Enhancing Cultural Sustainability: Making Rescue Excavations Accessible through Educational Applications and Virtual Reality

Abstract: The key questions of this article concern the extent to which digital documentation, 3D scanning technologies, and VR/AR technologies contribute to the preservation, enhancement, and sustainability of cultural heritage. The purpose of this research is to present an innovative methodology through which a platform for the diagnosis, management, and documentation of findings, progress, and reports and projections of rescue excavations in the area of Euboea, Greece was implemented. A holistic approach is proposed with a comprehensive system of archaeological documentation and visualization, which integrates the use of digital technologies in all steps of the archaeological process, enhances the tools of the scientific community, and provides immersive experiences for both researchers and the public. The results of the method include (1) a complex toolbox of innovative technologies that allow archaeologists to plan and execute a rescue excavation in an efficient and concise manner and (2) a VR installation at the Diachronic Museum of Chalkida “Arethousa”, which allows the public to participate in virtual rescue excavations and explore archaeological sites that are no longer accessible. Through this project, the impact of new technologies on cultural heritage and, in particular, on issues of sustainability, accessibility, education, and visitor participation is highlighted.


Filling the Gaps: Using Synthetic Low-Altitude Aerial Images to Increase Operational Design Domain Coverage

Abstract: A key necessity for the safe and autonomous flight of Unmanned Aircraft Systems (UAS) is their reliable perception of the environment, for example, to assess the safety of a landing site. For visual perception, Machine Learning (ML) provides state-of-the-art results in terms of performance, but the path to aviation certification has yet to be determined as current regulation and standard documents are not applicable to ML-based components due to their data-defined properties. However, the European Union Aviation Safety Agency (EASA) published the first usable guidance documents that take ML-specific challenges, such as data management and learning assurance, into account. In this paper, an important concept in this context is addressed, namely the Operational Design Domain (ODD) that defines the limitations under which a given ML-based system is designed to operate and function correctly. We investigated whether synthetic data can be used to complement a real-world training dataset which does not cover the whole ODD of an ML-based system component for visual object detection. The use-case in focus is the detection of humans on the ground to assess the safety of landing sites. Synthetic data are generated using the methods proposed in the EASA documents, namely augmentations, stitching and simulation environments. These data are used to augment a real-world dataset to increase ODD coverage during the training of Faster R-CNN object detection models. Our results give insights into the generation techniques and usefulness of synthetic data in the context of increasing ODD coverage. They indicate that the different types of synthetic images vary in their suitability but that augmentations seem to be particularly promising when there is not enough real-world data to cover the whole ODD. By doing so, our results contribute towards the adoption of ML technology in aviation and the reduction of data requirements for ML perception systems.


LangTest: A comprehensive evaluation library for custom LLM and NLP models

Abstract: The use of natural language processing (NLP) models, including the more recent large language models (LLM) in real-world applications obtained relevant success in the past years. To measure the performance of these systems, traditional performance metrics such as accuracy, precision, recall, and f1-score are used. Although it is important to measure the performance of the models in those terms, natural language often requires an holistic evaluation that consider other important aspects such as robustness, bias, accuracy, toxicity, fairness, safety, efficiency, clinical relevance, security, representation, disinformation, political orientation, sensitivity, factuality, legal concerns, and vulnerabilities. To address the gap, we introduce LangTest, an open source Python toolkit, aimed at reshaping the evaluation of LLMs and NLP models in real-world applications. The project aims to empower data scientists, enabling them to meet high standards in the ever-evolving landscape of AI model development. Specifically, it provides a comprehensive suite of more than 60 test types, ensuring a more comprehensive understanding of a model’s behavior and responsible AI use. In this experiment, a Named Entity Recognition (NER) clinical model showed significant improvement in its capabilities to identify clinical entities in text after applying data augmentation for robustness.


Online hate speech victimization: consequences for victims’ feelings of insecurity

Abstract: Abstract This paper addresses the question whether and to what extent the experience of online hate speech affects victims’ sense of security. Studies on hate crime in general show that such crimes are associated with a significantly higher feeling of insecurity, but there is little evidence concerning feeling of insecurity due to online hate speech. Based on a secondary data analysis of a representative population survey in Lower Saxony, Germany, on the topic of cybercrime in 2020 (N = 4,102), we tested three hypotheses regarding the effect of offline and online hate speech on feelings of insecurity. As a result, compared to non-victims, victims of online hate speech exhibit a more pronounced feeling of insecurity outside the Internet, while victims of other forms of cybercrime do not differ in this regard from non-victims. We found no effect for offline hate speech when relevant control variables were included in the statistical model. Possible reasons for this finding are assumed to lie in the characteristics of the phenomenon of online hate speech, for example, because the hateful content spreads uncontrollably on the Internet and reaches its victims even in protected private spheres.


Aspect-based sentiment classification with aspect-specific hypergraph attention networks

Abstract: Aspect-based sentiment classification aims to infer the sentiment expression towards a specific aspect in a sentence. The key to this task is to utilize the relationship between sentiment words and aspect words. The mainstream methods use Recurrent Neural Networks (RNN), Attention mechanisms, or Graph Neural Networks (GNN) to explore the syntactic information. Though these methods are undoubtedly effective, they still encounter several challenges: 1. Since most of the studies used only syntactic dependency graphs, they lacked a more optimal representation of inter-word relationships. 2. Some studies have explored multiple relationship graphs, but they fail to effectively integrate syntactic dependencies with semantic or other information, thereby impeding the exchange of multiple information elements. Moreover, the inclusion of more information graphs increases the computational burden on the model. In this paper, we construct a word-level relational hypergraph containing various syntactic and semantic relationships between aspect words and other context words. We propose an aspect-specific hypergraph attention network (ASHGAT) to thoroughly investigate the hypergraph’s information. Furthermore, we design an aspect-oriented syntactic distance-based weight distribution mechanism to optimize hypergraph attention. We conducted extensive experiments on four benchmark datasets from SemEval 14, 15, and 16. The results show that ASHGAT demonstrates the other SOTA baselines.


All-optical neural network nonlinear activation function based on the optical bistability within a micro-ring resonator

Abstract: Training all-optical neural networks in itself remains an unresolved problem, and the challenges compound when the problem is turned into the hardware implementations. In this paper, we propose a nonlinear activation function based on optical bistability within a micro-ring resonator (MRR), achieving threshold control without external modulation. Furthermore, a convolutional neural network similar to the Le-Net-5 architecture is designed, in which all nonlinear activation functions are composed of optical bistable hysteresis loop. The numerical simulation results demonstrate that the recognition rate on the Fashion-MNIST dataset can achieve 91.3%, which means that the optical neuromorphic computation can be implemented by utilizing the nonlinear optical effects themselves in the all-optical hardware. Such a scheme promises access to the all-optical neural network training in the optical hardware environment compared to numerical activation functions.


Immersive Experience and Virtual Reality

Abstract: Abstract Much of the excitement about virtual reality and its potential for things like entertainment, art, education, and activism is its ability to generate experiences that are powerfully immersive. However, discussions of VR tend to invoke the notion of immersive experience without subjecting it to closer scrutiny; and discussions often take it for granted that immersive experience is a single unified phenomenon. Against this, we argue that there are four distinct types or aspects of immersive experience that should be distinguished: representational immersion , which corresponds roughly to what is sometimes called “psychological presence”; participatory immersion , which is related to the interactive aspects of VR experience; affective immersion , which has to do with the subject’s emotional relation to the experience; and narrative immersion , which captures the phenomenon of being caught up in the flow of events experienced. We argue that this four-way distinction helps us understand the powerfully immersive character of VR, while also recognizing its continuity with the immersion we experience with media such as novels, films, and music. We also argue that the account of immersion we offer here lets us better understand the connection between VR and empathy, charting a middle course between extreme enthusiasts who view VR as the ultimate “empathy machine” and extreme skeptics who argue that this attitude is misguided.


Exploring better image captioning with grid features

Abstract: Abstract Nowadays, Artificial Intelligence Generated Content (AIGC) has shown promising prospects in both computer vision and natural language processing communities. Meanwhile, as an essential aspect of AIGC, image to captions has received much more attention. Recent vision-language research is developing from the bulky region visual representations based on object detectors toward more convenient and flexible grid ones. However, this kind of research typically concentrates on image understanding tasks like image classification, with less attention paid to content generation tasks. In this paper, we explore how to capitalize on the expressive features embedded in the grid visual representations for better image captioning. To this end, we present a Transformer-based image captioning model, dubbed FeiM, with two straightforward yet effective designs. We first design the feature queries that consist of a limited set of learnable vectors, which act as the local signals to capture specific visual information from global grid features. Then, taking augmented global grid features and the local feature queries as inputs, we develop a feature interaction module to query relevant visual concepts from grid features, and to enable interaction between the local signal and overall context. Finally, the refined grid visual representations and the linguistic features pass through a Transformer architecture for multi-modal fusion. With the two novel and simple designs, FeiM can fully leverage meaningful visual knowledge to improve image captioning performance. Extensive experiments are performed on the competitive MSCOCO benchmark to confirm the effectiveness of the proposed approach, and the results show that FeiM yields more eminent results than existing advanced captioning models.


