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arXiv 2024年

作者： Ji, Zhoulin Lin, Chenhao Wang, Hang Shen, Chao School of Software Engineering Xi’an Jiaotong University China School of Cyber Science and Engineering Xi’an Jiaotong University China School of Automation Science and Engineering Xi’an Jiaotong University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Detecting synthetic from real speech is increasingly crucial due to the risks of misinformation and identity impersonation. While various datasets for synthetic speech analysis have been developed, they often focus on specific areas, limiting their utility for comprehensive research. To fill this gap, we propose the Speech-Forensics dataset by extensively covering authentic, synthetic, and partially forged speech samples that include multiple segments synthesized by different high-quality algorithms. Moreover, we propose a TEmporal Speech LocalizaTion network, called TEST, aiming at simultaneously performing authenticity detection, multiple fake segments localization, and synthesis algorithms recognition, without any complex post-processing. TEST effectively integrates LSTM and Transformer to extract more powerful temporal speech representations and utilizes dense prediction on multi-scale pyramid features to estimate the synthetic spans. Our model achieves an average mAP of 83.55% and an EER of 5.25% at the utterance level. At the segment level, it attains an EER of 1.07% and a 92.19% F1 score. These results highlight the model’s robust capability for a comprehensive analysis of synthetic speech, offering a promising avenue for future research and practical applications in this field. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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UNDERSTANDING USERS’ UX NEEDS THROUGH THE LENSES OF PSYCHOLOGICAL NEEDS THEORIES: USERS’ EXPECTATIONS AND FELT EXPERIENCES 16

UNDERSTANDING USERS’ UX NEEDS THROUGH THE LENSES OF PSYCHOL...

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16th International Conference on Interfaces and Human Computer Interaction, IHCI 2022, and 15th International Conference on Game and Entertainment Technologies 2022, GET 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022

作者： Mkpojiogu, Emmanuel O.C. Okeke-Uzodike, Obianuju E. Omopariola, Victor School of Computing Universiti Utara Malaysia UUM Sintok06010 Malaysia Department of Software Engineering Veritas University Abuja Nigeria Department of Applied Management Durban University of Technology KwaZulu-Natal Pietermaritzburg South Africa Department of Computer and Information Technology Veritas University Abuja Nigeria

ISBN: (纸本)9789898704405

The purpose of this study was to conduct a literature review in order to gather material that would serve to underpin and support a link between UX requirements and psychological theories. In the paper, relevant sources from extant literature were accessed and synthesized. The outcome revealed that psychological theories support user experience needs. UX needs as psychological needs are fully supported by psychological theories. In the light of this, the search for ways to meet these needs should be done using psychological needs fulfillment strategies. Further studies will investigate ways of meeting these user experience needs and how these needs can be modeled for UX design and evaluation. © 2022 International Conference on Interfaces and Human Computer Interaction

关键词： Human computer interaction

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software Practice in Small software Companies: Development Context Constraints on Process Adoption. 22

Software Practice in Small Software Companies: Development C...

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Proceedings of the 2022 European Symposium on software engineering

作者： Micheal Tuape Victoria Hasheela-Mufeti Petrus Iiyambo Anna Kayanda Sharon Mensah Jussi Kasurinen Department of Software Engineering Lappeenranta-Lahti University of Technology Finland Dept of Computing Maths and Statistical Sciences University of Namibia Namibia Dept of Information Systems College of Business Education Tanzania Software Department B-Systems Limited Ghana

ISBN: (纸本)9781450397308

Small software companies (SSCs) interact with the immediate environment, exposing them to challenges that force the organization to undertake adjustments if it must survive and remain in business. These adjustments result into counterproductive practices and changes that create complexities in process adoption. This cross-sectional survey investigates the occurrences around the customer in the development context that affect the adoption of process in SSCs. To answer the research questions, we conducted a survey on 115 respondents and found out that although customer engagement has a significant relationship with reducing rework, inadequacies in the engagement due to the customer's lack of knowledge of software processes, triggers unstructured and ad-hoc methods in SSCs. The main contribution of this paper is a customer engagement framework that seeks to transform software processes by focusing on the customer as a pillar of achieving purpose and value to reduce development effort and time.

关键词： Small software Companies software processes software Development Context

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Aligning Academic with Industrial Needs: Investigating DevOps 24

Aligning Academic with Industrial Needs: Investigating DevOp...

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Proceedings of the 28th International Conference on Evaluation and Assessment in software engineering

作者： Saima Rafi Muhammad Azeem Akbar Sajjad Mahmood School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Software Engineering LUT University Finland Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (纸本)9798400717017

Over recent years, DevOps has gained more attention within the software industry, owing to its pivotal role in facilitating continuous software delivery. The significant demand for DevOps practitioners necessitates substantial adjustments in conventional software engineering courses in higher educational institutions. Recent studies have highlighted DevOps principles, challenges, best practices, and tools to facilitate continuous delivery and deployment pipelines from an industrial perspective. As numerous universities are now introducing courses related to DevOps for students majoring in software engineering and computer science, this high demand for DevOps practitioners requires non-trivial adjustments in traditional software engineering courses and educational methodologies, including practices for teaching DevOps, training in DevOps, the level to which DevOps is currently taught and applied to measure the gap between teaching practices compared to industrial requirements. Our vision paper aims to highlight the crucial need for updated policies and tools to improve DevOps training in higher education, providing guidelines. By aligning academic curricula with industry standards, we can ensure students are better equipped for the demands of the workforce, fostering a seamless transition from academia to industry and promoting innovation in software engineering practices.

