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Knowledge Graph and Knowledge Reasoning:A Systematic Review

Journal of Electronic science and Technology 2022年第2期20卷 159-186页

作者： Ling Tian Xue Zhou Yan-Ping Wu Wang-Tao Zhou Jin-Hao Zhang Tian-Shu Zhang Kashi Institute of Electronics and Information Industry Kashi 844099 Shenzhen Institute of Information Technology Shenzhen 518172 the School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054 the School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054

The knowledge graph(KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of KG and KG reasoning is provided. It introduces an overview of KGs, including representation, storage, and essential technologies. Specifically, it summarizes several types of knowledge reasoning approaches, including logic rules-based, representation-based, and neural network-based methods. Moreover, this paper analyzes the representation methods of knowledge hypergraphs. To effectively model hyper-relational data and improve the performance of knowledge reasoning, a three-layer knowledge hypergraph model is proposed. Finally, it analyzes the advantages of three-layer knowledge hypergraphs through reasoning and update algorithms which could facilitate future research.

关键词： methods. representation reasoning

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Cyber security skills development in South Africa: Addressing the Gender Gap in the Industry 8

Cyber security skills development in South Africa: Addressin...

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8th Conference on Information Communication Technology and Society, ICTAS 2024

作者： Ramonyai, Tebogo Motlatso Mpekoa, Noluntu Tom, Sheethal University of Johannesburg Academy of Computer Science & Software Engineering Cape Town South Africa IIE Varsity College School of Information Technology Cape Town South Africa

ISBN: (纸本)9798350314892

This paper investigates the gender gap in South Africa's cybersecurity sector and its effects on both the sector and the nation's economic growth. There is a significant shortage of skilled workers in the sector, and women are notably underrepresented. The main research question examines how the gender gap affects the industry's development, innovation, and national security posture, emphasizing the importance of utilizing the nation's talent pool to its fullest capacity to combat the evolving cyber threats. Insights and perspectives on the gender disparity in the cybersecurity sector were gathered through a questionnaire circulated amongst cybersecurity professionals and academics. These participants were chosen due to their knowledge and direct engagement in the subject, which offers valuable insights into the obstacles that women encounter in their workplace. The study highlights a number of problems, including gender stereotypes, a lack of access to education and training, and a non-inclusive workplace culture, that contribute to the gender gap. The paper makes the case that in order to solve these problems and advance diversity and inclusivity in the workplace, all stakeholders must work together to close the gender gap. In order to overcome the gender gap in cybersecurity in South Africa, the study suggests special programs aimed at expanding access to education and training opportunities, promoting gender diversity and inclusivity, and creating a more hospitable and equal work environment. This will boost the country's economic growth and strengthen security against cyberattacks. © 2024 IEEE.

关键词： Cybersecurity

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Unsupervised Semantic Retrieval via Neural Estimation of Pointwise Mutual Information

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2551-2564页

作者： Xiaohan Jiang Richong Zhang Yanzhao Zhang Junfan Chen CCSE School of Computer Science and Engineering Beihang University Beijing China School of Software Beihang University Beijing China

Semantic retrieval task aims to identify documents in a retrieval corpus that are semantically related to a query. Existing methods rely heavily on large-scale annotated training data, which are expensive to obtain. To relieve the reliance on annotated data, this paper investigates the semantic retrieval problem in an unsupervised setting, where a corpus of candidate documents is given without accessing the training data. In this setting, the utilization of deep learning techniques may be limited by the insufficient consideration of word-level correlations. To address this limitation, this study seeks to solve the unsupervised semantic retrieval problem by Pointwise Mutual Information (PMI). Specifically, we theoretically show that the solutions for semantic retrieval based on maximum a posteriori and maximum likelihood are equivalent to the maximumization of PMI, enabling us to tackle unsupervised semantic retrieval by PMI estimation. However, existing PMI estimation with the incidence matrix may suffer from the matrix-sparsity problem, which may cause the empirical distribution to be discordant with the ground truth distribution and lead to inaccurate PMI estimation. To tackle this problem, we propose a new unsupervised PMI estimation framework, i.e. UPMI, that leverages negative sampling to reformulate PMI estimation as a binary classification problem, whose optimal solution is exactly the PMI. We then employ neural networks equipped with attention mechanisms to realize the PMI function in the UPMI framework. Empirical studies on 13 benchmark datasets of three unsupervised semantic retrieval tasks confirm the effectiveness of our proposed model.