HousePrice_ML: An Efficient Framework for House Price Prediction Using Soft Computing

Abstract: Predicting housing prices is important to many people, such as home buyers, real estate agents, and investors. By harnessing the power of machine learning models, this paper aims to develop a highly efficient system to calculate reliable housing price forecasts. The results of this research can facilitate decision-making processes, enable more informed investments, and improve the overall buying and selling experience in the real estate market. The relationship between house prices and the economy is an important motivating factor for predicting house prices. This paper focuses on how to predict housing prices using machine learning techniques. This paper proposes an efficient framework For prediction houses using six machine learning algorithms ( SVM, Tree, Neural Network, KNN, Linear Regression, Gradient Boosting). In best model 1, the number of fields equals Gradient Boosting; in best model 2, the number of fields equals Linear Regression; in Best model 3 number of fields equals Gradient Boosting. The best all model equal model 2 equal Linear Regression.


How engagement in internet fraud impact academic activities of undergraduate students in Nigeria: A socio-legal study

Abstract: This study examined how engagement in internet fraud by undergraduate students impact their academic activities. Specifically, we examined the factors that drive the engagement of undergraduate students in internet fraud, how engagement in internet fraud activities affect their academic activities, and how they navigate their academic challenges to achieve academic objectives. We conducted this study between 2020 and 2022 and relied on both quantitative and qualitative data. 400 questionnaire instruments were randomly administered to students of four purposively selected tertiary institutions to gauge their perception of engagement in internet fraud activities among undergraduate students. We then relied on snowball sampling and semi-structured interview to elicit responses from sampled respondents. The respondents cut across five tertiary institutions in Nigeria. Analysis was done using Microsoft excel spread sheet for the quantitative data and inductive analysis for the qualitative data. We found that pressure from peers, failure of traditional morality/ethics gatekeepers, among others, are major drivers of engagement in internet fraud among undergraduate students, and that to navigate academic challenges and achieve academic objectives, they unethically induce some fellow students and staff.


Rarity updated ensemble with oversampling: An ensemble approach to classification of imbalanced data streams

Abstract: Abstract Today's ever‐increasing generation of streaming data demands novel data mining approaches tailored to mining dynamic data streams. Data streams are non‐static in nature, continuously generated, and endless. They often suffer from class imbalance and undergo temporal drift. To address the classification of consecutive data instances within imbalanced data streams, this research introduces a new ensemble classification algorithm called Rarity Updated Ensemble with Oversampling (RUEO). The RUEO approach is specifically designed to exhibit robustness against class imbalance by incorporating an imbalance‐specific criterion to assess the efficacy of the base classifiers and employing an oversampling technique to reduce the imbalance in the training data. The RUEO algorithm was evaluated on a set of 20 data streams and compared against 14 baseline algorithms. On average, the proposed RUEO algorithm achieves an average‐accuracy of 0.69 on the real‐world data streams, while the chunk‐based algorithms AWE, AUE, and KUE achieve average‐accuracies of 0.48, 0.65, and 0.66, respectively. The statistical analysis, conducted using the Wilcoxon test, reveals a statistically significant improvement in average‐accuracy for the proposed RUEO algorithm when compared to 12 out of the 14 baseline algorithms. The source code and experimental results of this research work will be publicly available at https://github.com/vkiani/RUEO .


Comparative Analysis of Transformer-Based Method In A Question Answering System for Campus Orientation Guides

Abstract: The campus introduction process is a stage where new students acquire information about the campus through a series of activities and interactions with existing students. However, the delivery of campus introduction information is still limited to conventional methods, such as using guidebooks. This limitation can result in students having a limited understanding of the information needed during their academic period. The one of solution for this case is to implement a deep learning system with knowledge-based foundations. This research aims to develop a Question Answering System (QAS) as a campus introduction guide by comparing two transformer methods, namely the RoBERTa and IndoBERT architectures. The dataset used is processed in the SQuAD format in the Indonesian language. The collected SQuAD dataset in the Indonesian language consists of 5046 annotated data. The result shows that IndoBERT outperforms RoBERTa with EM and F1-Score values of 81.17 and 91.32, respectively, surpassing RoBERTa with EM and F1-Score values of 79.53 and 90.18.


THE "YOUNG PROFESSIONALS" CHAMPIONSHIP AS A TOOL FOR DEVELOPING PROFESSIONAL SKILLS OF PARTICIPANTS IN THE COMPETENCE OF THE "INTERNET OF THINGS"

Abstract: The article discusses issues regarding the organization of the national championship “Young Professionals”, which takes place within the framework of the federal project “Young Professionals (increasing the competitiveness of vocational education)” of the national project “Education”. Currently, there are many competitions aimed at improving the level of vocational education. The Young Professionals Championship is no exception. Its main goal is to increase the prestige of working professions and the development of vocational education by combining the best practices and professional standards with the involvement of qualified specialists as judges and teachers who are able to transfer the accumulated experience to a new generation of young professionals. The "Young Professionals" championships in our country are held according to the international rules of WorldSkills International (WSI), which reflect the requirements for a specialist of the highest level in the conditions of modern realities. Of course, the Young Professionals Championship movement plays an important role in the training of qualified personnel. The championship makes an invaluable contribution to the education and upbringing of young professionals, as it encourages students to accumulate experience, search for non-standard solutions to problems. Subsequently, students show great independence and interest in learning.


Vulnerability issues in Automatic Speaker Verification (ASV) systems

Abstract: Abstract Claimed identities of speakers can be verified by means of automatic speaker verification (ASV) systems, also known as voice biometric systems. Focusing on security and robustness against spoofing attacks on ASV systems, and observing that the investigation of attacker’s perspectives is capable of leading the way to prevent known and unknown threats to ASV systems, several countermeasures (CMs) have been proposed during ASVspoof 2015, 2017, 2019, and 2021 challenge campaigns that were organized during INTERSPEECH conferences. Furthermore, there is a recent initiative to organize the ASVSpoof 5 challenge with the objective of collecting the massive spoofing/deepfake attack data (i.e., phase 1), and the design of a spoofing-aware ASV system using a single classifier for both ASV and CM, to design integrated CM-ASV solutions (phase 2). To that effect, this paper presents a survey on a diversity of possible strategies and vulnerabilities explored to successfully attack an ASV system, such as target selection, unavailability of global countermeasures to reduce the attacker’s chance to explore the weaknesses, state-of-the-art adversarial attacks based on machine learning, and deepfake generation. This paper also covers the possibility of attacks, such as hardware attacks on ASV systems. Finally, we also discuss the several technological challenges from the attacker’s perspective, which can be exploited to come up with better defence mechanisms for the security of ASV systems.


Silent and oral reading methods on improving English reading comprehension among generation alpha pupils

Abstract: Reading comprehension enables each child to make meaning of the world. Therefore, it is important to develop this during the child’s primary years in school. Using ex post facto design, this study investigated the effects of reading methods on English reading comprehension of randomly selected 75 Grade 2 pupils in a private school in the Philippines. The scores of pupils who read two comparable narrative passages in oral and silent were compared. Paired t-test results revealed a significant difference between oral reading and silent reading. Silent reading had a greater positive effect on the comprehension of the pupils. Likewise, it was the most preferred reading method among the respondents. Focus group discussion with English teachers' results suggested that silent reading was preferred specifically because it helps in understanding the story better, remembering words, and concentrating given its quiet and peaceful nature. It is recommended that teachers employ sustained silent reading classroom practices such as Silent Reading Activity (SRA) Reading Laboratory, Genuine Love for Reading activities through the Four-pronged approach, and Drop Everything and Read time. These methods will help to support the reading needs and preference of the generation alpha pupils and to further strengthen the positive effects of silent reading on improving their reading comprehension.


Human activity recognition with fine-tuned CNN-LSTM

Abstract: Abstract Human activity recognition (HAR) by deep learning is a challenging and interesting topic. Although there are robust models, there is also a bunch of parameters and variables, which affect the performance such as the number of layers, pooling type. This study presents a new deep learning architecture that is obtained by fine-tuning of the conventional CNN-LSTM model, namely, CNN (+3)-LSTM. Three changes are made to the conventional model to increase the accuracy. Firstly, kernel size is set to 1×1 to extract more information. Secondly, three convolutional layers are added to the model. Lastly, average pooling is used instead of max-pooling. Performance analysis of the proposed model is conducted on the KTH dataset and implemented on Keras. In addition to the overall accuracy of the proposed model, the contribution of each change is observed individually. Results show that adding layers made the highest contribution followed by kernel size and pooling, respectively. The proposed model is compared with state-of-art and outperformed some of the recent studies with a 94.1% recognition rate.


