Deposit type discrimination based on trace elements in sphalerite

Abstract: Sphalerite can form in different geological environments and is the main ore mineral of most Pb-Zn deposits. Trace elements in sphalerite combined with machine learning (ML) methods have been used for deposit type discrimination. However, the performance of different multivariate and ML methods is not systematically compared. Here more than four thousand laser ablation inductively coupled plasma mass spectrometry data from more than one hundred deposits were compiled and investigated by different multivariate and ML methods. Unsupervised multivariate and ML methods including principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), and supervised multivariate and ML methods including partial least squares-discriminant analysis (PLS-DA), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), naïve Bayes (NB), and neural network (NN) are used to classify deposit types. The models are constructed for five deposit types, Mississippi Valley-type (MVT), Volcanogenic Massive Sulfide (VMS), sedimentary exhalative (SEDEX), Epithermal, and Skarn deposits, based on twelve elements in sphalerite, Fe, Pb, Mn, Co, Cu, Ga, Ge, Ag, Cd, In, Sn, and Sb. The PCA and PLS-DA methods show similar clustering results, where Epithermal, MVT, SEDEX, and Skarn deposits can be separated from other deposit types. The t-SNE method is only efficient in discriminating MVT, SEDEX, and Epithermal deposits. The KNN, RF, and NN methods show better performance than LR, SVM, and NB in terms of average accuracy and precision. The PCA, t-SNE, and PLS-DA methods have advantages in data visualization, whereas KNN, NN, and RF methods are more suitable for classification tasks. Feature importance analyses of the PLS-DA and RF methods show that the most important discriminant elements in the classification are Ge, Mn, In, Sn, Ga and Sb. The models based on PCA, PLS-DA, KNN, NN, and RF are used to predict sphalerite samples with uncertain origins. All the models give similar classification results for deposit types, which are consistent with the ore-forming geological setting. The multivariate and ML models, including discrimination diagrams of PCA and PLS-DA, can be used as the first screening for the type of Pb-Zn mineralization in mineral exploration.


Korean Syntactic Complexity Analyzer (KOSCA): An NLP application for the analysis of syntactic complexity in second language production

Abstract: Given the lack of computational tools available for assessing second language (L2) production in Korean, this study introduces a novel automated tool called the Korean Syntactic Complexity Analyzer (KOSCA) for measuring syntactic complexity in L2 Korean production. As an open-source graphic user interface (GUI) developed in Python, KOSCA provides seven indices of syntactic complexity, including traditional and Korean-specific ones. Its validity was tested by investigating whether the syntactic complexity indices measured by it in L2 written and spoken production could explain the variability of L2 Korean learners’ proficiency. The results of mixed-effects regression analyses showed that all seven indices significantly accounted for learner proficiency in Korean. Subsequent stepwise multiple regression analyses revealed that the syntactic complexity indices explained 56.0% of the total variance in proficiency for the written data and 54.4% for the spoken data. These findings underscore the validity of the syntactic complexity indices measured by KOSCA as reliable indicators of L2 Korean proficiency, which can serve as a valuable resource for researchers and educators in the field of L2 Korean learning and assessment.


Multimodal human motion dataset of 3D anatomical landmarks and pose keypoints

Abstract: In this paper, we present a dataset that takes 2D and 3D human pose keypoints estimated from images and relates them to the location of 3D anatomical landmarks. The dataset contains 51,051 poses obtained from 71 persons in A-Pose while performing 7 movements (walking, running, squatting, and four types of jumping). These poses were scanned to build a collection of 3D moving textured meshes with anatomical correspondence. Each mesh in that collection was used to obtain the 3D locations of 53 anatomical landmarks, and 48 images were created using virtual cameras with different perspectives. 2D pose keypoints from those images were obtained using the MediaPipe Human Pose Landmarker, and their corresponding 3D keypoints were calculated by linear triangulation. The dataset consists of a folder for each participant containing two Track Row Column (TRC) files and one JSON file for each movement sequence. One TRC file is used to store the 3D data of the triangulated 3D keypoints while the other contains the 3D anatomical landmarks. The JSON file is used to store the 2D keypoints and the calibration parameters of the virtual cameras. The anthropometric characteristics of the participants are annotated in a single CSV file. These data are intended to be used in developments that require the transformation of existing human pose solutions in computer vision into biomechanical applications or simulations. This dataset can also be used in other applications related to training neural networks for human motion analysis and studying their influence on anthropometric characteristics.


Enhanced Aquila optimizer based on tent chaotic mapping and new rules

Abstract: Abstract Metaheuristic algorithms, widely applied across various domains due to their simplicity and strong optimization capabilities, play a crucial role in problem-solving. While the Aquila Optimizer is recognized for its effectiveness, it often exhibits slow convergence rates and susceptibility to local optima in certain scenarios. To address these concerns, this paper introduces an enhanced version, termed Tent-enhanced Aquila Optimizer (TEAO). TEAO incorporates the Tent chaotic map to initialize the Aquila population, promoting a more uniform distribution within the solution space. To balance exploration and exploitation, novel formulas are proposed, accelerating convergence while ensuring precision. The effectiveness of the TEAO algorithm is validated through a comprehensive comparison with 14 state-of-the-art algorithms using 23 classical benchmark test functions. Additionally, to assess the practical feasibility of the approach, TEAO is applied to six constrained engineering problems and benchmarked against the performance of the same 14 algorithms. All experimental results consistently demonstrate that TEAO outperforms other advanced algorithms in terms of solution quality and stability, establishing it as a more competitive choice for optimization tasks.


AI Model for Industry Classification Based on Website Data

Abstract: This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories. The study involved a detailed fine-tuning phase resulting in a consistent decrease in training loss, indicative of the model’s learning efficacy. Subsequent validation on a separate dataset revealed the model’s robust performance, with classification accuracies ranging from 83.5% to 92.6% across different industry classes. Our model showed a high overall accuracy of 88.23%, coupled with a robust F1 score of 0.88. These results highlight the model’s ability to capture and utilize the nuanced features of text data pertinent to various industries. The model has the capability to harness real-time web data, thereby enabling the utilization of the latest and most up-to-date information affecting to the company’s product portfolio. Based on the model’s performance and its characteristics, we believe that the process of relative valuation can be drastically improved.


Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models

Abstract: Large Language Models (LLMs) are reshaping the landscape of Machine Learning (ML) application development. The emergence of versatile LLMs capable of undertaking a wide array of tasks has reduced the necessity for intensive human involvement in training and maintaining ML models. Despite these advancements, a pivotal question emerges: can these generalized models negate the need for task-specific models? This study addresses this question by comparing the effectiveness of LLMs in detecting phishing URLs when utilized with prompt-engineering techniques versus when fine-tuned. Notably, we explore multiple prompt-engineering strategies for phishing URL detection and apply them to two chat models, GPT-3.5-turbo and Claude 2. In this context, the maximum result achieved was an F1-score of 92.74% by using a test set of 1000 samples. Following this, we fine-tune a range of base LLMs, including GPT-2, Bloom, Baby LLaMA, and DistilGPT-2—all primarily developed for text generation—exclusively for phishing URL detection. The fine-tuning approach culminated in a peak performance, achieving an F1-score of 97.29% and an AUC of 99.56% on the same test set, thereby outperforming existing state-of-the-art methods. These results highlight that while LLMs harnessed through prompt engineering can expedite application development processes, achieving a decent performance, they are not as effective as dedicated, task-specific LLMs.


Review of a Design Methodology in a Client-Based, Authentic Design Curriculum

Abstract: Abstract The curriculum at a small, urban, private school is centered around a series of hands-on, client-based design courses called the [redacted]. Projects are completed during an entire academic year. Faculty serve in a dual role of technical consultants and as academic coaches through the program. A faculty committee tasked with the responsibility to review, develop, and implement design course work performed a mid-semester progress report of students teams as part of a regular curricular review, and realized that a majority of teams were behind schedule for the prescribed project lifecycle and timeline. This realization from a collective team status update prompted a review of project expectations and milestone accomplishments across three levels of student teams (i.e. sophomore, junior, and senior). Design project teams in all three levels originally followed the same two semester project lifecycle, divided into four phases: Identify Requirements, Characterize Design, Optimize Design, and Validate Design, commonly referred to as ICOV. The status reports revealed that senior teams were on track with the prescribed project lifecycle, while the junior and sophomore teams required additional resources (e.g. time and faculty support). This performance evaluation prompted the faculty committee to develop new project timelines scaleable to the skill levels of the project teams. Additionally, this assessment of team progress also provided an opportunity to redefine the project phases to better represent the types of problems historically researched by the teams and the sequence of tasks performed throughout the semesters. The committee rebranded the four phase project lifecycle with custom phase definitions: Identifying requirements, Develop Preliminary Design, Develop Detailed Design, and Final System Design. The updated phase definitions were created to provide more structure for the student teams and better capture what the school's design process was in practice rather than in theory. This paper will present the original and revised project phases and the review of the design process. This process should be of interest to programs with capstone experiences.