关键词： Semantics Neural networks Adaptation models Training data Deep learning Training Data models Mutual information Question answering (information retrieval) Computational modeling

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Self-supervised Distillation and Multi-scale Network for Parkinson's Disease Classification

Self-supervised Distillation and Multi-scale Network for Par...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Lei, Shuyuan Liang, Chengjia Lei, Baiying Zhang, Yuchen Shenzhen University College of Computer Science & Software Engineering Shenzhen China Harbin University of Commerce College of Computer & Information Engineering Harbin China Shenzhen University Medical School Shenzhen University School of Biomedical Engineering Shenzhen China

ISBN: (纸本)9798350337488

Parkinson's disease (PD) is a common and irreversible neurodegenerative disease that the earlier it is diagnosed, the easier and better it can be controlled. This paper proposes a self-supervised distillation and multi-scale dynamic convolutional network for PD classification. Experiments indicate that our proposed method has outperformed most SOTA methods. © 2023 IEEE.

关键词： Multi-scale network Parkinson's disease Self-supervised distillation

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Degenerate asymmetric quantum concatenated codes for correcting biased quantum errors

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Chinese Physics B 2021年第12期30卷 206-213页

作者： Ji-Hao Fan Jun Li Han-Wu Chen Wen-Jie Liu School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Computer Science and Engineering Southeast UniversityNanjing 211189China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China

In most practical quantum mechanical systems,quantum noise due to decoherence is highly biased towards *** quantum state suffers from phase flip noise much more seriously than from the bit flip *** this work,we construct new families of asymmetric quantum concatenated codes(AQCCs)to deal with such biased quantum *** construction is based on a novel concatenation scheme for constructing AQCCs with large asymmetries,in which classical tensor product codes and concatenated codes are utilized to correct phase flip noise and bit flip noise,*** generalize the original concatenation scheme to a more general case for better correcting degenerate ***,we focus on constructing nonbinary AQCCs that are highly *** to previous literatures,AQCCs constructed in this paper show much better parameter performance than existed ***,we design the specific encoding circuit of the *** is shown that our codes can be encoded more efficiently than standard quantum codes.

关键词： asymmetric quantum codes concatenated code quantum channel degenerate code

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Deciphering urban traffic impacts on air quality by deep learning and emission inventory

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Journal of Environmental sciences 2023年第2期124卷 745-757页

作者： Wenjie Du Lianliang Chen Haoran Wang Ziyang Shan Zhengyang Zhou Wenwei Li Yang Wang School of Software Engineering University of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China Alibaba Inc. Hangzhou 310052China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China CAS Key Laboratory of Urban Pollutant Conversion Department of environmental science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China USTC-CityU Joint Advanced Research Center Suzhou 215123China

Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future ***,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air *** model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other *** identifies the ranking of major factors,local meteorological conditions have become a nonnegligible ***-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of ***,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our *** also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 *** insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.

关键词： PM_(2.5)concentration forecast Traffic emissions Deep learning Attention mechanism New energy vehicles

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Evaluating LoRaWAN performance in intentional and unintentional DoS attacks by legacy 900MHz network devices 6

Evaluating LoRaWAN performance in intentional and unintentio...