An improved column-generation-based matheuristic for learning classification trees

Abstract: Decision trees are highly interpretable models for solving classification problems in machine learning (ML). The standard ML algorithms for training decision trees are fast but generate suboptimal trees in terms of accuracy. Other discrete optimization models in the literature address the optimality problem but only work well on relatively small datasets. Firat et al. (2020) proposed a column-generation-based heuristic approach for learning decision trees. This approach improves scalability and can work with large datasets. In this paper, we describe improvements to this column generation approach. First, we modify the subproblem model to significantly reduce the number of subproblems in multiclass classification instances. Next, we show that the data-dependent constraints in the master problem are implied, and use them as cutting planes. Furthermore, we describe a separation model to generate data points for which the linear programming relaxation solution violates their corresponding constraints. We conclude by presenting computational results that show that these modifications result in better scalability.


Hierarchical Controller for Obstacle Avoidance Task in WMRs Considering Actuators and Power Electronics Subsystems: When Artificial Potential Fields Approach is Used

Abstract: Through the development of a three-level hierarchical controller, this study proposes a solution to the obstacle avoidance task in wheeled mobile robots (WMRs). The proposed controller takes into consideration the dynamics of the three subsystems that typically compose a WMR (mechanical structure, actuators, and power electronics). At the high level, a control based on input-output linearization that uses the artificial potential fields technique is used and a Lyapunov-alike function is proposed for stability analysis purposes. At the medium level, two controls based on differential flatness are employed and a Hurwitz-stable-type polynomial is defined with the aim of locate the poles on the left-hand-side of the complex plane. Finally, at the low level, two cascaded controls are implemented using sliding modes and PI control. The performance of the three-level hierarchical controller is evaluated through a co-simulation using Matlab-Simulink and PSpice software. The obtained results are assessed with a two-level controller that does not take into account the dynamics of the power stage. Such results demonstrate that both controllers accomplish the obstacle avoidance task when no kind of perturbations are considered. However, and unlike the two-level controller, the three-level hierarchical controller successfully achieves the obstacle avoidance task even when disturbances in some parameters of the system are introduced.


Generative adversarial network based on frequency domain data enhancement: Dual-way discriminator structure Copes with limited data

Abstract: The excellent image-generation ability of generative adversarial networks (GANs) has been widely used. However, training a GAN requires large-scale data support, which hinders in-depth development. Therefore, the research on stable training of GANs under limited data conditions is helpful to further expand the application scenarios. To solve this problem, a new network based on a dual-ways discriminator structure is designed, used to eliminate the problem that a single discriminator model is prone to overfitting under the condition of limited data. Then, the problem that the traditional data augmentation strategy is limited to pixel space and lacks attention to the overall structure and contour of the image is analyzed. An adaptive dynamic data augmentation strategy based on the Laplace convolution kernel is proposed from the frequency domain space, which realizes the purpose of implicitly increasing the training data in the training process. This new designed module improves the performance of the generative adversarial network. Through extensive experiments, it was confirmed that the new network, named FD-GAN, achieved prefer image generation ability, and its Fid score reached 4.58, 12.007, and 10.382 in the AFHQ-Cat, AFHQ-Dog, and TankDataSet datasets, respectively.


Identity fraud victimization: a critical review of the literature of the past two decades

Abstract: Abstract This study aims to provide an understanding of the nature, extent, and quality of the research evidence on identity fraud victimization in the US. Specifically, this article reviews, summarizes, and comments on the state of empirical research of identity fraud victimization in the US based on a narrative review of 52 published empirical studies. Studies included in this review suggest that the prevalence of identity fraud in the US has increased over the years and existing account frauds is the most prevalent type of identity fraud. There is a pressing need for more research on the prevalence of identity fraud victimization among minors, institutionalized individuals, and individuals from minority groups; long-term prevalence of identity fraud victimization; and emerging forms of identity fraud such as synthetic identity fraud victimization. Studies included in this review further suggest that identity fraud risk factors vary based on the fraud type considered. Identity fraud victims can experience a variety of harms. Longitudinal studies following identity fraud victims are essential for reliably estimating the risk factors for identity fraud victimization and the impact of identity fraud victimization on individual victims. The research on services for identity fraud victims is limited and suggests the positive impact of trauma-informed services for serious identity fraud victims. The overwhelming lack of research on the impact of programs and services for identity fraud victims necessitates more attention from scholars to study the impact of programs, interventions, and services for identity fraud victims on reporting of victimization, prevention of victimization, experiences of victims, and victim-centered cost benefit analysis of services. Policy and practice implications of these findings are discussed.


Confounder balancing in adversarial domain adaptation for pre-trained large models fine-tuning

Abstract: The excellent generalization, contextual learning, and emergence abilities in the pre-trained large models (PLMs) handle specific tasks without direct training data, making them the better foundation models in the adversarial domain adaptation (ADA) methods to transfer knowledge learned from the source domain to target domains. However, existing ADA methods fail to account for the confounder properly, which is the root cause of the source data distribution that differs from the target domains. This study proposes a confounder balancing method in adversarial domain adaptation for PLMs fine-tuning (CadaFT), which includes a PLM as the foundation model for a feature extractor, a domain classifier and a confounder classifier, and they are jointly trained with an adversarial loss. This loss is designed to improve the domain-invariant representation learning by diluting the discrimination in the domain classifier. At the same time, the adversarial loss also balances the confounder distribution among source and unmeasured domains in training. Compared to newest ADA methods, CadaFT can correctly identify confounders in domain-invariant features, thereby eliminating the confounder biases in the extracted features from PLMs. The confounder classifier in CadaFT is designed as a plug-and-play and can be applied in the confounder measurable, unmeasurable, or partially measurable environments. Empirical results on natural language processing and computer vision downstream tasks show that CadaFT outperforms the newest GPT-4, LLaMA2, ViT and ADA methods.


Multimodal sentiment analysis leveraging the strength of deep neural networks enhanced by the XGBoost classifier

Abstract: Multimodal sentiment analysis, an increasingly vital task in the realms of natural language processing and machine learning, addresses the nuanced understanding of emotions and sentiments expressed across diverse data sources. This study presents the Hybrid LXGB (Long short-term memory Extreme Gradient Boosting) Model, a novel approach for multimodal sentiment analysis that merges the strengths of long short-term memory (LSTM) and XGBoost classifiers. The primary objective is to address the intricate task of understanding emotions across diverse data sources, such as textual data, images, and audio cues. By leveraging the capabilities of deep learning and gradient boosting, the Hybrid LXGB Model achieves an exceptional accuracy of 97.18% on the CMU-MOSEI dataset, surpassing alternative classifiers, including LSTM, CNN, DNN, and XGBoost. This study not only introduces an innovative model but also contributes to the field by showcasing its effectiveness and balance in capturing the nuanced spectrum of sentiments within multimodal datasets. The comparison with equivalent studies highlights the model's remarkable success, emphasizing its potential for practical applications in real-world scenarios. The Hybrid LXGB Model offers a unique and promising perspective in the realm of multimodal sentiment analysis, demonstrating the significance of integrating LSTM and XGBoost for enhanced performance.


Data Augmentation for Offline Handwritten Text Recognition: A Systematic Literature Review

Abstract: Abstract Offline Handwritten Text Recognition (HTR) systems concern the automatic recognition and transcription of handwritten text from scanned images to digital media. Recently, HTR research field has become increasingly important due to the growing need for digitizing documents and automating data entry across various industries. However, achieving satisfactory results depend on the amount of available samples to train an optical model. Creating and labeling large enough datasets for this purpose often require significant time and effort, that in some situations may be impractical. To address this problem, data augmentation approaches are commonly used as an essential component of HTR systems. In this way, the present work aims to identify, explore, and analyze the scope of data augmentation approaches for offline HTR systems. Furthermore, we detailed our research protocol and answered four pertinent research questions, which enabled us to discuss trends and possible gaps. A search was conducted across five scientific databases, focusing on papers published between 2012 and 2023. The search yielded 976 primary papers, with 32 meeting the criteria for inclusion in this review. Our results indicate that handwriting synthesis is an emerging research field, and we observed that Digital Image Processing (DIP) is still widely used as an image generator. Nevertheless, the application of Generative Adversarial Networks (GAN) has gained traction in recent years owing to its impressive ability to synthesize images of handwritten text with arbitrary style and content. In addition, we explored and analyzed the most commonly used datasets and text recognition levels in the selected works.


Steady state engineering of a two-level system by the mixed-state inverse engineering scheme

Abstract: The mixed-state inverse engineering scheme is a control scheme used for engineering the quantum state of a driven open quantum system from an initial steady state to a final steady state. In this paper, we present an analytical study of this scheme applied to the driven two-level model coupled to a heat reservoir. Typically, when the purity of the quantum state varies, incoherent control techniques are required for mixed-state engineering. However, we show that for both Markovian and non-Markovian dynamics, coherent control protocols can transfer the quantum state into the target state. This simplification comes at a cost, as the evolution of the quantum state must be limited to restricted conditions, resulting in special trajectories in its Hilbert space that connect the initial and target states.


