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Enabling Generalized Zero-Shot Vulnerability Classification

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Hu, Jinghao Guo, Jinsong Luo, Chen Hu, Yang Lanzinger, Matthias Li, Zhanshan Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Software Changchun130012 China University College London Department of Computer Science United Kingdom Amazon United States United Kingdom University of Oxford Department of Computer Science United Kingdom Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Changchun130012 China

Regarding computer security, the growth of code vulnerability types presents a persistent challenge. These vulnerabilities, which may cause severe consequences, necessitate precise classification for effective mitigation. However, the rapid emergence of new vulnerability types complicates the classification process. Traditional methodologies, which often involve human expertise and the manual labeling or generation of example instances, are not only resource-intensive but also struggle to adapt to the dynamic nature of these vulnerabilities. This paper introduces VulnSense, an innovative method that harnesses the capabilities of Generalized Zero-Shot Learning (GZSL) to address the vulnerability classification problem. VulnSense learns about unseen vulnerability classes from the descriptions of these unseen classes, while not requiring to see any instances of these unseen classes. Specifically, VulnSense learns from three main resources: (1) seen classes with labeled code instances;(2) descriptions of these seen classes;and (3) descriptions of "unseen"classes which have no labeled instances. Our experiments underscore VulnSense's superiority over existing GZSL methods in classifying instances of unseen classes. Concurrently, it maintains a performance parity with traditional labeled-example based learning methods in classifying instances of seen vulnerabilities. VulnSense demonstrates the potential of using GZSL for vulnerability classification, while also highlighting challenges that inspire future work. © 2004-2012 IEEE.

关键词： Zero-shot learning

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Dynamic Integration of Digital Twins in the Metaverse: software engineering Perspectives

Dynamic Integration of Digital Twins in the Metaverse: Softw...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nedal Nwasra Khalil Omar Ahmed Alsalemi Software Engineering Department University of Petra Amman Jordan Computer Science Department University of Petra Amman Jordan Information Technology Department Al-Madinah College of Technology Madinah Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The paper presents an exploratory perspective of the seamless dynamic integration of the Digital Twin within the Metaverse from the software engineering viewpoint. Since the inception of Digital Twins as virtual counterparts of real-world entities, they have portrayed great promises of improving operational efficiency, especially in healthcare, smart manufacturing, and urban planning. However, their more and more seamless integration into such shared virtual environments as the Metaverse, dependent on immersive technologies, brings its specific software engineering challenges: real-time data synchronization, interoperability, scalability, and security. To cope with these challenges, this paper has proposed a layered software architecture belonging to data ingestion, processing, visualization, and user interaction. The key contributions take the form of a comprehensive literature review towards (1) identifying gaps in existing approaches in which this conceptual model and system architecture can facilitate modular and scalable integration of Digital Twins into the Metaverse and (2) practical implementation and benefits of such integration including real-time patient monitoring and interactive therapy simulations in which a healthcare use case can be demonstrated. This work addresses the technical and architectural challenges step by step and lays down the fundamental basis that will allow the integration of Digital Twins in real-time, secure, and user-centric immersive environments across a wide range of industries. Future research should be oriented towards standardization, improved data governance, and adaptive interaction paradigms.

关键词： Industries Metaverse Systems architecture Medical services Real-time systems Digital twins Security Synchronization software engineering Systematic literature review

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Performance Analysis of Self-adaptive User Interface for Mobile Users (Systematic Literature Review) 1st

Performance Analysis of Self-adaptive User Interface for Mob...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Ali, Muddassar Raheel, Muhammad Iqbal, Muhammad Waseem Irfan, Danish Department of Computer Science The Superior University Lahore54000 Pakistan Department of Software Engineering The Superior University Lahore54000 Pakistan Department of Information Technology The Superior University Lahore54000 Pakistan

ISBN: (纸本)9783031776168

Mobile devices have become essential to our daily lives, and the use of mobile applications has been rapidly increasing. The usability and effectiveness of mobile applications significantly influence user satisfaction. One approach to designing mobile apps is the adaptive user interface (AUI), which can modify its interface to meet the user’s demands and preferences. This study aims to assess the effectiveness of AUI across all types of applications. It summarizes previous research on AUI, outlines its advantages and disadvantages, and provides empirical evidence to support its effectiveness. The study also examines various methodologies and strategies used to evaluate self-adaptive mobile user interfaces. Additionally, the study analyzes the development of technology-enhanced adaptive/personalized learning over the past two decades. It explores different research areas, such as adaptive/personalized learning parameters, teaching aids, learning outcomes, participants, hardware, and more. The study demonstrates the effectiveness of personalized/adaptive user interfaces for mobile users and identifies essential features that improve UI effectiveness. It also highlights that while most studies focus on conventional mobile applications, personalized/adaptive learning has immense potential for wearable technologies, smartphones, and tablets. With the rapid growth of wearable computing and artificial intelligence, personalized/adaptive learning could have a significant impact on smart devices. Finally, the study identifies future research directions, issues, and challenges related to personalized/adaptive learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smartphones

