset (SSDD) and the NWPU VHR-10 dataset with a parameter count (Params) of 3.4 million, a Giga Floating Point Operations Per Second (GFLOPs) of 6.1 and an Inference Time (IT) of 4.8 milliseconds. YOLOv7-LDS effectively strikes a fine balance between computational cost and detection performance, surpassing many of the current state-of-the-art object detection models. As a result, it offers a more resilient solution for maritime ship monitoring.


Transformer-based time-to-event prediction for chronic kidney disease deterioration

Abstract: Deep-learning techniques, particularly the Transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. Previous methods focused on fixed-time risk prediction, however, time-to-event prediction is often more appropriate for clinical scenarios. Here, we present STRAFE, a generalizable survival analysis Transformer-based architecture for electronic health records.The input for STRAFE is a sequence of visits with SNOMED-CT codes in OMOP-CDM format. A Transformer-based architecture was developed to calculate probabilities of the occurrence of the event in each of 48 months. Performance was evaluated using a real-world claims dataset of over 130 000 individuals with stage 3 chronic kidney disease (CKD).STRAFE showed improved mean absolute error (MAE) compared to other time-to-event algorithms in predicting the time to deterioration to stage 5 CKD. Additionally, STRAFE showed an improved area under the receiver operating curve compared to binary outcome algorithms. We show that STRAFE predictions can improve the positive predictive value of high-risk patients by 3-fold. Finally, we suggest a novel visualization approach to predictions on a per-patient basis.Time-to-event predictions are the most appropriate approach for clinical predictions. Our deep-learning algorithm outperformed not only other time-to-event prediction algorithms but also fixed-time algorithms, possibly due to its ability to train on censored data. We demonstrated possible clinical usage by identifying the highest-risk patients.The ability to accurately identify patients at high risk and prioritize their needs can result in improved health outcomes, reduced costs, and more efficient use of resources.


Synthesis of Hybrid Data Consisting of Chest Radiographs and Tabular Clinical Records Using Dual Generative Models for COVID-19 Positive Cases

Abstract: To generate synthetic medical data incorporating image-tabular hybrid data by merging an image encoding/decoding model with a table-compatible generative model and assess their utility. We used 1342 cases from the Stony Brook University Covid-19-positive cases, comprising chest X-ray radiographs (CXRs) and tabular clinical data as a private dataset (pDS). We generated a synthetic dataset (sDS) through the following steps: (I) dimensionally reducing CXRs in the pDS using a pretrained encoder of the auto-encoding generative adversarial networks (αGAN) and integrating them with the correspondent tabular clinical data; (II) training the conditional tabular GAN (CTGAN) on this combined data to generate synthetic records, encompassing encoded image features and clinical data; and (III) reconstructing synthetic images from these encoded image features in the sDS using a pretrained decoder of the αGAN. The utility of sDS was assessed by the performance of the prediction models for patient outcomes (deceased or discharged). For the pDS test set, the area under the receiver operating characteristic (AUC) curve was calculated to compare the performance of prediction models trained separately with pDS, sDS, or a combination of both. We created an sDS comprising CXRs with a resolution of 256 × 256 pixels and tabular data containing 13 variables. The AUC for the outcome was 0.83 when the model was trained with the pDS, 0.74 with the sDS, and 0.87 when combining pDS and sDS for training. Our method is effective for generating synthetic records consisting of both images and tabular clinical data.


Automobile Insurance Fraud Detection Using Data Mining: A Systematic Literature Review

Abstract: Insurance is a pivotal element in modern society, but insurers face a persistent challenge from fraudulent behaviour performed by policyholders. This behaviour could be detrimental to both insurance companies and their honest customers, but the intricate nature of insurance fraud severely complicates its efficient, automated detection. This study surveys fifty recent publications on automobile insurance fraud detection, published between January 2019 and March 2023, and presents both the most commonly used data sets and methods for resampling and detection, as well as interesting, novel approaches. The study adopts the highly-cited Systematic Literature Review (SLR) methodology for software engineering research proposed by Kitchenham and Charters and collected studies from four online databases. The findings indicate limited public availability of automobile insurance fraud data sets. In terms of detection methods, the prevailing approach involves supervised machine learning methods that utilise structured, intrinsic features of claims or policies and that lack consideration of an example-dependent cost of misclassification. However, alternative techniques are also explored, including the use of graph-based methods, unstructured textual data, and cost-sensitive classifiers. The most common resampling approach was found to be oversampling. This SLR has identified commonly used methods in recent automobile insurance fraud detection research, and interesting directions for future research. It adds value over a related review by also including studies published from 2021 onward, and by detailing the used methodology. Limitations of this SLR include its restriction to a small number of considered publication years and limited validation of choices made during the process.


Lung cancer diagnosis based on weighted convolutional neural network using gene data expression

Abstract: Abstract Lung cancer is thought to be a genetic disease with a variety of unknown origins. Globocan2020 report tells in 2020 new cancer cases identified was 19.3 million and nearly 10.0 million died owed to cancer. GLOBOCAN envisages that the cancer cases will raised to 28.4 million in 2040. This charge is superior to the combined rates of the former generally prevalent malignancies, like breast, colorectal, and prostate cancers. For attribute selection in previous work, the information gain model was applied. Then, for lung cancer prediction, multilayer perceptron, random subspace, and sequential minimal optimization (SMO) are used. However, the total number of parameters in a multilayer perceptron can become extremely large. This is inefficient because of the duplication in such high dimensions, and SMO can become ineffective due to its calculating method and maintaining a single threshold value for prediction. To avoid these difficulties, our research presented a novel technique including Z-score normalization, levy flight cuckoo search optimization, and a weighted convolutional neural network for predicting lung cancer. This result findings show that the proposed technique is effective in precision, recall, and accuracy for the Kent Ridge Bio-Medical Dataset Repository.