Automated Assessment and Feedback in Higher Education Using Generative AI

Abstract: This chapter explores the integration of generative AI in higher education assessment, addressing the inadequacies of traditional methods in meeting the diverse needs of contemporary learners. It highlights the potential of AI technologies, such as natural language processing and computer vision, to offer personalized, scalable, and insightful evaluations. The chapter critically examines both the enhanced capabilities introduced by AI in educational settings and the ethical challenges it poses. Emphasizing the need for a balanced approach, it suggests synergizing AI's analytical strengths with human expertise to ensure equitable and effective assessments. This work aims to guide educators, administrators, and policymakers through the complexities of AI adoption in academic evaluation, focusing on maintaining academic integrity and inclusivity while leveraging the transformative potential of AI in education.


Kinematics-aware spatial-temporal feature transform for 3D human pose estimation

Abstract: 3D human pose estimation plays an important role in various human-machine interactive applications, but how to effectively extract and represent the kinematical features of human body structure in video has always been a challenge. This paper presents some inspiring observations on the human body properties that hold heuristic patterns of human poses: 1) There is distinct temporal coherence in any kind of human pose; 2) there exist evident spatial and temporal correlations among local joints even though the human is doing complex actions. According to the observed patterns, a locally structured feature encoder and a spatial–temporal feature transform are proposed for kinematics-aware feature extraction and enhancement. Unlike existing works directly projecting every bone joint to pose features without distinction, the proposed locally-structured feature encoder maps the local connection property of human body structure to kinematical features which are neural embeddings extracted from both local and global groups of human bone joints. Since the local and global bone-joint groups are pre-defined according to human body kinematics, the kinematical features are able to represent body kinematics. The kinematical features are then transformed by the proposed spatial–temporal feature transform to enhance the spatial and temporal correlations among human bone joints. The overall framework well promotes the representation of human body kinematics for 3D pose estimation. Extensive experimental results on commonly used datasets show that the mean per joint position error (MPJPE) is significantly reduced when compared with state-of-the-art methods under the same experimental condition. The improvement is expected to promote machines to better understand human poses for building superior human-centered automation systems.


The global volcano monitoring infrastructure database (GVMID)

Abstract: Monitoring volcanoes is of the most importance in volcano risk mitigation to safeguard lives and economies. Thanks to recent technological advances, both on-ground and in space, our understanding of volcanic processes has improved significantly. Though there is no one-system-fits-all, optimizing infrastructure for efficient monitoring stands as key objective. The impacts of volcanic hazards can span from local to global scales, affecting us both in the short and long term. This highlights the worldwide significance of improving volcano monitoring. Previously reliant on local ground-based instruments, today’s monitoring approach is enhanced by remote and space-based techniques such as satellite remote sensing, scanning-Differential Optical Absorption Spectroscopy (DOAS), and infrasound. Designing an effective monitoring infrastructure for volcano observatories involves careful consideration of various factors such as network coverage, type of sensors, data transmission, and power supply to ensure that the targeted parameters meet the specific needs of each volcano (e.g., type of activities, early warning systems). Additionally, fostering collaboration and information sharing within the global scientific community is essential for addressing the current challenges in volcanology. In line with this, we’ve established the Global Volcano Monitoring Infrastructure Database (GVMID) to compile data from volcano monitoring across the globe. Global Volcano Monitoring Infrastructure Database serves as an integral component of WOVOdat, the global volcano unrest database, aiming to enhance our understanding of eruptive processes and improve eruption forecasts. The database incorporates monitoring metadata comprising networks, stations, and instruments, all standardized and managed using a MySQL relational database management system. Accessed through a web-based interface ( https://wovodat.org/gvmid/home.php ), GVMID offers an informative snapshot and foundational overview of the techniques and instruments in place at diverse volcanoes. This interactive platform allows for queries, visualizations, and downloads, serving as a valuable resource for the volcano community. GVMID can assist observatories in various ways, by: (a) Facilitating the setup or enhancement of monitoring systems for specific volcanoes. (b) Providing insights into the latest monitoring technologies and instrumentation. (c) Identifying existing monitoring gaps that could be addressed through remote sensing infrastructure and future instrument deployments. We extend an invitation to the global volcano community to actively participate in the development and enrichment of GVMID. Our aim is for it to become a continually updated and indispensable resource that caters to diverse needs within the volcanology community.


AI Chatbots in Chinese higher education: adoption, perception, and influence among graduate students—an integrated analysis utilizing UTAUT and ECM models

Abstract: This study is centered on investigating the acceptance and utilization of AI Chatbot technology among graduate students in China and its implications for higher education. Employing a fusion of the UTAUT (Unified Theory of Acceptance and Use of Technology) model and the ECM (Expectation-Confirmation Model), the research seeks to pinpoint the pivotal factors influencing students’ attitudes, satisfaction, and behavioral intentions regarding AI Chatbots. The study constructs a model comprising seven substantial predictors aimed at precisely foreseeing users’ intentions and behavior with AI Chatbots. Collected from 373 students enrolled in various universities across China, the self-reported data is subject to analysis using the partial-least squares method of structural equation modeling to confirm the model’s reliability and validity. The findings validate seven out of the eleven proposed hypotheses, underscoring the influential role of ECM constructs, particularly “Confirmation” and “Satisfaction,” outweighing the impact of UTAUT constructs on users’ behavior. Specifically, users’ perceived confirmation significantly influences their satisfaction and subsequent intention to continue using AI Chatbots. Additionally, “Personal innovativeness” emerges as a critical determinant shaping users’ behavioral intention. This research emphasizes the need for further exploration of AI tool adoption in educational settings and encourages continued investigation of their potential in teaching and learning environments.


DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks

Abstract: Deep neural networks (DNNs) are widely used in various application domains such as image processing, speech recognition, and natural language processing. However, testing DNN models may be challenging due to the complexity and size of their input domain. Particularly, testing DNN models often requires generating or exploring large unlabeled datasets. In practice, DNN test oracles, which identify the correct outputs for inputs, often require expensive manual effort to label test data, possibly involving multiple experts to ensure labeling correctness. In this paper, we propose DeepGD , a black-box multi-objective test selection approach for DNN models. It reduces the cost of labeling by prioritizing the selection of test inputs with high fault-revealing power from large unlabeled datasets. DeepGD not only selects test inputs with high uncertainty scores to trigger as many mispredicted inputs as possible but also maximizes the probability of revealing distinct faults in the DNN model by selecting diverse mispredicted inputs. The experimental results conducted on four widely used datasets and five DNN models show that in terms of fault-revealing ability: (1) White-box, coverage-based approaches fare poorly, (2) DeepGD outperforms existing black-box test selection approaches in terms of fault detection, and (3) DeepGD also leads to better guidance for DNN model retraining when using selected inputs to augment the training set.


Creating and Implementing a Custom Chatbot in Engineering Education

Abstract: Abstract This paper investigates the development and use of a chatbot in an engineering curriculum. The chatbot helps students find course materials, answer general inquiries, schedule meetings with professors and teaching assistants, and much more. Students require academic and administrative assistance in their journey as undergraduates at universities. College life is stressful, and tasks such as keeping track of deadlines, scheduling meetings, and finding resources become daunting as the semester progresses. The constant email exchanges about general course information could also become tedious for the course instructors. The authors developed a chatbot to address these difficulties using The Microsoft Power Virtual Agents App (MS PVA). MS PVA was selected as the platform since it requires minimal coding. It is a graphical programming environment that requires configuring and connecting nodes. The development stages need to be well planned. Some items to consider are setting up a hierarchical system to store information that the chatbot needs to retrieve, and creating a shared department email address to send and receive meeting requests. The chatbot was designed to handle all repetitive tasks, thus freeing the instructors time for answering more meaningful questions. The chatbot also promotes self-learning as it allows the student to ask questions beyond office hours and get a response. The chatbot developed is set up to source information from existing databases of deadlines, file directories, questions, and answers. Hence, it does not require reprogramming for a new course. The instructor needs to tabulate all necessary information in a predetermined location. Future variations of the chatbot will help students with coursework by providing them with practice problems when prompted. It will assist them further by giving instant feedback on their responses. Thus, students will get to practice and prepare for a course as they see fit. Since this process will be automated and randomized, the chatbot will never run out of problems. The chatbot will record any question that it is unable to answer. The instructors can further develop the chatbot's repository to answer those questions. The chatbot's impact on the student's university experience is measured in a class by conducting class surveys among the students. The authors have planned a pilot study of the chatbot and its implementation for a course in Spring 2023. Results will be reported in the final paper.


