Reviewerly/exHarmony huggingface dataset:
Performance evaluation considering academic misconduct of China’s higher education institutions

Authors:
raw_author_name: Yufei Liu
raw_affiliation_string: School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan, 250002, Shandong, China
raw_author_name: Wenbin Liu
raw_affiliation_string: Faculty of Business and Management, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, 519087, Guangdong, China

Abstract: Performance evaluation is essential for managing and allocating resources in higher education institutions (HEIs), guiding their development. However, in the current evaluation system, the growing issue of academic misconduct (AM) as an undesirable output in research assessments is highly overlooked. To fill this gap, we propose the creative inclusion of AM as an undesirable output in the evaluation system. To validate the potentially questionable nature of the evaluation system without AM, we took China’s 32 HEIs within the period of 2016–2018 as an example, separately calculating efficiencies without and with AM through DEA approach. Then we use Wilcoxon test of paired samples and Pearson correlation coefficient confirming that there are increasingly significant differences between the two efficiencies and the two rankings in the sample period, respectively. Therefore, we believe that other performance evaluations for HEIs that do not incorporate AM are likely to be questionable, such as the influencing factors of research efficiency and returns to scale (RTS). Incorporating AM into the evaluation index system, we further explore the influencing factors of research efficiency in sample HEIs through Tobit regression model. Then we propose innovative models tailored specifically for analyzing RTS of HEIs in which undesirable outputs are under extended strong disposability and apply them to the empirical study of China’s HEIs. The results show that (1) AM is playing an increasingly prominent role in the research efficiency evaluation of HEIs; (2) local economic development, external exchanges and emphasis on AM in HEIs can improve research efficiency; however, government funding has a negative effect on it while the effect of human capital is not significant; (3) the investment scale in most China’s HEIs is optimize or excessive.
Why and how bug blocking relations are breakable: An empirical study on breakable blocking bugs

Authors:
raw_author_name: Hao Ren
raw_affiliation_string: Department of Computer Science and Technology, Nanjing University, 210023, China; State Key Laboratory for Novel Software Technology, Nanjing University, 210023, China
raw_author_name: Yanhui Li
raw_affiliation_string: Department of Computer Science and Technology, Nanjing University, 210023, China; State Key Laboratory for Novel Software Technology, Nanjing University, 210023, China
raw_author_name: Lin Chen
raw_affiliation_string: Department of Computer Science and Technology, Nanjing University, 210023, China; State Key Laboratory for Novel Software Technology, Nanjing University, 210023, China
raw_author_name: Yuming Zhou
raw_affiliation_string: Department of Computer Science and Technology, Nanjing University, 210023, China; State Key Laboratory for Novel Software Technology, Nanjing University, 210023, China
raw_author_name: Changhai Nie
raw_affiliation_string: Department of Computer Science and Technology, Nanjing University, 210023, China; State Key Laboratory for Novel Software Technology, Nanjing University, 210023, China

Abstract: Blocking bugs prevents other bugs from being fixed, which is difficult to repair and negatively impacts software quality. During software maintenance, developers usually try to break the blocking relationship between blocking and blocked bugs, e.g., propose a temporary fix. However, to our knowledge, no studies have investigated why and how blocking relations between bugs are breakable. In this study, we aim to construct an empirical analysis to explore breakable blocking bugs (BBBs). Specifically, we employ quantitative and qualitative analysis to study these BBBs from two aspects. One is to investigate the characteristics of these bugs, and the other is to explore why and how developers break the blocking relationship between bugs during software maintenance. We build a dataset on five large-scale open-source projects and classify bugs into three types (BBBs, normal blocking bugs, and other bugs) to compare the differences between BBBs and other types of bugs. We observe that BBBs have higher levels of involvement, take longer to fix, and involve more complex source code than other bugs. Moreover, we summarize four reasons blocking relationships between bugs are broken, i.e., partial association (41.87%), serious influence (26.40%), time pressure (19.73%), and flawed blocking (12.21%), and three measures developers adopt to break these blocking relationships, i.e., quick patch for blocking bugs (41.33%), quick patch for blocked bugs (38.67%), and ignore the blocking relation and fix blocked bugs directly (20.00%). Through these analyses, it is meaningful for software maintainers to have a deeper understanding of the characteristics and repair practices of BBBs, which will help solve these BBBs effectively in the future.
Joint task semi-supervised semantic segmentation for TRUS image