The Evaluation of Course Teaching Effect Based on Improved RBF Neural Network

Abstract: As basic education is increasingly digitized, the need for better teaching and learning quality also rises. Teaching reform is crucial to achieve this, and incorporating the Levenberg-Marquardt (L-M) into the Radial Basis Function (RBF) can help establish a fair online teaching evaluation system. The experimental results showed that the convergence ability of the model was significantly improved compared with the traditional RBF neural network. The overall mean square error of the improved model was 100. The actual value prediction accuracy of the improved model is higher than that of the Backpropagation (BP). When the actual value was at its peak, the accuracy reached 98%, the overall fluctuation range of absolute error was low, the highest absolute error value reached 0.78, and the average absolute error was below 0.5. With targeted improvements, teachers and students could better understand and change their own learning situations, as reflected in empirical evaluations.


Assessment of catastrophic forgetting in continual credit card fraud detection

Abstract: The volume of e-commerce continues to increase year after year. Buying goods on the internet is easy and practical, and took a huge boost during the lockdowns of the Covid crisis. However, this is also an open window for fraudsters and the corresponding financial loss costs billions of dollars. In this paper, we study e-commerce credit card fraud detection, in collaboration with our industrial partner, Worldline. Transactional companies are more and more dependent on machine learning models such as deep learning anomaly detection models, as part of real-world fraud detection systems (FDS). We focus on continual learning to find the best model with respect to two objectives: to maximize the accuracy and to minimize the catastrophic forgetting phenomenon. For the latter, we proposed an evaluation procedure to quantify the forgetting in data streams with delayed feedback: the plasticity/stability visualization matrix. We also investigated six strategies and 13 methods on a real-size case study including five months of e-commerce credit card transactions. Finally, we discuss how the trade-off between plasticity and stability is set, in practice, in the case of FDS.


A novel Elman neural network based on Gaussian kernel and Improved SOA and Its Applications

Abstract: To address challenges encountered in traditional Elman neural networks (ENNs), such as low convergence accuracy, difficulties in hyperparameter selection, and issues with gradient disappearance, a new ENN based on the Gaussian kernel and an improved seagull optimization algorithm (SOA) named GSENN is proposed. First, the principle of the ENN is introduced, and the effects of the network structure, parameters, and gradient descent algorithm on its output are analyzed. Building upon the analysis results, an input layer for the ENN using the Gaussian kernel is designed. The outstanding local feature extraction capability of the Gaussian kernel is used to enhance convergence accuracy, and the predicted trends are more in line with real data. In addition, we propose an SOA incorporating a nonlinear factor to optimize the weights and thresholds of the ENN. This approach aims to resolve the challenges in hyperparameter selection and problems of gradient vanishing in the ENN. Seven commonly used neural network structures and the latest improved techniques were chosen for comparative experiments across different datasets. The experimental results show that GSENN achieves a mean relative error rate of 4.286%. The MSE results indicate a 45.600% improvement over the original ENN and a 37.142% improvement over BP neural networks. The experimental results demonstrate that GSENN exhibits efficient forecasting capability and offers reliable and effective forecasting for the relevant departments.


Robust image steganography against JPEG compression based on DCT residual modulation

Abstract: Robust steganography is the technology of hiding secret message in cover image so that the message can be recovered after additional image processing across social network. Traditional robust steganography schemes mainly relied on error correction code and embedding domain selection, which could not effectively address the additional errors introduced during JPEG compression. In this paper, we analyze the lossy operations of JPEG compression and present the DCT Residual Modulation (DRM) algorithm to specifically address spatial pixel overflow, which is a predominant factor leading to alterations in quantized DCT coefficients following recompression. To enhance robustness, unit-based shuffling is applied to the encoded message, and the DRM algorithm is employed for processing the cover image and the stego image. To enhance resistance against steganalysis, the embedding domain is shifted to lower-frequency regions and the redundancy of error correction coding is reduced. Experimental results show that the proposed scheme outperforms existing schemes in terms of robustness and maintains a high level of anti-steganalysis capabilities.


RASNet: Renal Automatic Segmentation Using an Improved U-Net with Multi-scale Perception and Attention Unit

Abstract: For the treatment of renal disease, the application of radioactive equipment has become one of the important methods. Accurate segmentation of renal contour plays an important role in clinical diagnosis. However, manual renal contour drawing is not only inefficient but also prone to inaccurate outlining results due to different manual proficiency and fatigue caused by long-term work. There is little research on automatic renal segmentation with renal dynamic imaging. To address this issue, an improved model based on a deep neural network called Renal Automatic Segmentation Network (RASNet) is proposed, to aid in the automatic segmentation of renal contours. Besides, a multi-scale spatial perception module and a decoding module with attention connection are introduced to enrich the semantic information and further improve the accuracy of network segmentation. Extensive experiments were conducted on a renal dynamic medical image database established in this paper. Analysis results show the superiority of the proposed RASNet to several existing segmentation frameworks.


A heuristic method for discovering multi-class classification rules from multi-source data in cloud–edge system

Abstract: The integration of diverse devices has made the establishment of multi-class classification models for multi-source data a primary concern for data mining in cloud–edge system. Developing rule-based classifiers is essential because they present results that express the reasons for classification. However, existing rule learning methods are not compatible with multi-source tabular data containing mixed features, imbalanced labels and missing values, making it difficult to build cross-data source and cross-device data mining models. We developed a heuristic multi-class rule learning method that can handle complex tabular datasets without relying too much on cumbersome preprocessing techniques. We abstract the training process of the classifier into a multi-objective optimization problem and design a novel hybrid evolutionary algorithm to obtain Pareto-optimal solutions as rule-based classifier. Compared with the existing explainable classification methods, this method has obvious advantages in the classification performance of tabular data.