Exploring the Potential of Deep Learning for Personalized Learning Environments

Abstract: Abstract This study investigates the current potential for artificial intelligence (AI) to support personalized learning (PL). Personalized learning can provide a customized learning environment to support student learning processes based on individual needs, competencies, and interests. One way to conduct personalized learning is by using a recommender system that employs deep learning, an AI technique. To date, a limited number of researchers have discussed the application of deep learning methods to develop advanced recommenders in personalized learning environments. This study examines the literature that describe deep learning as a recommender system to support personalized learning environments. This initial phase of the project seeks to synthesize the issues and opportunities associated with personalized learning experiences and the potential of using deep learning to support the process. Because the topic intersects the education and information technology (IT) fields, we selected three databases for this literature review project: Scopus, ERIC, and Engineering Village. We used the phrase "deep learning recommender system for personalized learning environments" as our search string. We focused only on papers that experts had evaluated in the field to ensure accuracy. Therefore, terms such as "peer review," "literature review," and "systematic review" were added to the original search string. The initial search results included 409 documents. After applying inclusion/exclusion criteria, 20 papers emerged as the focus of this study. Thematic analysis was used to look for various themes to identify how deep learning methods are used in education and their potential to inform personalized learning environments. The analysis process utilized Mendeley and NVivo to quickly capture themes by focusing on six features to peruse within the articles. The features involved research questions, goals of studies, research methodology, research design, primary outcomes, and limitations. We then generated three themes from the six features in the analysis phase of the 20 papers, first, regarding the type of study. We grouped the articles into primary and secondary studies. By these categorizations, we could identify the types of studies that employed deep learning methods in the development of recommender systems and their integration with personalized learning. Second, recommender system (RS) techniques mainly emerged in the articles. This theme highlighted the AI methodologies that were most frequently utilized in previous research. And the third theme was a list of e-learning platforms that applied RS for personalized learning. The main findings revealed that the deep learning method was effective in big data analysis due to its ability to forecast students' achievements, behaviors, and future paths. Therefore, we considered that deep learning could be widely applied as a technique to develop recommender systems to support personalized learning environments. Furthermore, because we found that only a few studies have investigated the implementation of this AI technology, researchers will have a great opportunity to explore deep learning to develop more innovative solutions in educational fields.


AdvMix: Adversarial Mixing Strategy for Unsupervised Domain Adaptive Object Detection

Abstract: Recent object detection networks suffer from performance degradation when training data and test data are distinct in image styles and content distributions. In this paper, we propose a domain adaptive method, Adversarial Mixing (AdvMix), where the label-rich source domain and unlabeled target domain are jointly trained by the adversarial feature alignment and a self-training strategy. To diminish the style gap, we design the Adversarial Gradient Reversal Layer (AdvGRL), containing a global-level domain discriminator to align the domain features by gradient reversal, and an adversarial weight mapping function to enhance the stability of domain-invariant features by hard example mining. To eliminate the content gap, we introduce a region mixing self-supervised training strategy where a region of the target image with the highest confidence is selected to merge with the source image, and the synthesis image is self-supervised by the consistency loss. To improve the reliability of self-training, we propose a strict confidence metric combining both object and bounding box uncertainty. Extensive experiments conducted on three benchmarks demonstrate that AdvMix achieves prominent performance in terms of detection accuracy, surpassing existing domain adaptive methods by nearly 5% mAP.


Enhancing Industrial IoT Network Security through Blockchain Integration

Abstract: In the rapidly evolving landscape of industrial ecosystems, Industrial IoT networks face increasing security challenges. Traditional security methods often struggle to protect these networks adequately, posing risks to data integrity, confidentiality, and access control. Our research introduces a methodology that leverages blockchain technology to enhance the security and trustworthiness of IoT networks. This approach starts with sensor nodes collecting and compressing data, followed by encryption using the ChaCha20-Poly1305 algorithm and transmission to local aggregators. A crucial element of our system is the private blockchain gateway, which processes and classifies data based on confidentiality levels, determining their storage in cloud servers or the Interplanetary File System for enhanced security. The system’s integrity and authenticity are further reinforced through the proof of authority consensus mechanism. This system employs Zero Knowledge Proof challenges for device authorization, optimizing data retrieval while maintaining a delicate balance between security and accessibility. Our methodology contributes to mitigating vulnerabilities in Industrial IoT networks and is part of a broader effort to advance the security and operational efficiency of these systems. It reflects an understanding of the diverse and evolving challenges in IoT security, emphasizing the need for continuous innovation and adaptation in this dynamic field.


Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case–control study

Abstract: Abstract Background There is increasing evidence that artificial intelligence (AI) breast cancer risk evaluation tools using digital mammograms are highly informative for 1–6 years following a negative screening examination. We hypothesized that algorithms that have previously been shown to work well for cancer detection will also work well for risk assessment and that performance of algorithms for detection and risk assessment is correlated. Methods To evaluate our hypothesis, we designed a case-control study using paired mammograms at diagnosis and at the previous screening visit. The study included n = 3386 women from the OPTIMAM registry, that includes mammograms from women diagnosed with breast cancer in the English breast screening program 2010–2019. Cases were diagnosed with invasive breast cancer or ductal carcinoma in situ at screening and were selected if they had a mammogram available at the screening examination that led to detection, and a paired mammogram at their previous screening visit 3y prior to detection when no cancer was detected. Controls without cancer were matched 1:1 to cases based on age (year), screening site, and mammography machine type. Risk assessment was conducted using a deep-learning model designed for breast cancer risk assessment (Mirai), and three open-source deep-learning algorithms designed for breast cancer detection. Discrimination was assessed using a matched area under the curve (AUC) statistic. Results Overall performance using the paired mammograms followed the same order by algorithm for risk assessment (AUC range 0.59–0.67) and detection (AUC 0.81–0.89), with Mirai performing best for both. There was also a correlation in performance for risk and detection within algorithms by cancer size, with much greater accuracy for large cancers (30 mm+, detection AUC: 0.88–0.92; risk AUC: 0.64–0.74) than smaller cancers (0 to < 10 mm, detection AUC: 0.73–0.86, risk AUC: 0.54–0.64). Mirai was relatively strong for risk assessment of smaller cancers (0 to < 10 mm, risk, Mirai AUC: 0.64 (95% CI 0.57 to 0.70); other algorithms AUC 0.54–0.56). Conclusions Improvements in risk assessment could stem from enhancing cancer detection capabilities of smaller cancers. Other state-of-the-art AI detection algorithms with high performance for smaller cancers might achieve relatively high performance for risk assessment.


Navigating the security landscape of large language models in enterprise information systems

Abstract: In this letter, we present a comprehensive analysis of the security landscape surrounding large language models, leveraging a dataset from 2022 to 2023. We delve into the collaborative and global nature of this field, emphasizing the interdisciplinary approach required to address the evolving challenges. Prominent keywords like 'language model,' 'cybersecurity,' and 'data privacy' underscore central research themes. Beyond analysis, we introduce a cutting-edge deep CNN-based security framework, successfully tested with the KDDCup dataset, resulting in an impressive 94.73% accuracy on the testing dataset. As large language models continue to play a pivotal role in diverse applications, our research underscores the urgency of international cooperation and innovative security measures to ensure their responsible and secure usage in our interconnected world. This letter serves as a foundation for future research, driving the collective effort to safeguard large language models.


Deep panoramic depth prediction and completion for indoor scenes

Abstract: Abstract We introduce a novel end-to-end deep-learning solution for rapidly estimating a dense spherical depth map of an indoor environment. Our input is a single equirectangular image registered with a sparse depth map, as provided by a variety of common capture setups. Depth is inferred by an efficient and lightweight single-branch network, which employs a dynamic gating system to process together dense visual data and sparse geometric data. We exploit the characteristics of typical man-made environments to efficiently compress multi-resolution features and find short- and long-range relations among scene parts. Furthermore, we introduce a new augmentation strategy to make the model robust to different types of sparsity, including those generated by various structured light sensors and LiDAR setups. The experimental results demonstrate that our method provides interactive performance and outperforms state-of-the-art solutions in computational efficiency, adaptivity to variable depth sparsity patterns, and prediction accuracy for challenging indoor data, even when trained solely on synthetic data without any fine tuning.


A deep learning framework for non-functional requirement classification

Abstract: Abstract Analyzing, identifying, and classifying nonfunctional requirements from requirement documents is time-consuming and challenging. Machine learning-based approaches have been proposed to minimize analysts’ efforts, labor, and stress. However, the traditional approach of supervised machine learning necessitates manual feature extraction, which is time-consuming. This study presents a novel deep-learning framework for NFR classification to overcome these limitations. The framework leverages a more profound architecture that naturally captures feature structures, possesses enhanced representational power, and efficiently captures a broader context than shallower structures. To evaluate the effectiveness of the proposed method, an experiment was conducted on two widely-used datasets, encompassing 914 NFR instances. Performance analysis was performed on the applied models, and the results were evaluated using various metrics. Notably, the DReqANN model outperforms the other models in classifying NFR, achieving precision between 81 and 99.8%, recall between 74 and 89%, and F1-score between 83 and 89%. These significant results highlight the exceptional efficacy of the proposed deep learning framework in addressing NFR classification tasks, showcasing its potential for advancing the field of NFR analysis and classification.


