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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Sushma Kakkar Subha B L. Chandra Sekhar Reddy Subharun Pal Sushil Chandra Dimri Neerav Nishant Department of Electrical Electronics and Communication Engineering Sharda School of Engineering and Technology Sharda University Greater Noida Department of Management Kristu Jayanti College Bangalore India HOD-CSE(Data Science) CMR College of Engineering & Technology Kandlakoya(v) Medchal Telangana India Department of Computer Science and Engineering Indian Institute of Technology Jammu Jagti Jammu Jammu & Kashmir India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering School of Engineering Babu Banarasi Das University Lucknow India

Detecting and confirming MasterCard fraud (also known as “master card fraud detection”) is a popular course topic amongst students due to the usefulness of data processing and machine learning strategies in combating cybercrime. The three main parts of the proposed method are preprocessing, feature extraction, and model training. Preprocessing techniques are used to mine crucial information for identifying MasterCard fraud. Only by using the most advanced and efficient algorithms, the future transactions can be predicted with any degree of certainty. By employing these techniques, the proposed approach may confirm a variety of facts, including the correctness of authentication and the regularity of dealings. To accomplish feature extraction, the proposed approach used the PCA and SKM algorithms. In order to train the models following feature extraction, the proposed approach uses CNN, BiGRU, and BiGRU-CNN. Both the CNN and the BiGRU models are outperformed by the proposed model. With an approximate 98.87 percent accuracy, the proposed method is superior to other methods such as CNN and BiGRU.

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Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning

arXiv

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arXiv 2024年

作者： Du, Xiaohu Wen, Ming Zhu, Jiahao Xie, Zifan Ji, Bin Liu, Huijun Shi, Xuanhua Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China College of Computer National University of Defense Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab HUST Wuhan430074 China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security HUST Wuhan430074 China JinYinHu Laboratory Wuhan430077 China Cluster and Grid Computing Lab HUST Wuhan430074 China

Code Pre-trained Models (CodePTMs) based vulnerability detection have achieved promising results over recent years. However, these models struggle to generalize as they typically learn superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities, resulting in poor performance in real-world scenarios beyond the training instances. To tackle this challenge, we introduce VulLLM, a novel framework that integrates multi-task learning with Large Language Models (LLMs) to effectively mine deep-seated vulnerability features. Specifically, we construct two auxiliary tasks beyond the vulnerability detection task. First, we utilize the vulnerability patches to construct a vulnerability localization task. Second, based on the vulnerability features extracted from patches, we leverage GPT-4 to construct a vulnerability interpretation task. VulLLM innovatively augments vulnerability classification by leveraging generative LLMs to understand complex vulnerability patterns, thus compelling the model to capture the root causes of vulnerabilities rather than overfitting to spurious features of a single task. The experiments conducted on six large datasets demonstrate that VulLLM surpasses seven state-of-the-art models in terms of effectiveness, generalization, and robustness. © 2024, CC BY.

关键词： Large datasets

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Big data: A Technical Preview of Technology

Big Data: A Technical Preview of Technology

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Jagjit Singh Dhatterwal Sonali Gupta Kiran Malik Kuldeep Singh Kaswan Sunil Bharti Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science & Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun India Matu Ram Institute of Engineering and Management Rohtak Haryana India School of Computer Science & Engineering Galgotias University Greater Noida Uttar Pradesh India Galgotias College of Engineering and Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The spaceless accessibility of data has dramatically changed the environments of decision making in the digital age. “Big data” technically refers to datasets that are not only large, but also diverse & fast, thus speeding up the process of managing them with traditional procedures & tools. However, with this more abundance of data, making use of these information, and extracting particular utilities from them will have to be explored and studied. Firstly, decisions must be made that give significant insights from such diverse and changing data which move from the level of transactions, consumer comprehension to the network interpersonal. Massive data analytics, which is an implication of large-scale data analysing by firing on huge data collections, brings such value. The objective of this article is to pursue some of the different kinds of analytical methods and software tools that can be used to analyze data, as well as the alternative decision-making facilitation modes which may be achieved through advanced analytics.

