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35th IEEE International Symposium on software Reliability engineering Workshops, ISSREW 2024

作者： Su, Qingran Li, Xingze Ren, Yuming Ouyang, Xulang Hu, Chunming Yin, Yongfeng Beihang University School of Computer Science and Engineering Beijing China Beihang University School of Software Beijing China

ISBN: (纸本)9798350367041

Safety testing is a key method to ensure software quality. But the quality of the test depends on the level of the test engineers. The method of generating safety test cases based on state diagrams often perform poorly due to lack of personnel experience. Meanwhile, the manual construction of test cases constrains the improvement of safety testing efficiency. This article utilizes LLMs to achieve automated extension and testing of safety state diagrams. The main contributions include extracting safety properties from aviation standards and extending the basic state diagram with LLMs to enable safety testing capabilities. In addition, a genetic algorithm combining edge first depth traversal and LLMs optimization was proposed to improve the efficiency of test case generation. Compared to classical genetic algorithms, the average execution round achieved a 90% reduction. Our LLMs optimization strategy can also enhance other improved genetic algorithms, further improving the efficiency of safety testing. © 2024 IEEE.

关键词： Safety testing

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An Exploration of Human Pose Estimation Based Cheating Tools for FPS Video Game and its Defense Solution

An Exploration of Human Pose Estimation Based Cheating Tools...

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IEEE Workshop on Multimedia Signal Processing

作者： Chang Liu Zichun Gao Zhenyu Liao Yue Sun Xianglong Feng Department of Computer Science & Software Engineering Miami University Oxford USA

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Modern computer vision and AI algorithms have become highly effective in analyzing high-dimensional image and video content for various tasks. Recently, some have exploited the power of computer vision to develop cheating tools for video games, which pose a serious threat to the gaming community and the game industry. Using human pose estimation algorithms, these cheating tools can assist players by automatically targeting and shooting with very high precision and accuracy. Compared to classic cheating methods, these tools are generally much more undetectable as they can mimic real “competent” players. To counter this threat, we propose a machine learning-based approach that leverages the concept of adversarial attacks to generate perturbations that fool such human pose estimation algorithms, preventing cheaters from gaining unfair advantages. In this work, we first implement the video game cheating systems and then we propose and implement our solution. In the end, we use the cheating system to evaluate the efficiency of our proposed algorithms in defending against such cheating tools. Experimental results show that our algorithm can effectively deceive advanced human pose estimation algorithms by adding invisible perturbations to the characters in the video game, maintaining a fair and healthy gaming environment.

关键词： Industries Video games computer vision Solid modeling Machine learning algorithms Perturbation methods Pose estimation Signal processing algorithms Streaming media Artificial intelligence

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Understanding Blockchain Trilemma, Causes and Solutions

Understanding Blockchain Trilemma, Causes and Solutions

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Monu Chaudhary Suman Bhunia Department of Computer Science and Software Engineering Miami University Oxford Ohio

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

With extensive research and development in blockchain technology, the concern about scalability, security, and decentralization is still evident. Blockchain trilemma describes that it is realistically impossible to simultaneously achieve a high level of decentralization, security, and scalability. However, new developments in the sector of blockchain are making progress in simultaneous achievement of these three aspects. Layer 1 blockchain is the base level of blockchain network architecture. It includes blockchain networks like Bitcoin, Ethereum, etc. However, the Layer 1 blockchain network faces scalability issues. Layer 2 blockchain is a network built on top of Layer 1 blockchain to help eliminate scalability issues. This paper presents a comparative analysis of Layer 1 and Layer 2 blockchain networks and different approaches to solving the blockchain trilemma in Layer 1 and Layer 2 blockchain networks. From the study, we found that Layer 2 blockchain networks are easier and faster to implement.