Authors:
raw_author_name: Chao Gao
raw_affiliation_string: College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei 443002, China; Hubei Key Laboratory of Intelligent Vision Monitoring for Hydropower Engineering, China Three Gorges University, Yichang, Hubei 443002, China
raw_author_name: Yongtao Shi
raw_affiliation_string: College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei 443002, China; Hubei Key Laboratory of Intelligent Vision Monitoring for Hydropower Engineering, China Three Gorges University, Yichang, Hubei 443002, China
raw_author_name: Chang Zhou
raw_affiliation_string: The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei 443002, China; Yichang Central People’s Hospital, Yichang, Hubei 443002, China
raw_author_name: Bangjun Lei
raw_affiliation_string: College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei 443002, China; Hubei Key Laboratory of Intelligent Vision Monitoring for Hydropower Engineering, China Three Gorges University, Yichang, Hubei 443002, China

Abstract: Transrectal ultrasound (TRUS) is an important tool for prostate observation, playing a crucial role in medical processes such as multimodal image registration and surgical planning. However, TRUS images are often plagued by blurry boundary information and artifacts, making manual annotation a time-consuming and labor-intensive task, which results in insufficient pixel-labeled data for training neural networks. Therefore, this paper introduces a semi-supervised neural network based on joint tasks for TRUS image segmentation, which combines confidence information from target localization and semantic segmentation to generate pseudo-labels for collectively training unlabeled samples. Additionally, this method supports the generation of pseudo-labels using only location information for weakly supervised training of neural networks. Compared to other state-of-the-art models, our results show that this approach outperforms in segmentation performance with a smaller dataset, achieving an mean Dice similarity coefficient and mean Jaccard similarity coefficient of 92.8 % and 88.4 %, respectively. Furthermore, the experiments reveal that reducing the coupling of two decoders' parameters between the segmentation and detection tasks improves segmentation accuracy as dataset size decreases.
Comparative Study of HMD-based Virtual and Augmented Realities for Immersive Museums: User Acceptance, Medium, and Learning

Authors:
raw_author_name: Yunshui Jin
raw_affiliation_string: Tongji University, Shanghai, P.R. China
raw_author_name: Minhua Ma
raw_affiliation_string: Falmouth University, Cornwall, United Kingdom
raw_author_name: Liu Yun
raw_affiliation_string: Tongji University, Shanghai, P.R. China

Abstract: Technologies like Head Mounted Display (HMD)-based Virtual Reality (VR) and Augmented Reality (AR) have made HMD-based immersive museums possible. To investigate the user acceptance, medium, and interaction experience for HMD-based immersive museums, an app entitled The Extended Journey has been designed, developed, and deployed on both VR headsets and AR headsets. Subsequently, a between-subjects design experiment with 62 participants was conducted to measure the user experience and learning outcome in HMD VR and HMD AR conditions. Quantitative results revealed that HMD VR museums had statistically significantly better immersion and empathy compared to HMD AR museums. Qualitative data indicated HMD-based immersive museums were embraced by most young participants while HMD VR had better user acceptance than HMD AR for immersive museums. The interview also demonstrated that the advantage of the HMD-based immersive museum over the traditional online museum is not only the sensory immersion from the medium itself but also the interactive narrative experience that the HMD medium facilitates, especially the natural interaction with the CG characters and the environment in the story.
Cross-modal Multiple Granularity Interactive Fusion Network for Long Document Classification

