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International Conference on Advances in Information Technology (ICAIT)

作者： Mudit Mittal Praveen Gujjar J Guru Prasad M S Raghavendra M Devadas Lubna Ambreen Vikash Kumar Department of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Department of Computer Science and Engineering Gitam School of Technology Gitam (Deemed-to-be University) Bengaluru India Department of Computer Application Graphic Era (Deemed to be University) Dehradun India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The increasing complexity and high dimensionality of datasets in various fields, such as genomics, image analysis, and natural language processing, have posed significant challenges for effective data visualization and analysis. Dimensionality reduction techniques play a crucial role in addressing these challenges by transforming high-dimensional data into lower-dimensional representations while preserving the inherent structure and relationships within the data. This research focuses on the application of two prominent dimensionality reduction techniques: Uniform Manifold Approximation and Projection (UMAP) and t-distributed Stochastic Neighbor Embedding (t-SNE). UMAP and t-SNE have gained widespread popularity for their ability to capture complex nonlinear relationships and preserve local and global structures in the data. This paper Deals with parameter-tuning strategies for both UMAP and t-SNE to optimize their performance based on different data characteristics. Practical guidelines are provided to aid researchers and practitioners in selecting appropriate dimensionality reduction techniques and parameter settings for specific applications. The findings of this research contribute to a deeper understanding of the capabilities and limitations of UMAP and t-SNE, providing valuable insights into their practical applicability in the analysis and visualization of high-dimensional datasets.

关键词： Dimensionality reduction Manifolds Iris Image analysis Data analysis Data visualization Genomics

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Utilizing Anomaly Detection Methods for Identifying Fraudulent Activities in Credit Card Transactions 1

Utilizing Anomaly Detection Methods for Identifying Fraudule...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Prasad, N. Siva Rama Krishna Gangopadhyay, Amit Prabakaran, R. Mishra, Nilamadhab Prasad, D. Shyam Kapoor, Sneha Saveetha School of Engineering Dept. of CSE Chennai India Mohan Babu University Dept. of ECE Tirupati India Arignar Anna Institute of Science and Technology Dept. of Computer Application India VIT Bhopal University Dept. of CSE Bhopal India CVR College of Engineering Dept. of Electronics and Instrumentation Engineering Hyderabad India Swami Vivekanand Subharti University Dept of CSE Meerut India

ISBN: (纸本)9798350356816

Anomaly detection involves identifying unusual occurrences that could signal potential issues, such as security breaches, system failures, financial fraud, structural defects, or medical errors. In the context of digital finance, credit card fraud has become a significant concern, with fraudulent transactions often designed to appear legitimate. To address this, we employ the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset during sampling, and use the ARIMA model for time series forecasting to ensure accurate results. This research explores various fraud detection techniques and models, including K-Nearest Neighbors (KNN), Random Forest, Support Vector Classifier (SVC), and Decision Trees. We evaluate and compare the performance of these models to determine which offers the highest accuracy in detecting fraud. © 2024 IEEE.

关键词： Anomaly detection

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KVSAgg: Secure Aggregation of Distributed Key-Value Sets

KVSAgg: Secure Aggregation of Distributed Key-Value Sets

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International Conference on Data engineering

作者： Yuhan Wu Siyuan Dong Yi Zhou Yikai Zhao Fangcheng Fu Tong Yang Chaoyue Niu Fan Wu Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users’ privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem—key-value set secure aggregation—that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously.

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Security Challenges and Elucidations in Cloud Storage and File Systems: An Advanced Investigation Review

Security Challenges and Elucidations in Cloud Storage and Fi...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： S. P. Santhoshkumar S. Hariharasudhan S. Poorna Prakash Jim Mathew Philip S. Haritha T. Nalini Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai India Artificial Intelligence & Data Science Akshaya College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India Master of Computer Application Rathinam Technical Campus. Coimbatore India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

