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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Zehui Xu, Yuwei Huang, Ranfeng Fan, Xinhe Xu, Jingdong Cheng, Guang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Software College Northeastern University Shenyang China College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9789819615247

In light of the pervasive significance of privacy protection in contemporary society, the exploration of covert communication technology has been extensive, which is designed to safeguard communication behaviors and contents from disclosure. The decentralization, anonymity, and robustness of blockchain technology render it a more suitable medium for covert communication than traditional networks, which are vulnerable to tracing and destruction. Nevertheless, state-of-the-art blockchain-based covert communication schemes tend to focus on the concealment of a single type of constituent element in a blockchain transaction. These methods are able to resist detection methods for a particular element, yet are easily exposed under the joint detection of multiple classes of methods. The absence of resistance to joint detection methods targeting all elements presents a significant challenge that impedes the advancement of blockchain-based covert channels. Consequently, BlockWhisper is proposed as a blockchain-based hybrid covert channel that is capable of evading the recognition of various detection methods. Following the probability distribution model obtained from a blockchain transaction distribution analysis method, BlockWhisper is implemented using a top address list and a special hybrid coding method. The experiments demonstrated that Graph-based Detection (GD), Similarity-based Detection (SMD), Shape-based Detection (SPD), Entropy-based Detection (ETD), and Machine Learning-based Detection (MLD) yielded inferior results on this scheme, which substantiates the assertion that BlockWhisper possesses a strong ability to evade detection. Moreover, a comparison of BlockWhisper with other schemes reveals that it has a superior channel capacity on the basis of the guarantee of channel feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Covert communication Evading detection

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Exploration of software Field Engineer Training Based on AI+Capability Model 24

Exploration of Software Field Engineer Training Based on AI+...

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2nd International Conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Zhou, Junjie Wu, Qian School of Information Wuhan Software Engineering Vocational College Wuhan Open University Hubei Wuhan China School of Computer Science Wuhan University Hubei Wuhan China Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts Wuhan China

ISBN: (纸本)9798400712883

With the rapid development of artificial intelligence technology, its application in the field of education and teaching is becoming increasingly widespread, bringing revolutionary changes to traditional education models. To cultivate high-quality software vocational skills talents, a digital management platform based on AI+capability model is constructed to explore the innovative application of artificial intelligence in education and teaching, realizing intelligent auxiliary learning functions such as personalized resource recommendation, student growth monitoring, and achievement label portrait. At the same time, a series of intelligent teaching tools such as"AI+Code Generation, AI+Inspection Assistant, AI+Process Map" will be developed to explore and promote the vertical application of big models in talent cultivation. Through innovative models such as"introducing enterprises into education, cultivating students through real-life situations, AI+empowerment, teaching according to students’ aptitude, trinity, innovative training, strengthening processes, and diverse evaluations", it has been fully applied to the talent training scene of software technology field engineers, and its teaching effectiveness is significant. © 2024 Copyright held by the owner/author(s).

关键词： Students

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Automated Detection of COVID-19 Using Deep Convolutional Neural Network (CNNs) Using Chest Radiograph and CT Scan Images 1st

Automated Detection of COVID-19 Using Deep Convolutional Neu...

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

作者： Uallah, Naeem Khan, Javed Ali Raza, Asaf Shah, Syed Yaqub Assam, Muhammad Arshad, Hasna Department of Software Engineering University of Engineering and Technology Taxila Taxila Pakistan Department of Software Engineering University of Science and Technology Bannu Bannu Pakistan Department of Computer Science HITEC University Taxila Pakistan