MVDet: Encrypted malware traffic detection via multi-view analysis

Abstract: Detecting encrypted malware traffic promptly to halt the further propagation of an attack is critical. Currently, machine learning becomes a key technique for extracting encrypted malware traffic patterns. However, due to the dynamic nature of network environments and the frequent updates of malware, current methods face the challenges of detecting unknown malware traffic in open-world environment. To address the issue, we introduce MVDet, a novel method that employs machine learning to mine the behavioral features of malware traffic based on multi-view analysis. Unlike traditional methods, MVDet innovatively characterizes the behavioral features of malware traffic at 4-tuple flows from four views: statistical view, DNS view, TLS view, and business view, which is a more stable feature representation capable of handling complex network environments and malware updates. Additionally, we achieve a short-time behavioral features construction, significantly reducing the time cost for feature extraction and malware detection. As a result, we can detect malware behavior at an early stage promptly. Our evaluation demonstrates that MVDet can detect a wide variety of known malware traffic and exhibits efficient and robust detection in both open-world and unknown malware scenarios. MVDet outperforms state-of-the-art methods in closed-world known malware detection, open-world known malware detection, and open-world unknown malware detection.


d2kg: An integrated ontology for knowledge graph-based representation of government decisions and acts

Abstract: To implement Open Governance a crucial element is the efficient use of the big amounts of open data produced in the public domain. Public administration is a rich source of data and potentially new knowledge. It is a data intensive sector producing vast amounts of information encoded in government decisions and acts, published nowadays on the World Wide Web. The knowledge shared on the Web is mostly made available via semi-structured documents written in natural language. To exploit this knowledge, technologies such as Natural Language Processing, Information Extraction, Data mining and the Semantic Web could be used, embedding into documents explicit semantics based on formal knowledge representations such as ontologies. Knowledge representation can be made possible by the deployment of Knowledge Graphs, collections of interlinked representations of entities, events or concepts, based on underlying ontologies. This can assist data analysts to achieve a higher level of situational awareness, facilitating automated reasoning towards different objectives, such as for knowledge management, data maintenance, transparency and cybersecurity. This paper presents a new ontology d2kg [d(iavgeia) 2(to) k(nowledge) g(raph)] integrating in a unique way standard EU ontologies, core and controlled vocabularies to enable exploitation of publicly available data from government decisions and acts published on the Greek platform Diavgeia with the aim to facilitate data sharing, re-usability and interoperability. It demonstrates a characteristic example of a Knowledge Graph based representation of government decisions and acts, highlighting its added value to respond to real practical use cases for the promotion of transparency, accountability and public awareness. The developed d2kg ontology in owl is accessible at: http://w3id.org/d2kg, as well as documented at: http://w3id.org/d2kg/documentation.


A Survey on Implementing Augmented Reality in Learning Data Structures

Abstract: Data structures are fundamental building blocks of computer science, but their abstract nature can pose challenges for students. This research explores the potential of Augmented Reality (AR) to enhance the learning of data structures. The paper describes the development and analysis of an AR-based learning system for specific data structures. The system utilizes data visualization and interactive manipulation through 3D models and animations to explain how AR features aid in learning. The results were compelling, demonstrating significant improvement in both understanding and performance among students utilizing the AR tool. In general, this study indicates that AR can function as a valuable tool for studying data structures. This study not only highlights the effectiveness of AR in demonstrating data structures but also paves the way for future advancements in computer science education, ultimately leading to a more easily accessible and engaging learning environment for all.


Dura-Europos Stories: Developing interactive storytelling applications using knowledge graphs for cultural heritage exploration

Abstract: We introduce Dura-Europos Stories, a multimedia application for viewing artifacts and places related to the Dura-Europos archaeological excavation. We describe the process of mapping data to the Wikidata data model as well as the process of contributing data to Wikidata. We provide an overview of the functionality of an interactive application for viewing images of the artifacts in the context of their metadata. We contextualize this project as an example of using knowledge graphs in research projects in order to leverage technologies of the Semantic Web in such a way that data related to the project can be easily combined with other data on the web. Presenting artifacts in this story-based application allows users to explore these objects visually, and provides pathways for further exploration of related information.


Exponential Capacity of Dense Associative Memories

Abstract: Recent generalizations of the Hopfield model of associative memories are able to store a number P of random patterns that grows exponentially with the number N of neurons, P=exp(αN). Besides the huge storage capacity, another interesting feature of these networks is their connection to the attention mechanism which is part of the Transformer architecture widely applied in deep learning. In this work, we study a generic family of pattern ensembles using a statistical mechanics analysis which gives exact asymptotic thresholds for the retrieval of a typical pattern, α1, and lower bounds for the maximum of the load α for which all patterns can be retrieved, αc, as well as sizes of attraction basins. We discuss in detail the cases of Gaussian and spherical patterns, and show that they display rich and qualitatively different phase diagrams.Received 26 July 2023Revised 29 November 2023Accepted 11 January 2024DOI:https://doi.org/10.1103/PhysRevLett.132.077301© 2024 American Physical SocietyPhysics Subject Headings (PhySH)Physical SystemsDisordered systemsSpin glassesTechniquesDeep learningMachine learningReplica theoryStatistical Physics & ThermodynamicsInterdisciplinary Physics


Simulating photon counting from dynamic quantum emitters by exploiting zero-photon measurements

Abstract: Many applications of quantum optics demand delicate quantum properties of light carefully tailored to accomplish a specific task. To this end, numerical simulations of quantum light sources are vital for designing, characterizing, and optimizing quantum photonic technology. Here, I show that exploiting information hidden in zero-photon measurement outcomes provides an exponential speedup for time-integrated photon-counting simulations, realizing eight orders of magnitude reduction in the time to compute six-photon detection probabilities while achieving 10 orders of magnitude higher precision compared to the state of the art. This enables simulations of large photonic experiments with an unprecedented level of physical detail. It can accelerate the design of sources to generate photonic resource states for quantum sensing and measurement-based quantum computing while capturing realistic imperfections. It also establishes a general theoretical framework to study dynamic interactions between stationary qubits mediated by measurements of flying qubits, which can be used to model distributed quantum computing and quantum communication.