With the widespread adoption of cloud storage and file systems, security concerns have become paramount. This paper presents an advanced investigation into the security challenges encountered in cloud storage and file systems. We explore the vulnerabilities and threats that these systems face, ranging from unauthorized access to data breaches and insider attacks. Additionally, we examine the evolving landscape of security solutions, including encryption techniques, access control mechanisms, and intrusion detection systems. Through a comprehensive review of existing literature and case studies, we identify emerging trends and best practices for mitigating security risks in cloud storage and file systems. Our analysis provides valuable insights for researchers, practitioners, and organizations seeking to enhance the security posture of their cloud-based infrastructure. Cloud storage and file systems have revolutionized data management, offering scalability, flexibility, and accessibility. However, alongside their benefits, they present formidable security challenges. This paper conducts an advanced investigation into the security landscape of cloud storage and file systems, aiming to interpret key challenges and propose effective solutions. Through a thorough examination of prevalent security threats, including unauthorized access, data breaches, and insider attacks, this study identifies vulnerabilities inherent in cloud environments. Moreover, it scrutinizes emerging security technologies such as encryption, access control mechanisms, and intrusion detection systems, assessing their efficacy in mitigating risks. By synthesizing insights from existing literature and real-world case studies, this investigation provides valuable guidance for enhancing the security posture of cloud-based infrastructure. Ultimately, this paper contributes to the ongoing discourse on cloud security by offering nuanced analyses and actionable recommendations to researchers, practitioners,

关键词： Cloud computing File systems Reviews Scalability Cloud computing security Intrusion detection Data breach Market research Encryption Security

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Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models 30

Step-by-Step: Controlling Arbitrary Style in Text with Large...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Pusheng Wu, Lianwei Wang, Linyong Guo, Sensen Liu, Yang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China Research & Development Institute of Northwestern Polytechnical University Shenzhen China Chongqing Science & Technology Innovation Center of Northwestern Polytechnical University China State Key Laboratory of Integrated Service Networks Xidian University China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University China

ISBN: (纸本)9782493814104

Recently, the autoregressive framework based on large language models (LLMs) has achieved excellent performance in controlling the generated text to adhere to the required style. These methods guide LLMs through prompt learning to generate target text in an autoregressive manner. However, this manner possesses lower controllability and suffers from the challenge of accumulating errors, where early prediction inaccuracies might influence subsequent word generation. Furthermore, existing prompt-based methods overlook specific region editing, resulting in a deficiency of localized control over input text. To overcome these challenges, we propose a novel three-stage prompt-based approach for specific region editing. To alleviate the issue of accumulating errors, we transform the text style transfer task into a text infilling task, guiding the LLMs to modify only a small portion of text within the editing region to achieve style transfer, thus reducing the number of autoregressive iterations. To achieve an effective specific editing region, we adopt both prompt-based and word frequency-based strategies for region selection, subsequently employing a discriminator to validate the efficacy of the selected region. Experiments conducted on several publicly competitive datasets for text style transfer task confirm that our proposed approach achieves state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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A Multi-Objective Evolutionary Algorithm Based on Indicator and Performing Local Searches 4

A Multi-Objective Evolutionary Algorithm Based on Indicator ...

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4th International Conference on Information science and Education, ICISE-IE 2023

作者： Chen, Hanning Zeng, Xiangrui Jia, Kun Hu, Yabao Wang, Guopeng School of Computer Science and Technology Tiangong University Tianjin China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing China School of Artificial Intelligence Tiangong University Tianjin China Jingwei Textile Machinery Co. Ltd Beijing China College of Artificial Intelligence University of Science and Technology Tianjin China The Open University of China China

ISBN: (纸本)9798350394610

The indicator-based multi-objective optimization evolutionary algorithm has gained significant attention for its strong performance across various optimization problems. We draw on the advantages and try to make improvements in the environmental selection process based on different selection criteria. From this, we developed a distinctive multi-objective evolutionary algorithm that based on indicator and performing local searches, namely INLS. The INLS algorithm includes a novel selection mechanism that does not rely on weight vectors, which employs a strategy choosing solutions with relatively poor convergence but good diversity to perturb the decision variables of the selected solutions, aiming to explore and discover solutions that demonstrate enhanced convergence and diversity characteristics. The experimental results obtained from conducting tests on the ZDT and DTLZ test suites provide clear evidence of the remarkable performance exhibited by the proposed INLS in comparison to various other well-known MOEAs. These results serve to underscore the competitiveness of the proposed method. © 2023 IEEE.

