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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ebenezer, V. Nair, Akash Stewart Kirubakaran, S. Praising Linijah, N.L. Rajan, Jerlin Santhiya, P. Karunya Institute of Technology and Sciences Division of Data Sciences and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya School of Management Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331529635

The time-honored way that farmers have been used to apply to the identification of the best crops for better yield is not exhaustive. Under this technique, the management process of powerful and productive resources, cannot be done properly. The crop recommendation system comprising a machine learningbased model can be deployed to solve the problem. While using techniques such as Logistic Regression, Decision Trees, Random Forests, XGB boost, SVM and Stacking Classifier, the machine learning model attempts to predict the most suitable crops by taking and observing soil contents and environmental parameters. That study is devoted to helping farmers have comprehensive prescriptions and appropriate crop selections to optimize productivity and guarantee sustainability. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Wildcard Searchable Symmetric Encryption with Forward and Backward security 1st

Efficient Wildcard Searchable Symmetric Encryption with For...

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1st International Conference on Artificial Intelligence security and Privacy, AIS and P 2023

作者： Zhang, Xi Su, Ye Wei, Zhongkai Shen, Wenting Qin, Jing College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang050024 China School of Information Science and Engineering Shandong Normal University Jinan250358 China School of Mathematics Shandong University Jinan250100 China College of Computer Science and Technology Qingdao University Qingdao266071 China

ISBN: (纸本)9789819997848

Wildcard Searchable Symmetric Encryption can achieve flexibility and pattern matching while protecting data privacy. There is a promising future for combining secure wildcard search with emerging technologies such as AI. However, there are challenges in reducing the communication cost and improving security. In this paper, we propose an efficient wildcard searchable symmetric encryption with forward and backward security. The complexity of communication costs in the search protocol is O(1), independent of the number of characters of wildcard keywords, or files in the search result. It is achieved by a double-compressed index in which the character set and file identifiers are encoded simultaneously. Then, the double-compressed index provides a possibility to achieve oblivious keyword query and update, so that the proposed scheme only reveals the query type (search or update) without anything else. It achieves forward and backward security using the distributed multi-point function and an additively homomorphic symmetric encryption scheme. Detailed security proof and the theoretical comparison show the improvement in security and efficiency. The acceptable overheads of the proposed scheme are presented by the extensive performance evaluation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pattern matching

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Internet Public Safety Event Grading and Hybrid Storage Based on Multi-feature Fusion for Social Media Texts 28th

Internet Public Safety Event Grading and Hybrid Storage Bas...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Hu, Die Xie, Yulai Feng, Dan Zhao, Shixun Fu, Pengyu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031306365

With the rapid development of mobile Internet technology, various public events appear on social media platforms and attract a lot of attention. Since netizens can express their opinions freely on the Internet, some events that cause negative public opinion seriously threaten public security, which are called Internet Public Safety events (IPSe). Existing solutions use a single metric to realize event detection, which has a high false detection rate for Internet Public Safety events. In addition, they lack countermeasures after the outbreak of public opinion, resulting in inefficient information management. This paper proposes a novel Internet Public Safety event grading method based on multi-feature, which measures events from the three perspectives of heat, emotion, and sensitivity. In order to improve the retrieval efficiency of events information, we implement a smart dynamic hot/cold data migration mechanism using a hybrid storage system containing solid-state drives and hard-disk drives, which realizes real-time data adjustment in the storage layer and ensures efficient events management. In the experiments, we verify our method through several real internet events, and the results show that our method achieves the state-of-the-art accuracy in the detection of Internet Public Safety events and realizes efficient retrieval with a low query overhead. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Grading

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NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors 39

