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arXiv 2024年

作者： Li, Chufeng Chen, Jianyong School of Computer Science and Software Engineering Shenzhen University Shenzhen China

As a branch of time series forecasting, stock movement forecasting is one of the challenging problems for investors and researchers. Since Transformer was introduced to analyze financial data, many researchers have dedicated themselves to forecasting stock movement using Transformer or attention mechanisms. However, existing research mostly focuses on individual stock information but ignores stock market information and high noise in stock data. In this paper, we propose a novel method using the attention mechanism in which both stock market information and individual stock information are considered. Meanwhile, we propose a novel EMD-based algorithm for reducing short-term noise in stock data. Two randomly selected exchange-traded funds (ETFs) spanning over ten years from US stock markets are used to demonstrate the superior performance of the proposed attention-based method. The experimental analysis demonstrates that the proposed attention-based method significantly outperforms other state-of-the-art baselines. Code is available at https://***/DurandalLee/ACEFormer. © 2024, CC BY.

关键词： Empirical mode decomposition

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Bandwidth-Effective DRAM Cache for GPU s with Storage-Class Memory

Bandwidth-Effective DRAM Cache for GPU s with Storage-Class ...

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IEEE Symposium on High-Performance computer Architecture

作者： Jeongmin Hong Sungjun Cho Geonwoo Park Wonhyuk Yang Young-Ho Gong Gwangsun Kim Department of Computer Science and Engineering POSTECH Soongsil University School of Software

We propose overcoming the memory capacity limitation of GPUs with high-capacity Storage-Class Memory (SCM) and DRAM cache. By significantly increasing the memory capacity with SCM, the GPU can capture a larger fraction of the memory footprint than HBM for workloads that mandate memory oversubscription, resulting in substantial speedups. However, the DRAM cache needs to be carefully designed to address the latency and bandwidth limitations of the SCM while minimizing cost overhead and considering GPU's characteristics. Because the massive number of GPU threads can easily thrash the DRAM cache and degrade performance, we first propose an SCM-aware DRAM cache bypass policy for GPUs that considers the multi-dimensional characteristics of memory accesses by G PU s with SCM to bypass DRAM for data with low performance utility. In addition, to reduce DRAM cache probe traffic and increase effective DRAM BW with minimal cost overhead, we propose a Configurable Tag Cache (CTC) that repurposes part of the L2 cache to cache DRAM cacheline tags. The L2 capacity used for the CTC can be adjusted by users for adaptability. Furthermore, to minimize DRAM cache probe traffic from CTC misses, our Aggregated Metadata-In-Last-column (AMIL) DRAM cache organization co-locates all DRAM cacheline tags in a single column within a row. The AMIL also retains the full ECC protection, unlike prior DRAM cache implementation with Tag-And-Data (TAD) organization. Additionally, we propose SCM throttling to curtail power consumption and exploiting SCM's SLC/MLC modes to adapt to workload's memory footprint. While our techniques can be used for different DRAM and SCM devices, we focus on a Heterogeneous Memory Stack (HMS) organization that stacks SCM dies on top of DRAM dies for high performance. Compared to HBM, the HMS improves performance by up to 12.5× (2.9× overall) and reduces energy by up to 89.3% (48.1 % overall). Compared to prior works, we reduce DRAM cache probe and SCM write traffic by 91–9

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Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

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SN computer science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ML approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ML is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPIMLM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

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Fuzzy Deduplication Scheme Supporting Pre-verification of Label Consistency 17th

Fuzzy Deduplication Scheme Supporting Pre-verification of L...

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17th International Conference on Provable and Practical Security, ProvSec 2023

作者： Tang, Zehui Zeng, Shengke Li, Tao Cheng, Shuai Zheng, Haoyu School of Computer and Software Engineering Xihua University Chengdu610039 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Computer Science Qufu Normal University Rizhao276825 China

ISBN: (纸本)9783031455124

Efficiently and securely removing encrypted redundant data with cross-user in the cloud is challenging. Convergent Encryption (CE) is difficult to resist dictionary attacks for its deterministic tag. Server-aided mechanism is against such attacks while it may exist collusion. Focus on multimedia data, this paper proposes an efficient and secure fuzzy deduplication system without any additional servers. We also propose a notion of pre-verification of label consistency to compensate for the irreparable post-verification loss. Compared with other fuzzy deduplication schemes, our work has apparent advantages in deduplication efficiency and security based on a natural data set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Cryptography

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The Early Detection of Dementia Disease Using Machine Learning Approach

