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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

Frontiers of computer science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

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Intelligent Automation & Soft computing 2023年第8期37卷 1895-1907页

作者： S.Priya R.S.Ponmagal Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia

Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion *** address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different *** rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other ***,the methods suffer from mitigating intrusion attacks at a higher *** article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these *** method involves analyzing service growth,network growth,and quality of service *** process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user *** proposed MFTEM model improves intrusion detection accuracy with higher performance.

关键词： Intrusion detection cloud systems trusted service support trusted network support trust user score trusted QoS support

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RSMR: A Reliable and Sustainable Multi-Path Routing Scheme for Vehicle Electronics in Edge computing Networks

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Wang, Ye Gao, Honghao Xiang, Zhengzhe Zhu, Zhongzhi Al-Dulaimi, Anwer School of Computer Engineering and Science Shanghai University Shanghai China School of Computer and Computing Science Hangzhou City University Hangzhou China Senior Manager of Emerging technologies and Innovations and Distinguished Member of Technical Canada

Consumer electronic devices used to support data communication are integral components of vehicular networks. However, due to factors such as the limited reliability and failure of electronic devices, vehicle communication data may fail to be uploaded and downloaded in a timely manner, potentially leading to serious traffic accidents. With the emergence of edge computing technology, computing tasks are distributed from traditional centralized cloud computing to the network edge, thereby enabling faster response to the processing demands of vehicle data. However, even though edge computing offers faster data processing capabilities, the issue of effective routing of data within vehicular edge computing (VEC) networks remains to be addressed. Therefore, this paper proposes a two-phase multi-path routing scheme for VEC networks. In the route decision phase, the scheme introduces an integrated adaptive function, that plans the route reasonably by considering the transmission latency, energy balance and communication quality. On this basis, different routing requirements (e.g., maximizing network lifetime or transmission reliability) can be achieved by setting the weights of the proposed function. In the route maintenance phase, the scheme implements real-time multi-path adjustment based on the route maintenance mechanism to support data routing. The simulation results show that the proposed scheme has significant advantages over three baseline schemes in terms of routing reliability and energy balance. In addition, we explore the impacts of the weights and initial network configuration on the routing performance. The obtained results can provide guidance for planning reliable and sustainable routes. IEEE

关键词： Wireless sensor networks

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Amazon Web Services Cloud Compliance Automation with Open Policy Agent 4

Amazon Web Services Cloud Compliance Automation with Open Po...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Paul, Alen Manoj, Rishi Udhayakumar, S. Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350385793

The security challenges posed by Infrastructure as code (IaC) are outgrowing established security procedures in an era of rapidly adopting cloud computing and DevOps methodologies. Using security principles to be integrated into the CI/CD pipeline for continuous validation and remediation, this research work proposes a DevSecOps approach to enable cloud environment IaC security. The proposed architecture evaluates CloudFormation Template file to the specified security policy by utilizing Open Policy Agent (OPA). Any violations are flagged by OPA, which may force the deployment process to stop. By incorporating security and compliance considerations into the development pipeline, this method guarantees that vulnerabilities are kept out of production systems. Organizations can improve the overall security posture of their cloud systems by proactively identifying and remediating security problems by implementing a DevSecOps approach. By ensuring that IaC deployments adhere to specified security policies based on the compliance requirements, OPA's continuous security validation reduces the possibility of misconfigurations and security flaws. © 2024 IEEE.

关键词： Cloud security

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On the complexity of computing Markov perfect equilibrium in general-sum stochastic games

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National science Review 2023年第1期10卷 288-301页

作者： Xiaotie Deng Ningyuan Li David Mguni Jun Wang Yaodong Yang Center on Frontiers of Computing Studies School of Computer Science Peking University Center for Multi-Agent Research Institute for AI Peking University Huawei UK Computer Science University College London

Similar to the role of Markov decision processes in reinforcement learning,Markov games(also called stochastic games) lay down the foundation for the study of multi-agent reinforcement learning and se quential agent *** introduce approximate Markov perfect equilibrium as a solution to the computational problem of finite-state sto chastic games repeated in the infinite horizon and prove its *** solution concept preserves the Markov perfect property and opens up the possibility for the success of multi-agent reinforcement learning algorithms on static two-player games to be extended to multi-agent dynamic games,expanding the reign of the PPAD-complete class.