A target region extraction method for ultrasound medical images based on improved <scp>PRIDNet</scp> and <scp>UCTransNet</scp>

Abstract: Abstract Computer‐aided diagnosis is pivotal in augmenting the diagnostic efficiency of ultrasound images. Nonetheless, the substantial presence of noise and artifacts in ultrasound images presents a challenge to the precise segmentation of the target region. To highlight and analyze the diagnostic information such as tissues, organs, and lesions in ultrasound medical images more accurately, a target region extraction method combining the improved PRIDNet and UCTransNet for ultrasound medical images is proposed. The method sequentially enhances and segments the target region of the image, thereby solving the problem of inconspicuous target region caused by noise and artifacts in ultrasound images. There are three key characteristics: (i) The feature extraction part of the enhancement network (improved PRIDNet) is redesigned for speckle noise to improve the network's ability to extract information and highlight the feature information of the target region in ultrasound images. (ii) The segmentation network with the addition of the underlying information on UCTransNet would effectively improves Channel‐wise Cross fusion Transformer (CCT) and decoder feature fusion capability. (iii) By combining the enhancement network with the segmentation network, we can further improve the segmentation accuracy of the target region in the presence of noise interference. The experiments conducted on both UFSU that we prepared and on some public datasets including BUSI, FHC, and CT2US have demonstrated that The proposed method attains MIoU, DSC, Acc, and HD of 96.34%, 98.12%, 99.35%, and 8.64 in CT2US, respectively. The method significantly surpasses those certain state‐of‐the‐art methods, demonstrating its potential to offer valuable guidance for clinical treatment. The code will be publicly released at https://github.com/425877/Target-Region-Extraction-Method-for-Ultrasound-Medical-Images .


An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes

Abstract: An AI-empowered indoor digital contact-tracing system was developed using a centralized architecture and advanced low-energy Bluetooth technologies for indoor positioning, with careful preservation of privacy and data security. We analyzed the contact pattern data from two RCHs and investigated a COVID-19 outbreak in one study site. To evaluate the effectiveness of the system in containing outbreaks with minimal contacts under quarantine, a simulation study was conducted to compare the impact of different quarantine strategies on outbreak containment within RCHs. The significant difference in contact hours between weekdays and weekends was observed for some pairs of RCH residents and staff during the two-week data collection period. No significant difference between secondary cases and uninfected contacts was observed in a COVID-19 outbreak in terms of their demographics and contact patterns. Simulation results based on the collected contact data indicated that a threshold of accumulative contact hours one or two days prior to diagnosis of the index case could dramatically increase the efficiency of outbreak containment within RCHs by targeted isolation of the close contacts. This study demonstrated the feasibility and efficiency of employing an AI-empowered system in indoor digital contact tracing of outbreaks in RCHs in the post-pandemic era.


A Randomized Optimal k-mer Indexing Approach for Efficient Parallel Genome Sequence Compression

Abstract: Next Generation Sequencing (NGS) technology generates massive amounts of genome sequence that increases rapidly over time. As a result, there is a growing need for efficient compression algorithms to facilitate the processing, storage, transmission, and analysis of large-scale genome sequences. Over the past 31 years, numerous state-of-the-art compression algorithms have been developed. The performance of any compression algorithm is measured by three main compression metrics: compression ratio, time, and memory usage. Existing k-mer hash indexing systems take more time, due to the decision-making process based on compression results. In this paper, we propose a two-phase reference genome compression algorithm using optimal k-mer length (RGCOK). Reference-based compression takes advantage of the inter-similarity between chromosomes of the same species. RGCOK achieves this by finding the optimal k-mer length for matching, using a randomization method and hashing. The performance of RGCOK was evaluated on three different benchmark data sets: novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Homo sapiens, and other species sequences using an Amazon AWS virtual cloud machine. Experiments showed that the optimal k-mer finding time by RGCOK is around 45.28 min, whereas the time for existing state-of-the-art algorithms HiRGC, SCCG, and HRCM ranges from 58 min to 8.97 h.


Weakly supervised instance segmentation via peak mining and filtering

Abstract: Abstract Learning the full extent of pixel‐level instance response in a weakly supervised manner remains unsatisfactory. Peak response maps (PRMs) localizes the discriminative object regions but cannot provide complete instance information, suffering from incomplete segmentation and unreliable mask prediction by noisy proposal retrieval. This work tackles this challenging problem by mining diverse class peak responses that include more discriminative and complete object regions and retrieving more reliable proposals from noisy segment proposal galleries. First, the existing method is enhanced with two more classification branches, thus contributing to more diverse and abundant instance regions from peak response maps. The mined class peak responses from two of the branches are then merged to generate more complete peak response maps by a clustering approach in their deep feature space. Then, instance segmentation masks are retrieved from a noisy object segment proposal gallery with class confidence, which is calculated by a normal classifier to obtain cleaner mask prediction. Finally, the pseudo‐supervision can be used to train an instance segmentation network in a fully supervised manner. Experiments on the PASCAL VOC 2012 dataset and COCO dataset show that the approach works effectively and outperforms other counterparts by a margin of more than 6 %, 4%, and 3% with the mean average precision (mAP) at IoU threshold of 0.25, 0.5 and 0.75, respectively.


Physical scene understanding

Abstract: Abstract Current AI systems still fail to match the flexibility, robustness, and generalizability of human intelligence: how even a young child can manipulate objects to achieve goals of their own invention or in cooperation, or can learn the essentials of a complex new task within minutes. We need AI with such embodied intelligence: transforming raw sensory inputs to rapidly build a rich understanding of the world for seeing, finding, and constructing things, achieving goals, and communicating with others. This problem of physical scene understanding is challenging because it requires a holistic interpretation of scenes, objects, and humans, including their geometry, physics, functionality, semantics, and modes of interaction, building upon studies across vision, learning, graphics, robotics, and AI. My research aims to address this problem by integrating bottom‐up recognition models, deep networks, and inference algorithms with top‐down structured graphical models, simulation engines, and probabilistic programs.


Towards Generalist Biomedical AI

Abstract: BackgroundMedicine is inherently multimodal, requiring the simultaneous interpretation and integration of insights between many data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence systems that flexibly encode, integrate, and interpret these data might better enable impactful applications ranging from scientific discovery to care delivery. MethodsTo catalyze development of these models, we curated MultiMedBench, a new multimodal biomedical benchmark. MultiMedBench encompasses 14 diverse tasks, such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling. We then introduced Med-PaLM Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI system that flexibly encodes and interprets biomedical data including clinical language, imaging, and genomics with the same set of model weights. To further probe the capabilities and limitations of Med-PaLM M, we conducted a radiologist evaluation of model-generated (and human) chest x-ray reports. ResultsWe observed encouraging performance across model scales. Med-PaLM M reached performance competitive with or exceeding the state of the art on all MultiMedBench tasks, often surpassing specialist models by a wide margin. In a side-by-side ranking on 246 retrospective chest x-rays, clinicians expressed a pairwise preference for Med-PaLM Multimodal reports over those produced by radiologists in up to 40.50% of cases, suggesting potential clinical utility. ConclusionsAlthough considerable work is needed to validate these models in real-world cases and understand if cross-modality generalization is possible, our results represent a milestone toward the development of generalist biomedical artificial intelligence systems. (Funded by Alphabet Inc. and/or a subsidiary thereof.)


Information Retrieval Systems in Healthcare

Abstract: Healthcare systems generate an immense volume of data, which presents a unique opportunity to make a significant impact. This chapter examines the role of information retrieval systems in healthcare, specifically focusing on how text analysis can be utilized to enhance the understanding of medical data. By employing advanced text mining tools, this chapter demonstrates how we can extract valuable insights from these complex documents. It also presents text analytics as a solution-oriented approach, particularly beneficial in managing crises within healthcare systems and in making informed decisions based on accurate data analysis. The technical foundation of the study is rooted in the fields of natural language processing and artificial intelligence, with a focus on methodologies related to the semantics of words and text (e.g., text corpus, dictionaries, and text embeddings). Through this exploration, the chapter aims to highlight the transformative potential of text analysis and information retrieval systems in revolutionizing healthcare data understanding.


A robust bit-level image encryption based on Bessel map

Abstract: A chaotic map plays a critical role in image encryption (IME). The map used to generate chaotic sequences should perform high dynamic characteristics. In this study, a new chaotic system depending on the Bessel function, so-called Bessel map, and a novel Bessel map-based IME scheme are proposed for the IME. The Bessel map has three control parameters, which provide superior ergodicity and diversity. Bessel map has also order degree rather than Sine map, which is used as a control parameter boosting the security. The chaotic characteristic of the Bessel map is verified through different reliable measurements such as bifurcation diagram, trajectory phase, Lyapunov exponent (LE), sample entropy (SE), permutation entropy (PE), and 0-1 test. Then, the Bessel map is employed in a new bitwise IME (BIME) scheme based on bit-level permutation and diffusion processes. The Bessel map-based BIME is validated through various simulated cryptanalyses and cyberattacks as well as compared with the state-of-the-art schemes. The achieved results demonstrate that the BIME based on the Bessel map ensures the most secure ciphered images thanks to the excellent randomness and complexity performance.


