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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Feng Qiu Pu Ji Baojian Hua Yang Wang School of Software Engineering University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China Computer Science Department The Johns Hopkins University USA

Quantum chemistry software implements the first principle quantum computation and is indispensable in both scientific research and chemical industries. Any bugs in such software will lead to serious consequences, thus defeating its trustworthiness and reliability. However, bug detection techniques for such software have not been fully investigated. In this paper, to fill this gap, we propose a novel approach to fuzz quantum chemistry software with the aid of Large Language Models (LLMs). Our basic idea is utilize LLMs to mutate and generate syntactic and semantic valid input files from seed inputs, by proving valuable domain-specific knowledge of chemistry. With this basic idea, we have designed and implemented CHEMFuzz, a fully automatic fuzzing framework to fuzz quantum chemistry software for bugs. Our evaluation of CHEMFUZZ leverages popular LLMs including GPT3.5, Claude-2, and Bart as test oracles to generate parameters to mutate inputs and analyze computation results. CHEMFUZZ detected 40 unique bugs, which have been classified and reported to developers, with a code coverage of 17.4%.

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Interactive multi-character motion retargeting

Interactive multi-character motion retargeting

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作者： Kim, Jongmin Seol, Yeongho Kwon, Taesoo Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of NVIDIA Seoul Korea Republic of Department of Computer and Software Hanyang University Seoul Korea Republic of

A motion retargeting process is necessary as the body size and proportion of the actors are generally different from those of the target characters. However, the original spatial relationship between the multiple characters and the environment is easily broken when using previous motion retargeting methods, which are generally performed for each character independently. Therefore, time-consuming manual adjustments by animators are usually required to obtain satisfactory results. To address these issues, we present a novel multicharacter motion retargeting method that preserves various types of spatial relationships between characters and environments. We establish a unified deformation-based framework for the motion retargeting of multiple characters (more than two) or nonhuman characters with complex interactions. Also, an interactive motion editing interface with immediate feedback to the user is provided. We experimentally show that our method achieves a speedup when compared with previous motion retargeting methods. © 2021 John Wiley & Sons, Ltd.

关键词： Deformation

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Trustworthy Federated Learning: Privacy, Security, and Beyond

arXiv

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arXiv 2024年

作者： Chen, Chunlu Liu, Ji Tan, Haowen Li, Xingjian Wang, Kevin I-Kai Li, Peng Sakurai, Kouichi Dou, Dejing HiThink Research Hangzhou China Carnegie Mellon University Pittsburgh United States Information Science and Electrical Engineering Department Kyushu University Fukuoka Japan Electrical Computer and Software Engineering Department The University of Auckland Auckland New Zealand Computer Science and Engineering Department The University of Aizu Aizuwakamatsu Japan Information Science and Technology Department Zhejiang Sci-Tech University Hangzhou China Fudan University Shanghai and BEDI Cloud Beijing China

While recent years have witnessed the advancement in big data and Artificial Intelligence (AI), it is of much importance to safeguard data privacy and security. As an innovative approach, Federated Learning (FL) addresses these concerns by facilitating collaborative model training across distributed data sources without transferring raw data. However, the challenges of robust security and privacy across decentralized networks catch significant attention in dealing with the distributed data in FL. In this paper, we conduct an extensive survey of the security and privacy issues prevalent in FL, underscoring the vulnerability of communication links and the potential for cyber threats. We delve into various defensive strategies to mitigate these risks, explore the applications of FL across different sectors, and propose research directions. We identify the intricate security challenges that arise within the FL frameworks, aiming to contribute to the development of secure and efficient FL systems. © 2024, CC BY-NC-SA.

关键词： Differential privacy

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Improving Transformers with Probabilistic Attention Keys 39

Improving Transformers with Probabilistic Attention Keys

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39th International Conference on Machine Learning, ICML 2022

作者： Nguyen, Tam Nguyen, Tan M. Le, Dung D. Nguyen, Duy Khuong Tran, Viet-Anh Baraniuk, Richard G. Ho, Nhat Osher, Stanley J. FPT Software AI Center Ha Noi Viet Nam Department of Mathematics University of California Los Angeles United States College of Engineering and Computer Science VinUniversity Ha Noi Viet Nam Deezer Research France Department of Electrical and Computer Engineering Rice University Houston United States Department of Statistics and Data Sciences The University of Texas Austin United States

Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkable performance across a variety of natural language processing (NLP) and computer vision tasks. It has been observed that for many applications, those attention heads learn redundant embedding, and most of them can be removed without degrading the performance of the model. Inspired by this observation, we propose Transformer with a Mixture of Gaussian Keys (Transformer-MGK), a novel transformer architecture that replaces redundant heads in transformers with a mixture of keys at each head. These mixtures of keys follow a Gaussian mixture model and allow each attention head to focus on different parts of the input sequence efficiently. Compared to its conventional transformer counterpart, Transformer-MGK accelerates training and inference, has fewer parameters, and requires fewer FLOPs to compute while achieving comparable or better accuracy across tasks. Transformer-MGK can also be easily extended to use with linear attention. We empirically demonstrate the advantage of Transformer-MGK in a range of practical applications, including language modeling and tasks that involve very long sequences. On the Wikitext-103 and Long Range Arena benchmark, Transformer-MGKs with 4 heads attain comparable or better performance to the baseline transformers with 8 heads. Copyright © 2022 by the author(s)

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Ultra-Fast Core Mode Prediction in Plasmonic Crystal Fiber Sensor: A Machine Learning Approach

Ultra-Fast Core Mode Prediction in Plasmonic Crystal Fiber S...

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International Conference on Innovations in science, engineering and Technology (ICISET)

作者： Mahmudul Hoque Mahmud Ariful Hoque Maruf Mohammad Nasir Uddin Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Software Engineering Institute of Information Technology University of Dhaka Dhaka Bangladesh Department of Electrical and Electronic Engineering American International University-Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

This study introduces a novel approach to improving Photonic Crystal Fiber (PCF) Surface Plasmon Resonance (SPR) sensors by incorporating machine learning (ML) models for predicting core mode index. Traditionally, identifying core phenomena in PCF SPR sensors required manual visual inspection by experts, a process that is both time-consuming and prone to errors. To address this, a dual-core PCF model is developed and used Finite Element Method (FEM) simulations to create extensive training data for ML models. The proposed PCF model shows exceptional sensitivity to wavelength changes, with a recorded sensitivity of $\mathbf{6 0 0 0} \mathrm{nm} /$ RIU, making it highly effective for accurate mode prediction. The study evaluated nine ML models, including XGBoost, Random Forest, and Decision Tree, achieving accuracies of 0.9742,0.9645, and 0.9667, respectively. By integrating ML models, the core mode identification process is significantly faster and more efficient, offering major benefits for PCF SPR research. This work not only advances PCF sensor technology but also enhances efficiency and accuracy in various SPR-based applications.

关键词： Accuracy Sensitivity Photonic crystal fibers Predictive models Sensor phenomena and characterization Surface plasmon resonance Data models Sensors Finite element analysis Numerical models

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario

arXiv

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arXiv 2023年

作者： Guo, Jingcai Guo, Song Ma, Shiheng Sun, Yuxia Xu, Yuanyuan The Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200030 China The Department of Computer Science Jinan University Guangzhou510632 China The School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

We study the challenging task of malware recognition on both known and novel unknown malware families, called malware open-set recognition (MOSR). Previous works usually assume the malware families are known to the classifier in a close-set scenario, i.e., testing families are the subset or at most identical to training families. However, novel unknown malware families frequently emerge in real-world applications, and as such, require to recognize malware instances in an open-set scenario, i.e., some unknown families are also included in the test-set, which has been rarely and non-thoroughly investigated in the cyber-security domain. One practical solution for MOSR may consider jointly classifying known and detecting unknown malware families by a single classifier (e.g., neural network) from the variance of the predicted probability distribution on known families. However, conventional well-trained classifiers usually tend to obtain overly high recognition probabilities in the outputs, especially when the instance feature distributions are similar to each other, e.g., unknown v.s. known malware families, and thus dramatically degrades the recognition on novel unknown malware families. To address the problem and construct an applicable MOSR system, we propose a novel model that can conservatively synthesize malware instances to mimic unknown malware families and support a more robust training of the classifier. More specifically, we build upon the generative adversarial networks (GANs) to explore and obtain marginal malware instances that are close to known families while falling into mimical unknown ones to guide the classifier to lower and flatten the recognition probabilities of unknown families and relatively raise that of known ones to rectify the performance of classification and detection. A cooperative training scheme involving the classification, synthesizing and rectification are further constructed to facilitate the training and jointly improve the model p

关键词： Malware

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Segmentation of Retinal Blood Vessels in Fundus Images Using Fully Convolutional Networks