关键词： Challenges DevOps Guidelines Industry Practices Teaching

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METHODS FOR THE EVALUATION OF UX OVER TIME 16

METHODS FOR THE EVALUATION OF UX OVER TIME

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ISBN: (纸本)9789898704405

In this paper a literature review strategy was utilized to assess the methods of evaluating UX longitudinally. The review revealed that studies on user experience evaluation over time are rare. This rareness is attributed to the cost involved in carrying out such studies. This research nonetheless suggests that lack of sufficient information and adequate knowledge on methods used for the evaluation of UX over time may also be the reason for the scarcity of studies on long-term user experience evaluation. On this note, this paper reviewed previous works on long-term evaluation of UX to elicit the methods employed in conducting such evaluation to inform possible researchers on the existing methods. The outcome of the study indicated a number of methods for the evaluation of UX over time. © 2022 International Conference on Interfaces and Human Computer Interaction

关键词： Human computer interaction

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A formal model for multiagent Q-learning on graphs

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Science China(Information Sciences) 2025年

作者： Jinzhuo LIU Guangchen JIANG Chen CHU Yong LI Zhen WANG Shuyue HU School of Software Yunnan University School of Artificial Intelligence Optics and Electronics (iO PEN)Northwestern Polytechnical University Department of Statistics School of Statistics and MathematicsYunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics School of Information Science&Engineering Yunnan University School of Cybersecurity Northwestern Polytechnical University Shanghai Artificial Intelligence Laboratory

Understanding the dynamics of multi-agent learning has long been an important research topic. Existing research has focused mostly on 2-agent games or well-mixed populations. However, in real-world multi-agent systems, agents often interact in spatially or socially structured networks(or graphs). In this paper, we examine the dynamics of multi-agent Q-learning on graphs. Combining mean-field theory and combinatorics analysis, we present a new analytical approach to formally describe the time evolution of Q-values in the system with a topological structure. Through extensive numerical simulations, we show that our theory consistently provides an accurate depiction of the Q-learning dynamics across different typical games, initial conditions, and various graph structures, encompassing regular graphs, scale-free graphs, and random graphs. Moreover, we show that when comparing regular graphs to other types of graphs with the same average degree, the differences in the system evolution are largely attributed to the behaviors and Q-values of agents with lower degrees.

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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

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Soft computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection

arXiv

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arXiv 2024年

作者： Yuan, Shuai Qin, Hanlin Yan, Xiang Akhtar, Naveed Mian, Ajmal The School of Optoelectronic Engineering Xidian University Xi’an710071 China The School of Computing and Information Systems Faculty of Engineering and IT The University of Melbourne ParkvilleVIC3052 Australia The Department of Computer Science and Software Engineering The University of Western Australia Perth6009 Australia

This is the pre-acceptance version, to read the final version please go to IEEE TRANSACTION ON GEOSCIENCE AND REMOTE SENSING on IEEE Xplore. Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. However, largely overlooking effective global information modeling, existing techniques struggle when the target has high similarities with the background. We present a Spatial-channel Cross Transformer Network (SCTransNet) that leverages spatial-channel cross transformer blocks (SCTBs) on top of long-range skip connections to address the aforementioned challenge. In the proposed SCTBs, the outputs of all encoders are interacted with cross transformer to generate mixed features, which are redistributed to all decoders to effectively reinforce semantic differences between the target and clutter at full levels. Specifically, SCTB contains the following two key elements: (a) spatial-embedded single-head channel-cross attention (SSCA) for exchanging local spatial features and full-level global channel information to eliminate ambiguity among the encoders and facilitate high-level semantic associations of the images, and (b) a complementary feed-forward network (CFN) for enhancing the feature discriminability via a multi-scale strategy and cross-spatial-channel information interaction to promote beneficial information transfer. Our SCTransNet effectively encodes the semantic differences between targets and backgrounds to boost its internal representation for detecting small infrared targets accurately. Extensive experiments on three public datasets, NUDT-SIRST, NUAA-SIRST, and IRSTD-1K, demonstrate that the proposed SCTransNet outperforms existing IRSTD methods. Our code will be made public at https://***/xdFai/SCTransNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Revolutionizing Image Recognition: Next-Generation CNN Architectures for Handwritten Digits and Objects

Revolutionizing Image Recognition: Next-Generation CNN Archi...

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IEEE Symposium on Wireless Technology and Applications (ISWTA)

作者： Md Nurul Absur Kazi Fahim Ahmad Nasif Sourya Saha Sifat Nawrin Nova Department of Computer Science City University of New York New York USA College of Computing and Software Engineering Kennesaw State University Georgia USA Department of Computer Science Chalmers University of Technology Gothenburg Sweden

ISBN: (数字)9798350351354

ISBN: (纸本)9798350351361

This study addresses the pressing need for computer systems to interpret digital media images with a level of sophistication comparable to human visual perception. By leveraging Convolutional Neural Networks (CNNs), we introduce two innovative architectures tailored to distinct datasets: the MNIST handwritten digit dataset and the Fashion MNIST dataset. Unlike traditional machine learning methods such as Support Vector Machines (SVM) and Random Forests, our customized CNN models remarkably enhance image attribute comprehension and recognition accuracy. Specifically, the model developed for the MNIST dataset achieved an unprecedented accuracy of 98.71% without any bias, while the Fashion MNIST model reached 91.39%, marking significant advancements over conventional algorithms without any bias. This research showcases the superior efficiency of CNNs in processing and understanding digital images. It underscores the potential of deep learning technologies in bridging the gap between computational systems and human-like visual recognition. Through meticulous experimentation and analysis, we illustrate how deep CNNs require less preparatory work than other image-processing algorithms, setting a new benchmark in computer vision.

关键词： Support vector machines Handwriting recognition Visualization Accuracy Image recognition Computational modeling Computer architecture

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