关键词： Evolutionary algorithms

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Evaluation of Machine-Learned Price Prediction Models for Block-Chain Technology 1

Evaluation of Machine-Learned Price Prediction Models for Bl...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Goel, Vikas Sharma, Ashutosh Ranjan, Raju Sharma, Amit Kumar KIET Group of Institutions Dept. of IT Ghaziabad India IMSUC-Ghaziabad Dept. of Computer Application Ghaziabad India School of Computing Science & Engineering Galgotia University Greater Noida India Dept. of CS&E Ch. Charan Singh University Campus Meerut India

ISBN: (纸本)9798350345957

As a new form of technology, blockchain has much to offer. The technology has a wide range of potential uses and advancements, depending on the field of application. However, further study is required to realise the potential of advanced technologies like Blockchain and smart contracts. There is a lot of potential for investigation because the technology is so new. This research looked at the origins of the Blockchain and analysed several different blockchain solutions. Ethereum was discovered to be the most often used platform for constructing distributed apps after being compared to other platforms. Therefore, a distributed smart energy microgrid application will be built on the Ethereum platform. There is a coin, or token, exclusive to each blockchain platform. Bitcoin's relationship to other cryptocurrencies was measured using the Karl Pearson correlation coefficient. The research concludes that ether's price is most sensitive to changes in the price of bitcoin. On a preferred Ethereum network, a distributed application for managing smart microgrids has been built. The smart contract constitutes the heart of this programme. As a result, we also take care of the smart contract's layout, coding, and optimization. In comparison to the original, less GAS is consumed by this smart contract. The entire distributed app has been built and thoroughly tested. Smart Energy Microgrid Distributed application framework results demonstrate that the application functioned as expected. © 2023 IEEE.

关键词： Blockchain

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: heterogeneous hypergraph attention network with counterfactual learning for multimodal sentiment analysis

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Complex & Intelligent Systems 2025年第4期11卷 1-16页

作者： Huang, Changqin Lin, Zhenheng Huang, Qionghao Huang, Xiaodi Jiang, Fan Chen, Jili Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China School of Education Zhejiang Normal University Jinhua China School of Computer Science and Technology Zhejiang Normal University Jinhua China School of Computing Mathematics and Engineering Charles Sturt University Albury Australia

Multimodal sentiment analysis (MSA) has garnered significant attention for its immense potential in human-computer interaction. While cross-modality attention mechanisms are widely used in MSA to capture inter-modality interactions, existing methods are limited to pairwise interactions between two modalities. Additionally, these methods can not utilize the causal relationship to guide attention learning, making them susceptible to bias information. To address these limitations, we introduce a novel method called Heterogeneous Hypergraph Attention Network with Counterfactual Learning $$(\text {H}^2\text {CAN}).$$ The method constructs a heterogeneous hypergraph based on sentiment expression characteristics and employs Heterogeneous Hypergraph Attention Networks (HHGAT) to capture interactions beyond pairwise constraints. Furthermore, it mitigates the effects of bias through a Counterfactual Intervention Task (CIT). Our model comprises two main branches: hypergraph fusion and counterfactual fusion. The former uses HHGAT to capture inter-modality interactions, while the latter constructs a counterfactual world using Gaussian distribution and additional weighting for the biased modality. The CIT leverages causal inference to maximize the prediction discrepancy between the two branches, guiding attention learning in the hypergraph fusion branch. We utilize unimodal labels to help the model adaptively identify the biased modality, thereby enhancing the handling of bias information. Experiments on three mainstream datasets demonstrate that $$\text {H}^2\text {CAN}$$ sets a new benchmark.

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A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural Networks

A New Method for Predefined-Time Synchronization Control of ...

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Memristive Computing and applications (ICMCA), IEEE International Conference on

作者： Aidi Liu Wenyue Yu Lili Huang Jian Guo Tengfei Lei Hui Zhao School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan China

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory.

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An Auxiliary Industrial Equipment Maintenance System Using Mixed Reality 8

An Auxiliary Industrial Equipment Maintenance System Using M...

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8th IEEE International Conference on Industrial engineering and applications, ICIEA 2021

作者： Su, Xia Cheng, Zheng Luo, Bin School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9781665428958

The intellectualization of modern industrial equipment presents a great challenge to its maintenance. The traditional maintenance of equipment requires the repairer to reference related manual, resulting in low maintenance efficiency. And the attempt based on the Virtual Reality can not produce the real and operable experience. To solve the problems, we proposes an auxiliary equipment maintenance system based on Mixed Reality technology that superposes CAD model and other virtual information to the actual equipment using HoloLens. We take the maintenance of a gasoline engine as an example, mainly including troubleshooting, maintenance guidance and equipment part training, where users only need to follow the steps set by the system, and the experiment about system evaluation shows that it makes equipment maintenance work more simple, intuitive, convenient and efficient. © 2021 IEEE.

关键词： Maintenance

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