Geometrical Structure of Bifurcations during Spatial Decision-Making

Abstract: In the study, authors reveal new geometric principles of bifurcations during spatiotemporal decision-making and demonstrate that a non-Euclidean neural representation of space simplifies decision-making in multi-target environments.


Using quantum annealing to design lattice proteins

Abstract: Quantum annealing has shown promise for finding solutions to difficult optimization problems, including protein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problem in a coarse-grained lattice model, the HP model, in which amino acids are classified into two broad groups: hydrophobic (H) and polar (P). Using a set of 22 HP sequences with up to 64 amino acids, we demonstrated the fast and consistent identification of the correct HP model ground states using the D-Wave hybrid quantum-classical solver. An equally relevant biophysical challenge, called the protein design problem, is the inverse of the above, where the task is to predict protein sequences that fold to a given structure. Here, we approach the design problem by a two-step procedure, implemented and executed on a D-Wave machine. In the first step, we perform a pure sequence-space search by varying the type of amino acid at each sequence position, and seek sequences which minimize the HP-model energy of the target structure. After mapping this task onto an Ising spin glass representation, we employ a hybrid quantum-classical solver to deliver energy-optimal sequences for structures with 30-64 amino acids, with a 100% success rate. In the second step, we filter the optimized sequences from the first step according to their ability to fold to the intended structure. In addition, we try solving the sequence optimization problem using only the QPU, which confines us to sizes $\le$20, due to exponentially decreasing success rates. To shed light on the pure QPU results, we investigate the effects of control errors caused by an imperfect implementation of the intended Hamiltonian on the QPU, by numerically analyzing the Schr\"odinger equation. We find that the simulated success rates in the presence of control noise semi-quantitatively reproduce the modest pure QPU results for larger chains.


Bayesian estimation of the Kullback-Leibler divergence for categorical systems using mixtures of Dirichlet priors

Abstract: In many applications in biology, engineering, and economics, identifying similarities and differences between distributions of data from complex processes requires comparing finite categorical samples of discrete counts. Statistical divergences quantify the difference between two distributions. However, their estimation is very difficult and empirical methods often fail, especially when the samples are small. We develop a Bayesian estimator of the Kullback-Leibler divergence between two probability distributions that makes use of a mixture of Dirichlet priors on the distributions being compared. We study the properties of the estimator on two examples: probabilities drawn from Dirichlet distributions and random strings of letters drawn from Markov chains. We extend the approach to the squared Hellinger divergence. Both estimators outperform other estimation techniques, with better results for data with a large number of categories and for higher values of divergences.


Multi-source adaptive thresholding adaboost with application to virtual metrology

Abstract: To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks.


Real-time adaptive estimation of decoherence timescales for a single qubit

Abstract: Characterizing the time over which quantum coherence survives is critical for any implementation of quantum bits, memories, and sensors. The usual method for determining a quantum system's decoherence rate involves a suite of experiments probing the entire expected range of this parameter, and extracting the resulting estimation in postprocessing. Here we present an adaptive multiparameter Bayesian approach, based on a simple analytical update rule, to estimate the key decoherence timescales (${T}_{1}$, ${T}_{2}^{\ensuremath{\ast}}$, and ${T}_{2}$) and the corresponding decay exponent of a quantum system in real time, using information gained in preceding experiments. This approach reduces the time required to reach a given uncertainty by a factor up to an order of magnitude, depending on the specific experiment, compared to the standard protocol of curve fitting. A further speedup of a factor approximately $2$ can be realized by performing our optimization with respect to sensitivity as opposed to variance.


Understanding New Machine Learning Architectures: Practical Generative Artificial Intelligence for Anesthesiologists

Abstract: Recent advances in neural networks have given rise to generative artificial intelligence, systems able to produce fluent responses to natural questions or attractive and even photorealistic images from text prompts. These systems were developed through new network architectures that permit massive computational resources to be applied efficiently to enormous data sets. First, this review examines autoencoder architecture and its derivatives the variational autoencoder and the U-Net in annotating and manipulating images and extracting salience. This architecture will be important for applications like automated x-ray interpretation or real-time highlighting of anatomy in ultrasound images. Second, this article examines the transformer architecture in the interpretation and generation of natural language, as it will be useful in producing automated summarization of medical records or performing initial patient screening. The author also applies the GPT-3.5 algorithm to example questions from the American Board of Anesthesiologists Basic Examination and find that, under surprisingly reasonable conditions, it correctly answers more than half the questions.