Asynchronous Threshold ECDSA With Batch Processing

Abstract: Threshold Elliptic Curve Digital Signature Algorithm (ECDSA) has attracted a lot of attention due to the wide applications of ECDSA in crypto asset. Although several variants of threshold signature protocols can provide functions, such as key generation and signing, they suffer from two shortfalls. First, these schemes only discuss a single signature computation task in a synchronous algorithm context, which is difficult to adapt to real crypto-asset applications, such as custody. Second, these schemes are computing intensive and not scalable, hence can hardly support large-scale processing operations in real life even after traditional optimization, such as multithreading, is applied. In this article, we propose an innovative computation method called asynchronous threshold ECDSA with batch processing, based on the interactive threshold signature protocols. The method provides a reliable solution for critical operational scenarios, such as threshold signing and distributed key generation (DKG) in crypto-asset custody, and can be a future reference in secure data distribution mechanisms. The performance and scalability of our methods are validated through a benchmark testing.


Multitask Particle Swarm Optimization with Heterogeneous Domain Adaptation

Abstract: The main goal of multitask optimization (MTO) is the parallel optimization of multiple different tasks. However, since different tasks in the MTO problem usually have heterogeneous characteristics, it is difficult to realize the positive knowledge transfer among tasks, resulting in poor convergence. To cope with this problem, a multi-task particle swarm optimization with a heterogeneous domain adaptation strategy (MTPSO-HDA) is proposed to transfer positive knowledge among heterogeneous tasks. First, a nonlinear mapping between the source task and the target task is constructed based on the adaptive kernel function. Then, source tasks are mapped to the target task space to reduce the differences among heterogeneous tasks. Second, a multi-source domain adaptive strategy based on fitness landscape similarity is designed to implement domain adaptation. Then, the importance of each source domain is quantitatively described to reduce the differences between multiple source domains and a target domain and achieve domain adaptation among heterogeneous tasks. Third, a heterogeneous multitask particle swarm optimization mechanism is introduced to facilitate positive knowledge transfer among heterogeneous tasks. Then, an appropriate evolutionary mechanism is designed according to the fitness landscape similarity to achieve positive knowledge transfer. Finally, to assess the effectiveness of the MTPSO-HDA algorithm, some experiments are designed based on some benchmark problems and real-world application of wastewater treatment process. The results demonstrate that the proposed MTPSO-HDA algorithm can promote positive knowledge transfer among heterogeneous tasks to improve convergence.


Cybercrime

Abstract: The Elgar Encyclopedia of Crime and Criminal Justice stands apart as the most comprehensive global reference title in its field. New entries will be added every month and PDF downloads will be available once the Encyclopedia is complete.


Neural Architecture Search via Multi-Hashing Embedding and Graph Tensor Networks for Multilingual Text Classification

Abstract: Neural architecture search (NAS) has been demonstrated to be promising in deep learning for text classification. Most existing NAS algorithms, however, are proposed for single-language text classification. They may become ineffective when extended to multilingual text classification because the differences in the syntactic structure and contextual semantics of different languages increase the computational complexity of NAS search. To address the above issue, this article proposes a differential neural architecture search approach using multi-hashing embedding for multilingual text representation. A multi-hashing network capable of processing heterogeneous graph information is constructed so that cross-language syntactic and contextual semantic information can be effectively represented. In addition, a neural tensor network with multi-hashing embedding is adopted as a continuous encoder to estimate the probability for each candidate operation in the search space, and reparameterization-based gradient search is employed to efficiently search for network architectures for multilingual text representation. Our experimental results on two multilingual text classification datasets demonstrate that the proposed approach outperforms the state-of-the-art NAS methods for text classification in terms of both classification accuracy and computational efficiency.


Multi-Hop Task Routing in Vehicle-Assisted Collaborative Edge Computing

Abstract: Collaborative edge computing has emerged as a novel paradigm that allows edge servers (ESs) to share data and computing resources, effectively mitigating network congestion in traditional multi-access edge computing (MEC) scenarios. However, existing research in collaborative edge computing often limits offloading to only one hop, which may lead to suboptimal computing resource sharing due to challenges such as poor channel conditions or high computing workload at ESs located just one hop away. To address this limitation and enable more efficient computing resource utilization, we propose a multi-hop MEC approach that leverages omnipresent vehicles in urban areas to create a data transportation network for task delivery. Here, we propose a general multi-hop task offloading framework for vehicle-assisted collaborative edge computing where tasks from users can be offloaded to powerful ESs via potentially multi-hop transmissions. Under the proposed framework, we formulate an aggregated service throughput maximization problem by designing the task routing path subject to end-to-end latency requirements, spectrum, and computing resources. To efficiently address the curse of dimensionality problem due to vehicular mobility and channel variability, we develop a deep reinforcement learning, i.e., multi-agent deep deterministic policy gradient, based multi-hop task routing approach. Numerical results demonstrate that the proposed algorithm outperforms existing benchmark schemes.


Distinct but correct: generating diversified and entity-revised medical response

Abstract: Medical dialogue generation (MDG) is applied for building medical dialogue systems for intelligent consultation. Such systems can communicate with patients in real time, thereby improving the efficiency of clinical diagnosis. However, predicting correct entities and correctly generating distinct responses remain a great challenge. Inspired by actual doctors’ responses to patients, we consider MDG a two-stage task: entity prediction and dialogue generation. For entity prediction, we design an ent-mac post pre-training strategy by leveraging external medical entity knowledge to enhance the pre-trained model. For dialogue generation, we propose an entity-aware fusion MDG method in which predicted entities are integrated into the dialogue generation model through different encoding fusion mechanisms, using information from different sources. Because the diverse beam search algorithm can produce responses with entities that deviate from the predicted entities, an entity-revised diverse beam search is proposed to correct the entities entailed in the generated responses and make the generated responses more distinct. The experimental results on the China Conference on Knowledge Graph and Semantic Computing 2021 (A/B tests) and the International Conference on Learning Representations 2021 (online test) datasets show that the proposed method outperforms several state-of-the-art methods, which demonstrates its practicability and effectiveness.


Multitask Kernel-based Learning with Logic Constraints

Abstract: This paper presents a general framework to integrate prior knowledge in the form of logic constraints among a set of task functions into kernel machines. The logic propositions provide a partial representation of the environment, in which the learner operates, that is exploited by the learning algorithm together with the information available in the supervised examples. In particular, we consider a multi-task learning scheme, where multiple unary predicates on the feature space are to be learned by kernel machines and a higher level abstract representation consists of logic clauses on these predicates, known to hold for any input. A general approach is presented to convert the logic clauses into a continuous implementation, that processes the outputs computed by the kernel-based predicates. The learning task is formulated as a primal optimization problem of a loss function that combines a term measuring the fitting of the supervised examples, a regularization term, and a penalty term that enforces the constraints on both supervised and unsupervised examples. The proposed semi-supervised learning framework is particularly suited for learning in high dimensionality feature spaces, where the supervised training examples tend to be sparse and generalization difficult. Unlike for standard kernel machines, the cost function to optimize is not generally guaranteed to be convex. However, the experimental results show that it is still possible to find good solutions using a two stage learning schema, in which first the supervised examples are learned until convergence and then the logic constraints are forced. Some promising experimental results on artificial multi-task learning tasks are reported, showing how the classification accuracy can be effectively improved by exploiting the a priori rules and the unsupervised examples.


Joint Gaussian Distribution and Attention for Time-Aware Recommendation Systems

Abstract: Sequential models have achieved admirable success in recommendation systems. However, most sequential models typically only consider the chronological order of items through timestamps and ignore the relative distances in the sequence, which weakens the temporal relationships between items. To address this issue, we propose a temporal recommendation system using the Gaussian distribution and attention mechanism, which considers the sequentiality and interaction among items. Technically, we first deploy the word vector space along the time dimension as sequence features. Then, we use the Gaussian process to effectively represent the duration influence of items and the context interaction between items as high-level features. Finally, an innovative attention mechanism is used to capture the hidden correlation relationships between representation subspaces of different levels of features. Experiments conducted on two widely used real public datasets show that our model outperforms the state-of-the-art recommendation systems.


Progressive Learning of 3D Reconstruction Network From 2D GAN Data

Abstract: This paper presents a method to reconstruct high-quality textured 3D models from single images. Current methods rely on datasets with expensive annotations; multi-view images and their camera parameters. Our method relies on GAN generated multi-view image datasets which have a negligible annotation cost. However, they are not strictly multi-view consistent and sometimes GANs output distorted images. This results in degraded reconstruction qualities. In this work, to overcome these limitations of generated datasets, we have two main contributions which lead us to achieve state-of-the-art results on challenging objects: 1) A robust multi-stage learning scheme that gradually relies more on the models own predictions when calculating losses, 2) A novel adversarial learning pipeline with online pseudo-ground truth generations to achieve fine details. Our work provides a bridge from 2D supervisions of GAN models to 3D reconstruction models and removes the expensive annotation efforts. We show significant improvements over previous methods whether they were trained on GAN generated multi-view images or on real images with expensive annotations. Please visit our web-page for 3D visuals: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://research.nvidia.com/labs/adlr/progressive-3d-learning</uri> .