Authors:
raw_author_name: Tengfei Liu
raw_affiliation_string: Beijing University of Technology, China
raw_author_name: Yongli Hu
raw_affiliation_string: Beijing University of Technology, China
raw_author_name: Junbin Gao
raw_affiliation_string: The University of Sydney, Australia
raw_author_name: Yanfeng Sun
raw_affiliation_string: Beijing University of Technology, China
raw_author_name: Baocai Yin
raw_affiliation_string: Beijing University of Technology, China

Abstract: Long Document Classification (LDC) has attracted great attention in Natural Language Processing and achieved considerable progress owing to the large-scale pre-trained language models. In spite of this, as a different problem from the traditional text classification, LDC is far from being settled. Long documents, such as news and articles, generally have more than thousands of words with complex structures. Moreover, compared with flat text, long documents usually contain multi-modal content of images, which provide rich information but not yet being utilized for classification. In this article, we propose a novel cross-modal method for long document classification, in which multiple granularity feature shifting networks are proposed to integrate the multi-scale text and visual features of long documents adaptively. Additionally, a multi-modal collaborative pooling block is proposed to eliminate redundant fine-grained text features and simultaneously reduce the computational complexity. To verify the effectiveness of the proposed model, we conduct experiments on the Food101 dataset and two constructed multi-modal long document datasets. The experimental results show that the proposed cross-modal method outperforms the single-modal text methods and defeats the state-of-the-art related multi-modal baselines.
A Hypersphere Information Granule-Based Fuzzy Classifier Embedded With Fuzzy Cognitive Maps for Classification of Imbalanced Data

Authors:
raw_author_name: Wei Lu
raw_affiliation_string: School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China
raw_author_name: Jianhua Yang
raw_affiliation_string: School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China

Abstract: In this article, a hypersphere information granule-based fuzzy classifier integrated with Fuzzy Cognitive Maps (FCM), named FCM-IGFC, is proposed for the classification of imbalanced data. The proposed FCM-IGFC is structured by sequentially linking a Takagi-Sugeno-Kang (TSK) fuzzy system (FCM-TSK) – which is embedded with FCMs – and a rule-based fuzzy system (IGFS) based on hypersphere information granules. The FCM-TSK leverages the inference capabilities of the FCM to allow for the movement of data within the original space, while the IG-FS creates a mapping between samples and majority and minority classes using information granules. In this study, we introduced an innovative bottom-up granulation method and an overlap elimination technique for constructing hypersphere information granules. These methods facilitate the creation of information granules that accurately represent the structure of classes, even when dealing with im- balanced data. Moreover, the stacked structure of the FCM-IGFC offers data transfer capabilities. This helps reduce the complexity of distributions such as small, disjointed clusters and irregular class boundaries, with the support of the FCM, thereby making it easier to use information granules to describe the class structure. A series of experiments conducted on 12 publicly available datasets demonstrated that the performance of FCM-IGFC significantly surpasses that of existing granule-based fuzzy classifiers. Additionally, it is competitive with top-tier classifiers that incorporate sampling methods.
Semi-supervised, Neural Network based approaches to face mask and anomaly detection in surveillance networks

Authors:
raw_author_name: Sabir Saheel
raw_affiliation_string: Department of Computer Science, University of Minnesota Duluth, Duluth, MN, USA
raw_author_name: Anik Alvi
raw_affiliation_string: Department of Computer Science, New Mexico State University, Las Cruces, NM, USA
raw_author_name: Aninda Roy Ani
raw_affiliation_string: Department of Computer Science and Engineering, Independent University, Bangladesh (IUB), Dhaka, Bangladesh
raw_author_name: Tarem Ahmed
raw_affiliation_string: Department of Computer Science and Engineering, Independent University, Bangladesh (IUB), Dhaka, Bangladesh
raw_author_name: Mohammad Faisal Uddin
raw_affiliation_string: Department of Computer Science and Engineering, Independent University, Bangladesh (IUB), Dhaka, Bangladesh