Sentiment Analysis of Finnish Twitter Discussions on COVID-19 During the Pandemic

Abstract: Abstract With the outbreak of the COVID-19 pandemic, researchers have studied how people reacted on social media during the pandemic. Sentiment analysis has been leveraged to gain insight. However, much of the research conducted on both sentiment analysis and social media analysis of COVID-19 often focuses on widespread languages, such as English and Chinese. This is partly due to the scarcity of resources for natural language processing and sentiment analysis for morphologically complex and less prevalent languages such as Finnish. This paper aims to analyze sentiments on Twitter in the Finnish language during the COVID-19 pandemic. We manually annotate with sentiments a random sample of 1943 tweets about COVID-19 in Finnish. We use it to build binomial and multinomial logistic regression models with Lasso penalty by exploiting ngrams and two existing sentiment lexicons. We also build two similar models using an existing (pre-COVID-19) Twitter dataset for comparison. The best-performing model for the Finnish language is then used to determine the trends of positive, negative, and neutral opinions on a collection of tweets in Finnish extracted between April 21 and June 18, 2020. The best sentiment polarity prediction model for the Finnish language attain 0.785 AUC, 0.710 balanced accuracy, and 0.723 macro-averaged F1 for predicting positive and negative polarity (binomial classification), and 0.667 AUC, 0.607 balanced accuracy, and 0.475 F1 when adding neutral tweets (multinomial classification). On the other hand, the pre-COVID-19 model trained on the same number of tweets exhibits higher accuracy for the multinomial model (0.687 balanced accuracy, and 0.588 F1). We hypothesize that this loss of performance is due to the COVID-19 context that makes sentiment analysis of neutral tweets more difficult for the machine learning algorithm to predict. Running the model on all the extracted Finnish tweets, we observe a decrease in negativity and an increase in positivity over the observed time as the Finnish government lifts restrictions. Our results show that applying an existing general-purpose sentiment analyzer on tweets that are domain-specific, such as COVID-19, provides lower accuracy. More effort in the future needs to be invested in using and developing sentiment analysis tools tailored to their application domain when conducting large-scale social media analysis of specific medical issues, such as a global pandemic.


A machine learning approach for early identification of patients with severe imported malaria

Abstract: The aim of this study is to design ad hoc malaria learning (ML) approaches to predict clinical outcome in all patients with imported malaria and, therefore, to identify the best clinical setting.This is a single-centre cross-sectional study, patients with confirmed malaria, consecutively hospitalized to the Lazzaro Spallanzani National Institute for Infectious Diseases, Rome, Italy from January 2007 to December 2020, were recruited. Different ML approaches were used to perform the analysis of this dataset: support vector machines, random forests, feature selection approaches and clustering analysis.A total of 259 patients with malaria were enrolled, 89.5% patients were male with a median age of 39 y/o. In 78.3% cases, Plasmodium falciparum was found. The patients were classified as severe malaria in 111 cases. From ML analyses, four parameters, AST, platelet count, total bilirubin and parasitaemia, are associated to a negative outcome. Interestingly, two of them, aminotransferase and platelet are not included in the current list of World Health Organization (WHO) criteria for defining severe malaria.In conclusion, the application of ML algorithms as a decision support tool could enable the clinicians to predict the clinical outcome of patients with malaria and consequently to optimize and personalize clinical allocation and treatment.


Learning to Detect Attended Objects in Cultural Sites with Gaze Signals and Weak Object Supervision

Abstract: Cultural sites such as museums and monuments are popular tourist destinations worldwide. Visitors come to these places to learn about the cultures, histories and arts of a particular region or country. However, for many cultural sites, traditional visiting approaches are limited and may fail to engage visitors. To enhance visitors’ experiences, previous works have explored how wearable devices can be exploited in this context. Among the many functions that these devices can offer, understanding which artwork or detail the user is attending to is fundamental to provide additional information on the observed artworks, understand the visitor’s tastes and provide recommendations. This motivates the development of algorithms for understanding visitor attention from egocentric images. We considered the attended object detection task, which involves detecting and recognizing the object observed by the camera wearer, from an input RGB image and gaze signals. To study the problem, we collect a dataset of egocentric images collected by subjects visiting a museum. Since collecting and labeling data in cultural sites for real applications is a time-consuming problem, we present a study comparing unsupervised, weakly supervised, and fully supervised approaches for attended object detection. We evaluate the considered approaches on the collected dataset, assessing also the impact of training models on external datasets such as COCO and EGO-CH. The experiments show that weakly supervised approaches requiring only a 2D point label related to the gaze can be an effective alternative to fully supervised approaches for attended object detection. To encourage research on the topic, we publicly release the code and the dataset at the following url: https://iplab.dmi.unict.it/EGO-CH-Gaze/.


Simulation-based mathematical learning for higher education students from heterogeneous backgrounds

Abstract: This paper explores the use of simulation-based training for mathematical learning in undergraduate and graduate mathematics, science, and engineering courses. Simulation-based training offers the advantages of active learning and inquiry-based learning techniques. Furthermore, it provides extensive flexibility, ranging from user-level usage of simulations to the modification or creation of new possibilities by the student, thus engaging different cognitive levels to achieve the learning objectives. This is particularly interesting in groups consisting of students from diverse backgrounds and levels, due to factors such as their international origin or varying prior education, especially in interdisciplinary Master's degree programmes. Additionally, in online or blended environments (which have become widespread during the last years), simulation-based learning has the advantage of granting students a certain degree of autonomy, which can, to some extent, compensate for the absence of the instructor's physical presence.


Multi-channels Prototype Contrastive Learning with Condition Adversarial Attacks for Few-shot Event Detection

Abstract: Abstract Few-shot Event Detection (FSED) is a sub-task of Event Detection that aims to accurately identify event types with limited training instances and enable smooth transfer to newly-emerged event types. Recently, the dominant works have used the prototypical network to accomplish this task and employ contrastive learning to alleviate the issue of semantically-close categories. Nevertheless, these methods still suffer from two serious problems: (1) inadequate learning of prototype representations resulting from limited training data; (2) hard-easy sample imbalance and categories imbalance caused by the large number of non-trigger word("O" tags) in the token-level classification task. To address the problems, this paper proposes the Multi-channels Prototype and Contrastive learning method with Conditional Adversarial attack, which introduces the improved multi-channels prototype and contrastive networks to alleviate the categories and hard-easy samples imbalance. Moreover, we devise a constrained adversarial attack to improve the problem of limited training data. Extensive experimental results show that our model performs better than other FSED methods. All the code and data will be available for online public access.