NumbOD: A Spatial-Frequency Fusion Attack Against Object Det...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhou, Ziqi Li, Bowen Song, Yufei Yu, Zhifei Hu, Shengshan Wan, Wei Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (e.g., NMS and RPN), yielding some results but simultaneously constraining their scalability. Moreover, most efforts against ODs stem from image-level attacks originally designed for classification tasks, resulting in redundant computations and disturbances in object-irrelevant areas (e.g., background). Consequently, how to design a model-agnostic efficient attack to comprehensively evaluate the vulnerabilities of ODs remains challenging and unresolved. In this paper, we propose NumbOD, a brand-new spatial-frequency fusion attack against various ODs, aimed at disrupting object detection within images. We directly leverage the features output by the OD without relying on its internal structures to craft adversarial examples. Specifically, we first design a dual-track attack target selection strategy to select high-quality bounding boxes from OD outputs for targeting. Subsequently, we employ directional perturbations to shift and compress predicted boxes and change classification results to deceive ODs. Additionally, we focus on manipulating the high-frequency components of images to confuse ODs’ attention on critical objects, thereby enhancing the attack efficiency. Our extensive experiments on nine ODs and two datasets show that NumbOD achieves powerful attack performance and high stealthiness. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting

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Journal of Reliable Intelligent Environments 2023年第2期9卷 183-200页

作者： Rahman, Md. Mijanur Shakeri, Mohammad Khatun, Fatema Tiong, Sieh Kiong Alkahtani, Ammar Ahmed Samsudin, Nurul Asma Amin, Nowshad Pasupuleti, Jagadeesh Hasan, Mohammad Kamrul Department of Computer Science and Engineering Jatiya Kabi Kazi Nazrul Islam University Trishal Mymensingh2224 Bangladesh Department of Electrical and Electronic Engineering Bangabandhu Sheikh Mujibur Rahman Science & Technology University Gopalganj Dhaka8100 Bangladesh Jalan Ikram-Uniten Selangor Kajang43000 Malaysia Center for Cyber Security School of Information Science and Technology Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia

The increasing energy demand and expansion of power plants are provoking the effects of greenhouse gas emissions and global warming. To mitigate these issues, renewable energies (like solar, wind, and hydropower) are blessings for modern energy sectors. The study focuses on wind-speed prediction in energy forecasting applications. This paper is a comprehensive review of deep neural network based approaches, like the "nonlinear autoregressive exogenous inputs (NARX)", "nonlinear input-output (NIO)" and "nonlinear autoregressive (NAR)" neural network models, in time-series forecasting applications. This study proposed NARX based prediction models in wind-speed forecasting for short-term scheme. The meteorological parameters related to wind time-series have been analyzed, and used for evaluating the performance of the proposed models. The experiments revealed the best performance of the prediction models in terms of "mean square error (MSE)", "correlation-coefficient (R2)", "auto-correlation", "error-histogram", and "input-error cross-correlation". Comparing with the other neural network models, like "recurrent neural network (RNN)" and "curve fitting neural network (CFNN)" models, the NARX-based prediction model achieved better performance in regard to "auto-correlation", "error-histogram", "input-error cross-correlation", and training time. The results also showed that the RNN and CFNN models performed better prediction accuracy with R2 and MSE values. While this performance index is slightly higher, it is negligible in forecasting applications and concluded that the proposed NARX-based model achieved the better prediction accuracy in terms of other performance evaluation measures. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Neural network models

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Speech-to-text Emotion Detection System using SVM, CNN, and BERT 1

Speech-to-text Emotion Detection System using SVM, CNN, and ...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Yadav, Uma Bondre, Shweta Thakre, Bhakti Likhar, Kiran Shri Ramdeobaba College of Engineering and Management Nagpur India Shri Ramdeobaba College of Engineering and Management Information Technology Department Nagpur India St. Vincent Pallotti College of Engineering and Technology Cyber Security Department Nagpur India Computer Science & Engineering G H Raisoni Institute of Engineering and Technology Nagpur India