ISBN: (纸本)9783031776199

Many countries are severely affected by COVID-19, and massive efforts are required to mitigate the terrible impacts of the COVID-19 global epidemic. To stop COVID-19 from spreading widely and minimize COVID-19 patient treatment issues, early discovery of the virus in its early stages is essential. Conventional methods for COVID-19 detection are time-consuming, cumbersome, unreliable, and yield an incorrect diagnosis. Most deep learning (DL) methods for COVID-19 identification described in the past only support one data type (chest radiographs or CT scan images). Examining unusual patterns in various data types may yield high classification performance. For this purpose, we use two image datasets: chest radiographs and chest computer tomography (CT). This research study proposed an excellent COVID-19 detection strategy based on the Resnet-18 deep convolutional neural network (CNN) to timely and efficiently identify COVID-19 spread. Also, we used each image data type to conduct extensive Covid-19 detection experiments with the proposed DL model. We performed a trinary classification task (normal, covid-19, and pneumonia) using chest radiographs and a binary classification (COVID-19 and Normal) using CT scan images. We validated the performance of the proposed approach on two standard Kaggle datasets, i.e., the COVID-19 Radiography Database and the SARS-CoV-2 CT scan dataset. The Resnet model's detection accuracy of 99.42% using chest radiographs (CRs) and 100% using CT scans for COVID-19 detection indicates its efficacy in detecting COVID-19. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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MedKAN: An Advanced Kolmogorov-Arnold Network for Medical Image Classification

arXiv

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arXiv 2025年

作者： Yang, Zhuoqin Zhang, Jiansong Luo, Xiaoling Lu, Zheng Shen, Linlin School of Computer Science & Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Nottingham Ningbo China Ningbo China

Recent advancements in deep learning for image classification predominantly rely on convolutional neural networks (CNNs) or Transformer-based architectures. However, these models face notable challenges in medical imaging, particularly in capturing intricate texture details and contextual features. Kolmogorov-Arnold Networks (KANs) represent a novel class of architectures that enhance nonlinear transformation modeling, offering improved representation of complex features. In this work, we present MedKAN, a medical image classification framework built upon KAN and its convolutional extensions. MedKAN features two core modules: the Local Information KAN (LIK) module for fine-grained feature extraction and the Global Information KAN (GIK) module for global context integration. By combining these modules, MedKAN achieves robust feature modeling and fusion. To address diverse computational needs, we introduce three scalable variants—MedKANS, MedKAN-B, and MedKAN-L. Experimental results on nine public medical imaging datasets demonstrate that MedKAN achieves superior performance compared to CNN- and Transformer-based models, highlighting its effectiveness and generalizability in medical image analysis. Copyright © 2025, The Authors. All rights reserved.

关键词： Image classification

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DALC: Distributed Arithmetic Coding Aided by Linear Codes

arXiv

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arXiv 2025年

作者： Zhou, Junwei Xiao, HaoYun Xiang, Jianwen Lin, Qiuzhen The School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China College of Computer Science and Software Engineering Shenzhen University China

Distributed Arithmetic Coding (DAC) has emerged as a feasible solution to the Slepian-Wolf problem, particularly in scenarios with non-stationary sources and for data sequences with lengths ranging from small to medium. Due to the inherent decoding ambiguity in DAC, the number of candidate paths grows exponentially with the increase in source length. To select the correct decoding path from the set of candidates, DAC decoders utilize the Maximum A Posteriori (MAP) metric to rank the decoding sequences, outputting the path with the highest MAP metric as the decoding result of the decoder. However, this method may still inadvertently output incorrect paths that have a MAP metric higher than the correct decoding path, despite not being the correct decoding path. To address the issue, we propose Distributed Arithmetic Coding Aided by Linear Codes (DALC), which employs linear codes to constrain the decoding process, thereby eliminating some incorrect paths and preserving the correct one. During the encoding phase, DALC generates the parity bits of the linear code for encoding the source data. In the decoding phase, each path in the set of candidate paths is verified in descending order according to the MAP metric until a path that meets the verification criteria is encountered, which is then outputted as the decoding result. DALC enhances the decoding performance of DAC by excluding candidate paths that do not meet the constraints imposed by linear codes. Our experimental results demonstrate that DALC reduces the Bit Error Rate(BER), with especially improvements in skewed source data scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

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A Switch-Transformer Predictive Process Monitoring Model for Next Activity Prediction in Business Process Management 24

A Switch-Transformer Predictive Process Monitoring Model for...