关键词： data analysis Firing Decision making Big data Information age data mining Software tools

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Artificial Intelligence enabled Employee Performance Prediction using Comprehensive Learning Metrics

Artificial Intelligence enabled Employee Performance Predict...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： P. GururamaSenthilvel N. Duraimutharasan Hannah Rose Esther T P. N. Periyasamy Rajesh Kumar. K Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of Information Technology AMET University Kanathur Tamil Nadu Department of Artificial Intelligence and Data Science Velammal Engineering College Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore Tamil Nadu Department of Electronics and Communication Engineering Chendu College of Engineering Madurantakam Tamil Nadu India

Human capital is crucial to the success of businesses in the new knowledge economy. There is a compelling financial justification for developing a system to assess and enhance employee performance, making performance measurement at the level of the individual worker a must. With data Mining, HR professionals may find new and relevant information (HRM). The success of an employee is not dependent on a single trait. Humans vary greatly in their innate talents and the traits that define their personalities. Hence, data from many sectors must be collected for performance monitoring. The purpose of this article is to present an overall grade of an employee's performance taking into account a number of criteria. We begin by contrasting many popular Machine Learning algorithmsagainst one another. We then recommend an ensemble method that draws from the aforementioned methods. The empirical findings show that the suggested model outperforms the state-of-the-art models in terms of precision, recall, F1-score, and accuracy, as compared to the more conventional models.

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Delay Alignment Modulation with Hybrid Analog/Digital Beamforming for Millimeter Wave and Terahertz Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Jieni Zeng, Yong Yu, Xiangbin Jin, Shi Yuan, Jinhong Liang, Ying-Chang Zhang, Rui Southeast University National Mobile Communication Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210016 China The University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia Chengdu611731 China The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

For millimeter wave (mmWave) or Terahertz (THz) communications, by leveraging the high spatial resolution offered by large antenna arrays and the multi-path sparsity of mmWave/THz channels, a novel inter-symbol interference (ISI) mitigation technique called delay alignment modulation (DAM) has been recently proposed. The key ideas of DAM are delay pre-compensation and path-based beamforming. However, existing research on DAM is mainly based on fully digital beamforming, which requires the number of radio frequency (RF) chains to be equal to the number of antennas. This paper proposes the hybrid analog/digital beamforming based DAM, including both fully and partially connected structures. The analog and digital beamforming matrices are designed to achieve performance close to DAM based on fully digital beamforming. While DAM was considered for the path-based channel model with integer delays in the previous work, this paper extends DAM to a more general tap-based model that accounts for fractional path delays. To further reduce the cost of channel estimation and improve the performance for wireless channels with fractional delays, DAM with codebook-based beam alignment and DAM-orthogonal frequency division multiplexing (DAM-OFDM) with hybrid beamforming are proposed. The effectiveness of the proposed techniques is verified by extensive simulation results. © 2002-2012 IEEE.

关键词： Beamforming

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STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting

STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lincan Li Kaixiang Yang Jichao Bi Fengji Luo Data and Knowledge Research Group University of New South Wales Sydney Australia School of Computer Science and Engineering South China University of Technology Guangzhou China Zhejiang Institute of Industry and Information Technology Hangzhou China The University of Sydney Sydney Australia

Efficiently capturing the complex spatiotemporal representations from large-scale traffic data with uneven data quality remains to be a challenging task. In considering of the dilemma, this work employs the advanced contrastive learning and proposes a novel Spatial-Temporal Synchronous Contextual Contrastive Learning (STS-CCL) model. First, we elaborate the basic and strong augmentation methods for spatiotemporal graph data. Second, we introduce a Spatial-Temporal Synchronous Contrastive Module (STS-CM) to simultaneously capture the decent spatial-temporal dependencies and realize graph-level contrasting. To further discriminate node individuals in negative filtering, a Semantic Contextual Contrastive method is designed based on semantic features and spatial heterogeneity, achieving node-level contrastive learning along with negative filtering. Finally, we present a hard mutual-view contrastive training scheme and extend the classic contrastive loss to an integrated objective function, yielding better performance. Extensive experiments and evaluations demonstrate that building a predictor upon STS-CCL contrastive learning model gains superior performance than existing traffic forecasting benchmarks. The proposed STS-CCL is highly suitable for large datasets with only a few labeled data and other spatiotemporal tasks with data scarcity issue.