Fuzzy Representation Learning on Dynamic Graphs

Abstract: Exploring dynamic patterns from complex and large-scale networks is a significant and challenging task in graph analysis. One of the most advanced solutions is dynamic graph representation learning, which embeds structural and temporal correlations into a representative vector for each node or subgraph. Existing models have made some successes, such as overcoming the problems of induction for unseen nodes and scalability for large-scale evolving networks. However, these models usually rely on crisp representation learning that is incapable of modeling feature fuzziness and capturing uncertainties in dynamic graphs. While real-world dynamic networks as complex systems always contain non-negligible but inestimable uncertainties in node/link attributes and network topology. These uncertainties may cause the learned representations from crisp models hard to precisely reflect network evolution. To address the issues, we propose a new dynamic graph representation learning model, called FuzzyDGL, which first incorporates fuzzy representation learning to handle the uncertainties in dynamic graphs. Through combining CDGRL with fuzzy logic, the FuzzyDGL digests both of their advantages. On the one hand, it has flexible model scalability and brilliant inductive capability. On the other hand, it can model feature fuzziness to reduce the impact of uncertainties in dynamic graphs, improving the quality of learned representations. To demonstrate its effectiveness, we conduct two important tasks of network analysis, including link prediction and node classification, over eight real-world datasets. The experimental results show the strong competitiveness and generalization of the FuzzyDGL against a number of baseline models.


An improved Arabic text classification method using word embedding

Abstract: Feature selection (FS) is a widely used method for removing redundant or irrelevant features to improve classification accuracy and decrease the model’s computational cost. In this paper, we present an improved method (referred to hereafter as RARF) for Arabic text classification (ATC) that employs the term frequency-inverse document frequency (TF-IDF) and Word2Vec embedding technique to identify words that have a particular semantic relationship. In addition, we have compared our method with four benchmark FS methods namely principal component analysis (PCA), linear discriminant analysis (LDA), chi-square, and mutual information (MI). Support vector machine (SVM), k-nearest neighbors (K-NN), and naive Bayes (NB) are three machine learning based algorithms used in this work. Two different Arabic datasets are utilized to perform a comparative analysis of these algorithms. This paper also evaluates the efficiency of our method for ATC on the basis of performance metrics viz accuracy, precision, recall, and F-measure. Results revealed that the highest accuracy achieved for the SVM classifier applied to the Khaleej-2004 Arabic dataset with 94.75%, while the same classifier recorded an accuracy of 94.01% for the Watan-2004 Arabic dataset.


Predicting automobile insurance fraud using classical and machine learning models

Abstract: Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases.


Data integrity aware system for executing dynamic operations on outsourced cloud data

Abstract: The cloud computing platform of today handles a greater amount of real-time data, which requires dynamic data operations. The majority of the currently available cloud techniques, however, do not concentrate on dynamic data operations. Hence, this work provides a system that enables dynamic data operations to be carried out on a cloud computing platform, such as the insertion, deletion, and updating of data. The performance of the work is tested in terms of time consumption with respect to data storage, retrieval, data block insertion and deletion operations. The analysis is carried out by altering the data volume and the performance of the work is satisfactory with reasonable time consumption.


Exploiting Physical Referent Features as Input for Multidimensional Data Selection in Augmented Reality

Abstract: Embedding data into the physical environment using augmented reality (AR) is a practical approach for data visualization as it offers a large and flexible display space on or around the physical referent, i.e. the physical object to which the data is related. Yet current interaction in such context is often performed using cumbersome dedicated devices, tiring mid-air gestures or awkward on-body input. In this paper, we investigate the use of the physical referent itself as a support for input interaction with an embedded space-time cube (STC) representation. Hence, we first identify the most promising mappings between the physical features of the referent (edges, faces, corners) and the STC dimensions. Then, we design three data selection techniques using the physical referent and compare them to mid-air gestures when performing selection tasks on the STC. Overall, our work demonstrates that using the physical referent to support input interaction with embedded data representations is an efficient and comfortable approach for data selection in standing and sitting situations.


Deep learning enabled blockchain based electronic heathcare data attack detection for smart health systems

Abstract: The idea of networked personal medical devices is a component of contemporary Smart Health Systems (SHS). These gadgets offer remote observing and the exchange of wellbeing information, which enormously further develop the patient's personal satisfaction while at the same time reducing treatment expenses for both the patient and the medical care suppliers (telemedicine). For individuals' wellbeing, a cutting edge individual wellbeing record framework is fundamental. Here, there are still difficulties with information mix from different EHRs, information interoperability, and guaranteeing that admittance to information is totally under the power of the patient. Some security issues are caused by the Network. To settle these issues, we propose a novel profound learning-based framework that circuits state of the art decentralized innovations like IPFS and blockchain with wellbeing information interoperability principles and advances like FHIR's APIs. In this review, we show that correspondence between private clinical gadgets is as a matter of fact powerless to different cyber attacks. We show how an outer assailant could involve man-in-the-center, replay, bogus information infusion, and refusal of-administration assaults to block delicate wellbeing information stream by capturing the correspondence of the individual clinical gadget. We likewise suggest an Interruption Recognition Framework (IDS), GAN, to additional screen traffic on private clinical gear and spot attacks against them. Our extensive investigation shows that GAN, with an F1-score of 98 % and an accuracy of 98.7 %, can successfully and accurately recognise numerous assaults on personal medical equipment.


Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

Abstract: Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom\'s epistemic taxonomy based on textual comments in educational discussion forums. Our proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom\'s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis.


Professional and personal traits of the teacher and the relationship with didactic strategies

Abstract: During the context of virtual classes due to the pandemic, it was evidenced that many teachers had to face accelerated adaptation stages, which is why teachers presented difficulties in designing their teaching strategies and students showed certain difficulties in their learning. In this sense, this article aims to determine how the professional and personal traits of the teacher are related to their teaching strategies, in the context of COVID-19, from the perspective of the students. For which an investigation was developed with a quantitative approach, correlational level and non-experimental design. It was determined that there is a moderate direct relationship between the professional and personal traits of the teacher with his didactic strategies in the context of virtual teaching. Thus, it was also identified that teachers do not pour their professional experiences into the development of the subject, additionally it does not favor the solution of problems related to real life applications; this leads teacher to reflect on the importance of the construction of student knowledge, especially if it is about subjects that are taught in the last cycles, since the student is waiting for how and in which processes their knowledge will be applied theoretical knowledge.


DGTRL: Deep graph transfer reinforcement learning method based on fusion of knowledge and data

Abstract: Deep reinforcement learning has shown promising application effects in many fields. However, issues such as low sample efficiency and weak knowledge transfer and generalization capabilities constrain its in-depth development. This paper proposes a deep graph transfer reinforcement learning method based on knowledge and data fusion. Through knowledge transfer between tasks and data training in exploration, efficient learning driven by knowledge and data fusion is realized. First, we map the trajectories of agents in the source and target domains into graph structures to represent knowledge and environmental models and use a graph matching method to match the knowledge adaptation domain based on similarity. Second, real-time transfer of source domain knowledge is carried out in a similar mapping domain, and policy mapping, verification, and adaptive transfer mechanisms based on knowledge reuse frequency are designed to effectively improve the transfer effect. Finally, we evaluate and validate the proposed algorithm, mechanism, and their application effectiveness in diverse and multitask knowledge transfer scenarios using three indicators in environments such as maze. The results show that the method improves the target task time indicators by 84.7% and 70% under the conditions of no verification exploration and a standard transfer setting, respectively, achieving significant knowledge reuse effects.


Teaching speaking in Kampung Inggris: the tutors’ challenges and solutions

Abstract: Teaching English speaking has become a centre of attention among scholars and teaching English to speakers of other languages (TESOL) practitioners for a few decades. However, the issue of teaching English speaking in Kampung Inggris/English Village remains under-researched, notably viewed from the tutors’ challenges and solutions in the Indonesian English as a foreign language (EFL) milieu. This study addressed this gap. Two English tutors from Kampung Inggris/English Village got involved as the participants. The data were collected through semi-structured interviews and analyzed with thematic analysis. The findings reported that there were five challenges encountered by tutors when teaching English speaking, namely tutors' inability of exploring English language teaching materials, tutors' insufficient English vocabulary, tutors' ineffective time management during language teaching practices, demotivated tutees to learn English, and perceived teaching anxiety. In addition, the tutors stipulated four solutions to cope with such challenges, namely building a good rapport between tutor and tutees, tutors' self-motivation to English vocabulary enrichment, selecting appropriate and providing updated English language teaching materials, and motivating English language tutees. Therefore, teaching English speaking should not only rely on immersing students in fluency-based activities but also manage tutees' psychological factors to attain more effective teaching English speaking objectives.