关键词： Social computing Sharding Scalability Pressing Bitcoin Network architecture Blockchains Security Research and development Faces

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Group Signature with Time-Bound Keys for Secure E-health Record Sharing

Group Signature with Time-Bound Keys for Secure E-health Rec...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lu, Junze Hu, Chunqiang Ruan, Conghao Cai, Bin Xiang, Tao Chongqing University School of Big Data & Software Engineering China Chongqing University College of Computer Science China

ISBN: (纸本)9798350386226

With the advent of various mobile IoT devices, a large amount of e-health record (EHR) data has been generated. This data has great potential to improve medical research. However, there are many challenges regarding the sharing of medical data. Firstly, users are more inclined to interact anonymously. Secondly, verifying the validity of certificates in the case of anonymous interactions is challenging. In addition, it is necessary to uncover the identities of the actual interacting parties in the event of malicious behavior. Therefore, we address the above challenges and propose group signatures with time constraints to support anonymous and traceable EHR data sharing. First, we propose a group signature scheme that supports traceability. Second, to address the issue of validating anonymous certificates, we propose group signatures with time constraints that enable dynamic updates to validity. Through this, we can dynamically revoke group members. Lastly, security proofs and efficiency analyses demonstrate that our scheme is both secure and efficient. © 2024 IEEE.

关键词： Electronic health record

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Image Inpainting with GANs on LOKI Dataset

Image Inpainting with GANs on LOKI Dataset

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Binici, Rıfkı Can Onan, Aytug Koch, Reinhard Schröder, Simon-Martin Software Engineering Izmir Katip Celebi University Izmir Turkey Computer Science Kiel University Kiel Germany

ISBN: (纸本)9798350379433

Inpainting images becomes crucial, especially when dealing with the challenging LOKI zooplankton dataset. This research presents a novel framework for image inpainting, designed to optimize results through a systematic approach. Our proposed framework effectively addresses these challenges, enhancing both the qualitative and quantitative aspects of inpainting quality. Through evaluation on the LOKI dataset, we achieved an overall Dice score of 0.7604, indicating a high level of accuracy. Additionally, the inclusion of squared masks proved advantageous in predicting missing components across images. Our innovative framework for inpainting plankton images in the LOKI dataset overcomes limitations by enabling inpainting within square-shaped regions, without requiring prior pixel location knowledge. This approach leverages a MultiRes-UNet generator, two discriminator networks with EfficientNet architecture, and a pretrained feature extractor network (VGG19), resulting in efficient performance in terms of our task. © 2024 IEEE.

关键词： Image reconstruction

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GPT-Based Models Meet Simulation: How to Efficiently use Large-Scale Pre-Trained Language Models Across Simulation Tasks 23

GPT-Based Models Meet Simulation: How to Efficiently use Lar...

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Proceedings of the Winter Simulation Conference

作者： Philippe J. Giabbanelli Department of Computer Science & Software Engineering Miami University Oxford USA

ISBN: (纸本)9798350369663

The disruptive technology provided by large-scale pre-trained language models (LLMs) such as ChatGPT or GPT-4 has received significant attention in several application domains, often with an emphasis on high-level opportunities and concerns. This paper is the first examination regarding the use of LLMs for scientific simulations. We focus on four modeling and simulation tasks, each time assessing the expected benefits and limitations of LLMs while providing practical guidance for modelers regarding the steps involved. The first task is devoted to explaining the structure of a conceptual model to promote the engagement of participants in the modeling process. The second task focuses on summarizing simulation outputs, so that model users can identify a preferred scenario. The third task seeks to broaden accessibility to simulation platforms by conveying the insights of simulation visualizations via text. Finally, the last task evokes the possibility of explaining simulation errors and providing guidance to resolve them.

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A Rearrangement and Restore-Based Mixer Model for Target-Oriented Multimodal Sentiment Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3109-3119页

作者： Jia, Li Ma, Tinghuai Rong, Huan Sheng, Victor S. Huang, Xuejian Xie, Xintong Nanjing University of Information Science and Technology School of Computer Science Nanjing Jiangsu210044 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing Jiangsu210044 China Jiangsu Ocean University School of Computer Engineering Lianyungang Jiangsu222005 China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing Jiangsu210044 China Texas Tech University Department of Computer Science LubbockTX79409 United States

