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Research Progress and Trend of Temporal Knowledge Graph Representation and Reasoning

Ruan Jian Xue Bao/Journal of software 2024年第8期35卷 3923-3951页

作者： Wang, Yu-Han Chen, Zi-Yang Zhao, Xiang Tan, Zhen Xiao, Wei-Dong Cheng, Xue-Qi Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha410073 China College of Systems Engineering National University of Defense Technology Changsha410073 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

As a research hotspot in artificial intelligence in recent years, knowledge graphs have been applied to many fields in reality. However, with the increasingly diversified application scenarios of knowledge graphs, people gradually find that static knowledge graphs which do not change with time cannot fully adapt to the scenarios of high-frequency knowledge update. To this end, researchers propose the concept of temporal knowledge graphs containing temporal information. This study organizes all existing temporal knowledge graph representation and reasoning models and summarizes and constructs a theoretical framework for these models. Then, on this basis, it briefly introduces and analyzes the current research progress of temporal representation reasoning, and carries out the future trend prediction to help researchers develop and design better models. © 2024 Chinese Academy of Sciences. All rights reserved.

关键词： Knowledge graph

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A Collaborative Consumption Digital Platform for Government Organizations using Design Science

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Digital Government: Research and Practice 2022年第1期3卷 1–14页

作者： Gebeyehu, Seffi Twinomurinzi, Hossana Faculty of Computing Bahirdar Technology Institute Bahirdar University P.O.Box 26 Bahirdar Ethiopia Applied Information Systems Department School of Consumer Intelligence and Information Systems College of Business and Economics Office 17 Auckland Park Bunting Road Campus Johannesburg South Africa

The growth of digital technologies that enable the sharing of resources, underutilized assets, and services, is bringing new ways of shared consumption commonly called the "sharing economy"or "collaborative consumption". This study used a design science paradigm and the elaborated action design method (eADR) to design and develop a "collaborative consumption"(CC) digital artifact under the government to government (G2G) domain towards improving resource limitations in developing country contexts. The Ethiopian WoredaNet government project was used as a case study. The new artifact was evaluated using the post-study system usability framework (n = 35) to reveal its practical usefulness in improving resource limitations. A new design principle was also elicited: intentionally elicit user responses for unfamiliar digital innovations. The study recommends for policymakers of low-income countries (LICs) to create new sharing-capable policies in the G2G domain, and consider extending it towards non-government organizations and citizens. Overall, the findings contribute to information systems discourse and digital government practice by identifying the importance of novel CC digital platforms to optimize government resources in LICs. The study also contributes to the eADR method by identifying that the action research cycle is not a strict requirement but helpful as an entry point into design science efforts. © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Developing countries

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Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

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Computers, Materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim Aljabhan Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Ports and Maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia S 7Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective *** traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical *** recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)*** this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)*** presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the *** attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTM)model for predicting traffic *** order to enhance the performance of the ABLSTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(MSE),root mean square error(RMSE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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Investigating the Performance in SDN Based Data Centers Under Different Network Topologies 2

Investigating the Performance in SDN Based Data Centers Unde...

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2nd International Conference on Advanced Research in computing, ICARC 2022

作者： Hemachandra, K.G.R.P. Jayasena, K.P.N. Rankothge, Windhya Wijesiri, M.P.M. Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665407410

A data center architecture can be determined as the basic structure of a cloud computing data center. However, the expected outcome cannot be obtained even though the tools, technologies, and elements are advanced. It is crucial to select the right architecture as architecture used, which directly affects the operational cost and power consumption of the data center. The SDN, software Defined Networking, is a novel technology of cloud computing. This SDN is used to separate the control panel and forwarding plane as well, as it enables programmable network components. Therefore, the components of the SDN can easily control and managed. This research aimed to select an SDN-based architecture for a data center. Here, there are two parameters of average throughput and average packet delay on the three existing architectures that are tested using simulation. Then the results are compared with each other. In this research, the accuracy correlations among average packet delay and average throughput are investigated. The Obtained results show that Fat-Tree architecture is the best one among VL2 and hybrid architecture consists with flat tree and VL2 technology in terms of their performances. © 2022 IEEE.

关键词： software defined networking

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A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第3期16卷 136-150页

作者： Alsarhan, Ayoub Al-Ghuwairi, Abdel-Rahman Alshdaifat, Esra’a Idhaim, Hasan alkhawaldeh, Omar Department of Information Technology Hashemite University Zarqa Jordan Software Engineering Department Hashemite University Zarqa Jordan Computer Information Systems Department Hashemite University Zarqa Jordan Hashemite University Zarqa Jordan

Cloud security vulnerabilities have recently become more prevalent around the world, posing a threat to cloud service providers’ (CSPs) ability to respond to client demands. In cloud market, the requests are announced by the client nodes to their CSP. A malicious node can alter a client’s request, resulting in the next cloud market collapse, decreased reliability, and data leaking. To identify malicious nodes in the cloud market, a novel fuzzy multiple criterion decision making scheme is suggested. Authentication test, trust level, traffic size, and node activity levels are all taken into consideration simultaneously as the major criteria for identifying malicious nodes. For each node, the CSP uses fuzzy Integral to generate a composite value based on these criteria. The malicious node is then removed from the cloud market using this composite value. The simulation results demonstrated the potential of the proposed method to prevent nodes in the cloud market from running malware or software that can be used to degrade quality of service by exhausting resources in the cloud market © 2022,International Journal of Interactive Mobile *** Rights Reserved

关键词： Intrusion detection

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Computer-Aided Potato Disease Detection by Using Deep Learning Techniques

Computer-Aided Potato Disease Detection by Using Deep Learni...

