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24th IEEE International Conference on data Mining, ICDM 2024

作者： Haghighi, Venus Soltani, Behnaz Shabani, Nasrin Wu, Jia Zhang, Yang Yao, Lina Sheng, Quan Z. Yang, Jian School of Computing Macquarie University SydneyNSW2109 Australia School of Computer Science and Engineering University of New South Wales NSW Australia CSIRO's Data61 Australia

ISBN: (纸本)9798331506681

Graph-based fraud detection has attracted increasing attention in recent years, reflecting its growing potential in mitigating sophisticated fraudulent activities. The main objective of graph-based fraud detection is to discern between fraud-sters and normal entities within graphs. As fraudsters adopt increasingly sophisticated camouflage tactics, combating them has become an urgent task. Despite the complex interactions within real-world networks involving high-order relations, ex-isting graph-based fraud detection methods often neglect non-pairwise relationships among entities in graphs. Thus, we empha-size the significance of investigating beyond pairwise relationships for building an effective fraud detection model. In this paper, we propose constructing a hypergraph from the original input graph to encapsulate comprehensive high-order relations and present TROPICAL, a novel TRansfOrmer-based hyPergraph LearnIng for detecting CAmouflaged maLicious actors in online social networks. TROPICAL learns representations by processing different hyperedge groups and incorporates positional encodings into the aggregated information to enhance their distinctiveness. Subsequently, the model feeds the learned aggregated sequential information into the transformer encoder, achieving rich rep-resentations for effective camouflaged fraudster detection. The superiority of TROPICAL is demonstrated through experiments conducted on two real-world datasets, compared against the state-of-the-art fraud detection models. The source codes and datasets of our work are available at https://***/TROPICAL. © 2024 IEEE.

关键词： HTTP

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Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

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computers, Materials & Continua 2023年第1期74卷 1351-1374页

作者： Nasir Sayed Muhammad Shoaib Waqas Ahmed Sultan Noman Qasem Abdullah M.Albarrak Faisal Saeed Department of Computer Science Islamia College PeshawarPeshawar25120Pakistan Department of Computer Science CECOS University of IT and Emerging SciencesPeshawar25000Pakistan Department of Electrical Engineering HITEC University TaxilaTaxila47080Pakistan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK

Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to ***,the rapid growth of IoT devices in homes increases the risk of *** detection systems(IDS)are commonly employed to prevent *** systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate *** have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been *** this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT ***,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 *** accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network *** also propose two baseline models to demonstrate the performance of the proposed ***,both models achieve high accuracy in detecting the majority of these nine attacks.

关键词： Internet of things intrusion detection system deep learning convolutional neural network supervised learning

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Artificial Intelligence Strategies for Accurate Segmentation and Categorization of Unveiling Genetic Disorders in Bioinformatics 2

Artificial Intelligence Strategies for Accurate Segmentation...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sarathamani, T. Kavitha, K. Thirumoorthi, C. Vagini, K Jayanthi Manikandaprabhu, P. Sumathi, P. Kolkata India Mohan Babu University School of Computing Department of Ai and Ml Sree Sainath Nagar Tirupati India Psg College of Arts and Science Department of Computer Science Tamilnadu Coimbatore India Kpr College of Arts Science and Research Department of Computer science with Data Analytics Tamilnadu Coimbatore India Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science Karnataka Mysuru India Dr. G R Damodaran College of Science Department of Computer Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350368413

Women all across the world are affected by the potentially fatal condition known as breast cancer. According to clinical experts, early cancer detection helps to save lives. Several machine learning algorithms have been developed to classify cancers in order to diagnose breast cancer, and medical image processing has been found to be a beneficial area for increasing classification accuracy. One of the most crucial signs for detecting breast cancer early is micro classifications. Machine learning methods have been employed in numerous studies to address this problem. Various classification algorithms are used to find the presence of breast cancer whether it is Benign or Malignant. For earlier cancer detection and diagnosis, an image enhancement approach is being developed. A machine learning technique is employed in this work to attempt to categorize breast cancer. The purpose of the present research was to increase the accuracy of machine learning methods for disease diagnosis using ML techniques. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hybrid Model Using Coordinate Attention Mechanisms for Stock Price Prediction 24

A Hybrid Model Using Coordinate Attention Mechanisms for Sto...

