Reviewerly/exHarmony huggingface dataset:
Kinematics-aware spatial-temporal feature transform for 3D human pose estimation

Authors:
raw_author_name: Songlin Du
raw_affiliation_string: Shenzhen Research Institute of Southeast University, Shenzhen 518063, China; Southeast University, Nanjing 210096, China
raw_author_name: Zhiwei Yuan
raw_affiliation_string: Shenzhen Research Institute of Southeast University, Shenzhen 518063, China; Southeast University, Nanjing 210096, China
raw_author_name: Takeshi Ikenaga
raw_affiliation_string: Waseda University, Kitakyushu 808-0135, Japan

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

Authors:
raw_author_name: Yu Zhao
raw_affiliation_string: College of Foreign Languages, Xinjiang University, Urumqi, China

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

Authors:
raw_author_name: Zohreh Aghababaeyan
raw_affiliation_string: University of Ottawa, Ottawa, Canada
raw_author_name: Manel Abdellatif
raw_affiliation_string: École de Technologie Supérieure, Montreal, Canada
raw_author_name: Mahboubeh Dadkhah
raw_affiliation_string: University of Ottawa, Ottawa, Canada
raw_author_name: Lionel Briand
raw_affiliation_string: University of Ottawa, Ottawa, Canada and Lero Centre, University of Limerick, Ireland

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.
AdvMix: Adversarial Mixing Strategy for Unsupervised Domain Adaptive Object Detection

Authors:
raw_author_name: Ruimin Chen
raw_affiliation_string: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; Zhejiang Geely Holding Group Co., Ltd., Hangzhou 310051, China; Zhejiang Green Intelligent Vehicle and Spare Parts Technology Innovation Center, Ningbo 315336, China
raw_author_name: Dailin Lv
raw_affiliation_string: Zhejiang Geely Holding Group Co., Ltd., Hangzhou 310051, China; Zhejiang Green Intelligent Vehicle and Spare Parts Technology Innovation Center, Ningbo 315336, China
raw_author_name: Li Dai
raw_affiliation_string: Zhejiang Geely Holding Group Co., Ltd., Hangzhou 310051, China; Zhejiang Green Intelligent Vehicle and Spare Parts Technology Innovation Center, Ningbo 315336, China
raw_author_name: Zhiyu Xiang
raw_affiliation_string: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China

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

Authors:
raw_author_name: Gokuleshwaran Narayanan
raw_affiliation_string: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
raw_author_name: Raja Soosaimarian Peter Raj
raw_affiliation_string: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
raw_author_name: Ivan Cvitić
raw_affiliation_string: Department of Information and Communiation Traffic, Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia
raw_author_name: Dragan Peraković
raw_affiliation_string: Department of Information and Communiation Traffic, Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia

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

Authors:
raw_author_name: Giovanni Montana
raw_affiliation_string: Warwick Manufacturing Group, University of Warwick, Coventry, CV4 7AL, England, UK
raw_author_name: Adam R. Brentnall
raw_affiliation_string: Wolfson Institute of Population Health, Queen Mary University of London, Charterhouse square, London, EC1M 6BQ, England, UK

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 &lt; 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 &lt; 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

Authors:
raw_author_name: Brij B. Gupta
raw_affiliation_string: Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan; Department of Electrical and Computer Engineering, Lebanese American University, Beirut, Lebanon; Kyung Hee University, Seoul, Korea; Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India
raw_author_name: Akshat Gaurav
raw_affiliation_string: Computer Engineering, Ronin Institute, Montclair, NJ, USA
raw_author_name: Varsha Arya
raw_affiliation_string: Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India; Department of Business Administration, Asia University, Taichung, Taiwan

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

Authors:
raw_author_name: Giovanni Pintore
raw_affiliation_string: Visual and Data-intensive Computing, CRS4, Cagliari, 09134, Italy
raw_author_name: Eva Almansa
raw_affiliation_string: Visual and Data-intensive Computing, CRS4, Cagliari, 09134, Italy
raw_author_name: Enrico Gobbetti
raw_affiliation_string: Visual and Data-intensive Computing, CRS4, Cagliari, 09134, Italy

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

Authors:
raw_author_name: Kiramat Rahman
raw_affiliation_string: Department of Software Engineering, International Islamic University, Islamabad, 44000, Pakistan
raw_author_name: Anwar Ghani
raw_affiliation_string: Department of Computer Science, International Islamic University, Islamabad, 44000, Pakistan
raw_author_name: Sanjay Misra
raw_affiliation_string: Department of Computer Science and Communication, Østfold University College, Halden, Norway
raw_author_name: Arif Ur Rahman
raw_affiliation_string: Department of Computer Science, Bahria University, Islamabad, 44000, Pakistan

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

Authors:
raw_author_name: Brian Meneses-Claudio
raw_affiliation_string: Universidad Tecnológica del Perú, Facultad de Negocios. Lima, Perú.

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

Authors:
raw_author_name: Saravanan Srinivasan
raw_affiliation_string: Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India, Chennai, India
raw_author_name: Sandeep Kumar Mathivanan
raw_affiliation_string: School of Computing Science and Engineering, Galgotias University, Greater Noida, 203201, India
raw_author_name: P. Karthikeyan
raw_affiliation_string: Department of Computer Applications,School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
raw_author_name: Mohd Asif Shah
raw_affiliation_string: Department of Economics, Kabridahar University, Po Box 250, Kabridahar, Ethiopia

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.