Transformation and digital challenges in Peru during the COVID-19 pandemic, in the educational sector between 2020 and 2023: Systematic Review

Abstract: Introduction: Digital transformation in the Peruvian educational sector has experienced a significant boost after facing the COVID-19 pandemic. During the period between 2020 and 2023, various innovative methods have been implemented to ensure the continuity of the academic year.Objective: Explain how the digital transformation was carried out in the Peruvian educational sector after facing the COVID-19 pandemic to the present (2020 – 2023).Method: Examples from many institutions, statistical studies and scientific and technological references were taken into account to achieve the objective. Throughout this work we are analyzing the different and innovative methods used by teachers to provide continuity to the academic year and how digital challenges were overcome.Results: 78 documents from Scopus and Scielo were reviewed, leaving 62 after filtering. These cover 8 categories on the impact of the pandemic on education, the transition to online teaching, job skills, challenges and advantages of virtual education, innovation in higher education, educational evaluation in virtual environments, educational internationalization and challenges for teachers during the COVID-19 pandemic.Conclusions: In conclusion, the digital transformation in the Peruvian educational sector after the COVID-19 pandemic has been fundamental to guarantee the continuity of the teaching-learning process.


Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network

Abstract: Abstract Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall excellence in segmenting multimodal medical images. Therefore, we propose several modifications to the existing cutting-edge U-Net model. The technical approach involves applying a Multi-Dimensional U-Convolutional Neural Network to achieve accurate segmentation of multimodal biomedical images, enhancing precision and comprehensiveness in identifying and analyzing structures across diverse imaging modalities. As a result of the enhancements, we propose a novel framework called Multi-Dimensional U-Convolutional Neural Network (MDU-CNN) as a potential successor to the U-Net framework. On a large set of multimodal medical images, we compared our proposed framework, MDU-CNN, to the classical U-Net. There have been small changes in the case of perfect images, and a huge improvement is obtained in the case of difficult images. We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively.


Interacting with Smart Virtual Assistants for Individuals with Dysarthria: A Comparative Study on Usability and User Preferences

Abstract: This study explores the effectiveness and user experience of different interaction methods used by individuals with dysarthria when engaging with Smart Virtual Assistants (SVAs). It focuses on three primary modalities: direct speech commands through Alexa, non-verbal voice cues via the Daria system, and eye gaze control. The objective is to assess the usability, workload, and user preferences associated with each method, catering to the varying communication capabilities of individuals with dysarthria. While Alexa and Daria facilitate voice-based interactions, eye gaze control offers an alternative for those unable to use voice commands, including users with severe dysarthria. This comparative approach aims to determine how the usability of each interaction method varies, conducted with eight participants with dysarthria. The results indicated that non-verbal voice interactions, particularly with the Daria system, were favored because of their lower workload and ease of use. The eye gaze technology, while viable, presented challenges in terms of the higher workload and usability. These findings highlight the necessity of diversifying interaction methods with SVAs to accommodate the unique needs of individuals with dysarthria.


Exploring the role of blockchain technology in modern high-value food supply chains: global trends and future research directions

Abstract: Abstract Trust, safety, and quality are among the most important factors in the agri-food supply chains. Traceability is a powerful tool to ensure them, but implementing a transparent and effective system is a complex operation. As a result, innovative systems, like blockchain, could be introduced. Although research on its impacts in the agri-food is recent, the literature appears fragmented. The objective is to investigate the studied aspects of the blockchain adoption in agri-food, with the purpose of retrieving meaningful considerations about the current state of the art about strategic high-value supply chains, such as wine and olive oil, particularly subjected to fraudulent behaviors. A productivity measurement was applied to retrieve the evolution of the number of documents through the years, the most productive countries, the sources, the research areas, and the most significant papers in terms of number of citations received. To understand the research trends, a co-occurrence analysis was employed. Results show that most of the existing studies focus on the role of blockchain in the resolution of some critical issues as food safety and frauds. While wine is currently an emerging sector in which this approach can be implemented, olive oil still needs more attention. In both cases, blockchain could potentially help to support the profitability and sustainability of the production. The research underlines the importance of focusing on the environmental and social dimension of the blockchain phenomenon and the use of technology to improve the efficiency of agri-food chains and reduce waste and resource use.


Hybrid structural graph attention network for POI recommendation

Abstract: In the era of big data, information overload poses a challenge, complicating user decision-making. Recommendation systems aim to assist in this process. In recent years, research on point-of-interest (POI) recommendations has been gaining momentum with some studies pointing to issues that need to be resolved. Previous studies often used heterogeneous graphs to learn across different entity types, overlooking same-type entity relationships. Certain studies solely extract raw node features from a single source, thus disregarding information diversity, whereas others employ inappropriate methods that fail to preserve the inherent characteristics of the relevant information in the design of raw inputs. The integration of multiple sources of information can introduce a certain amount of noise into the data; however, the approaches used in related research may not be effective in handling this situation. To address these issues, we propose a hybrid structural graph attention network (HS-GAT) for POI recommendation. In this approach, multisource data are first preprocessed and relevant raw features are initialized. Subsequently, heterogeneous graphs are built for user-POI-POI attributes and POI-user-user attributes. These heterogeneous graphs are aggregated using a dual-attention mechanism, to create embedding matrices for users and POIs, which are then used to construct user-user and POI-POI homogeneous graphs. These graph structures are then combined with user and POI embeddings obtained from heterogeneous graphs and fed into a graph attention network (GAT) , which yields the final embedding representations for users and POIs. Finally, recommendations for POIs are made in the form of inner products. A comprehensive performance evaluation of HS-GAT on the Yelp, Boston, Chicago and London datasets demonstrated that the proposed approach outperforms other state-of-the-art methods.


Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning

Abstract: For the precise measurement of complex surfaces, determining the position, direction, and path of a laser sensor probe is crucial before obtaining exact measurements. Accurate surface measurement hinges on modifying the overtures of a laser sensor and planning the scan path of the point laser displacement sensor probe to optimize the alignment of its measurement velocity and accuracy. This manuscript proposes a 3D surface laser scanning path planning technique that utilizes adaptive ant colony optimization with sub-population and fuzzy logic (SFACO), which involves the consideration of the measurement point layout, probe attitude, and path planning. Firstly, this study is based on a four-coordinate measuring machine paired with a point laser displacement sensor probe. The laser scanning four-coordinate measuring instrument is used to establish a coordinate system, and the relationship between them is transformed. The readings of each axis of the object being measured under the normal measuring attitude are then reversed through the coordinate system transformation, thus resulting in the optimal measuring attitude. The nominal distance matrix, which demonstrates the significance of the optimal measuring attitude, is then created based on the readings of all the points to be measured. Subsequently, a fuzzy ACO algorithm that integrates multiple swarm adaptive and dynamic domain structures is suggested to enhance the algorithm’s performance by refining and utilizing multiple swarm adaptive and fuzzy operators. The efficacy of the algorithm is verified through experiments with 13 popular TSP benchmark datasets, thereby demonstrating the complexity of the SFACO approach. Ultimately, the path planning problem of surface 3D laser scanning measurement is addressed by employing the proposed SFACO algorithm in conjunction with a nominal distance matrix.


Face masks and fake masks: the effect of real and superimposed masks on face matching with super-recognisers, typical observers, and algorithms

Abstract: Abstract Mask wearing has been required in various settings since the outbreak of COVID-19, and research has shown that identity judgements are difficult for faces wearing masks. To date, however, the majority of experiments on face identification with masked faces tested humans and computer algorithms using images with superimposed masks rather than images of people wearing real face coverings. In three experiments we test humans (control participants and super-recognisers) and algorithms with images showing different types of face coverings. In all experiments we tested matching concealed or unconcealed faces to an unconcealed reference image, and we found a consistent decrease in face matching accuracy with masked compared to unconcealed faces. In Experiment 1, typical human observers were most accurate at face matching with unconcealed images, and poorer for three different types of superimposed mask conditions. In Experiment 2, we tested both typical observers and super-recognisers with superimposed and real face masks, and found that performance was poorer for real compared to superimposed masks. The same pattern was observed in Experiment 3 with algorithms. Our results highlight the importance of testing both humans and algorithms with real face masks, as using only superimposed masks may underestimate their detrimental effect on face identification.