关键词： Markov game multi-agent reinforcement learning Markov perfect equilibrium PPAD-completeness stochastic game

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An EETR Approach for Therapeutic Response Prediction Using Gene Expression and Drug Properties 3rd

An EETR Approach for Therapeutic Response Prediction Using G...

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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Selvi Rajendran, P. Jawahar, Janiel Computer Science and Engineering School of Computing Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819726707

In recent years, the role of computational methods such as machine learning and deep learning has evolved to help better understand an individual’s response to drugs. Through advancements in the discipline of precision medicine, cancer therapies are made based on an individual's pharmacogenomics data. In this way, doctors can make informed decisions about patient outcomes and reduce treatment costs. Beyond these advancements, there is enough void to precisely predict drug response. A drug’s response to therapy is dependent on a number of factors such as the drug’s physicochemical properties, pharmacokinetics, metabolism, protein, mutation, somatic variation, environment, and much more. Studying the correlation of more of these factors and their association with the drug is one of the promising ways to bridge the gap in improving results. Extra tree regression is an ensemble-based algorithm and is less prone to overfitting than other ensemble learning algorithms particularly when low bias and reduced overfitting are desired, and computational efficiency is important. In this paper, we have applied the drug’s physicochemical properties to predict the Inhibition values using an enhanced extra tree regression (EETR) algorithm. The EETR algorithm prediction outperformed both the random forest and original ETR algorithm in terms of both MSE and R2 score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Road Traffic Accident Prediction using Deep Learning 1

Road Traffic Accident Prediction using Deep Learning

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Girija, M. Divya, V. School of Computing Science Vistas Department of Computer Science Chennai India School of Computing Science Vistas Department of Information Technology Chennai India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Maintaining public safety and reducing the effects of unplanned incidents need real-time accident prevention. This research work presents a comprehensive plan for developing an advanced computer vision-based accident-avoidance system. The proposed system's primary data source is live video recordings, and it employs a number of procedures to detect anomalies in the environment and issue alerts in a timely manner. The proposed approach begins with the extraction of frames from the continuous video stream in order to enhance the relevance and quality of the returned frames. Effective image preparation techniques follow next. Data augmentation is then used to diversify the dataset and increase the model's generalization ability. In order to enable prompt reaction and intervention, the proposed system instantly notifies users when it detects irregularities, such as potential accidents or dangerous situations. The proposed method is then implemented using Python software. Utilizing a rule-based system, it delivers dynamic recommendations to drivers with enhanced road safety through adaptive speed limits, route suggestions, and real-time hazard alerts through car dashboard. The proposed modified YOLOv8 method shows better accuracy with 98.4%, which is 11.85% higher when compared with Faster RCNN, FAYOLO and FESSD. YOLO-v8 results in a powerful toolkit for accurate object tracking and recognition, guaranteeing a proactive and responsive system. The proposed model's evaluations show the way the method may be utilized to raise public safety by anticipating anomalies and preventing accidents. © 2024 IEEE.