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A Review on Feature Selection Techniques for Anxiety Classification from Physiological Signals

A Review on Feature Selection Techniques for Anxiety Classif...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Shikha Shikha Divyashikha Sethia S. Indu Department of Computer Science and Engineering Delhi Technological University New Delhi India Department of Software Engineering Delhi Technological University New Delhi India Department of Electronics and Communication Delhi Technological University New Delhi India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Anxiety disorders significantly impact individuals' quality of life and are traditionally assessed using subjective methods, which often lack accuracy. Recent advancements have enabled the use of physiological signals for anxiety classification, providing a more objective approach. However, the multidomain nature of these signals results in high-dimensional feature sets, necessitating effective feature selection. Therefore, this paper reviews various feature selection techniques-filter, wrapper, embedded, and hybrid-used in physiological-based anxiety classification, analyzing the advantages and limitations. It also highlights key challenges and suggests future research directions. Furthermore, this review introduces a proposed study utilizing a novel Corr-FGA hybrid feature selection algorithm to develop an efficient anxiety classification framework. The proposed framework achieves the highest accuracy of 88.93% using 8 features. This review aims to guide researchers in developing a robust, efficient anxiety classification model while providing insights that can help streamline feature selection and optimize machine learning model performance in real-world applications.

关键词： computer science Accuracy Machine learning algorithms Reviews Anxiety disorders Machine learning Feature extraction Physiology Data models Load modeling

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Performance Comparison of Interpolation Methods for Enriching WiFi Fingerprint Database 24

Performance Comparison of Interpolation Methods for Enrichin...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Thi, Luong Nguyen Lee, Seon-Woo Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Mathematics and Informatics Dalat University Dalat Viet Nam Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of Department of Electronics and Telecommunications Dalat University Dalat Viet Nam

ISBN: (纸本)9798400717161

Recently, WiFi Fingerprinting-based indoor positioning systems have gained significant attention due to the low cost of infrastructure deployment. In the data collection process, a higher density of reference points may lead to better positioning results. However, it makes this process time-consuming and laborious. To solve this problem, this study applies and compares different interpolation methods to enrich the fingerprint database without increasing the cost of the collection process. To evaluate the performance of the chosen methods, five interpolation methods are implemented in a multi-condition WiFi Fingerprinting dataset. Moreover, to check the robustness of the methods, different numbers of reference points are considered when creating the new interpolated fingerprint databases. According to the results, inverse distance weighting (IDW) is the most suitable interpolation method with low complexity and stable performance. The IDW-based database achieves an average interpolation error of less than 6 dBm compared to the original database. When applying this interpolated database to perform positioning tasks, it helps reduce the collection cost by 30% with slightly lower positioning results compared to the original database. This work demonstrates the feasibility of using interpolation to ease the data collection process of WiFi Fingerprinting, especially in complex and big areas such as airports or hospitals. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Wi-Fi

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CorLMI-FSA: An Efficient Feature Selection Approach for Stress Classification Using Physiological Signals

CorLMI-FSA: An Efficient Feature Selection Approach for Stre...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Mental stress in academic settings significantly impacts students' health and performance. Physiological signals such as Electrodermal Activity (EDA) and Heart Rate Variability (HRV) offer valuable insights for stress classification. However, the multidomain nature of these signals often introduces redundancy and irrelevant data, reducing both classification accuracy and computational efficiency [1]. Hence, this paper proposes an efficient feature selection algorithm, CorLMI-FSA (Correlation-Logistic Mutual Information Feature Selection Algorithm), which dynamically adjusts redundancy weights through a logistic function to capture nonlinear dependencies among features. The proposed algorithm achieves accuracies of 96.82% and 95.84% for binary and three-level stress classification tasks using Gradient Boosting (GB), enhancing computational efficiency by selecting only the most relevant features. These results underscore CorLMI-FSA's potential as a low-complexity solution for realtime stress monitoring in wearable devices, facilitating timely intervention.

关键词： Accuracy Heuristic algorithms Redundancy Human factors Feature extraction Classification algorithms Computational efficiency Biomedical monitoring Wearable devices Stress

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There Is More to Dwell Than Meets the Eye: Toward Better Gaze-Based Text Entry Systems With Multi-Threshold Dwell 25

There Is More to Dwell Than Meets the Eye: Toward Better Gaz...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Aunnoy K Mutasim Mohammad Raihanul Bashar Christof Lutteroth Anil Ufuk Batmaz Wolfgang Stuerzlinger School of Interactive Arts + Technology (SIAT) Simon Fraser University Vancouver British Columbia Canada Department of Computer Science & Software Engineering Concordia University Montreal Quebec Canada Computer Science University of Bath Bath United Kingdom

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The Impact of Transformer Models and Regression Algorithms on Automated Short Answer Grading