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2024 International Conference on Digital Economy, Blockchain and Artificial Intelligence, DEBAI 2024

作者： Sun, Ying Zhang, Bowen Peng, Cheng The Institute for Sustainable Development Macau University of Science and Technology China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Computer Science University of Electronic Science and Technology of China Zhongshan China

ISBN: (纸本)9798400710261

Predicting stock prices is a complex and challenging task in financial markets due to the intricate and non-linear nature of stock price movements. This study explores the application of Coordinate Attention mechanisms within advanced Temporal Convolutional Network (TCN) frameworks for stock price prediction based solely on historical stock price data. We present a hybrid model that combines the advantages of modern TCNs with Coordinate Attention mechanisms to improve the precision and robustness of stock prediction models. Extensive experiments are conducted on the well-known TSLA stock dataset. The results show varying levels of alignment with actual stock prices, highlighting the strengths and weaknesses of each model. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading 23

AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Duan, Zhuohui Feng, Hao Liu, Haikun Liao, Xiaofei Jin, Hai Li, Bangyu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133458

The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of Value region, especially for garbage collection (GC) operation that is used to reduce the redundant space occupation. In response, many efforts have been made to optimize the GC mechanism for KV separation. However, our analysis indicates that such solution based on trade-offs between CPU and I/O overheads cannot simultaneously satisfy the three requirements of KV separated systems in terms of throughput, tail latency, and space usage. This limitation hinders their real-world application. In this paper, we introduce AegonKV, a "three-birds-one-stone" solution that comprehensively enhances the throughput, tail latency, and space usage of KV separated systems. AegonKV first proposes a SmartSSD-based GC offloading mechanism to enable asynchronous GC operations without competing with LSM read/write for bandwidth or CPU. AegonKV leverages offload-friendly data structures and hardware/software execution logic to address the challenges of GC offloading. Experiments demonstrate that AegonKV achieves the largest throughput improvement of 1.28-3.3 times, a significant reduction of 37%-66% in tail latency, and 15%-85% in space overhead compared to existing KV separated systems. © 2025 FAST. All Rights Reserved.

关键词： Digital storage

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Dynamic Modeling of Antibody Level Changes with Individual Age: The Case of COVID-19 Spread in China

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International Journal of Applied and Computational Mathematics 2023年第5期9卷 1-23页

作者： Li, Li Wang, Yangqing Zhang, Wei School of Computer and Information Technology Shanxi University Shanxi Taiyuan 030006 China Data Science and Technology North University of China Taiyuan 030051 China

COVID-19 is a ribonucleic acid virus with a high mutation frequency and will continue to mutate as time goes on. At the same time, the number of antibodies in the human body will gradually decrease with time. This paper analyzed the dissemination mechanism of COVID-19 in China after implementing the liberalization policy, established a waning immunity model with age heterogeneity, and explored the threshold dynamics of the system and the dynamic behavior of equilibrium. According to the latest release of COVID-19 data from China, the model parameters were fitted and analyzed using the least squares method to verify the model. We also predicted that the peak of the next epidemic in China would come around June 27. Furthermore, we used numerical simulations to research the impact of different infection rates on disease and compare them with the absence of waning immunity. Finally, using the cost-effectiveness analysis method, we determined the best strategy based on the number of deaths and medical resources. The results show that when β₁₁> 0.27 , controlling the transmission rate between people under 65 is the optimal solution;however, when β₁₁< 0.27 , we should pay more attention to the infected people aged 65 and above. © 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Age heterogeneity COVID-19 Dynamical model Epidemic control Waning immunity

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Implicit Reasoning in Transformers is Reasoning through Shortcuts

arXiv

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arXiv 2025年

作者： Lin, Tianhe Xie, Jian Yuan, Siyu Yang, Deqing School of Data Science Fudan University China School of Computer Science Fudan University China