The Early Detection of Dementia Disease Using Machine Learni...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kabir, Md. Shariar Khanom, Jakia Bhuiyan, Md. Atik Tumpa, Zerin Nasrin Rabby, S.K. Fazlee Bilgaiyan, Saurabh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350348217

Early dementia detection is a crucial but challenging task in Bangladesh. Often, dementia is not recognized until it is too late to receive effective care. This results in part from a lack of knowledge about the illness and its signs and symptoms. Recent improvements in machine learning algorithms, however, may change this. In a recent study, we developed a model that can identify early dementia in Bangladesh using machine learning algorithms. This research paper proposed an efficient machine learning-based approach for early detection of dementia disease A dataset of 199 people with dementia and 175 healthy controls was used to develop the model. In 96% of the cases, the algorithm correctly identified dementia. This is a significant accomplishment that could revolutionize Bangladesh's dementia detection process. For patients to get the care they require, early dementia detection is essential. This study offers a proof-of-concept for the use of machine learning in dementia early detection & The results of this study suggest that machine learning models can be used as a powerful tool for early detection of dementia. © 2023 IEEE.

关键词： Machine learning

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Bank Customer Churn Prediction based on Random Forest Algorithm 24

Bank Customer Churn Prediction based on Random Forest Algori...

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5th International Conference on computer Information and Big Data Applications, CIBDA 2024

作者： Zhang, Nini Zheng, Yuzhi Duan, Chao Software Engineering Institute of Guangzhou Guangzhou510000 China School of Computer Science South China Normal University Guangzhou510000 China Guangzhou Xinhua University Guangzhou510000 China

ISBN: (纸本)9798400718106

Customer churn is a key concern for service industries such as banking, telecommunications and insurance. If they can know in advance which users have a higher probability of churn, institutions can formulate corresponding plans to restore them. This study is based on the random forest model and builds a credit card customer churn classification model by analyzing credit card customers’ historical transactions, credit card usage and other related characteristics. Use the data set on the kaggle platform for training and verification, and evaluate the performance of the model through cross-validation. This model can predict whether there is a risk of customer churn based on customer characteristics. Through the verification and optimization of the model, the accuracy and robustness of the model are improved. Experimental results show that the algorithm based on the random forest model outperforms the decision tree and support vector machine algorithms in predicting credit card customer churn, and its time complexity outperforms the neural network algorithm. © 2024 ACM.

关键词： Decision trees

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Impact of Reenactment Programs on Young Generation 2nd

Impact of Reenactment Programs on Young Generation

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2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Fatima, Anam Iqbal, Muhammad Waseem Aqeel, Muhammad Rana, Toqir A. Chen, Shuhong Arif, Muhammad Department of Computer Science Superior University Lahore Lahore54000 Pakistan Department of Software Engineering Superior University Lahore Lahore54000 Pakistan Department of Computer Science and IT The University of Lahore Lahore54000 Pakistan School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789819902712

The influence of reenactment-based violence programs on young people is the subject of this survey-based research project. A sample of 200 respondents (100 respondents were male and 100 were female) was gathered by the distribution of a questionnaire, and their responses were examined for the study. The data was broken down into five categories: violence, instigation, language, seduction, and fear. It was intriguing to learn that a lot of teenagers believe that violence and filthy language are permissible in the reenactment program circumstances. A considerable number of young people believe that drinking and smoking are wrong and that lying in some situations is unacceptable. A significant majority of adolescent respondents considered the representation of love, relationship, flirting, seduction, physical contact, and rape attractive in various situations of reenactment crime shows. Following the violence and crimes depicted in the reenactment shows, some young viewers believed that society was unsafe for them or others. The results are shown that 53% of respondents think that the use of violence and abusive language is justified. On the other hand, only 21% of respondents believe that criminal activity and violence are justified. 53% of those polled stated that watching the shows had made them fearful. The findings demonstrate that young viewers are afraid of violence and criminality when they see it on television. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crime

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SPUFChain: Permissioned Blockchain Lightweight Authentication Scheme for Supply Chain Management Using PUF of IoT

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IEEE Access 2025年 13卷 88662-88682页

作者： Assaqty, Mohammad Iqbal Saryuddin Gao, Ying Alqarni, Abeer D. Khan, Siraj Chakraborty, Mohuya Nur Siddiqi, Hafied Ahmad, Sadique South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Universitas Nahdlatul Ulama Indonesia Faculty of Engineering and Computer Science Jakarta10320 Indonesia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Research Institute for Future Media Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China University of Engineering and Management Cybersecurity Centre of Excellence Kolkata700160 India Riyadh12435 Saudi Arabia