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8th International Conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Nguyen, Thanh-Hai Kim, Kyoung-Sook Kim, Kwanghoon Pio Thai Nguyen Thai Nguyen Viet Nam Contents Convergence Software Research Institute Kyonggi University Gyeonggi Suwon Korea Republic of AI Computer Science and Engineering Kyonggi University Gyeonggi Suwon Korea Republic of

ISBN: (纸本)9798400718014

This paper proposes a deep learning model for predicting the next activities in a temporal sequence of activity-events associated with a single process instance. The proposed model is based on a mixture of experts (MoE) mechanism supported by the Switch-Transformer neural network architecture. The deep learning model is trained from scratch on process enactment event log histories. The ultimate goal is to predict the next activities, generate their probabilities of occurrence, and visualize all the predicted next activities in a graphical form of temporal order representing each running case of the corresponding process model. To assess the operational effectiveness of the proposed model, we successfully conducted performance evaluations through a series of experiments on synthetic and real-life process event log datasets, which have been typically and usefully applied in other approaches in previous research works. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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Multi-View Incongruity Learning for Multimodal Sarcasm Detection 31

Multi-View Incongruity Learning for Multimodal Sarcasm Detec...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761964

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model's generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL's advancement in mitigating the effect of spurious correlation. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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CorLMI-FSA: An Efficient Feature Selection Approach for Stress Classification Using Physiological Signals 5

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

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

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

ISBN: (纸本)9798350356236

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. © 2025 IEEE.

关键词： Stress analysis

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Ownership Verification for Federated Recommendation

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ACM Transactions on Information Systems 2025年第3期43卷 1-27页

作者： Yang, Enyue Pan, Weike Fan, Lixin Gu, Hanlin Li, Zhitao Yang, Qiang Ming, Zhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Department WeBank Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Shenzhen Technology University Shenzhen China Shenzhen China

Most federated learning-based recommender systems allow clients to access a well-trained high-quality model locally, which provides adversaries with the opportunity to infringe the legitimate copyright of the model. In response, we study an emerging and important problem, i.e., copyright protection of a federated recommendation model, which has not yet been addressed in the community of federated learning or recommender systems. We propose the first backdoor-based ownership verification scheme for federated recommendation (OVFR), which enables the server to claim its ownership for a given suspicious recommendation model. First, we propose to generate a trigger set tailored to recommendation scenarios. In particular, we generate some fake users and items, and then construct a set of fake users with fake interaction records as a trigger set. Moreover, we ensure that the distribution of the popularity of the fake items follows a long-tailed distribution for the effectiveness of the incorporated watermarking. To provide robustness assurance, we propose two different hybrid strategies to make the embeddings of the fake items similar to those of the real items. Second, we focus on effectively learning from a trigger set for recommendation scenarios. In particular, we design an MSE loss function and a contrastive loss function for incorporating the backdoor-based watermarking into the item embeddings, since the item embeddings are often more valuable and easier to be accessed than other parameters of a federated recommendation model. We then design a contrastive loss function to reduce the risk of the fake items being detected. Extensive experiments on three public datasets show the effectiveness of our OVFR in terms of ownership verification, model performance, and robustness. © 2025 Copyright held by the owner/author(s).

关键词： Federated learning

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EvoChain: A Framework for Tracking and Visualizing Smart Contract Evolution

arXiv

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arXiv 2025年

作者： Qasse, Ilham Hamdaqa, Mohammad Jónsson, Björn Þór Department of Computer Science Reykjavik University Reykjavik Iceland Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

Tracking the evolution of smart contracts is challenging due to their immutable nature and complex upgrade mechanisms. We introduce EvoChain, a comprehensive framework and dataset designed to track and visualize smart contract evolution. Building upon data from our previous empirical study, EvoChain models contract relationships using a Neo4j graph database and provides an interactive web interface for exploration. The framework consists of a data layer, an API layer, and a user interface layer. EvoChain allows stakeholders to analyze contract histories, upgrade paths, and associated vulnerabilities by leveraging these components. Our dataset encompasses approximately 1.3 million upgradeable proxies and nearly 15,000 historical versions, enhancing transparency and trust in blockchain ecosystems by providing an accessible platform for understanding smart contract evolution. © 2025, CC BY-NC-SA.

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