Chaos based Medical Image Encryption Techniques: A Comprehensive Review and Analysis

Abstract: In the past two decades, we have seen the proliferation of Internet and its applications. The information exchange happens via images, videos, texts, audio clips, etc. Medical experts use images as the most common form of information exchange such as X-rays, MRI scans, CT scans, etc. These scanned images contain confidential information about a patient. With images being the most attractive mode for medical information exchange, it becomes the target of many security attacks. Many researchers have put out their best efforts to protect the images from these attacks by proposing several techniques. Despite all efforts, many cryptosystems still compromise with one or more key attributes like efficiency or time required for them to be top-notch. Along with an appreciation of work, careful inspection is also required that can influence the future work and make them better. Hence, this survey aims to summarize and analyze the existing chaos-based medical image encryption techniques introduced in the last five years. The paper starts with a brief introduction about the basic chaos maps, security parameters, and then, progresses to analysis of the encryption algorithms based on categories followed by comparative study and open security issues.


Digital competence and students’ engagement: a comprehensive analysis of smartphone utilization, perceived autonomy and formal digital learning as mediators

Abstract: Purpose The integration of digital technologies into education has brought about a profound transformation, fundamentally reshaping the learning landscape. The purpose of this study is to underscore the importance of investigating the factors influencing students’ engagement (SE) in this evolving digital era, particularly within formal digital learning environments. To address this need, the study is grounded in self-determination theory (SDT) and presents a comprehensive model comprising interconnected elements: digital competence (DC), smartphone use (SPU), perceived autonomy (PA), digital formal learning (DFL) and SE. Design/methodology/approach The research conducted an investigation within Saudi Arabian universities, collecting a robust data set of 392 cases. This data set underwent rigorous analysis to validate the proposed model. To untangle the intricate relationships within the framework, the study used partial least squares structural equation modelling. Given the distinct dimensions of the two constructs under study, the researcher used a disjoint two-stage approach to establish reflective-formative higher-order constructs (HOC). Findings The findings revealed that digital literacy and digital skills (DS) constitute the foundational constituents of DC. Simultaneously, the study identified facilitation, distraction and connectedness as integral components of SPU. Importantly, the study established that DC, SPU, PA and DFL significantly influence SE. Furthermore, the research illuminated the mediating roles played by SPU, PA and DFL in the complex relationship between DC and SE. Originality/value This study advances the literature by delineating the dynamic interplay between DC, SPU and SE in digital learning. It extends SDT within educational contexts, emphasizing the role of internal motivations and DS. Methodologically, it innovates through reflective-formative HOCs, deepening the analysis of complex educational constructs. Managerially, it guides institutions in enhancing DC and integrating smartphones effectively into learning, advocating for tailored strategies to foster engaging and autonomous digital learning environments, thereby enriching both theoretical understanding and practical application in education.


INFORMATE: When Are the Data?

Abstract: In a recent Upstream blog post we explored where data connected to papers funded by several U.S. Federal Agencies are published. Different data sharing practices across these agencies led to very different distributions of datasets across various repositories. We used CHORUS reports that combine linked article and dataset metadata as input for that work.


Is the Tamil language Conjugation or Grafting

Abstract: Language is the main tool that helps people to communicate their thoughts, emotions, likes and dislikes to others. Research about the origin of the language are often biased. Hence the origin of the language cannot be determined. In order to understand the language, linguists classify it in various ways based on its nature, character, usage, number of users etc. Among these, the languages of the world are divided into independent, conjunctive, and so on. Although many languages have appeared in the world, only a few languages have survived. Among the languages that have survived, some have lost its value as spoken language and living only as literary languages. Among the languages that appear, grow and disappear, Tamil is the most important language that appeared in ancient times and has grown with influence and is still alive today. This Tamil language, which has an ancient grammatical and literary richness and the unique feature of adapting itself according to the times, has two different linguistic elements in itself, grafting and conjugation. Pointing to the interdisciplinary nature of Tamil, which is often labeled as a hybrid language, this article is to analyse whether Tamil is a conjugation or grafting.


A design team’s perspective-mixing for shared empathic understandings

Abstract: Empathic design involves two social practices – collaboration with users and intra team collaboration forshared empathic understandings. This study is focused on the latter phenomenon, aiming to characterise a social mechanism underlying it. To achieve this objective, we used a framework of perspective-mixing in empathic design, including three distinct perspectives: first, second, and third-person perspectives. With this framework, we traced how team members share and integrate individuals' user knowledge and insights to develop and apply their shared empathic understandings. We conducted conversation analysis to examine one design team's conversations over a design journey from need-finding to initial ideation to prototyping and testing. To characterise this team's perspective-mixing during their conversations, we analysed how team members took and combined the three perspectives within and between their utterances (perspective transitions). Our analysis revealed that the team used the third-person perspective as an underlying mechanism for perspective-mixing. In addition, the nature of perspective-mixing was distinct for specific design activities – the transitions involving the first and second-person perspectives were prominent during need-finding and initial ideation, while the third-person perspective was predominant during prototyping and testing. Based on these findings, we discuss what and how perspective-mixing can support intrateam collaboration for shared empathic understandings.