Multimodal Detection and Classification of Robot Manipulation Failures

Abstract: An autonomous service robot should be able to interact with its environment safely and robustly without requiring human assistance. Unstructured environments are challenging for robots since the exact prediction of outcomes is not always possible. Even when the robot behaviors are well-designed, the unpredictable nature of the physical robot-object interaction may lead to failures in object manipulation. In this work, we focus on detecting and classifying both manipulation and post-manipulation phase failures using the same exteroception setup. We cover a diverse set of failure types for primary tabletop manipulation actions. In order to detect these failures, we propose FINO-Net [1], a deep multimodal sensor fusion-based classifier network architecture. FINO-Net accurately detects and classifies failures from raw sensory data without any additional information on task description and scene state. In this work, we use our extended FAILURE dataset [1] with 99 new multimodal manipulation recordings and annotate them with their corresponding failure types. FINO-Net achieves 0.87 failure detection and 0.80 failure classification F1 scores. Experimental results show that FINO-Net is also appropriate for real-time use.


Precision in spatial working memory examined with mouse pointing

Abstract: The capacity of visuospatial working memory (VSWM) is limited. However, there is continued debate surrounding the nature of this capacity limitation. The resource model (Bays et al., 2009) proposes that VSWM capacity is limited by the precision with which visuospatial features can be retained. In one of the few studies of spatial working memory, Schneegans and Bays (2016) report that memory guided pointing responses show a monotonic decrease in precision as set size increases, consistent with resource models. Here we report two conceptual replications of this study that use mouse responses rather than pointing responses. Overall results are consistent with the resource model, as there was an exponential increase in localisation error and monotonic increases in the probability of misbinding and guessing with increases in set size. However, an unexpected result of Experiment One was that, unlike Schneegans and Bays (2016), imprecision did not increase between set sizes of 2 and 8. Experiment Two replicated this effect and ruled out the possibility that the invariance of imprecision at set sizes greater than 2 was a product of oculomotor strategies during recall. We speculate that differences in imprecision are related to additional visuomotor transformations required for memory-guided mouse localisation compared to memory-guided manual pointing localisation. These data demonstrate the importance of considering the nature of the response modality when interpreting VSWM data.


Customer sentiment recognition in conversation based on bidirectional LSTM and self-attention mechanism

Abstract: In the E-commerce environment, conversations between customers and businesses contain lots of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. For conversational sentiment analysis, most existing approaches take contextual information into account. On this basis, we focus on the degree of association between utterances, which can more effectively capture overall and useful sentiment information in conversation. For this purpose, we propose a hybrid model to recognize customer sentiment in conversation. The model obtains utterance vectors with sentiment information through Sentiment Knowledge Enhanced Pre-training (SKEP), then uses the bidirectional long short-term memory network (BiLSTM) to generate contextual semantic information, and further obtains customer sentiment information by applying the self-attention mechanism to focus on the degree of association between utterances. The experimental results on the JD Dialog dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation.


Scalable and resilient 360-degree-video adaptive streaming over HTTP/2 against sudden network drops

Abstract: Realistic Virtual Reality is supported by 360° video, which provides viewers with an immersive watching experience. However, 360°video is bulky in size, while the transmission system has limited ability to provide bandwidth. As such, intelligent adaptive delivery solutions play a crucial role in enabling users to stream high-quality 360°video. In this research, we propose a novel approach for 360-degree video streaming over HTTP/2 that can provide consumers with a good watching experience (QoE) even in varying network circumstances and head-eye movements over time. The proposed method deploys the so-called BBAG algorithm (Buffer and Bandwidth Allocation Algorithm) using Scalable Video Coding to choose appropriate tile layers to resolve the trade-off between network and user adaptivity. With the support of HTTP/2's stream termination capability, the delivery of late tile layers is terminated to handle abrupt interruptions. By employing multiple buffer thresholds, BBAG is able to adapt bitrates to changes in users' perspective while watching a 360-degree video. BBAG is proven to improve QoE up to about 90% by maintaining high and stable buffer level, while enhancing average viewport bitrates by about 80% compared to state-of-the-art methods in different scenarios of network bandwidths.


Sensor-based agitation prediction in institutionalized people with dementia A systematic review

Abstract: Early detection of agitation in individuals with dementia can lead to timely interventions, preventing the worsening of situations and enhancing their quality of life. The emergence of multi-modal sensing and advances in artificial intelligence make it feasible to explore and apply technology for this goal. We conducted a literature review to understand the current technical developments and challenges of its integration in caregiving institutions. Our systematic review used the Pubmed and IEEE scientific databases, considering studies from 2017 onwards. We included studies focusing on linking sensor data to vocal and/or physical manifestations of agitation. Out of 1622 identified studies, 12 were selected for the final review. Analysis was conducted on study design, technology, decisional data, and data analytics. We identified a gap in the standardized semantic representation of both behavioural descriptions and system event generation configurations. This research highlighted initiatives that leverage existing information in a caregiver's routine, such as correlating electronic health records with sensor data. As predictive systems become more integrated into caregiving routines, false positive reduction needs to be addressed as those will discourage their adoption. Therefore, to ensure adaptive predictive capacity and personalized system re-configuration, we suggest future work to evaluate a framework that incorporates a human-in-the-loop approach for detecting and predicting agitation.


An Efficient Hybrid Deep Learning Accelerator for Compact and Heterogeneous CNNs

Abstract: Resource-efficient Convolutional Neural Networks (CNNs) are gaining more attention. These CNNs have relatively low computational and memory requirements. A common denominator among such CNNs is having more heterogeneity than traditional CNNs. This heterogeneity is present at two levels: intra-layer type and inter-layer type. Generic accelerators do not capture these levels of heterogeneity, which harms their efficiency. Consequently, researchers have proposed model-specific accelerators with dedicated engines. When designing an accelerator with dedicated engines, one option is to dedicate one engine per CNN layer. We refer to accelerators designed with this approach as single-engine single-layer (SESL). This approach enables optimizing each engine for its specific layer. However, such accelerators are resource-demanding and unscalable. Another option is to design a minimal number of dedicated engines such that each engine handles all layers of one type. We refer to these accelerators as single-engine multiple-layer (SEML). SEML accelerators capture the inter-layer-type but not the intra-layer-type heterogeneity. We propose the Fixed Budget Hybrid CNN Accelerator (FiBHA), a hybrid accelerator composed of an SESL part and an SEML part, each processing a subset of CNN layers. FiBHA captures more heterogeneity than SEML while being more resource-aware and scalable than SESL. Moreover, we propose a novel module, Fused Inverted Residual Bottleneck (FIRB), a fine-grained and memory-light SESL architecture building block. The proposed architecture is implemented and evaluated using high-level synthesis (HLS) on different Field Programmable Gate Arrays representing various resource budgets. Our evaluation shows that FiBHA improves the throughput by up to 4 x and 2.5 x compared to state-of-the-art SESL and SEML accelerators, respectively. Moreover, FiBHA reduces memory and energy consumption compared to an SEML accelerator. The evaluation also shows that FIRB reduces the required memory by up to 54%, and energy requirements by up to 35% compared to traditional pipelining.


Optimization of Expert System Based on Interpolation, Forward Chaining, and Certainty Factor for Diagnosing Abdominal Colic

Abstract: Abdominal colic is a common condition that affects infants and it can be difficult to diagnose because it shares many symptoms with other conditions, such as gastric disease and appendicitis. Limitations of existing diagnostic methods include the unreliability of physical examinations and medical histories and the high cost and time-consuming nature of imaging tests. This research proposes an expert system based on interpolation, forward chaining, and certainty factors for diagnosing abdominal colic. This system has the potential to provide a more accurate and efficient way to diagnose abdominal colic, which could lead to better patient outcomes. This research proposes an expert system based on interpolation, forward chaining, and certainty factors for diagnosing abdominal colic. This system is implemented as a web application model. The forward chaining method is used to establish rules for the expert system. The rules are based on the symptoms and diseases that are included in the system's knowledge base. The interpolation method is used to normalize lab results and the certainty factor method is used to process medical history and physical examinations. This is necessary because medical history and physical examinations can be imprecise. The expert system was tested on a dataset of 100 cases and it was able to accurately diagnose 96 patients, achieving a 96% accuracy rate. This suggests that the expert system has the potential to provide a more accurate and efficient way to diagnose abdominal colic, which could lead to better patient outcomes.


Playing With Monte-Carlo Tree Search [AI-eXplained]

Abstract: This paper provides an accessible explanation of the working mechanism of Monte-Carlo Tree Search, an influential search algorithm. The paper summarizes the procedure of Monte-Carlo Tree Search, including selection, expansion, simulation, and backpropagation. Additionally, immersive examples based on Tic-Tac-Toe, Go, and Sokoban, two two-player competitive games and a classic single-player puzzle game, are presented to illustrate how Monte-Carlo Tree Search works. The full article with interactive contents is published on IEEE Xplore.