Simulation in engineering education: The transition from physical experimentation to digital immersive simulated environments

Abstract: Besides its use as a powerful systems analysis tool, simulation has also been used for decades in educational settings as a teaching and learning method. Simulation can replace or augment real-world inquiry-based experiences by providing learners with a low-cost and risk-free experimentation platform to develop knowledge and skills in a simulated environment. This paper presents an overview of current applications and the ongoing transition from physical experimentation to digital simulations and immersive simulated learning environments in engineering education. The paper highlights major implementation and research gaps related to simulation-based learning and immersive simulated learning environments, namely, lack of integration with learning theories and limited formal assessments of effectiveness. Potential implementation approaches and important areas for future educational research are discussed and exemplified in response to the identified gaps. The discussions presented are intended for simulationists, educational researchers, and instructors who are interested in designing and/or utilizing engineering education interventions involving simulated learning environments and immersive technologies in their teaching and educational research. In particular, the Immersive Simulation-Based Learning (ISBL) approach discussed in the paper provides a framework for simulationists to reuse the models developed as part of their simulation projects for educational purposes.


Discrete-Time Quantum Walks Community Detection in Multi-Domain Networks

Abstract: Abstract The problem of detecting communities in real-world networks has been extensively studied in the past, but most of the existing approaches work on single-domain networks, i.e. they consider only one type of relationship between nodes. Single-domain networks may contain noisy edges and they may lack some important information. Thus, some authors have proposed to consider the multiple relationships that connect the nodes of a network, thus obtaining multi-domain networks. However, most community detection approaches are limited to multi-layer networks, i.e. networks generated from the superposition of several single-domain networks (called layers) that are regarded as independent of each other. In addition to being computationally expensive, multi-layer approaches might yield inaccurate results because they ignore potential dependencies between layers. This paper proposes a multi-domain discrete-time quantum walks (MDQW) model for multi-domain networks. First, the walking space of network nodes in multi-domain network is constructed. Second, the quantum permutation circuit of the coin state is designed based on the coded particle state. Then, using different coin states, the shift operator performs several quantum walks on the particles. Finally, the corresponding update rule is selected to move the node according to the measurement result of the quantum state. With continuous update iteration, the shift operator automatically optimizes the discovered community structure. We experimentally compared our MDQW method with four state-of-the-art competitors on five real datasets. We used the normalized mutual information (NMI) to compare clustering quality, and we report an increase in NMI of up to 3.51 of our MDQW method in comparison with the second-best performing competitor. The MDQW method is much faster than its competitors, allowing us to conclude that MDQW is a useful tool in the analysis of large real-life multi-domain networks. Finally, we illustrate the usefulness of our approach on two real-world case studies.


Combining Critical and Quantum Metrology

Abstract: Critical metrology relies on the precise preparation of a system in its ground state near a quantum phase transition point where quantum correlations get very strong. Typically, this increases the quantum Fisher information with respect to changes in system parameters and thus improves the optimally possible measurement precision limited by the Cram\'er-Rao bound. Hence critical metrology involves encoding information about the unknown parameter in changes of the system's ground state. Conversely, in conventional metrology methods like Ramsey interferometry, the eigenstates of the system remain unchanged, and information about the unknown parameter is encoded in the relative phases that excited system states accumulate during their time evolution. Here we introduce an approach combining these two methodologies into a unified protocol applicable to closed and driven-dissipative systems. We show that the quantum Fisher information in this case exhibits an additional interference term originating from the interplay between eigenstate and relative phase changes. We provide analytical expressions for the quantum and classical Fisher information in such a setup, elucidating as well a straightforward measurement approach that nearly attains the maximum precision permissible under the Cram\'er-Rao bound. We showcase these results by focusing on the squeezing Hamiltonian, which characterizes the thermodynamic limit of Dicke and Lipkin-Meshkov-Glick Hamiltonians.


Physical Reservoir Computing Enabled by Solitary Waves and Biologically Inspired Nonlinear Transformation of Input Data

Abstract: Reservoir computing (RC) systems can efficiently forecast chaotic time series using the nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more-efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper, we experimentally validated a physical RC system that substitutes the effect of randomness that underpins the operation of the traditional RC algorithm for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the ‘next-generation’ improvement of the traditional RC algorithm.


Entanglement and entropy in multipartite systems: a useful approach

Abstract: Abstract Quantum entanglement and quantum entropy are crucial concepts in the study of multipartite quantum systems. In this work, we show how the notion of concurrence vector, re-expressed in a particularly useful form, provides new insights and computational tools for the analysis of both. In particular, using this approach for a general multipartite pure state, one can easily prove known relations in an easy way and to build up new relations between the concurrences associated with the different bipartitions. The approach is also useful to derive sufficient conditions for genuine entanglement in generic multipartite systems that are computable in polynomial time. From an entropy-of-entanglement perspective, the approach is powerful to prove properties of the Tsallis-2 entropy, such as the subadditivity, and to derive new ones, e.g., a modified version of the strong subadditivity which is always fulfilled; thanks to the purification theorem these results hold for any multipartite state, whether pure or mixed.


Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions

Abstract: The development of applications based on artificial intelligence and implemented over wireless networks is increasingly rapidly and is expected to grow dramatically in the future. The resulting demand for the aggregation of large amounts of data has caused serious communication bottlenecks in wireless networks and particularly at the network edge. Over-the-air federated learning (OTA-FL), leveraging the superposition feature of multi-access channels (MACs), enables users at the network edge to share spectrum resources and achieves efficient and low-latency global model aggregation. This paper provides a holistic review of progress in OTA-FL and points to potential future research directions. Specifically, we classify OTA-FL from the perspective of system settings, including single-antenna OTA-FL, multi-antenna OTA-FL, and OTA-FL with the aid of the emerging reconfigurable intelligent surface (RIS) technology, and the contributions of existing works in these areas are summarized. Moreover, we discuss the trust, security and privacy aspects of OTA-FL, and highlight concerns arising from security and privacy. Finally, challenges and potential research directions are discussed to promote the future development of OTA-FL in terms of improving system performance, reliability, and trustworthiness. Specifical challenges to be addressed include model distortion under channel fading, the ineffective OTA aggregation of local models trained on substantially unbalanced data, and the limited accessibility and verifiability of individual local models.


Fake news detection using recurrent neural network based on bidirectional LSTM and GloVe

Abstract: In the world of technology, the electronic and technical development of the fields of communication and the internet has increased, which has caused a renaissance in the virtual world. This development has greatly impacted virtual communities for the ease and speed of communication and information transfer through social media platforms, making these platforms likable and easy to use. The social network faces major challenges due to its extensive use. As a result, many people have become involved in cybercrimes. There are accounts on the internet that are malicious. Platforms for social networking online, such as Facebook and Twitter, allow all users to freely generate and consume massive volumes of material regardless of their traits. While individuals and businesses utilize this information to gain a competitive edge, spam or phony users create important data. According to estimates, 1 in 200 posts on social media contain spam, and 1 in 21 tweets contain spam. The problem was centered around the accuracy of detecting false news and correcting it or preventing its dissemination before it spread in the network. A new method is given based on improving the false news detection system; the level of improvement was significant in the preprocessing stage where Glove is used, which is an unsupervised learning algorithm developed by researchers at Stanford University aiming to generate word embeddings by aggregating global word co-occurrence matrices from a given corpus. The basic idea behind the GloVe word embedding is to derive the relationship between the words from statistics. The proposed method contains deep learning algorithms of convolutional neural network (CNN), deep neural network (DNN), and long short-term memory (LSTM). The RNN with GloVe in the preprocessing stage using the Curpos fake news dataset to enhance the system, due to the sequential processes and classification, has the highest accuracy of 98.974%.


Artificial intelligence for international business: Its use, challenges, and suggestions for future research and practice

Abstract: Abstract The emergence of artificial intelligence (AI) has transformed global business, aiding operational efficiency and innovation. It utilizes machine learning and big data analytics, driving predictive market trends and strategic decision‐making. However, despite the rising discussion and accessibility of AI tools, understanding its impact on international business remains limited. This article explores AI's potential in international business strategies, practices, and activities. To address this aim, we reviewed 37 articles in the existing literature to critically explore AI within the context of international business. More specifically, we explored how AI can be applied to innovation approaches in international business, international market selection, entry modes, foreign exchange, international human resource management, international supply chains, managing across cultures, and more topics. AI has necessitated changes in workplace configurations and the need for organizational and employee adjustments in response to this technology. As a result of the foregoing issues on AI integration within international business, our analysis provided an exploratory discussion around its use, challenges, managerial implications, and suggested areas requiring future studies.