Abstract: In the post-pandemic world, surveillance cameras play a key aspect when it comes to detecting various kinds of security risks. These can range from burglars entering a premises to an individual wearing or not wearing a mask where convention dictates one way versus the other. We are proposing a system that would allow the autonomous detection of these security risks with minimal human intervention. We propose using Multi-task Cascaded Convolutional Neural Networks as the face detector, followed by a Gabor image feature extractor, and the Kernel-based Online Anomaly Detection algorithm for detecting and identifying potential risks in real-time. We have tested our proposed framework on five different datasets comprising two datasets from public online repositories, one publicly available dataset from the Georgia Institute of Technology, and two from real-life settings. We compared our results to the Viola Jones legacy face detector, and with two other anomaly detection algorithms based on Principal Component Analysis (PCA) and the Autoencoder scheme. Our proposed framework has yielded high detection rates with low false alarm rates, in addition to being adaptive, portable, and requiring minimal infrastructure.
Cyber offending predictors and pathways in middle adolescence: Evidence from the UK Millennium Cohort Study

Authors:
raw_author_name: Katie Maras
raw_affiliation_string: Department of Psychology, University of Bath, UK

Abstract: Despite the pervasiveness of cyber crime victimisation, knowledge is limited regarding the prevalence, characteristics and pathways of offenders. The present study examines predictors of self-reported engagement in cyber crime in middle adolescence in a large (N = 13,277) longitudinal dataset from the UK Millennium Cohort Study. We adopted an ecological systems approach to examine a range of multicausal, intersecting factors across individual, familial, psychosocial and environmental systems. The overall prevalence of self-reported cyber offending (account hacking or the deployment of viruses) was 5.6% at age 14 and 3.8% at age 17, although persistence over time by the same individuals was relatively low (1.1%). Significant predictors of cyber offending at age 17 were being male, domestic violence between parents, low parental monitoring, low wellbeing, self-harm, exclusion from school, spending more time online gaming, participating in offline leisure activities, and engaging in serious violence (weapon carrying or use), assault, and cyber crime at age 14. Findings indicate that young cyber offenders are often males and those who have experienced a range of risk factors that are connected to poorer wellbeing and engaging in multiple risky/offending behaviours. Implications for theory, policy and practice are discussed.
BERT based severity prediction of bug reports for the maintenance of mobile applications

Authors:
raw_author_name: Asif Ali
raw_affiliation_string: School of Automation, Beijing Institute of Technology, Beijing 100081, China
raw_author_name: Yuanqing Xia
raw_affiliation_string: School of Automation, Beijing Institute of Technology, Beijing 100081, China
raw_author_name: Qasim Umer
raw_affiliation_string: Department of Computer Science, Hanyang University, Seoul, 04763, Republic of Korea; Department of Computer Sciences, COMSATS University Islamabad, Vehari 61000, Pakistan

Abstract: Mobile application maintenance is crucial to ensuring the accurate operation and continuous improvement of mobile applications (mobile apps). To effectively address issues and enhance the user experience, developers utilize issue-tracking systems that gather bug reports to refine mobile apps. Users can submit bugs through these systems, allowing them to determine the severity of each reported issue. The severity level plays a pivotal role in prioritizing bug resolution, enabling developers to address critical bugs promptly. Nonetheless, manually assessing the severity of each issue can be laborious and prone to errors. To overcome this challenge, this paper presents Bidirectional Encoder Representations from Transformers (BERT) based severity prediction of bug reports (called BERT-SBR) that leverages a deep neural network for automatic bug severity classification for mobile app maintenance. We collect the publicly available mobile apps bug reports dataset from the Hugging Face. BERT-SBR first computes the sentiment of reporters of bug reports and preprocesses them by leveraging BertTokenizer input formatting techniques. Next, it passes the formatted text and computed sentiment of each bug report to generate word embeddings. Then, it introduces a fine-tuned BERT classifier for bug report severity prediction. After that, it passes the generated word embeddings to the fine-tuned BERT classifier for training and testing. Finally, the proposed classifier’s performance is evaluated. The BERT-SBR assessment results confirm that the fine-tuned BERT classifies bug reports significantly more effectively than other deep learning classifiers. On average, BERT-SBR achieves a remarkable improvement of 40.43%, 67.78%, 40.71%, and 58.14% in the accuracy, precision, recall, and f-measure. This indicates its superiority in accurately predicting the severity of bug reports for mobile application maintenance.
Dynamic-group-aware networks for multi-agent trajectory prediction with relational reasoning