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Research Artifacts in Software engineering Publications: Status and Trends

arXiv

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arXiv 2024年

作者： Liu, Mugeng Huang, Xiaolong He, Wei Xie, Yibing Zhang, Jie M. Jing, Xiang Chen, Zhenpeng Ma, Yun Ministry of Education School of Computer Science Peking University Beijing China School of Software and Microelectronics Peking University Beijing China Department of Informatics King’s College London London United Kingdom National Key Laboratory of Data Space Technology and System Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute for Artificial Intelligence Peking University Beijing China

The Software engineering (SE) community has been embracing the open science policy and encouraging researchers to disclose artifacts in their publications. However, the status and trends of artifact practice and quality remain unclear, lacking insights on further improvement. In this paper, we present an empirical study to characterize the research artifacts in SE publications. Specifically, we manually collect 1,487 artifacts from all 2,196 papers published in top-tier SE conferences (ASE, FSE, ICSE, and ISSTA) from 2017 to 2022. We investigate the common practices (e.g., URL location and format, storage websites), maintenance activities (e.g., last update time and URL validity), popularity (e.g., the number of stars on GitHub and characteristics), and quality (e.g., documentation and code smell) of these artifacts. Based on our analysis, we reveal a rise in publications providing artifacts. The usage of Zenodo for sharing artifacts has significantly increased. However, artifacts stored in GitHub tend to receive few stars, indicating a limited influence on real-world SE applications. We summarize the results and provide suggestions to different stakeholders in conjunction with current guidelines. © 2024, CC BY.

关键词： Software engineering

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Aspect-Based Capsule Network With Mutual Attention for Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第3期2卷 228-237页

作者： Yang, Zhenyu Wang, Xuesong Cheng, Yuhu Liu, GuoJing The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The Qilu University of Technology Shandong Academy of Sciences Software Integration Institute Jinan250353 China The Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The School of Computer Science and Technology Qilu University of Technology Jinan250353 China

Review text is a valuable source of information for recommendation systems and often contains rich semantics with user preferences and item attributes. Recently, mainstream recommendation approaches have been using deep learning techniques to utilize review text. These efforts employ an affinity matrix to model the correlations between users and items and in turn aggregate the contextual features of users and items to form latent representations with some interpretability. However, constructing this simple interaction does not capture the complex correlations between users and items. Furthermore, a single representation of latent features is not sufficient to express user preferences and item attributes in reviews with multiple granularities. To this end, we propose an aspect-based capsule network and mutual attention for recommendation systems, named Acapnet. We design a novel contextual mutual attention mechanism for modeling the fine-grained correlations in the contextual features of users and items. We employ reverse dynamic routing to aggregate user and item context features into aspect features (capsules) for rating predictions;the vector length of a capsule can be used as a basis for estimating aspect importance. Extensive experiments are conducted on five real-world datasets with different features. The results show that the proposed Acapnet is superior to recent state-of-the-art models in terms of performance. Further analysis reveals that our model effectively mitigates the homogeneity among various aspect capsules with strong interpretability. © 2021 IEEE.

关键词： Recommender systems

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Real-Valued Group Testing for Quantitative Molecular Assays 26th

Real-Valued Group Testing for Quantitative Molecular Assays

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26th International Conference on Research in Computational Molecular Biology, RECOMB 2022

作者： Saeedi, Seyran Serrano, Myrna Yang, Dennis G. Brooks, J. Paul Buck, Gregory A. Arodz, Tomasz Department of Computer Science College of Engineering Virginia Commonwealth University RichmondVA United States Department of Microbiology and Immunology School of Medicine Virginia Commonwealth University RichmondVA United States Department of Mathematics College of Arts and Sciences Drexel University PhiladelphiaPA United States Department of Information Systems School of Business Virginia Commonwealth University RichmondVA United States Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States Center for Microbiome Engineering and Data Analysis Virginia Commonwealth University RichmondVA United States

Combinatorial group testing and compressed sensing both focus on recovering a sparse vector of dimensionality n from a much smaller number m © 2022, The Author(s), under exclusive license to Springer Nature Switz... 详细信息

ISBN: (纸本)9783031047480

关键词： SARS

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Research on layout algorithm of all steel attached wall climbing frame walkway boards based on Revit

Research on layout algorithm of all steel attached wall clim...

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Chinese Automation Congress (CAC)

作者： Zhong Hui An Di Liaoning Province Big Data Management and Analysisline Laboratory of Urban Construction School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China

In order to realize intelligent processing of drawings in building construction, improve the accuracy of drawings and provide more security for construction personnel, this paper proposes a layout algorithm of all steel attached wall climbing frame walkway boards, which can read and process the building contour data. Based on the building contour data, the layout drawing of building peripheral climbing frame walkway boards can be generated by one click in Revit and the walkway board data can be obtained Read and process the recognized building contour in the database, analyze the coordinate change and summarize the law by using the processed data, and obtain the walkway board contour map, walkway board partition map and walkway board data. The implementation of this algorithm greatly improves the work efficiency of technicians and lays the foundation for the layout of other parts. It has great application value in the field of computer- aided building construction design.

关键词： Visualization databases Buildings Layout Partitioning algorithms C# languages Steel

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