Physics-informed differentiable method for piano modeling

Abstract: Numerical emulations of the piano have been a subject of study since the early days of sound synthesis. High-accuracy sound synthesis of acoustic instruments employs physical modeling techniques which aim to describe the system’s internal mechanism using mathematical formulations. Such physical approaches are system-specific and present significant challenges for tuning the system’s parameters. In addition, acoustic instruments such as the piano present nonlinear mechanisms that present significant computational challenges for solving associated partial differential equations required to generate synthetic sound. In a nonlinear context, the stability and efficiency of the numerical schemes when performing numerical simulations are not trivial, and models generally adopt simplifying assumptions and linearizations. Artificial neural networks can learn a complex system’s behaviors from data, and their application can be beneficial for modeling acoustic instruments. Artificial neural networks typically offer less flexibility regarding the variation of internal parameters for interactive applications, such as real-time sound synthesis. However, their integration with traditional signal processing frameworks can overcome this limitation. This article presents a method for piano sound synthesis informed by the physics of the instrument, combining deep learning with traditional digital signal processing techniques. The proposed model learns to synthesize the quasi-harmonic content of individual piano notes using physics-based formulas whose parameters are automatically estimated from real audio recordings. The model thus emulates the inharmonicity of the piano and the amplitude envelopes of the partials. It is capable of generalizing with good accuracy across different keys and velocities. Challenges persist in the high-frequency part of the spectrum, where the generation of partials is less accurate, especially at high-velocity values. The architecture of the proposed model permits low-latency implementation and has low computational complexity, paving the way for a novel approach to sound synthesis in interactive digital pianos that emulates specific acoustic instruments.


The Use of Pronoun Interpretation Biases in L1 Spanish: The Role of Language Proficiency, Exposure, and Use in Heritage Speakers

Abstract: Abstract In an offline sentence comprehension task, we test the interpretation biases for null and overt pronouns and anaphoric and cataphoric pronouns in heritage speakers ( HS s) whose first language (L1) is Spanish (L2 English). Previous studies have shown that Spanish HS s can interpret overt anaphoric pronouns differently than monolingual speakers. A question that remains unexplored is how HS s interpret cataphoric pronouns and how L1 experience affects pronoun resolution biases in these individuals. We recruited 48 HS s and 48 monolingual Spanish speakers. The results show that pronoun interpretation in HS s differs from monolingual biases, with HS s demonstrating a higher subject antecedent preference for anaphoric and cataphoric pronouns. The results further show that a continuous measure of language proficiency, exposure, and use may contribute to explaining pronoun interpretation biases in heritage Spanish. We discuss the findings in light of recent research demonstrating that language experience can contribute to shape discourse patterns.


End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

Abstract: Abstract Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.


Wansing's bi-intuitionistic logic: semantics, extension and unilateralisation

Abstract: The well-known algebraic semantics and topological semantics for intuitionistic logic (Int) is here extended to Wansing's bi-intuitionistic logic (2Int). The logic 2Int is also characterised by a quasi-twist structure semantics, which leads to an alternative topological characterisation of 2Int. Later, notions of Fregean negation and of unilateralisation are proposed. The logic 2Int is extended with a ‘Fregean negation’ connective ∼, obtaining 2Int∼, and it is showed that the logic N4⋆ (an extension of Nelson's paraconsistent logic) results to be the unilateralisation of 2Int∼ via ∼. The logic 2Int∼ is also characterised by a Kripke-style semantics, a twist structure semantics and a topological semantics.


Incorporating Intensity Distance Attenuation Into PLUM Ground‐Motion‐Based Earthquake Early Warning in the United States: The APPLES Configuration

Abstract: Abstract We develop Attenuated ProPagation of Local Earthquake Shaking (APPLES), a new configuration for the United States West Coast version of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm that incorporates attenuation into its ground‐motion prediction procedures. Under APPLES, instead of using a fixed radius to forward‐predict observed peak ground shaking to the area surrounding a seismic station, the forward‐predicted intensity at a location depends on the distance from the station using an intensity prediction relationship. We conduct conceptual tests of maximum intensity distribution predictions in APPLES and PLUM using a catalog of ShakeMaps to confirm that the attenuation relationship in APPLES is appropriately modeling shaking distributions for West Coast earthquakes. Then, we run APPLES and PLUM in simulated real‐time tests to determine warning time performance. Finally, we compare real‐time alert behavior during the 2022 M 6.4 Ferndale, California, earthquake and other recent events. We find that APPLES presents two potential improvements to PLUM by reducing over‐alerting during smaller magnitude earthquakes and by increasing warning times in some locations during larger earthquakes. APPLES can produce missed and late alerts in locations that experience shaking intensities close to the level used to issue alerts, so preferred alerting strategies with APPLES would use alert thresholds that are lower than the intensities targeted for EEW alerts. We find alerts using APPLES are also similar to those for the source‐based approaches currently used in the ShakeAlert EEW system, which will make APPLES easier to integrate into the system.


Sparse Embedded Convolution Based Dual Feature Aggregation 3D Object Detection Network

Abstract: Abstract The algorithm design of compatible detection speed and accuracy based on LiDAR point clouds is a challenging issue in various practical applications of 3D object detection, including the field of autonomous driving. This paper designs a single-stage object detection algorithm that is lightweight and compatible with detection speed and accuracy for the above issue. To achieve these objectives, we propose a framework for a 3D object detection algorithm using a single-stage detection network as the backbone network. Firstly, we design a dual feature extraction module to reduce the occurrence of vehicle miss and error detection problems. Then, we use a multi-scale feature fusion scheme to fuse feature information with different scales. Furthermore, we design a data enhancement scheme suitable for this network architecture. Experimental results in the KITTI dataset show that the proposed method achieves improvement ratios of 38.5% for the detection speed and 2.88% $$\sim $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>∼</mml:mo> </mml:math> 13.65% in terms of the average precision of vehicle detection compared to the existing algorithm based on single-stage object detection (SECOND).


