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作者： Bez, Jean Luca Tang, Houjun Breitenfeld, Scot Zheng, Huihuo Liao, Wei-Keng Hou, Kaiyuan Huang, Zanhua Byna, Suren Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States HDF Group ChampaignIL United States Data Science Group Argonne National Laboratory LemontIL United States Electrical and Computer Engineering Department Northwestern University EvanstonIL United States Computer Science Department Northwestern University EvanstonIL United States Department of Computer Science and Engineering The Ohio State University OH United States

Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputers. With massive amounts of data produced or consumed by compute nodes, high-performant parallel I/O is essential. I/O benchmarks play an important role in this process;however, there is a scarcity of I/O benchmarks representative of current workloads on HPC systems. Toward creating representative I/O kernels from real-world applications, we have created h5bench, a set of I/O kernels that exercise hierarchical data format version 5 (HDF5) I/O on parallel file systems in numerous dimensions. Our focus on HDF5 is due to the parallel I/O library's heavy usage in various scientific applications running on supercomputing systems. The various tests benchmarked in the h5bench suite include I/O operations (read and write), data locality (arrays of basic data types and arrays of structures), array dimensionality (one-dimensional arrays, two-dimensional meshes, three-dimensional cubes), I/O modes (synchronous and asynchronous). In this paper, we present the observed performance of h5bench executed along several of these dimensions on existing supercomputers (Cori and Summit) and pre-exascale platforms (Perlmutter, Theta, and Polaris). h5bench measurements can be used to identify performance bottlenecks and their root causes and evaluate I/O optimizations. As the I/O patterns of h5bench are diverse and capture the I/O behaviors of various HPC applications, this study will be helpful to the broader supercomputing and I/O community. © 2024 Lawrence Berkeley National laboratory, The HDF Group, UChicago Argonne, LLC and The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd.

关键词： Supercomputers

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Research on Metaverse Security and Forensics

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computers, Materials & Continua 2023年第10期77卷 799-825页

作者： Guangjun Liang Jianfang Xin Qun Wang Xueli Ni Xiangmin Guo Pu Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Key Laboratory of Digital Forensics Department of Public Security of Jiangsu ProvinceNanjingChina School of Intelligent Engineering Nanjing Institute of Railway TechnologyNanjingChina

As a subversive concept,the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic,industrial,and investment ***,while the metaverse brings unprecedented opportunities for transformation to human society,it also contains related *** is a digital living space with information infrastructure,interoperability system,content production system,and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and *** social incentives and governance rules,the metaverse reflects the digital migration of human *** article will conduct an in-depth analysis of the metaverse from the perspective of electronic data ***,from the perspective of Internet development,the background and development process of the metaverse is *** systematically elaborating on the concept and connotation of the metaverse,this paper summarizes the different views of current practitioners,experts,and scholars on the ***,from the perspective of metaverse security,the social risk and crime risks of the metaverse are *** the importance of metaverse forensics is ***,from the perspective of blockchain,smart wearable devices,and virtual reality devices,the objects and characteristics of metaverse forensics have been studied in *** smart wearable devices as an example,this paper gives the relevant experimental process of smart bracelet ***,many challenges faced by metaverse forensics are summarized by us which provide readers with some exploratory guidance.

关键词： Metaverse forensics blockchain smart wear virtual reality

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Spatio-Temporal Graph Neural Networks for Accurate Crime Prediction

Spatio-Temporal Graph Neural Networks for Accurate Crime Pre...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Rojan Roshankar Mohammad Reza Keyvanpour Department of Computer Engineering Faculty of Engineering Data Mining Laboratory Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

As a matter of public safety and resource allocation, crime prediction is of paramount importance. As a result of applying data preprocessing techniques and a graph-based approach, this paper presents a crime prediction model. This study shows a comprehensive analysis of crime incidents reported in Chicago over five years, utilizing several preprocessing steps in preparing the dataset. Irrelevant features are eliminated, missing values are handled, and a new feature is extracted. An algorithm based on the k-nearest neighbors method is proposed for representing crime incidents by constructing a graph representation. Each crime incident is connected to its k nearest neighbors based on spatial coordinates. This study compares graph-based machine learning algorithms and conventional approaches, including logistic regression, decision trees, and the recently related work, Spatial-Temporal Meta-path Guided Explainable Crime Prediction (STMEC). The results reveal that the graph-based model demonstrates superior accuracy, recall, and F1 scores compared to traditional models, notably outperforming the recently related work, STMEC. Based on the current study, the graph-based approach effectively captures crime data's spatial dependencies and patterns. Incorporating additional features and fine-tuning hyperparameters of the model can provide valuable insights into crime prevention strategies and urban planning applications.

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Fuzzy-ID3 Algorithm for Breast Cancer Disease Classification

Fuzzy-ID3 Algorithm for Breast Cancer Disease Classification

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International Conference on Web Research (ICWR)

作者： Zahra Sadat Hosseini Alast Mohammad Reza Keyvanpour Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

In terms of death rates, breast cancer comes in second, among women with cancer. Despite the fact that cancer cells grow in a multistep process involving a number of different types of cells, prevention of breast cancer stays a challenge inside the modern world. As a method of breast cancer detection, this paper proposes ENTROPYMOC strategy, a fuzzy decision tree with a new formula of Entropy. It aims to improve the classification accuracy, precision, recall and F1-Measure of the decision tree by overcoming the limitations of the ID3 algorithm, which is not able to classify continuous-valued data. In the field of machine learning, fuzzy decision trees are becoming increasingly popular. This algorithm reduces the complexity of the logarithmic entropy formula by simplifying the Shannon entropy principle. WBCD (Original), WDBC (Diagnostic) and Coimbra datasets are used to test the improved algorithm. Based on the experimental results, the improved fuzzy-ID3 algorithm outperforms the other four classification algorithms (SVM, Naïve Bayes, Random forest and FId3) in terms of accuracy. In Coimbra dataset, accuracy increased by 3.448%.