ISBN: (纸本)9798350377002

Emotions have a tremendous impact on human experiences and decision-making processes. People are frequently motivated to repeat happy behaviors while avoiding depressing ones. Emotions may spread like wildfire in the modern digital era due to the quick broadcast of information and the heavy use of emotive language in communication. The identification of these emotional undercurrents in a variety of literary forms has been made easier by the analysis of indigenous languages, allowing for prompt interventions when needed. This study presents an algorithm that can analyze statements and then categorize them into sentiments in order to determine a person's emotional state. An analysis of the algorithm's performance shows that BERT performs better than SVM and CNN in a variety of emotional categories, showing better recall, accuracy, and F1-scores in the identification of emotions including Joy, Sadness, Fear, and Anger. For example, BERT outperforms SVM and CNN by large margins, achieving an average F1-score of 0.85 across all emotions. CNN performs competitively, especially when it comes to identifying emotions such as Fear and Sadness, but SVM performs somewhat worse overall than BERT and CNN. Interestingly, all models are good at recognizing Neutral emotions, with BERT and CNN doing somewhat better than SVM. As a result, BERT stands out as the best model for classifying emotions, demonstrating its effectiveness in identifying complex emotional states. © 2024 IEEE.

关键词： Convolutional neural networks

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S-chain: A Shard-based Blockchain Scheme for Cross-chain Supervision 29

S-chain: A Shard-based Blockchain Scheme for Cross-chain Sup...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Yuwei Song, Yuxing Zeng, Junyu He, Ran Xu, Jingdong Southeast University School of Cyber Science and Engineering Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Nankai University College of Computer Science Tianjin China

ISBN: (纸本)9798350330717

With the popularization of consortium blockchain, how to supervise numerous business chains has become a problem. Recently, some researchers have proposed the concept of 'governing chain by chain', which means that multiple organizations deploy a consortium blockchain to supervise many business chains in the industry. However, as the number of business chains increases, a performance bottleneck occurs on the supervision chain. Sharding is a promising direction to improve scalability, but existing studies focus on public chains. If transplanted to the supervision chain, these solutions will bring two challenges. Firstly, data from multiple business chains will cause storage conflicts and unbalanced sharding. Secondly, sharding makes it difficult for inter-shard transaction validation. Aiming at the challenges, we propose S-chain, a shard-based supervision chain scheme. The contribution of our work lies in three points. Firstly, we design a unified label-based data sharding method that can evenly map data from different business chains to all shards without conflict. Secondly, we propose a mechanism to validate general smart-contract-based inter-shard transactions. At first, we design a smart contract splitting algorithm that splits one inter-shard smart contract into multiple sub-contracts and depicts their dependencies via a Directed Acyclic Graph(DAG). Then, we design an inter-shard transaction validation method by analyzing the dependencies of all sub-contracts. Finally, we implement a prototype system of S-chain and test its performance. The results show that our S-chain outperforms the scheme without sharding on throughput and storage overhead. © 2023 IEEE.

关键词： Blockchain Cross-chain supervision Sharding Smart contract

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An unsupervised medical image registration network for intelligent medical education

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Neural Computing and Applications 2024年第19期37卷 13251-13266页

作者： Shi, Ke Mu, Jie Xu, Jian Zhang, Jing Yan, Tiantian Wang, Wei Zhang, Hua Ren, Wenqi Institute of Information Engineering Chinese Academy of Sciences Sanlihe-52 Beijing100045 China School of Cyber Security University of Chinese Academy of Sciences Yuquan-19 Beijing100049 China School of Data Science and Artificial Intelligence Dongbei University of Finance and Economics Jianshan-217 Liaoning Dalian116025 China The National and Local Joint Engineering Laboratory of Computer Aided Design Dalian University Xuefu-10 Liaoning Dalian116622 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Xinhu-666 Guangdong Shenzhen528406 China