TLC-XML: Transformer with Label Correlation for Extreme Multi-label Text Classification

Abstract: Abstract Extreme multi-label text classification (XMTC) annotates related labels for unknown text from large-scale label sets. Transformer-based methods have become the dominant approach for solving the XMTC task due to their effective text representation capabilities. However, the existing Transformer-based methods fail to effectively exploit the correlation between labels in the XMTC task. To address this shortcoming, we propose a novel model called TLC-XML, i.e., a Transformer with label correlation for extreme multi-label text classification. TLC-XML comprises three modules: Partition, Matcher and Ranker. In the Partition module, we exploit the semantic and co-occurrence information of labels to construct the label correlation graph, and further partition the strongly correlated labels into the same cluster. In the Matcher module, we propose cluster correlation learning, which uses the graph convolutional network (GCN) to extract the correlation between clusters. We then introduce these valuable correlations into the classifier to match related clusters. In the Ranker module, we propose label interaction learning, which aggregates the raw label prediction with the information of the neighboring labels. The experimental results on benchmark datasets show that TLC-XML significantly outperforms state-of-the-art XMTC methods.


Path planning of water surface garbage cleaning robot based on improved immune particle swarm algorithm

Abstract: In order to effectively improve the efficiency of surface garbage cleaning robot, an intelligent control algorithm was applied to plan the robot path. To do so, an improved immune particle swarm algorithm was developed based on the robot model. This algorithm introduced the adaptive information dynamic adjustment strategy to dynamically adjust the main link indices, which improved the global searchability and convergence of particles and facilitated the quick identification of the optimal path by the robot. Through comparative simulation experiments with the particle swarm optimization algorithm, genetic algorithm, and immune particle swarm optimization algorithm, it was found that the robot based on the Adaptive Immune Particle Swarm Optimization (AIPSO) algorithm had the shortest planning path and search time, the lowest energy consumption, and the highest efficiency. A robot prototype platform was built. Compared to other algorithms, the efficiency of the robot space search based on the AIPSO algorithm was the highest, the search time was the shortest, and the energy consumption was also the lowest. Especially in the complex level 4 wave water environment, the AIPSO algorithm had the best adaptability and robustness, and the robot had the highest working efficiency and comprehensive performance. The experimental results revealed that the AIPSO algorithm effectively improved the path search and garbage cleaning efficiency of the robots and reduced the working time, which further verified the reliability and accuracy of the designed algorithm.


GPU Shader Analysis and Power Optimization Model

Abstract: With the rapid advancements in 3D game technology, workload characterization has become crucial for each new generation of games. The increased complexity of scenes in 3D games allows for stunning real-time visual quality. However, handling such workloads results in significant power consumption over the GPU rendering pipeline. The focus of the current paper is low power optimization, targeting texture memory, geometry engine, pixel, and rasterization, as these components are significant contributors to the power consumption of a typical GPU. The proposed methodology integrates the Dynamic Voltage Frequency Scaling (DVFS) technique, adjusting voltage and frequency based on the workload analysis of frame rates with respect to the scenes of 3D games. Frame rates of 60 fps and 30 fps are set up to understand and manage the workload on frames. Furthermore, for comparative analysis, various frame-level power analysis schemes such as No DVFS implemented, Frame History Method, Frame Signature Method, and Tiled History-based are introduced. The proposed scheme consistently surpasses these frame-level schemes, with fewer missed deadlines, while having the lowest energy consumption per frame rate. The implementation resulted in a remarkable 65% improvement in quality, indicated by a reduction in deadline misses, along with a substantial 60% energy saving.


Advanced Fraud Detection in Blockchain Transactions: An Ensemble Learning and Explainable AI Approach

Abstract: In recent years, cryptocurrencies have experienced rapid growth and adoption, revolutionizing the financial sector. However, the rise of digital currencies has also led to an increase in fraudulent transactions and illegal activities. In this paper, we present a comprehensive study on the detection of fraudulent transactions in the context of cryptocurrency exchanges, with a primary focus on the Ethereum network. By employing various Machine Learning (ML) techniques and ensemble methods, including the hard voting ensemble model, which achieved a remarkable 99% accuracy, we aim to effectively identify suspicious transactions while maintaining high accuracy and precision. Additionally, we delve into the importance of eXplainable Artificial Intelligence (XAI) to enhance transparency, trust, and accountability in AI-based fraud detection systems. Our research contributes to the development of reliable and interpretable models that can significantly improve the cryptocurrency ecosystem security and integrity.


The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

Abstract: Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.


A Light Weight Depthwise Separable Layer Optimized CNN Architecture for Object-Based Forgery Detection in Surveillance Videos

Abstract: Abstract The present era is at the peak of technological advancement in image and video processing techniques, with user-friendly accessible tools/techniques. This immersive technology development makes video forensics enormously challenging. Specifically, the passive approaches to object-based forgeries in videos are crucial for legal and judicial matters. Hence, to ensure the integrity of the videos, a scientific, statistical and passive investigation of videos is required to maintain the spatial and temporal information content. This paper aims to develop a passive approach for digging out the forgery traces by applying the motion residue windowing technique for object removal forgery in surveillance videos. The novel max averaging windowing techniques improve visual imprints of the object removal forgery in the videos from the existing methods in the literature. A deep learning approach is the next step for achieving forgery detection in surveillance videos. The proposed lightweight depth-separable layer-optimized CNN has fast execution speed, optimized in terms of parameters without compromising the desired accuracy. This network is trained at a frame level with 98.60% testing accuracy, followed by a pipeline architecture of the proposed model for detection of forgery at video level with 99.01% accuracy. The suggested model works better than current models regarding post-processing operations, compression rates, forged video detection accuracy, precision, recall and F1 score.


Enhancing higher education art students’ learning experience through virtual reality: a comprehensive literature review of product design courses

Abstract: Since the turn of the millennium, virtual reality (VR) has become a popular global research topic. Educators are eager to employ VR for interactive learning, potentially enhancing student engagement and efficiency. Researchers have conducted numerous trials, with students across disciplines achieving notable results, affirming VR's effectiveness, even in art and design education. The field of product design, in particular, requires high-dimensional interaction throughout the learning process, making it a promising application area for VR technology. This study takes art and design majors as the analysis object and aims to explore the development trend of VR technology in education related to product design courses. In addition, this study conducted a systematic review of relevant literature over the past five years to assess the current state of VR technology in arts education, the types of VR used in teaching, and the impact of the technology on the student population. The results reveal that, despite the growing interest in VR technology, there have been relatively few cases of educators applying this technology to product design education. Moreover, immersive VR technology in art and design classrooms is still in the new stage. Consequently, researchers have ample scope for further exploring and expanding this field.


How to choose a proper representation of compositional data for mineral exploration?

Abstract: Regional mineral exploration is based on geochemical data of which the nature is compositional and frequently involves a large number of components. Consequently, it mostly needs multivariate dimension reduction methods such as principal component analysis (PCA) and its various robust versions. The application of such methods, defined for real random variables, require the data to be represented in coordinates supported in the real space. However, a common problem in exploration geochemistry is to select the appropriate representation. Using centered (clr) and isometric (ilr) logratio coordinates to discriminate anomalous zones for orogeny gold exploration throughout Sweden revealed that there is, as expected, no difference between the two representation methods. The main difference affects the interpretation of the coordinates used. This is observed for regional scale exploration, while it is also needed to study different ways of representing geochemical data in local scale.


Lightweight Vehicle Detection Based on Improved YOLOv5s

Abstract: A vehicle detection algorithm is an indispensable component of intelligent traffic management and control systems, influencing the efficiency and functionality of the system. In this paper, we propose a lightweight improvement method for the YOLOv5 algorithm based on integrated perceptual attention, with few parameters and high detection accuracy. First, we propose a lightweight module IPA with a Transformer encoder based on integrated perceptual attention, which leads to a reduction in the number of parameters while capturing global dependencies for richer contextual information. Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. The test results show that, compared with the original model, the model parameters decrease by about 9%, the average accuracy (mAP@50) increases by 3.1%, and the FLOPS does not increase.


FairGAT: Fairness-aware Graph Attention Networks

Abstract: Graphs can facilitate modeling various complex systems such as gene networks and power grids, as well as analyzing the underlying relations within them. Learning over graphs has recently attracted increasing attention, particularly graph neural network-based (GNN) solutions, among which graph attention networks (GATs) have become one of the most widely utilized neural network structures for graph-based tasks. Although it is shown that the use of graph structures in learning results in the amplification of algorithmic bias, the influence of the attention design in GATs on algorithmic bias has not been investigated. Motivated by this, the present study first carries out a theoretical analysis in order to demonstrate the sources of algorithmic bias in GAT-based learning for node classification. Then, a novel algorithm, FairGAT, that leverages a fairness-aware attention design is developed based on the theoretical findings. Experimental results on real-world networks demonstrate that FairGAT improves group fairness measures while also providing comparable utility to the fairness-aware baselines for node classification and link prediction.