The Impact of Transformer Models and Regression Algorithms o...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Abhijith. N. Nampoothiri M. G. Thushara Pradeesh. N Anand Krishna K. R Abhai Santhosh Abhijith Kumar S Suryan Kumar Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Omnex Software Solutions Pvt. Ltd Chennai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Grading short answer questions in higher education often poses significant challenges due to its subjective nature and time-consuming process. As educational institutions expand, the need for efficient and objective grading solutions becomes critical. This paper investigates the use of machine learning and natural language processing to automate the evaluation of short answer responses. Our study employs a two-phase methodology: first, several transformer models—including BERT, Sentence-BERT, T5, Genism, and XLNet—are used to transform text into numerical representations. In the second phase, various regression techniques, such as Linear Regression, Support Vector Machine (SVM), Random Forest, and AdaBoost, are applied to these representations to estimate scores. Our findings indicate that the combination of the BERT model with Random Forest performs the best, demonstrating superior accuracy with the lowest error metrics. This optimal model pairing is proposed for integration into the Ample Learning Management System (LMS) to enhance grading efficiency and provide quicker, more reliable feedback. The significant implications of this research provides teachers with a robust tool for automated grading, which can enhance the educational experience by delivering timely and accurate evaluations.

关键词： Support vector machines Measurement Accuracy Education Forestry Transforms Transformers Numerical models Reliability Random forests

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TMAE: Entropy-Aware Masked Autoencoder for Low-Cost Traffic Flow Map Inference

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IEEE Internet of Things Journal 2025年

作者： Luo, Xucheng Wu, Qiong Wang, Ye Zhang, Kuan Dai, Hong-Ning Chen, Dajiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Pengcheng laboratory Shenzhen China University of Nebraska-Lincoln United States Hong Kong Baptist University Department of Computer Science Hong Kong

Accurate traffic flow measurement is essential for the development of smart cities, yet the deployment of ubiquitous monitoring sensors using traditional methods is often cost-prohibitive. This paper proposes an innovative entropy-aware masked autoencoder framework, namely TMAE, for low-cost traffic flow inference. TMAE leverages a small number of selectively measured regions with few deployed sensors to infer traffic flow across entire urban areas, incorporating prior knowledge from road distribution maps. Specifically, TMAE employs a shared encoder to process traffic flow context, using self-attention scores to identify the importance of each region and guide a masking policy that retains regions rich in traffic flow information. The road distribution map, reflecting inherent traffic flow patterns, is incorporated as prior knowledge by substituting masked tokens during training. A cross-attention mechanism in the decoder further refines inference, where embeddings from the road distribution map serve as queries, and retained visible patches act as keys and values. Additionally, regional traffic entropy is introduced to quantify the information richness of each region, enabling the selection of minimal measurement regions to optimize inference for other areas. Extensive experiments conducted on datasets from various cities demonstrate the effectiveness and efficiency of TMAE, highlighting its potential as a scalable solution for low-cost traffic flow inference in urban environments. The source code of this work is released at https://***/TextGraph/TMAE. © 2025 IEEE.

关键词： Costs

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Implementation of IoT and WSN Technologies for Health Monitoring 1st

Implementation of IoT and WSN Technologies for Health Monito...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Mazhar, Tehseen Iqbal, Muhammad Iqbal, Muhammad Ghadi, Yazeed Yasin Rehman, Ateeq Ur Abbasi, Waseem Department of Computer Science Virtual University of Pakistan Lahore54000 Pakistan Institute of Computing and Information Technology Gomal University Dera Ismail Khan29220 Pakistan Department of Computer Science and Software Engineering Al Ain Universit Abu Dhabi15322 United Arab Emirates Department of Electrical Engineering Government College University Lahore54000 Pakistan Department of Computer Science and IT Superior University Sargodha Pakistan

ISBN: (纸本)9783031776168

IoT-based applications make innovative use of millions of sensors. Research has risen in the medical and technological fields in the previous ten years. The Internet of Things impacts the development of medical research. Improved medical care is possible because the Internet of Things can link sensors, medical equipment, and remote healthcare professionals. Patient treatment has been enhanced in terms of effectiveness, cost, accessibility, and safety. Due to COVID-19, receiving medical treatment in the convenience of one’s home is more important than ever. To enhance healthcare, internet of things devices is utilized to monitor patients. His article examines how the Internet of Things has improved, enabling technology, healthcare services, and healthcare-related challenges. IoT system concerns are also taken into consideration. This effort aims to help future researchers understand the topic better by giving a thorough overview of all of HIoT’s many uses. The applications, technology, and challenges of the healthcare industry are all examined in this article. As a result, the IoT has the potential to improve healthcare’s effectiveness and ability to treat patients. The idea put out in this article is that the Internet of Things may be made widely used, lucrative, and easily available everywhere. The integration provides a faster calculation. The last section includes a number of practical recommendations and suggestions for implementing the IoT healthcare system during COVID-19 and future severe pandemics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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