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A Comprehensive Approach to SMS Spam Filtering Integrating Embedded and Statistical Features

A Comprehensive Approach to SMS Spam Filtering Integrating E...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Shaghayegh Hosseinpour Mohammad Reza Keyvanpour Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

Modern society relies heavily on mobile phones to communicate. One of the most valuable mobile phone services is SMS (Short Message Service), which simplifies communication greatly. There have been spammers who have misused this platform by sending inappropriate messages to users, provoking them and costing them money. Due to imbalanced data, unclear semantics, and the inability to extract sufficient features from short messages, SMS spam can be difficult to filter. While spam messages have been filtered so far using various methods, their accuracy is still a work in progress. This study uses embeddings and TF-IDF to provide more information from short text messages while improving SMS spam filtering accuracy. The proposed approach was tested on a real dataset. Experiments analyzing evaluation parameters demonstrate that this model is effective.

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Evaluation of Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison

Evaluation of Supervised Machine Learning Algorithms for Cre...

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International Conference on Web Research (ICWR)

作者： Qazaleh Sadat Mirhashemi Negar Nasiri Mohammad Reza Keyvanpour Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

Every day the modern world is moving towards digitalization and cashless transactions are becoming more common, credit cards are rapidly becoming more popular. Online and offline purchases using credit cards have become increasingly popular, which results in more fraudulent transactions every day. A large number of credit card fraud incidents occur every year and lead to huge financial losses. accordingly, it could be important to choose the best fraud detection method is essential so that it can detect fraud before criminal consumers a stolen card. To detect fraud, one method is to evaluate historical transaction data, as well as both normal and fraudulent transactions, to obtain usual and fraudulent behavior features by using machine learning techniques. we can use machine learning algorithms to solve this problem if we have access to enough data. In this study, our goal is to compare three algorithms for detecting credit card fraud (Decision Tree, Regression Logistic and Random Forest). we want to use a model that is new and based on a hybrid approach for detecting credit card fraud. According to this study, the proposed model is more capable of identifying fraudulent transactions than previous studies.

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P-TECS: An Energy Balance Algorithm for Opportunistic Networks Integrating Multiple Node Attributes 5th

P-TECS: An Energy Balance Algorithm for Opportunistic Netwo...

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5th International Conference on Security and Privacy in New Computing Environments, SPNCE 2022

作者： Xu, Gang Yang, Xiaoying Chen, Jingjian Huang, Baoqi College of Computer Science Inner Mongolia University Hohhot010021 China Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9783031306228

This paper proposes an energy balance opportunistic networks routing algorithm P-TECS. The P-TECS solves the problems of energy consumption of key nodes in existing opportunistic routing algorithms. This paper defines the relay degree of communication between nodes, and designs a P-TECS opportunistic routing algorithm integrating dynamic attributes of multiple nodes. Simulation results show that in resource-constrained opportunistic networks, P-TECS significantly improves the message delivery rate and average remaining energy of nodes, and significantly reduces the routing overhead rate. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Routing algorithms

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Few-Shot Object Detection Based on the Transformer and High-Resolution Network

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computers, Materials & Continua 2023年第2期74卷 3439-3454页

作者： Dengyong Zhang Huaijian Pu Feng Li Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

Now object detection based on deep learning tries different *** uses fewer data training networks to achieve the effect of large dataset ***,the existing methods usually do not achieve the balance between network parameters and training *** makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection *** improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the *** and spatial attention are used to make the network focus on features that are more useful to the *** addition,the recently popular transformer is used to fuse the features of the existing *** compensates for the previous network failure by making full use of existing object *** on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

关键词： Object detection few shot object detection transformer high-resolution

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Enhancing QSAR Modeling: A Fusion of Sequential Feature Selection and Support Vector Machine

Enhancing QSAR Modeling: A Fusion of Sequential Feature Sele...

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Conference on Information and Knowledge Technology

作者： Farzaneh Khajehgili-Mirabadi Mohammad Reza Keyvanpour Department of Computer Engineering Faculty of Engineering Data Mining Laboratory Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

Quantitative Structure-Activity Relationship (QSAR) modeling is an approach employed to predict the biological response of chemical compounds by considering their structural attributes. Classification machine learning algorithms can learn patterns and relationships between chemical structure (descriptors) and biological activity from datasets and then use this knowledge to predict active or inactive compounds. This study introduces a new approach that combines Sequential Feature Selection (SFS) with the Support Vector Machine (SVM) algorithm to select the most relevant molecular descriptors for QSAR modeling. SFS and SVM work collaboratively to identify the best subset of descriptors, resulting in improved predictive accuracy. The key steps include selecting an appropriate subset of descriptors using SFS from a larger set, SVM models are built using different subsets of descriptors, and the most accurate model is chosen for final use. As shown by measuring Accuracy and precision, In our study, the SFS-SVM model demonstrated superior performance in QSAR modeling on both DKPES and PubChem datasets. It achieved the highest accuracy and precision among the compared models, with 92.87% accuracy and 86.66% precision on DKPES, and 98.18% accuracy and 98.00% precision on PubChem. This outperformed other models like SFS-LR, Cluster-GA, RF, DT, and SVM, highlighting its efficiency in feature selection and predictive accuracy.

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5731-5748页

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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