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Promoting rigor in blockchain energy and environmental footprint research:A systematic literature review

Blockchain(Research and Applications) 2024年第1期5卷 133-151页

作者： Ashish Rajendra Sai Harald Vranken department of Computer Science Open UniversiteitHeerlen6419 ATthe Netherlands department of Advanced computing sciences Maastricht UniversityMaastricht6211 LKthe Netherlands Institute for computing&Information sciences Radboud UniversityNijmegen6525 XZthe Netherlands

There is a growing interest in understanding the energy and environmental footprint of digital currencies,specifically in cryptocurrencies such as Bitcoin and *** cryptocurrencies are operated by a geographically distributed network of computing nodes,making it hard to estimate their energy consumption *** studies,both in academia and industry,attempt to model cryptocurrency energy consumption often based on a number of assumptions,for instance,about the hardware in use or the geographic distribution of the computing nodes.A number of these studies have already been widely criticized for their design choices and subsequent over-or under-estimation of energy *** this study,we evaluate the reliability of prior models and estimates by leveraging existing scientific literature from fields cognizant of blockchain,such as social energy sciences and information *** first design a quality assessment framework based on existing research,and we then conduct a systematic literature review examining scientific and non-academic literature demonstrating common issues and potential avenues of addressing these *** goal with this article is to advance the field by promoting scientific rigor in studies focusing on blockchain energy *** that end,we provide a novel set of codes of conduct for the five most widely used research methodologies:Quantitative energy modeling,literature reviews,data analysis and statistics,case studies,and *** envision that this code of conduct would assist in standardizing the design and assessment of studies focusing on blockchain-based systems’energy and environmental footprint.

关键词： Blockchain Energy consumption Environmental footprint Scientific rigor Reliability

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TULAM:trajectory-user linking via attention mechanism

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Science China(Information sciences) 2024年第1期67卷 51-68页

作者： Hao LI Shuyu CAO Yaqing CHEN Min ZHANG Dengguo FENG Trusted computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences University of Chinese Academy of sciences

Recently,the application of location-based services(LBS) has become a prevalent means to provide convenience in customers’ everyday ***,because massive volumes of location information are collected by LBS applications,users may suffer from serious privacy *** studies have shown that the identities of users can be inferred from historical anonymous trajectories,which is formulated as the trajectory-user linking(TUL) *** some recurrent neural network(RNN)-based models have been proposed to capture implicit movement patterns among trajectories to improve TUL performance,they cannot learn the sequential and contextual semantics within any individual trajectory completely,leaving the advantages of RNNs *** therefore propose an RNN model with an attention mechanism called TULAM to improve the accuracy of the TUL *** learns sequential relationships within individual trajectories via RNN and captures contextual semantics from trajectories via a multi-head attention ***,we propose a novel location encoding method called approximate one-hot to solve the corpus shortage problem of trajectory *** were conducted on real datasets from the Gowalla and Foursquare LBS *** experimental results indicate that TULAM is a practical solution that achieves significant improvements over existing methods with satisfactory model complexity and convergence.

关键词： information security data security and privacy trajectory-user linking deep learning recurrent neural network attention mechanism

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Extracting Integrated Features of Electronic Medical Records Big Data for Mortality and Phenotype Prediction

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Chinese Journal of Electronics 2024年第3期33卷 776-792页

作者： Fei LI Yiqiang CHEN Yang GU Yaowei WANG Institute of computing Technology University of Chinese Academy of Sciences Pengcheng Laboratory

The key to synthesizing the features of electronic medical records(EMR) big data and using them for specific medical purposes,such as mortality and phenotype prediction,is to integrate the individual medical event and the overall multivariate time series feature extraction automatically,as well as to alleviate data imbalance *** paper provides a general feature extraction method to reduce manual intervention and automatically process large-scale *** processing uses two variational auto-encoders(VAEs) to automatically extract individual and global *** avoids the well-known posterior collapse problem of Transformer VAE through a uniquely designed "proportional and stabilizing" mechanism and forms a unique means to alleviate the data imbalance *** conducted experiments using ICU-STAY patients' data from the MIMIC-Ⅲ database and compared them with the mainstream EMR time series processing *** results show that the method extracts visible and comprehensive features,alleviates data imbalance problems and improves the accuracy in specific predicting tasks.

关键词： Databases Time series analysis MIMICs Manuals Big Data Feature extraction Transformers

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Privacy-preserving human activity sensing:A survey

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High-Confidence computing 2024年第1期4卷 108-117页

作者： Yanni Yang Pengfei Hu Jiaxing Shen Haiming Cheng Zhenlin An Xiulong Liu School of Computer Science and Technology Shandong UniversityQingdao 266237China department of computing and Decision sciences Lingnan UniversityHong KongChina department of computing The Hong Kong Polytechnic UniversityHong KongChina department of Computer Science Princeton UniversityNJ 08544USA College of Intelligence and computing Tianjin UniversityTianjin 300350China

With the prevalence of various sensors and smart devices in people’s daily lives,numerous types of information are being *** using such information provides critical and convenient services,we are gradually exposing every piece of our behavior and *** are aware of the privacy risks and have been working on preserving privacy while sensing human *** survey reviews existing studies on privacy-preserving human activity *** first introduce the sensors and captured private information related to human *** then propose a taxonomy to structure the methods for preserving private information from two aspects:individual and collaborative activity *** each of the two aspects,the methods are classified into three levels:signal,algorithm,and ***,we discuss the open challenges and provide future directions.