Intelligent education, which applies artificial intelligence technologies to the education field, has attracted increasing attention in recent years. Intelligent medical education is an effective learning way to assist medical practitioners in mastering complex medical knowledge through artificial intelligence technologies like big data analysis and machine learning. With the development of medical imaging technology, the types of medical images are becoming abundant, such as computed tomography image and magnetic resonance imaging image. Medical experts with long clinical experience can analyze the patient’s condition from different types of medical images to achieve more accurate diagnosis. Nevertheless, cultivating an expert who can accurately analyze different types of medical images is time-consuming and laborious. Therefore, it is necessary to utilize the intelligent medical education way to design medical image processing method that can guide medical practitioners in simultaneously accurately analyzing different types of medical images. The medical image registration, which can automatically align different types of medical images and guide medical practitioners in diagnosing, has become popular. However, existing methods lack labeled training data and ignore interactive information between registered images. To solve these problems, we propose a cross-view interactive network (CVIN). First, CVIN utilizes alternate stages with information interaction modules to extract data features, which can use unlabeled medical data to train the model. Second, we introduce an InfoMax module to learn the mutual information between registered images. The experiment results show that CVIN is promising and can use to guide the medical practitioners to conduct accurate disease diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： computerized tomography

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Combined Effort Estimation Framework for Heterogeneous Projects Using Machine Learning: A Comparative Analysis

Combined Effort Estimation Framework for Heterogeneous Proje...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Amrita Sharma Neha Chaudhary Anand Sharma Department of Computer Science Indus University Ahmedabad India Computer Science & Engineering Manipal University Jaipur India Cyber Resilience Eventus Security Ahmedabad India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

The essential role of software development effort estimation is to predict the required work to develop software. The three main types of cost estimation approaches are algorithmic models, non-algorithmic models, and machine learning models. Algorithmic models use statistical or analytical formulae to estimate. Machine learning approaches are used with algorithmic or non-algorithmic models to increase accuracy. Other techniques include regression analysis, fuzzy logic, artificial neural networks, and others soft computing, meta-heuristic algorithms, and search-based algorithms. This paper present a combined framework developed for the effort estimation for diversifying projects. The combined framework uses the databases that include the projects from procedural, agile, and object-oriented approaches. A comparison is presented for the different projects using the single model as well as with the combined models. The combined models are developed for the projects with single feature and multiple features that are selected with the correlation.

关键词： Machine learning algorithms Costs Accuracy Object oriented modeling Computational modeling Software algorithms Estimation Machine learning Prediction algorithms Software

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An overview Scientific Cloud computing Web-based cloud services issues and challenges 5

An overview Scientific Cloud computing Web-based cloud servi...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Majumder, Sayan Raju, Kommisetti Murthy Arya, Nancy Shaik, Nazeer Tiwari, Sanjay Singh, Akanksha Department of cyber security The Heritage Academy Kolkata India Department of Electronics and Communication Engineering Shri Vishnu Engineering College For Women Bhimavaram India Department of Computer Science and Engineering Shree Guru Gobind Singh Tricentenary University Gurugram India Department Of Computer Science and Engineering Srinivasa Ramanujan Institute Of Technology-Autonomous-Anantapur Andhra Pradesh India Department of Computer Science and Engineering ARYA Institute of Engineering Technology and Management Jaipur India Computer Science and Engineering Babu Banarasi Das University Lucknow India

ISBN: (纸本)9798400709418

This research investigates the crucial role of cloud computing services in web development for creating scalable applications. Our study explores the benefits, challenges, and best practices associated with leveraging these services. Real-world case studies will be examined to showcase their significance. Additionally, we examine the integration of blockchain technologies in cloud-based web development and their potential impact on scalability. This paper presents a comprehensive analysis of the indispensability of cloud computing services in building scalable applications for the web. This research project delves into the transformative impact of cloud computing services on the field of web development. By providing scalable and flexible infrastructure, these services have revolutionized the way applications are built. Through an in-depth analysis of benefits, challenges, and best practices, along with real- world case studies, the project highlights the indispensable nature of cloud computing in creating scalable web applications. Furthermore, the exploration of blockchain integration within cloud-based web development offers insights into potential advancements in scalability and opens new avenues for innovation in the field. Additionally, the integration of blockchain technologies in cloud-based web development is investigated, providing insights into its potential impact on scalability and fostering innovation in the field. The research presents a comprehensive analysis of the indispensability of cloud computing services in creating scalable web applications and highlights their transformative impact on the development process. © 2023 ACM.

关键词： Blockchain

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