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International Conference on Green Energy, computing, and Intelligent technology, GEn-CITy 2023

作者： Razaq, Fareeha Bilal, Muhammad Ramzan, Muhammad Naveed, Muhammad Razzaq, Samreen Department of Computer Science University of Sargodha Sargodha Pakistan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (纸本)9789819998326

Potato is the most widely grown and consumed food throughout the world. There are a number of potato crop diseases that affect production, and these diseases differ in symptoms, circumstances, and controls. Early detection and recognition of disease information can aid in disease prevention and production. This paper presents deep learning models using proposed CNN and pre-trained models for potato disease detection and classification. The proposed model is more efficient and accurate at detection and classification. To perform classification, we used two datasets: PLD and PlantVillage. The Xception CNN model serves as the foundation for our proposed model. It achieved 1.00 accuracy, 0.99 precision, 1.00 recall, and 0.99 F1-score on the PLD dataset. On the PlantVillage dataset, it achieved 1.00 accuracy, 1.00 precision, 1.00 recall, and 1.00 F1-score. We also compared the results of Inception-ResNet-V2, MobileNet-V2, VGG-19, Inception-V3, and Xception models with the performance of our proposed model. For three classes of potato leaves, the proposed CNN model produced more accurate results than other pre-trained models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Utilizing Organizational Patterns to Build High-Performing Remote Teams 28

Utilizing Organizational Patterns to Build High-Performing R...

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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Toldy, Kristián Khail, Waheedullah Sulaiman Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Helmholtz-Zentrum Berlin für Materialien Energie Gmbh Germany

ISBN: (纸本)9798400700408

In recent years, remote work has become a more popular option for companies across various industries. While remote work provides numerous benefits, such as flexibility and increased work-life balance, it also presents unique challenges that can hinder team collaboration, productivity, and overall success. In order to overcome these obstacles, it is essential to adopt effective organizational patterns that can help build high-performing remote teams. Building a high-performing remote team requires a set of strategies with some key focus points to be kept in mind such as communication, trust, goals, collaboration, and performance. For all of these, there have been an extensive research and publication of observed patterns to which we have also contributed with a new set of these four patterns: Use Common Language, Discuss Publicly, Lights, Camera, Action!, and Praise in Public, Correct in Private. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Computer programming

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Fuzz-classification(p,l)-Angel:An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

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Digital Communications and Networks 2023年第5期9卷 1131-1140页

作者： Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan Gwanggil Jeon Department of Computer Science COMSATS University IslamabadPakistan Department of Information Technology Quaid-e-Azam University IslamabadPakistan College of Science and Engineering School of Computing and MathsUniversity of DerbyDE221GBUK Department of Embedded Systems Engineering Incheon National UniversitySouth Korea

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS,SLAMSA,(p,k)-Angelization,and(p,l)-Angelization,but these were found to be insufficient in terms of robust privacy and performance.(p,l)-Angelization was successful against different privacy disclosures,but it was not *** the best of our knowledge,no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple *** this paper,we suggest an improved version of(p,l)-Angelization based on a hybrid AI approach and privacy-preserving approach like ***-classification(p,l)-Angel uses artificial intelligence based fuzzy logic for classification,a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive *** demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri *** results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

关键词： Generalization Fuzzy-logic MSA Privacy disclosures Membership function (p,l)-Angelization QT HLPN

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Multi-Granularity Detector for Vulnerability Fixes

arXiv

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arXiv 2023年

作者： Nguyen, Truong Giang Le-Cong, Thanh Kang, Hong Jin Widyasari, Ratnadira Yang, Chengran Zhao, Zhipeng Xu, Bowen Zhou, Jiayuan Xia, Xin Hassan, Ahmed E. Le, Xuan-Bach D. Lo, David The School of Computing and Information Systems Singapore Management University Singapore The Software Engineering Application Technology Lab Huawei China School of Computing Queen’s University Canada School of Computing and Information Systems The University of Melbourne Australia

With the increasing reliance on Open Source software, users are exposed to third-party library vulnerabilities. software Composition Analysis (SCA) tools have been created to alert users of such vulnerabilities. SCA requires the identification of vulnerability-fixing commits. Prior works have proposed methods that can automatically identify such vulnerability-fixing commits. However, identifying such commits is highly challenging, as only a very small minority of commits are vulnerability fixing. Moreover, code changes can be noisy and difficult to analyze. We observe that noise can occur at different levels of detail, making it challenging to detect vulnerability fixes accurately. To address these challenges and boost the effectiveness of prior works, we propose MiDas (Multi-Granularity Detector for Vulnerability Fixes). Unique from prior works, MiDas constructs different neural networks for each level of code change granularity, corresponding to commit-level, file-level, hunk-level, and line-level, following their natural organization. It then utilizes an ensemble model that combines all base models to generate the final prediction. This design allows MiDas to better handle the noisy and highly imbalanced nature of vulnerability-fixing commit data. Additionally, to reduce the human effort required to inspect code changes, we have designed an effort-aware adjustment for MiDas’s outputs based on commit length. The evaluation results demonstrate that MiDas outperforms the current state-of-the-art baseline in terms of AUC by 4.9% and 13.7% on Java and Python-based datasets, respectively. Furthermore, in terms of two effort-aware metrics, EffortCost@L and Popt@L, MiDas also outperforms the state-of-the-art baseline, achieving improvements of up to 28.2% and 15.9% on Java, and 60% and 51.4% on Python, respectively. © 2023, CC BY.

关键词： Python

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