关键词： Human activity sensing Privacy-preserving sensing Activity sensing algorithms Human sensors Privacy protection

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Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

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Computer Modeling in Engineering & sciences 2024年第4期139卷 893-909页

作者： Yu Li Mingxiao Li Dongyang Ou Junjie Guo Fangyuan Pan department of computing Hangzhou Dianzi UniversityHangzhou310018China

With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore *** crowdsourcing consists of many different types of applications,such as spatial crowd-sensing *** terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction *** collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and *** task assignment and worker selection dominate the service quality for spatial crowdsourcing *** research conducted task assignments via traditional matching approaches or using simple network ***,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in ***,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal ***,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task *** experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.

关键词： Historical behavior analysis spatial crowdsourcing deep double dueling Q-networks

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Identity-Preserving Adversarial Training for Robust Network Embedding

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Journal of Computer Science & Technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data Intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

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Quantum search with prior knowledge

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Science China(Information sciences) 2024年第9期67卷 321-329页

作者： Xiaoyu HE Xiaoming SUN Jialing ZHANG State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences

The combination of contextual side information and search is a powerful paradigm in the scope of artificial intelligence. The prior knowledge enables the identification of possible solutions but may be imperfect. Contextual information can arise naturally, for example in game AI where prior knowledge is used to bias move decisions. In this work we investigate the problem of taking quantum advantage of contextual information, especially searching with prior knowledge. We propose a new generalization of Grover's search algorithm that achieves the optimal expected success probability of finding the solution if the number of queries is fixed. Experiments on small-scale quantum circuits verify the advantage of our algorithm. Since contextual information exists widely, our method has wide applications. We take game tree search as an example.

关键词： quantum computing quantum search quantum query algorithm prior knowledge

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Cervical Cancer Prediction Empowered with Federated Machine Learning

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Computers, Materials & Continua 2024年第4期79卷 963-981页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy M.A.Khan Khan Muhammad Adnan department of Computer sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE department of computing Skyline University CollegeSharjah999041UAE department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

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Caching Strategies in NDN Based Wireless Ad Hoc Network:A Survey

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Computers, Materials & Continua 2024年第7期80卷 61-103页

作者： Ahmed Khalid Rana Asif Rehman Byung-Seo Kim department of Computer Science FAST School of ComputingNational University of Computer and Emerging SciencesLahore54000Pakistan department of Software and Communications Engineering Hongik UniversitySejong City30016Republic of Korea

Wireless Ad Hoc Networks consist of devices that are wirelessly *** Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc *** is used in wireless ad hoc *** is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined *** fetches data from a fixed *** redundancy,mobility,and location dependency are the main issues of the IP network *** these factors result in poor performance of wireless ad hoc *** main disadvantage of IP is that,it does not provide in-network ***,there is a need to move towards a new network that overcomes these *** Data Network(NDN)is a network that overcomes these *** is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user *** NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and *** considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless *** cachingmechanism-based results are also *** the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.

关键词： Content centric network Internet of Things mobile ad hoc network named data network vehicular ad hoc network

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Skyway:Accelerate Graph Applications with a Dual-Path Architecture and Fine-Grained Data Management

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Journal of Computer Science & Technology 2024年第4期39卷 871-894页

作者： Mo Zou Ming-Zhe Zhang Ru-Jia Wang Xian-He Sun Xiao-Chun Ye Dong-Rui Fan Zhi-Min Tang Institute of computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of sciences Beijing 100049China Institute of Information Engineering Chinese Academy of SciencesBeijing 100045China department of Computer Science Illinois Institute of TechnologyChicagoIL 60616U.S.A.

Graph processing is a vital component of many AI and big data ***,due to its poor locality and complex data access patterns,graph processing is also a known performance killer of AI and big data *** this work,we propose to enhance graph processing applications by leveraging fine-grained memory access patterns with a dual-path architecture on top of existing software-based graph *** first identify that memory accesses to the offset,edge,and state array have distinct locality and impact on *** then introduce the Skyway architecture,which consists of two primary components:1)a dedicated direct data path between the core and memory to transfer state array elements efficiently,and 2)a data-type aware fine-grained memory-side row buffer hardware for both the newly designed direct data path and the regular memory hierarchy data *** proposed Skyway architecture is able to improve the overall performance by reducing the memory access interference and improving data access efficiency with a minimal *** evaluate Skyway on a set of diverse algorithms using large real-world *** a simulated fourcore system,Skyway improves the performance by 23%on average over the best-performing graph-specialized hardware optimizations.

关键词： graph application computer architecture memory hierarchy

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