关键词： Video recording

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Enhancing Connectivity of AODV in Urban Scenarios for VANETs: A QoS-Aware Approach 4

Enhancing Connectivity of AODV in Urban Scenarios for VANETs...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Janet Kumari, J Jesy Thangam, S. Ghosh, Archishman Kolhe, Dhruv Vijay Ghosh, Soumish Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523923

As cities expand, vehicles and congestion become more complex. Efficient vehicle-to-vehicle contact networks are needed for road safety and efficient traffic flow. Thus, Vehicular Ad Hoc Networks are needed to overcome all the issues. The Adhoc On-Demand Distance Vector (AODV) routing protocol is used in VANETs. However, this protocol has many challenges in urban settings due to high vehicle density, rapid topology changes, and non-line-of-sight conditions. This work improves AODV by integrating Quality of Service (QoS) metrics. The prioritizing factors such as overhead and end-to-end delay are used to create a more efficient protocol for urban traffic. The SUMO simulator is used for realistic traffic patterns and NS-2 for network analysis. An evaluation of the protocol against the classical AODV is done and important metrics for real-time communication are focused. The enhanced AODV showed marked improvements. It achieved lower overhead in route finding, an increase in the number of packets sent, and lower end-to-end delay. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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Signup page using Face recognition authentication

Signup page using Face recognition authentication

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Modi, Akash Rayar, Prajwal V Kavya, A.G. Vaishnavi, R. Dravya, B.S. Tamilarisi, R. Alliance School of Advanced Computing Department of Computer Science Bengaluru India

ISBN: (纸本)9798331529833

Improvements in artificial intelligence and machine learning have led to the growing integration of face recognition technology for a wide range of applications, from security systems to social media. Three related uses of facial processing technology are examined in this abstract: face detection, face recognition, and authentication through a face-based signup page. Finding and differentiating human faces in an image frame requires the first step, face detection. It also recognizes the facial features and separates them from other items in the frame using computer vision algorithms. Histogram of Oriented Gradients (HOG), and the Haar Cascade, are the methods used for face recognition. Still, due to failure in extracting facial features in harsh conditions, we use the more recent deep learning models like Convolutional Neural Networks (CNNs) to do this project. Building on detection and face recognition compares the observed faces to known faces in a database to identify or to confirm a person's identity. To assess distinctive facial traits (embeddings) or features and increase recognition accuracy across a variety of circumstances, like varying lighting and angles, modern face recognition systems employ deep learning models, such as Face Net and Deep Face. The Facial Recognition The signup page serves as an example of how face recognition can be used in authentication systems for secure apps. A user registers for this program by having their face captured, and this information is safely kept and acts as a unique identification. The solution gives a hands-free and password-less authentication mechanism during login by comparing the captured face with the stored data in the database. © 2025 IEEE.

关键词： Convolutional neural networks

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Smart Farming: IoT-Driven Crop Yield Prediction for Rice Cultivation 4

Smart Farming: IoT-Driven Crop Yield Prediction for Rice Cul...

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4th International Conference on Soft computing for Security Applications, ICSCSA 2024

作者： Sumesh, Nandana Navaneeth, R. Raj, Vimal Rajesh, Vismaya Ani, R. Department of Computer Science Amrita School of Computing Amritapuri India

ISBN: (纸本)9798331515720

In recent years, the agricultural sector has faced challenges in achieving optimal crop yields due to complex environmental factors and limited access to data-driven decision-making tools. An innovative IoT-based approach to predict crop yields is presented, aiming to improve crop productivity for farmers. The system utilizes a network of IoT sensors, including DHT22 and rainfall sensors, to gather real-Time data on temperature, humidity, and rainfall. Soil-integrated sensors are also used to monitor specific soil properties such as nitrogen, phosphorus, potassium, pH, electrical conductivity, humidity, and temperature. This data is sent to a web interface for live display and stored for predictive modeling. Several regression models-decision tree, random forest, ridge regression, and linear regression-were implemented to predict crop yields, with the random forest regression model achieving the highest accuracy and an error rate of 6.46 percent. The stacking model was also analyzed, but the random forest model's MAE and R-metrics proved superior, leading to its selection for deployment. A user-friendly web-based interface has been developed to enable farmers to interact with the system and predict crop yields effectively. This implementation smoothly integrates IoT technology with advanced data analytics, providing valuable insights to optimize agricultural practices, which in turn enhances food security and improves livelihoods. © 2024 IEEE.

关键词： Smart agriculture

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