Omniscient Video Super-Resolution with Explicit-Implicit Alignment

Abstract: When considering the temporal relationships, most previous video super-resolution (VSR) methods follow the iterative or recurrent framework. The iterative framework adopts neighboring low-resolution (LR) frames from a sliding window, while the recurrent framework utilizes the output generated in the previous SR procedure. The hybrid framework combines them but still cannot fully leverage the temporal relationships. Meanwhile, the existing methods are limited in the receptive field of the optical flow or lack semantic constrains on motion information. In this work, we propose an omniscient framework to fully explore the temporal relationships in the video, which encompasses both LR frames and SR outputs from the past, present, and future. The omniscient framework is more generic because the iterative, recurrent, and hybrid frameworks can be regarded as its special cases. Besides, when addressing the motion information, most previous VSR methods adopt the explicit motion estimation and compensation, while many recent methods turn to implicit alignment. In implicit alignment methods, because basic non-local means suffers from heavy computational costs, we improve it by capturing the non-local correlations in a relatively local manner to reduce the complexity. Moreover, we integrate the explicit and implicit methods into an explicit-implicit alignment module to better utilize motion information. We have conducted extensive experiments on public datasets, which show that our method is superior over the state-of-the-art methods in objective metrics, subjective visual quality, and complexity. In particular, on datasets of Vid4 and UDM10, our method improves PSNR by 0.19 dB, 0.49 dB against the most advanced method BasicVSR++, respectively.


Knowledge graph embedding in a uniform space

Abstract: Knowledge graph embedding (KGE) is typically used for link prediction to automatically predict missing links in knowledge graphs. Current KGE models are mainly based on complicated mathematical associations, which are highly expressive but ignore the uniformity behind the classical bilinear translational model TransE, a model that embeds all entities of knowledge graphs in a uniform space, enabling accurate embeddings. This study analyses the uniformity of TransE and proposes a novel KGE model called ConvUs that follows uniformity with expressiveness. Based on the convolution neural network (CNN), ConvUs proposes constraints on convolution filter values and employs a multi-layer, multi-scale CNN architecture with a non-parametric L2 norm-based scoring function for the calculation of triple scores. This addresses potential uniformity-related issues in existing CNN-based KGE models, allowing ConvUs to maintain a uniform embedding space while benefiting from the powerful expressiveness of CNNs. Furthermore, circular convolution is applied to alleviate the potential orderliness contradictions, making ConvUs more suitable for conducting uniform space KGE. Our model outperformed the base model ConvKB and several baselines on the link prediction benchmark WN18RR and FB15k-237, demonstrating strong applicability and generalization and indicating that the uniformity of embedding space with high expressiveness enables more efficient knowledge graph embeddings.


Corrigendum to “Omnisolver: An extensible interface to Ising spin–glass and QUBO solvers” [SoftwareX 24 (2023) 101559]

Abstract: The authors regret imprecise statement of acknowledgements. The acknowledgement section should contain the following statement: “This work is supported by the project “Near-term quantum computers Challenges, optimal implementations and applications” under Grant Number POIR.04.04.00–00–17C1/18–00, which is carried out within the Team-Net programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.” The authors would like to apologise for any inconvenience caused. Omnisolver: An extensible interface to Ising spin–glass and QUBO solversSoftwareXVol. 24PreviewWe introduce a new framework for implementing Binary Quadratic Model (BQM) solvers called Omnisolver. The framework provides an out-of-the-box dynamically built command-line interface as well as an input/output system, thus heavily reducing the effort required for implementing new algorithms for solving BQMs. The proposed software should be of benefit for researchers focusing on quantum annealers or discrete optimization algorithms as well as groups utilizing discrete optimization as a part of their daily work. Full-Text PDF Open Access


Optimized in-loop filtering in versatile video coding using improved fast guided filter

Abstract: Devices with varying display capabilities from a common source may face degradation in video quality because of the limitation in transmission bandwidth and storage. The solution to overcome this challenge is to enrich the video quality. For the mentioned purpose, this paper introduces an improved fast guided filter (IFGF) for the contemporary video coding standard H.266/VVC (versatile video coding), a continuation of H.265/HEVC (high efficiency video coding). VVC includes several types of coding techniques to enhance video coding efficiency over existing video coding standards. Despite that, blocking artifacts are still present in the images. Hence, the proposed method focuses on denoising the image and the increase of video quality, which is measured in terms of peak signal-to-noise (PSNR). The objective is achieved by using an IFGF for in-loop filtering in VVC to denoise the reconstructed images. VTM (VVC test model)-17.2 is used to simulate the various video sequences with the proposed filter. This method achieves a 0.67% Bjontegaard delta (BD)-rate reduction in low-delay configuration accompanied by an encoder run time increase of 4%.


Security enhancement of cyber-physical system using modified encryption AESGNRSA technique

Abstract: A cyber-physical system (CPS) is a combination of physical components with computational elements to interact with the physical world. The integration of these two systems has led to an increase in security concerns. Traditional encryption algorithms designed for general-purpose computing environments may not adequately address the distinct challenges of CPS, such as limited processing power, delay, and resource-constrained hardware. Therefore, there is a pressing need to develop an encryption algorithm that is optimized for CPS security without compromising the critical real-time aspects of these systems. This research has designed a modified encryption technique named the advanced encryption standard in galois counter mode with nonce and rivest-shamir-adleman algorithm (AESGNRSA). A smart medical system is designed to monitor the health of remotely located patients. The AESGNRSA algorithm is applied to the three servers of this system. The data of 1.5 lakh patients is fed to this system to verify the effectiveness of the AESGNRSA algorithm. The performance parameters like encryption and decryption time, encryption and decryption throughput, and encrypted file size are calculated for the AESGNRSA algorithm. The comparative analysis proved that AESGNRSA has the highest performance as compared to other algorithms and it can protect CPS against many cyber-attacks.


Methods for optimizing the assignment of cloud computing resources and the scheduling of related tasks

Abstract: Efficient scheduling algorithms are necessary in the cloud paradigm to optimize service provision to clients while minimizing time duration, energy consumption, and violations of service level agreements (SLAs). Disregarding task appropriateness in resource scheduling can have a detrimental effect on the quality of service provided by cloud providers. Moreover, the utilization of resources in an ineffective manner will necessitate a substantial expenditure of energy to execute activities, leading to prolonged processing duration that adversely affect the temporal duration. Many research projects have focused on employment scheduling problems, and the algorithms used in these studies have offered answers that were deemed nearly flawless. This study presents a chaos bird swarm algorithm (Chaos BSA) approach that use machine learning to consider task priority while allocating tasks to the cloud platform. The method calculates the priorities of task virtual machines and incorporates these values into the scheduler. The scheduler will select tasks that align with the specified priorities and are compatible with the virtual machines. The implementation of the system utilized the openstack cloud platform and the cloudsim tool. The results and comparison with the baseline approach genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) clearly demonstrate that our Chaos BSA outperforms them by 18% in terms of efficiency.


Multi-domain aspect-oriented sentiment analysis for movie recommendations using feature extraction

Abstract: Sentiment analysis is a well-recognized research field that has acknowledged significant attention in recent years. Researchers have made extensive efforts in employing various methodologies to explore these domains. Sentiment classification plays a fundamental role in natural language processing (NLP). However, studies have shown that sentiment classification models heavily depend on the specific domain. In the context of movie industry, where the demand for reliable movie reviews is high and not all movies are of exceptional quality and worthy of viewers time. Therefore, people depend on movie reviews before watching a movie. This explores the use of data from various domains to improve classification performance within each domain, addressing the difficulty of multi-domain sentiment classification in natural language processing. Therefore, it is crucial to effectively utilize shared sentiment knowledge across different domains for real-world applications. To solve these issues, a multi-domain aspect-oriented sentiment analysis for movie recommendation using feature extraction techniques. The main contribution of this work is to eliminate the time for users to go through a lengthy list of movies to make their decision. The novelty of this work is analysis of different movie genres, TV shows genres with accurate results. The presented approach's performance is validated by evaluating various metrics, including precision, recall, mean square error (MSE) and F1-score.


Convolutional neural network-based techniques and error level analysis for image tamper detection

Abstract: Photographs are the foremost powerful and trustworthy media of expression. At present, digital pictures not only serve forged information but also disseminate deceptive information. Users and experts with various objectives edit digital photographs. Images are frequently used as proof of reality or fact, therefore fake news or any publication that makes use of photos that have been altered in any way has a larger chance of deceiving readers. There is a need for a high-resolution image analysis model that processes individual pixels in images and a substantial amount of diverse image data, to detect image falsification. Convolutional neural network (CNN) with error level analysis (ELA) adopted in this research is found to be an ideal deep learning concept for detecting image manipulation. The model exhibited a validation accuracy of 99.6%, 99.7%, and 99.4% for CASIA V1.0, CASIA V2.0 and MICC datasets respectively. The accuracy for handmade tampered images was found to be 99.2%.


Automatic question generation using extended dependency parsing

Abstract: The importance of automatic question generation (AQG) systems in education is recognized for automating tasks and providing adaptive assessments. Recent research focuses on improving quality with advanced neural networks and machine learning techniques. However, selecting the appropriate target sentences and concepts remains challenging in AQG systems. To address this problem, the authors created a novel system that combined sentence structure analysis, dependency parsing approach, and named entity recognition techniques to select the relevant target words from the given sentence. The main goal of this paper is to develop an AQG system using syntactic and semantic sentence structure analysis. Evaluation using manual and automatic metrics shows good performance on simple and short sentences, with an overall score of 3.67 out of 5.0. As the field of AQG continues to evolve rapidly, future research should focus on developing more advanced models that can generate a wider range of questions, especially for complex sentence structures.