Authors:
raw_author_name: Chenxin Xu
raw_affiliation_string: Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Bohan Tang
raw_affiliation_string: Oxford-Man Institute and the Department of Engineering Science, University of Oxford, Oxford, UK
raw_author_name: Sheng Yin
raw_affiliation_string: Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Ya Zhang
raw_affiliation_string: Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, China; Shanghai AI Laboratory, Shanghai, China
raw_author_name: Yanfeng Wang
raw_affiliation_string: Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, China; Shanghai AI Laboratory, Shanghai, China

Abstract: Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly consider static, pairwise interactions with limited relational reasoning. To more comprehensively model interactions and reason relations, we propose DynGroupNet, a dynamic-group-aware network, which (i) models time-varying interactions in highly dynamic scenes; (ii) captures both pairwise and group-wise interactions; and (iii) reasons both interaction strength and category without direct supervision. Based on DynGroupNet, we further design a prediction system to forecast socially plausible trajectories with dynamic relational reasoning. The proposed prediction system leverages the Gaussian mixture model, multiple sampling and prediction refinement to promote prediction diversity for multiple future possibilities capturing, training stability for efficient model learning and trajectory smoothness for more realistic predictions, respectively. The proposed complex interaction modeling of DynGroupNet, future diversity capturing, efficient model training and trajectory smoothing of prediction system together to promote more accurate and plausible future predictions. Extensive experiments show that: (1) DynGroupNet can capture time-varying group behaviors, infer time-varying interaction category and interaction strength during prediction; (2) DynGroupNet significantly outperforms the state-of-the-art trajectory prediction methods by 28.0%, 34.9%, 13.0% in FDE on the NBA, NFL and SDD datasets.
A study on classifying Stack Overflow questions based on difficulty by utilizing contextual features

Authors:
raw_author_name: Md. Jubair Ibna Mostafa
raw_affiliation_string: Department of Computer Science and Engineering, University of Technology (IUT), Gazipur, 1704, Bangladesh; Software Engineering Lab (SELab), Islamic University of Technology (IUT), Gazipur, 1704, Bangladesh

Abstract: Technical question-answering sites like Stack Overflow are gaining enormous attention from practitioners of specialized fields looking to exchange their programming knowledge. They ask questions on different topics with varying degrees of complexity and difficulty. All practitioners do not have the same level of expertise on those topics to respond to such questions. However, the current approach used by Stack Overflow mostly filters questions based on topics alone and does not take difficulty into account. For this reason, a large percentage of questions fail to attract the attention of appropriate users, resulting in questions having no answer or a significant delay in response time. To address these limitations, we incorporate three models, TF-IDF, LDA, and Doc2Vec, to extract semantic and context-dependent features that can measure the difficulty of questions. Each of these models is paired with different classifiers along with other features to classify the questions based on difficulty. Extensive experiments on three different datasets exhibit the effectiveness of our models, and Doc2Vec outperforms the other models. We also identified that the contextual features are correlated with question difficulty, and one subset of features outperforms others. The proposed approach can be beneficial for building an automatic tagger based on question difficulty.