A novel quantum key distribution resistant against large‐pulse attacks

Abstract: Abstract Quantum key distribution (QKD) offers information‐theoretic security by leveraging the principles of quantum mechanics. This means the security is independent of all future advances in algorithm or computational power. However, due to the non‐availability of single‐photon sources, most traditional QKD protocols are vulnerable to various attacks, such as photon number‐splitting (PNS) attacks. Also, the imperfections in the measuring devices open a loophole for side channels that an eavesdropper may exploit to launch attacks such as large‐pulse attacks. As a result, this compromises the security of transmitted information. To address these challenges, the authors present a QKD protocol that is secure against both large‐pulse attacks and PNS attacks at zero‐error, in which the eavesdropper does not introduce any error, but still, the legitimate users of the system cannot distil a secure key. A notable feature of the proposed protocol is that it promotes greater robustness against both attacks than the Bennett‐Brassard 1984 (BB84) protocol or the Scarani‐Acin‐Ribordy‐Gisin 2004 (SARG04) protocol.


Integrated Photonics for Quantum Communications and Metrology

Abstract: Over the last two decades, integrated photonics has profoundly revolutionized the domain of quantum technologies. The ongoing second quantum revolution stands as a timely opportunity for a state-of-the-art review and, most important, an exploration of the directions undertaken by integrated quantum photonics. Within this perspective, based on the recent advances, we discuss the current challenges and future trends related to different technological platforms. Key examples will be considered across various subfields, including quantum communication, quantum metrology, and quantum memories. Our discussion encompasses disruptive concepts, progress, and potential limitations. The main objective of this Perspective is to provide the reader with a forward-looking discussion ranging from state-of-the-art developments to open challenges of the field.Received 28 October 2022Revised 24 October 2023DOI:https://doi.org/10.1103/PRXQuantum.5.010101Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum computationQuantum correlations in quantum informationQuantum engineeringQuantum entanglementQuantum memoriesQuantum metrologyQuantum Information, Science & Technology


Education reform and change driven by digital technology: a bibliometric study from a global perspective

Abstract: Abstract Amidst the global digital transformation of educational institutions, digital technology has emerged as a significant area of interest among scholars. Such technologies have played an instrumental role in enhancing learner performance and improving the effectiveness of teaching and learning. These digital technologies also ensure the sustainability and stability of education during the epidemic. Despite this, a dearth of systematic reviews exists regarding the current state of digital technology application in education. To address this gap, this study utilized the Web of Science Core Collection as a data source (specifically selecting the high-quality SSCI and SCIE) and implemented a topic search by setting keywords, yielding 1849 initial publications. Furthermore, following the PRISMA guidelines, we refined the selection to 588 high-quality articles. Using software tools such as CiteSpace, VOSviewer, and Charticulator, we reviewed these 588 publications to identify core authors (such as Selwyn, Henderson, Edwards), highly productive countries/regions (England, Australia, USA), key institutions (Monash University, Australian Catholic University), and crucial journals in the field ( Education and Information Technologies , Computers & Education , British Journal of Educational Technology ). Evolutionary analysis reveals four developmental periods in the research field of digital technology education application: the embryonic period, the preliminary development period, the key exploration, and the acceleration period of change. The study highlights the dual influence of technological factors and historical context on the research topic. Technology is a key factor in enabling education to transform and upgrade, and the context of the times is an important driving force in promoting the adoption of new technologies in the education system and the transformation and upgrading of education. Additionally, the study identifies three frontier hotspots in the field: physical education, digital transformation, and professional development under the promotion of digital technology. This study presents a clear framework for digital technology application in education, which can serve as a valuable reference for researchers and educational practitioners concerned with digital technology education application in theory and practice.


The metaverse digital environments: A scoping review of the techniques, technologies, and applications

Abstract: The “metaverse” has gained immense attention as the next internet frontier, holding significant economic implications, especially for the IT sector. To establish the groundwork for immersive metaverse spaces, our comprehensive scoping review uncovers the current techniques and technologies in metaverse development. This study investigates post-COVID-19 metaverse techniques, technologies, and applications, spanning from January 2020 to December 2022. Findings reveal potential metaverse applications across 11 industries, with education, manufacturing, healthcare, and real estate being notable. We identified 12 major technologies and four key development techniques that have gained prominence in the metaverse landscape. Notably, “Extended Reality (XR),” “Artificial Intelligence (AI),” and “Decentralized Technologies” were the three most frequently mentioned technologies, appearing in 73%, 40%, and 30% of the reviewed articles, respectively. Furthermore, “VR space convergence” and “fundamental technology” emerged as primary enablers in 73% and 43% of the selected articles, with “object connection” and “communication computing infrastructure” playing supportive roles. Integrating these technologies is vital for holistic metaverse development. Our report serves as a catalyst for discussions among stakeholders, promoting further research and the transformative potential of the metaverse. It highlights the need for advancing understanding and technological evolution in this emerging digital realm.


Generative AI in Higher Education

Abstract: This chapter provides a comprehensive exploration of generative artificial intelligence (AI), particularly focusing on its implications and applications in higher education. It discusses the evolution and fundamental concepts of AI, including large language models and their development, emphasizing the intricate processes involved in creating and refining these models. The chapter delves into the ethical considerations and potential biases inherent in AI systems, highlighting the importance of responsible AI development. Moreover, the chapter examines the transformative potential of generative AI in enhancing learning, creativity, and information processing in higher education settings.


Kernel-based mapping of reliability in predictions for consensus modelling

Abstract: Approaches of high-level data fusion, also known as consensus, combine predictions of individual models to increase reliability and overcome limitations of single models. Consensus strategies are frequently applied in the framework of Quantitative Structure - Activity Relationships (QSARs) to reduce the uncertainties in the prediction of molecular activities and provide better accuracy of the model outcomes. However, specific regions of the chemical space may systematically be associated with low accuracy and even consensus modelling cannot improve prediction reliability through the multiple outcomes of individual models. In this study, a new heuristic metric to assess the degree of accuracy of consensus predictions in the chemical space is proposed. This metric can assist the mapping of reliability in prediction and enhance the delineation of a safe zone, where consensus predictions are expected to have better accuracy. The new metric is calculated by kernel-based potential functions and it can be used in the framework of both classification and regression consensus modelling. Four case studies, including extensive datasets for consensus modelling, were used to test the proposed approach. Results demonstrated that a potential can be associated with regions of the chemical space as a function of accuracy of consensus modelling and it can be used to enable the mapping of reliability in prediction and the definition of specific regions where predictions are expected to be more reliable.


Optical computing metasurfaces: applications and advances

Abstract: Abstract Integrated photonic devices and artificial intelligence have presented a significant opportunity for the advancement of optical computing in practical applications. Optical computing technology is a unique computing system based on optical devices and computing functions, which significantly differs from the traditional electronic computing technology. On the other hand, optical computing technology offers the advantages such as fast speed, low energy consumption, and high parallelism. Yet there are still challenges such as device integration and portability. In the burgeoning development of micro–nano optics technology, especially the deeply ingrained concept of metasurface technique, it provides an advanced platform for optical computing applications, including edge detection, image or motion recognition, logic computation, and on-chip optical computing. With the aim of providing a comprehensive introduction and perspective for optical computing metasurface applications, we review the recent research advances of optical computing, from nanostructure and computing methods to practical applications. In this work, we review the challenges and analysis of optical computing metasurfaces in engineering field and look forward to the future development trends of optical computing.


Continuous-time quantum walks for MAX-CUT are hot

Abstract: By exploiting the link between time-independent Hamiltonians and thermalisation, heuristic predictions on the performance of continuous-time quantum walks for MAX-CUT are made. The resulting predictions depend on the number of triangles in the underlying MAX-CUT graph. We extend these results to the time-dependent setting with multi-stage quantum walks and Floquet systems. The approach followed here provides a novel way of understanding the role of unitary dynamics in tackling combinatorial optimisation problems with continuous-time quantum algorithms.


Assessment framework for deepfake detection in real-world situations

Abstract: Abstract Detecting digital face manipulation in images and video has attracted extensive attention due to the potential risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been employed and have exhibited remarkable performance. However, the performance of such detectors is often assessed on related benchmarks that hardly reflect real-world situations. For example, the impact of various image and video processing operations and typical workflow distortions on detection accuracy has not been systematically measured. In this paper, a more reliable assessment framework is proposed to evaluate the performance of learning-based deepfake detectors in more realistic settings. To the best of our acknowledgment, it is the first systematic assessment approach for deepfake detectors that not only reports the general performance under real-world conditions but also quantitatively measures their robustness toward different processing operations. To demonstrate the effectiveness and usage of the framework, extensive experiments and detailed analysis of four popular deepfake detection methods are further presented in this paper. In addition, a stochastic degradation-based data augmentation method driven by realistic processing operations is designed, which significantly improves the robustness of deepfake detectors.