Segmentation of Retinal Blood Vessels in Fundus Images Using...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Vijayalakshmi G Mohammed Al-Farouni Sourav Ch Satya Sravani Rajesh Kumar Singh R. Saiharsha Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computer Science & Engineering Aditya Engineering College Surampalem India Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Department of CSE-AI Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Segmentation is mandatory to analyze retinal blood vessel indication for diagnosis of various ophthalmic and systemic disorders. This paper takes a look at this problem, specifically, the application of Fully Convolutional Networks (FCNs) in this whole process, with a primary focus on the U-Net architecture. In this work, the authors conduct the experiments on three datasets, namely DRIVE, STARE and CHASE_DB1, and prepare the images for modeling through normalization, resizing, and data augmentation. Due to the binary structure of the task, the loss function of the proposed method is binary cross-entropy along with the Dice coefficient, and the model is trained with the Adam optimizer. The improvement scores that was obtained shows that the proposed model has a high accuracy, precision, recall and F1 score on all the datasets used and has proven to be better than other existing methods. The study establishes the usefulness of FCNs in segmenting the tree structure of the retinal blood vessel structures in uniterrupted manner, and presented promising improvement for early diagnosis and treatment in the clinical application. it emphasizes on the necessity of utilizing far improved deep learning architectures and more holistic training mechanisms in striving to create more accurate diagnosis support systems in medical image analysis.

关键词： Training Image segmentation Accuracy Image analysis Transfer learning Blood vessels Retina Data augmentation Convolutional neural networks Medical diagnostic imaging

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Cacomp: A Cloud-assisted Collaborative Deep Learning Compiler Framework for DNN Tasks on Edge

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IEEE Transactions on computers 2025年

作者： Lin, Weiwei Lin, Jinhui Zhang, Haotong Wu, Wentai Wu, Weizheng Li, Zhetao Li, Keqin South China University of Technology School of Computer Science and Engineering Guangzhou510641 China Pengcheng Laboratory Shenzhen518066 China South China University of Technology School of Software Engineering Guangzhou510641 China Jinan University Department of Computer Science College of Information Science and Technology Guangzhou510632 China Jinan University College of Information Science and Technology Guangzhou510632 China State University of New York Department of Computer Science New PaltzNY12561 United States

With the development of edge computing, DNN services have been widely deployed on edge devices. The deployment efficiency of deep learning models relies on the optimization of inference and scheduling policy. However, traditional optimization methods on edge devices still suffer from prohibitively long tuning time due to devices’ low computational power. Meanwhile, the widely used scheduling algorithm, the dominant resource fairness algorithm(DRF algorithm), struggles to maximize the efficiency of model execution on edge devices and inevitably increases average waiting time as it is not applicable in the real-time distributed computing environment. In this paper, we propose Cacomp, a distributed cloud-assisted deep learning compiler framework that features accelerating the optimization on edge devices with assistance from the cloud and a novel inference task scheduling algorithm. Our framework utilizes the tuning records from the cloud devices and proposes a two-step distillation strategy to obtain the best tuning record set for the edge device. For the scheduling process, we propose an RD-DRF algorithm to allocate inference tasks to edge devices based on dominant resource matching in real time. Extensive results show that our framework can achieve up to 2.19× improvement in the optimization time compared with other methods on edge devices. Our proposed scheduling algorithm significantly shortens the average waiting time of inference tasks by 30% and improves resource utilization by 20% on edge devices. © 1968-2012 IEEE.

关键词： Distributed cloud

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Contrastive Self-supervised EEG Representation Learning for Emotion Classification

Contrastive Self-supervised EEG Representation Learning for ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Keya Hu Ren-Jie Dai Wen-Tao Chen Hao-Long Yin Bao-Liang Lu Wei-Long Zheng Department of Computer Science and Engineering Shanghai Jiao Tong University School of Software Engineering Tongji University RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Self-supervised learning provides an effective approach to leverage a large amount of unlabeled data. Numerous previous studies have indicated that applying self-supervision to physiological signals can yield better representations of the signals. In the paper, we aim to apply this method to the crucial field of emotion recognition. We perform the experiment with several state-of-the-art contrastive self-supervised methods to explore their effectiveness in pre-training feature encoders on raw electroencephalography (EEG) signals and fine-tuning the pre-trained encoders on the downstream emotion classification tasks. We attempt to vary the proportion of labeled data used during fine-tuning and find that the improvement from self-supervised methods is more pronounced when the proportion of labeled data is small. Additionally, we explore the transferability of the feature encoders pre-trained on various datasets and observe that most self-supervised methods exhibit a certain degree of transferability. Methods that effectively utilize the temporal information in EEG signals show superior stability, accuracy, and transferability.

关键词： Training Representation learning Emotion recognition Accuracy Time series analysis Training data Feature extraction Electroencephalography Physiology engineering in medicine and biology

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