Test-time compute is emerging as a new paradigm for enhancing language models’ complex multi-step reasoning capabilities, as demonstrated by the success of OpenAI’s o1 and o3, as well as DeepSeek’s R1. Compared to explicit reasoning in test-time compute, implicit reasoning is more inference-efficient, requiring fewer generated tokens. However, why does the advanced reasoning capability fail to emerge in the implicit reasoning style? In this work, we train GPT-2 from scratch on a curated multi-step mathematical reasoning dataset and conduct analytical experiments to investigate how language models perform implicit reasoning in multi-step tasks. Our findings reveal: 1) Language models can perform step-by-step reasoning and achieve high accuracy in both in-domain and out-of-domain tests via implicit reasoning. However, this capability only emerges when trained on fixed-pattern data. 2) Conversely, implicit reasoning abilities emerging from training on unfixed-pattern data tend to overfit a specific pattern and fail to generalize further. Notably, this limitation is also observed in state-of-the-art large language models. These findings suggest that language models acquire implicit reasoning through shortcut learning, enabling strong performance on tasks with similar patterns while lacking generalization. Resources are available on the GitHub. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational linguistics

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of computer science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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Exploring the Efficiency of Text-Similarity Measures in Automated Resume Screening for Recruitment 10

Exploring the Efficiency of Text-Similarity Measures in Auto...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Alsharef, Ahmad Sonia Nassour, Hasan Sharma, Jitender Yogananda School of AI Computer and Data Science Shoolini University Solan India School of Management Sciences and Liberal Arts Shoolini University Solan India

ISBN: (纸本)9789380544472

With many online recruitment portals requesting job applicants to upload their resumes, the automated process of screening and shortlisting candidates can accelerate selection and decision-making. This study explores the use of text similarity measures as an alternative to experienced human hiring managers in processing resumes. Three text similarity measures: Cosine, Sqrt-Cosine, and Improved Sqrt-Cosine (ISC) similarity were utilized as computer programs in scanning resumes for the recruitment of a business development manager and a software engineer. The decisions of the algorithms were compared to those of an expert hiring manager within the same scenarios. The findings indicate that ISC and Sqrt-Cosine were closer to the expert-human decision than Cosine similarity. These specific text-similarity algorithms can also make acceptable decisions even when recruiting for high-level positions and can do so in seconds when executed as a program on a normal CPU processor. This study suggests that these algorithms can efficiently facilitate the process of decision-making in recruitment and shortlisting candidates and can be an effective alternative to expert hiring managers. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Decision making

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HOLMES: An Efficient and Lightweight Semantic Based Anomalous Email Detector 19

HOLMES: An Efficient and Lightweight Semantic Based Anomalou...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Wu, Peilun Guo, Hui Data Security & Compliance Cdo Data & Cloud PwC Cn China School of Computer Science and Engineering Australia

ISBN: (纸本)9798350346558

Email threat is a serious issue for enterprise security. The threat can be in various malicious forms, such as phishing, fraud, blackmail and malvertisement. The traditional anti-spam gateway often maintains a greylist to filter out unexpected emails based on suspicious vocabularies present in the email's subject and contents. However, this type of signature-based approach cannot effectively discover novel and unknown suspicious emails that utilize various evolving malicious payloads. To address the problem, in this paper, we present HOLMES, an efficient and lightweight semantic based engine for anomalous email detection. HOLMES can convert each email event log into a sentence through word embedding and then identify abnormalities that deviate from a historical baseline based on those translated sentences. We have evaluated the performance of HOLMES in a real-world enterprise environment, where around 5,000 emails are sent/received each day. In our experiments, HOLMES shows a high capability to detect email threats, especially those that cannot be handled by the enterprise anti-spam gateway. It is also demonstrated through our experiment that HOLMES can discover more concealed malicious emails that are immune from several commercial detection tools. © 2022 IEEE.

关键词： Intrusion detection

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