The complexity of the entire process of supply chain management (SCM) is quite cumbersome and traditional way of handling it is devoid of proper authentication and security and very often suffers from human errors in dealing with flaws in quality control process of SCM. While it may have started with shipment tracking, the outcome of using IoT on supply chains has spread to every link in the chain. For instance, manufacturers are employing Internet-enabled sensors in production to find product faults, resulting in higher-quality production runs. Physical Unclonable Function (PUF) is a defense countermeasure that exploits the exclusive, unrepeatable features of hardware components to generate distinct cryptographic keys or identifiers, typically for a semiconductor device like an Internet of Things (IoT) device. The distinctive proof of identity of IoT devices in the supply chain is employed by means of PUFs as inviolable IDs. Blockchain, the distributed, immutable ledger, on the other hand is the disruptive technology that provides higher security as compared to traditional centralized systems. The integration of PUF and blockchain proves to be quite interesting while handling the above issues of authentication. A smart contract on the blockchain is a software code that executes spontaneously as and when the conditions of the contract or agreement are satisfied. Hence after authentication process the results are fed to blockchain smart contract for the final validation. This paper presents a novel permissioned blockchain smart contract-based lightweight authentication scheme for SCM using PUF of IoT known as SPUFChain. Informal and formal security analysis (using AVISPA and BAN logic) of the proposed framework show its efficiency to resist several threat scenarios like man-in-the middle, non-repudiation, impersonation, replay attacks and many other security features as compared to other related schemes. The processing time (~13.8ms) is better than existing lightweig

关键词： Supply chain management

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GAN-IVDS: An Intrusion Detection System for Intelligent Connected Vehicles Based on Generative Adversarial Networks 8

GAN-IVDS: An Intrusion Detection System for Intelligent Conn...

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8th International Conference on Data science in Cyberspace, DSC 2023

作者： Chen, Xiansen Xiao, Kun Luo, Lei Li, Yun Chen, Lirong School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Guangdong Weichen Information Technology Co. Ltd Dongguan China

ISBN: (纸本)9798350331035

This paper proposes an anomaly detection algorithm based on Generative Adversarial Networks (GANs) for intrusion detection in Intelligent Connected Vehicles (ICVs). To address the imbalance between normal network traffic and attack traffic in vehicular networks, as well as the difficulty in collecting attack data, we train the neural network solely with normal traffic. We employ the Gini index to perform data dimensionality reduction, selecting 34 features with the highest purity of information and enhancing model training speed via normalization functions. Our algorithm combines the generative capabilities of GANs and the reconstructive power of autoencoders, detecting abnormal network data by learning the distribution of normal network data, thus eliminating the need to analyze packet payloads and safeguarding user privacy. We also develop a vehicular network traffic data collection scheme to collect network traffic data from ICVs. Experimental results indicate that our algorithm demonstrates high accuracy in detecting anomalous network traffic in ICVs, with an accuracy rate of 94.9%. © 2023 IEEE.

关键词： Anomaly Detection Algorithm Generative Adversarial Networks Intelligent Connected Vehicles Intrusion Detection Network Security

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CoSiNet: Dual-Branch Collaborative Siamese Network for Visual Object Tracking

CoSiNet: Dual-Branch Collaborative Siamese Network for Visua...

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International Conference on computer Supported Cooperative Work in Design

作者： Wenjun Zhou Yao Liu Nan Wang Yifan Wang Bo Peng School of Computer Science and Software Engineering Southwest Petroleum University China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

This article presents a dual-branch Collaborative Siamese network architecture designed for visual object tracking, which we refer to as CoSiNet. The dual-branch collaborative Siamese network comprises two network branches: a shallow branch, which focuses on target localization to enhance resistance to interference from objects with similar characteristics, and a deep branch, which emphasizes the extraction of more abstract semantic information related to the object. Furthermore, we have devised a Channel Attention Feature Enhancement Module and a Spatial Channel Attention Feature Enhancement Module to augment feature extraction while mitigating the influence of background noise. In the concluding stages, an Adaptive Fusion Module is employed to amalgamate the response maps from both branches, resulting in an enhanced final response map. Experimental results, conducted on two publicly available datasets, demonstrate that our algorithm outperforms other state-of-the-art techniques in terms of tracking performance.

关键词： Location awareness Resistance Visualization Semantics Collaboration computer architecture Interference

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