Hardness of Random Optimization Problems for Boolean Circuits, Low-Degree Polynomials, and Langevin Dynamics

Abstract: .We consider the problem of finding nearly optimal solutions of optimization problems with random objective functions. Such problems arise widely in the theory of random graphs, theoretical computer science, and statistical physics. Two concrete problems we consider are (a) optimizing the Hamiltonian of a spherical or Ising \(p\)-spin glass model and (b) finding a large independent set in a sparse Erdős–Rényi graph. The following families of algorithms are considered: (a) low-degree polynomials of the input—a general framework that captures many prior algorithms; (b) low-depth Boolean circuits; (c) the Langevin dynamics algorithm, a canonical Monte Carlo analogue of the gradient descent algorithm. We show that these families of algorithms cannot have high success probability. For the case of Boolean circuits, our results improve the state-of-the-art bounds known in circuit complexity theory (although we consider the search problem as opposed to the decision problem). Our proof uses the fact that these models are known to exhibit a variant of the overlap gap property (OGP) of near-optimal solutions. Specifically, for both models, every two solutions whose objectives are above a certain threshold are either close to or far from each other. The crux of our proof is that the classes of algorithms we consider exhibit a form of stability (noise-insensitivity): a small perturbation of the input induces a small perturbation of the output. We show by an interpolation argument that stable algorithms cannot overcome the OGP barrier. The stability of Langevin dynamics is an immediate consequence of the well-posedness of stochastic differential equations. The stability of low-degree polynomials and Boolean circuits is established using tools from Gaussian and Boolean analysis—namely hypercontractivity and total influence, as well as a novel lower bound for random walks avoiding certain subsets, which we expect to be of independent interest. In the case of Boolean circuits, the result also makes use of Linial–Mansour–Nisan's classical theorem. Our techniques apply more broadly to low influence functions, and we expect that they may apply more generally.Keywordsspin glassesmaximum independent setrandom graphsrandom optimization problemsaverage-case hardnessBoolean circuitslow-degree methodsLangevin dynamicsoverlap gap propertyMSC codes68Q0668Q1768Q2568Q8782D3058J6590C27


Context Transformer and Adaptive Method with Visual Transformer for Robust Facial Expression Recognition

Abstract: In real-world scenarios, the facial expression recognition task faces several challenges, including lighting variations, image noise, face occlusion, and other factors, which limit the performance of existing models in dealing with complex situations. To cope with these problems, we introduce the CoT module between the CNN and ViT frameworks, which improves the ability to perceive subtle differences by learning the correlations between local area features at a fine-grained level, helping to maintain the consistency between the local area features and the global expression, and making the model more adaptable to complex lighting conditions. Meanwhile, we adopt an adaptive learning method to effectively eliminate the interference of noise and occlusion by dynamically adjusting the parameters of the Transformer Encoder’s self-attention weight matrix. Experiments demonstrate the accuracy of our CoT_AdaViT model in the Oulu-CASIA dataset as (NIR: 87.94%, VL: strong: 89.47%, weak: 84.76%, dark: 82.28%). As well as, CK+, RAF-DB, and FERPlus datasets achieved 99.20%, 91.07%, and 90.57% recognition results, which achieved excellent performance and verified that the model has strong recognition accuracy and robustness in complex scenes.


Sequential quantum simulation of spin chains with a single circuit QED device

Abstract: Quantum simulations of many-body systems in materials science and chemistry are promising application areas for quantum computers. However, the limited scale and coherence of near-term quantum processors pose a significant obstacle to realizing this potential. Here, we theoretically outline how a single-mode circuit quantum electrodynamics device, consisting of a transmon qubit coupled to a long-lived cavity mode, can be used to simulate the ground state of a highly entangled quantum many-body spin chain. We exploit recently developed methods for implementing quantum operations to sequentially build up a matrix product state (MPS) representation of a many-body state. This approach reuses the transmon qubit to read out the state of each spin in the chain and exploits the large state space of the cavity as a quantum memory encoding intersite correlations and entanglement. We show, through simulation, that analog (pulse-level) control schemes can accurately prepare a known MPS representation of a quantum critical spin chain in significantly less time than digital (gate-based) methods, thereby reducing the exposure to decoherence. We then explore this analog-control approach for the variational preparation of an unknown ground state. We demonstrate that the large state space of the cavity can be used to replace multiple qubits in a qubit-only architecture, and could therefore simplify the design of quantum processors for materials simulation. We explore the practical limitations of realistic noise and decoherence and discuss avenues for scaling this approach to more complex problems that challenge classical computational methods.


Multi-strategy-based adaptive sine cosine algorithm for engineering optimization problems

Abstract: Sine Cosine Algorithm(SCA) is a population-based optimization algorithm, to find the optimal solution. However, SCA has problems such as premature convergence, insufficient solution precision for high-dimensional functions, and slow convergence speed. To solve the problems above, this paper proposes a multi-strategy-based Adaptive Sine Cosine Algorithm (ASCA). Firstly, a more uniform initial population is generated by the Halton sequence so that the initial population covers the entire search space to maintain the diversity of the initial population. Secondly, the adaptive grading strategy is adopted to sort according to the fitness value, and the population dynamics are divided into 4 grades: excellent, good, medium and poor. For the purpose of improving the convergence accuracy of the algorithm and enhancing the ability to jump out of the local optimum, hybrid mutation and elite guidance methods are applied to different levels of populations for perturbing mutations. Finally, in order to improve the convergence speed of the algorithm, a dynamic opposition-based learning global search strategy is proposed. The ASCA is tested on a set of 20 functions in low- dimensional and high-dimensional, and the improved algorithm is compared with Particle Swarm Optimization (PSO), Backtracking Search Algorithm(BSA), Genetic Algorithm(GA)and other improved Sine Cosine Algorithms. The results show the improved convergence accuracy and speed of the ASCA. Moreover, the ASCA proposed in this paper is applied to engineering optimization design. The solution results show that the ASCA is better than other algorithms in superiority-seeking ability, and can effectively solve the optimization problems in engineering.


Connecting through chatbots: residents’ insights on digital storytelling, place attachment, and value co-creation

Abstract: Despite the widespread adoption of chatbots in tourism, there is limited research on their role in digital storytelling from residents' perspectives. Drawing on Social Presence Theory, the primary objective of this study was to examine the impact of a chatbot's anthropomorphic conversation style (emotional vs. neutral) on perceived digital storytelling components in destination marketing. Additionally, the research explored how digital storytelling through chatbots influenced residents' place attachment and, consequently, their engagement in value co-creation. To address these research objectives, the study adopted a quantitative perspective and employed an experimental design. Data were collected from 176 residents of Side, Turkey, using convenience sampling, and all hypotheses were confirmed. The perceptions of digital storytelling expressed as emotional (experimental group) were found to be more positive than the neutral (control group). In addition, digital storytelling significantly influenced place attachment, which was a significant predictor of value co-creation. Finally, place attachment partially mediated the relationship between digital storytelling and residents' value co-creation. The findings provided valuable insights for tourism stakeholders such as businesses, policymakers, and researchers, guiding the effective implementation of chatbots in destination marketing, investigating residents' perspectives regarding emotional digital storytelling by chatbots, and enhancing residents' engagement in value co-creation.


A Patch Distribution-based Active Learning Method for Multiple Instance Alzheimer's Disease Diagnosis

Abstract: Medical data, particularly the complex brain imaging structures, acquisition presents significant difficulties and high diagnostic expenses, resulting in a scarcity of the trainable samples in the real-world scenarios. To overcome this limitation, we present an active learning-based sampling strategy that selects the most informative samples from the unlabeled candidate sample pool for expert annotation, leading to high classification performance with a reduced number of training samples. This study adopts a patch-level perspective and introduces a multi-instance learning framework for Alzheimer's Disease diagnosis. Initially, a patch pre-selection module is designed to identify pathology-prone regions while excluding background areas and irrelevant information. Subsequently, an inner-patch local attention mechanism block and an outer-patch global attention mechanism block are developed to enhance the extraction of discriminative local and global information by the network model. Finally, an active learning sampling strategy is devised to minimize the costs associated with data acquisition and expert annotation in medical domain. The effectiveness of the proposed network framework and active learning strategy was validated through four sets of control experiments on the ADNI dataset.


Enhancing User Experience With a Generative AI Chatbot

Abstract: With the rapid evolution of artificial intelligence (AI), this study aims to examine the interplay among the perceived usability, perceived enjoyment, perceived responsiveness, and intention to continue using ChatGPT. Structural equation modeling (SEM) was used to investigate our proposed model. We recruited 446 ChatGPT users through an online survey conducted on the Connect platform, powered by CloudResearch. Perceived usability (β = 0.254) and enjoyment (β = 0.438) significantly influence satisfaction with ChatGPT. However, perceived responsiveness is not significantly related to perceived attachment or satisfaction. Furthermore, we established that perceived attachment (β = 0.405) and satisfaction (β = 0.447) are pivotal in shaping users' intentions to continue using ChatGPT, providing insights into human–AI interactions. The practical implications of our findings suggest that generative AI chatbots should be crafted with a focus on enjoyable, user-centered interfaces that foster long-term user satisfaction and engagement. AI developers should design AI conversation flows and chatbot personas to optimize the user experience.