With the development of fine-grained multimodal sentiment analysis tasks, target-oriented multimodal sentiment (TMSC) analysis has received more attention, which aims to classify the sentiment of target with the help of textual and associated image features. Existing methods focus on exploring fine-grained image features and incorporate transformer-based complex fusion strategies, while ignoring the heavy computational burden. Recently, some lightweight multilayer perceptrons (MLP)-based methods have been successfully applied to multimodal sentiment classification tasks. In this article, we propose an effective rearrangement and restore mixer model (RR-Mixer) for TMSC, which dedicates the interaction of image, text, and targets along the modal-axis, sequential-axis, and feature channel-axis through rearrangement and restore operations. Specifically, we take vision transformer (ViT) and robustly optimized BERT (RoBERTa) pretrained models to extract image and textual features, respectively. Further, we adopt cosine similarity to select the most semantically relevant image features. Then, an RR-Mixer module is designed for mixed multimodal features, with the core technology consisting of rolling, grouping rearrangement and restore operations. Moreover, we introduce MLP Unit to learn the information of different modalities for intermodal interaction. The results show that our model achieves superior performance on two benchmark multimodal datasets, TWITTER-15 and TWITTER-17, with a significant improvement of 4.66% and 1.26% in terms of macro-F1. © 2020 IEEE.

关键词： Feature extraction

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Resisting Over-Smoothing in Graph Neural Networks via Dual-Dimensional Decoupling 24

Resisting Over-Smoothing in Graph Neural Networks via Dual-D...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Shen, Wei Ye, Mang Huang, Wenke National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

Graph Neural Networks (GNNs) are widely employed to derive meaningful node representations from graphs. Despite their success, deep GNNs frequently grapple with the oversmoothing issue, where node representations become highly indistinguishable due to repeated aggregations. In this work, we consider the oversmoothing issue from two aspects of the node embedding space: dimension and instance. Specifically, while existing methods primarily concentrate on instance-level node relations to mitigate oversmoothing, we propose to mitigate oversmoothing at dimension level. We reveal the heightened information redundancy between dimensions which diminishes information diversity and impairs node differentiation in GNNs. Motivated by this insight, we propose the Dimension-Level Decoupling (DLD) to reduce dimension redundancy, enhancing dimensional-level node differentiation. Besides, at the instance level, the neglect of class differences leads to vague classification boundaries. Hence, we introduce the Instance-Level Class-Difference Decoupling (ICDD) that repels inter-class nodes and attracts intra-class nodes, improving the instance-level node discrimination with clear classification boundaries. Additionally, we introduce a novel evaluation metric that considers the impact of class differences on node distances, facilitating precise oversmoothing measurement. Extensive experiments demonstrate the effectiveness of our method Dual-Dimensional Class-Difference Decoupling (DDCD) across diverse scenarios. © 2024 ACM.

关键词： Graph neural networks

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Parallel Cloth Simulation Using OpenGL Shading Language

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computer Systems science & engineering 2022年第5期41卷 427-443页

作者： Hongly Va Min-Hyung Choi Min Hong Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Science and Engineering University of Colorado DenverDenverCO 80217USA Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

The primary goal of cloth simulation is to express object behavior in a realistic manner and achieve real-time performance by following the fundamental concept of *** general,the mass–spring system is applied to real-time cloth simulation with three types of ***,hard spring cloth simulation using the mass–spring system requires a small integration time-step in order to use a large stiffness ***,to obtain stable behavior,constraint enforcement is used instead of maintenance of the force of each *** force computation involves a large sparse linear solving *** to the large computation,we implement a cloth simulation using adaptive constraint activation and deactivation techniques that involve the mass-spring system and constraint enforcement method to prevent excessive elongation of *** the same time,when the length of the spring is stretched or compressed over a defined threshold,adaptive constraint activation and deactivation method deactivates the spring and generate the implicit *** method that uses a serial process of the Central Processing Unit(CPU)to solve the system in every frame cannot handle the complex structure of cloth model in *** simulation utilizes the Graphic Processing Unit(GPU)parallel processing with compute shader in OpenGL Shading Language(GLSL)to solve the system *** this paper,we design and implement parallel method for cloth simulation,and experiment on the performance and behavior comparison of the mass-spring system,constraint enforcement,and adaptive constraint activation and deactivation techniques the using GPU-based parallel method.

关键词： Adaptive constraint cloth simulation constraint enforcement GLSL compute shader mass–spring system parallel GPU

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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