Chain-of-event prompting for multi-document summarization by large language models

Abstract: Purpose In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task. Design/methodology/approach To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning. Findings Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets. Originality/value This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.


Time Series Prediction of ESN Based on Chebyshev Mapping and Strongly Connected Topology

Abstract: Abstract This paper introduces a novel approach called Chebyshev mapping and strongly connected topology for optimization of echo state network (ESN). To enhance the predictive performance of ESNs for time series data, Chebyshev mapping is employed to optimize the irregular input weight matrix. And the reservoir of the ESN is also replaced using an adjacency matrix derived from a digital chaotic system, resulting in a reservoir with strong connectivity properties. Numerical experiments are conducted on various time series datasets, including the Mackey–Glass time series, Lorenz time series and solar sunspot numbers, validating the effectiveness of the proposed optimization methods. Compared with the traditional ESNs, the optimization method proposed in this paper has higher predictive performance, and effectively reduce the reservoir’s size and model complexity.


Video dataset of Balinese dance basic movement for action recognition

Abstract: In the convergence of cultural heritage preservation and computational research, this paper presents a comprehensive dataset capturing the nuanced artistry of traditional Balinese dancing. This endeavour not only documents but also aims to protect these intricate dance movements, bridging technological advancements with rich cultural traditions. The dataset includes 1740 and 828 high-definition video recordings of female and male dancers, respectively, showcasing 24 unique dance movements. These recordings, made using multiple smartphone cameras from various perspectives, reflect the dance's complex nature and emphasize gender-based movement variations, key for detailed cultural analysis. Our systematic approach involved three strategically placed cameras, capturing diverse angles for a holistic view. The subsequent preprocessing stage, including segmenting and labelling, has enhanced the dataset's clarity, making it a valuable resource for cultural studies and computational analysis in preserving intangible cultural heritage. The videos, stored in MP4 format, are categorized by dancer gender, dance type, and camera angle, offering researchers a rich, multifaceted tool for exploring this traditional art form.


The Salient360! toolbox: Handling gaze data in 3D made easy

Abstract: Eye tracking has historically been a very popular tool. The data it records allow us to understand how people behave and what they attend to within our visual world; under this perspective the experiments, applications and use-cases are endless. Therefore, it is not surprising to witness a strong rise in the use of eXtended Reality (XR) devices with embedded eye trackers in research. These devices allow for less obtrusive experimenting conditions, and a significantly higher experimental control compared to traditional desktop testing. The use of eye tracking in XR is increasing and so is the need for a toolbox enabling consensus about eye tracking methods in 3D. We present the Salient360! toolbox: it implements functions to identify saccades and fixations and output gaze features (e.g., saccade directions) to generate saliency maps, fixation maps, and scanpath data. It implements comparisons of gaze data with methods adapted to 3D. We plan continuous improvements of the toolbox as the community develops new tools and methods dedicated to 360°gaze tracking. We hope that this toolbox will spark discussions about the methodology of 3D gaze processing, facilitate running experiments, and improve studying gaze in 3D. https://github.com/David-Ef/salient360Toolbox


English Code-Mixings in WhatsApp interactions among Spanish adolescents and their orthographic competence

Abstract: Aims: The widespread use of computer-mediated communication (CMC) among adolescents has favored the creation of a newly written code called digitalk. This new code includes, among other characteristics, the use of foreign words as textisms, mainly anglicisms. These textisms also serve as a mark of identity among young speakers. The aim of this paper is twofold: first, to describe which are the more frequent anglicisms used by Spanish adolescents in their CMC and the code-mixing pathways of inclusion; second, to examine the possible influence between the use of anglicisms in the CMC of Spanish adolescents and their orthographic competence. Method: Based on an exploratory methodology, a corpus of interactions through WhatsApp of a group of adolescents attending Compulsory Secondary Education in a region of southern Spain has been analyzed. Data analysis: Sketch Engine software was used for corpus analysis, presenting the normalized frequency (1/1,000) of textisms, misspellings, and multimodal elements. Finally, for the descriptive statistical analysis of the data and for the bivariate correlation analysis applying Pearson’s coefficient, IBM SPSS v.26 software was used. Findings: The results indicate that young people use English Code-Mixings (ECM) as discursive framers with an expressive aim and as a sign of belonging to a specific community of speakers. Moreover, no positive correlation is observed between the use of these ECMs and a lower orthographic competence. Implications: ECMs show an important pragmatic function in CMC among Spanish adolescents. Furthermore, they are one of the elements that have been incorporated into the digital norm of Spanish and can be used in Compulsory Education as a resource for the acquisition of communicative competence in adolescents along with other textisms. Originality: This is the first research that analyzes the frequency and function of anglicisms in Spanish CMC and their impact on the orthographic competence of Spanish adolescents.


Examining successful and unsuccessful time management through process data: two novel indicators of test-taking behaviors

Abstract: Abstract Test-taking behavior is a potential source of construct irrelevant variance for test scores in international large-scale assessments where test-taking effort, motivation, and behaviors in general tend to be confounded with test scores. In an attempt to disentangle this relationship and gain further insight into examinees’ test-taking processes, researchers can now utilize process and timing data to obtain a more comprehensive view of test-taking behaviors, such as test-taking effort. The purpose of this study is to propose and evaluate two novel response-based, standardized indicators of test-taking behaviors that utilize a combination of examinee response and process (timing) data to better understand and describe test-taking effort in ILSAs. These indices were empirically estimated with USA data from two booklets from e-TIMSS 2019 in mathematics for grade 4. In addition, their predictive validity was examined with respect to achievement estimates. Their network of associations with other relevant variables such as motivation, interest in the subject, as well as across subjects were also examined to test their intra-individual stability in e-TIMSS.