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6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Bang, Seokhyeon Jang, Junyoung Ro, Minju Choi, Yuseon Kwon, Doyong Lee, Kangyeon Smith, Anthony Chung-Ang University School of Computer Science and Engineering Seoul Korea Republic of Chonnam National University Department of Software Engineering Gwangju Korea Republic of Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Purdue University Department of Computer and Information Technology IN United States

ISBN: (纸本)9798350329391

The deployment of LoRaWAN on the Internet of Things (IoT) has increased since its advent and LoRaWAN now predominates the IoT market over other Low Powered Wide Area Networks (LPWAN). However, since LoRaWAN uses Chirp Spread Spectrum (CSS), it is susceptible to wideband jamming attacks. In this paper, we demonstrate with experiments and concrete numerical results that jamming US915 LoRaWAN frequency is possible by the usual data transmission and reception process of 900MHz Canopy, one of the legacy 900MHz network device. Intentional attack is possible in the same manner. The experiments emulate the real-world environment operated in medical and agriculture industries, in outdoor and indoor conditions, respectively. In addition, this paper introduces and utilizes the novel metric, Jamming Effect (JE), that indicates the network performance of wireless networks that spread the data on air. © 2023 IEEE.

关键词： Risk assessment

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Enriching molecular graph representation via substructure learning

Enriching molecular graph representation via substructure le...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, HongHao Zhang, Acong Li, Ping Southwest Petroleum University School of Computer Science and Software Engineering Chengdu China China Mobile Jiangxi Co. Ltd. Network Department Ganzhou China

ISBN: (纸本)9798350386226

Continuous molecular graph representations are highly useful for effective molecule property predictions. However, learning graph-specific structure information remains challenging. Current graph neural network models typically aggregate all node embeddings of a graph into a single vector, which, while being an efficient compressor, may not capture the high-level function-related substructure information, e.g., motifs. Moreover, the nodes that are loosely relevant to the graph representation may lead to the sub-optimal representation learning. Towards these limitations, in this paper we propose a new approach to enrich molecular graph representation, where the most relevant motif are selected to represent the graph at a high level and the key-node spanned subgraph is constructed to filter out irrelevant nodes. By comparing to recently proposed unimodal graph neural network models and multi-modal methods, we show that our method achieves state-of-the-art performance on molecular property prediction task. © 2024 IEEE.

关键词： Molecular docking

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Classification of Helpful and Unhelpful Online Customer Reviews Using XLNet and BERT Variants 1

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International Conference on Green Energy, Computing, and Intelligent Technology, GEn-CITy 2023

作者： Bilal, Muhammad Arshad, Muhammad Haseeb Ramzan, Muhammad Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Computer Science National University of Computer and Emerging Sciences Chiniot-Faisalabad Campus Chiniot Pakistan Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (数字)9789819998333

ISBN: (纸本)9789819998326

The majority of businesses have made public appearances on various social media platforms as a result of recent advances in e-commerce and the popularity of social media websites. Customers can share their experiences in the form of online customer reviews, which can assist potential customers in determining the quality of a company and making purchasing decisions. Due to the large volume of published reviews, it becomes difficult for customers to read all of the reviews and assess the quality of the business, resulting in the problem of information overload. Several solutions have been proposed in the literature by researchers using statistical and machine learning techniques to predict the helpfulness of online customer reviews. However, most of the existing solutions are based on the use of various business, review, and reviewer features, which lead to generalizability issues. Moreover, very limited studies have examined the effectiveness of state-of-the-art pre-trained language models for the classification of helpful and unhelpful reviews. Therefore, this study aims to examine the effectiveness of XLNet, Albert, DistilBert, and Roberta for review helpfulness prediction using textual features. The models were fine-tuned using a publicly available dataset of Yelp reviews. The results showed that XLNet achieved the highest F1-score of 0.730 compared to a benchmark of 0.717 achieved by the BERT base model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sales

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Optimization of Building Construction Control System Based on Nonlinear Image Enhancement

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computer-Aided Design and Applications 2023年 20卷 213-224页

作者： Liu, Congru Lin, Mingsen Ghafoor, Kayhan Zrar Al-Dabagh, Mustafa Zuhaer Nayef Karras, Dimitrios A. School of Urban Construction Wuhan University of Science and Technology Wuhan China Changjiang Polytechnic Wuhan China Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software & Informatics Engineering Salahaddin University-Erbil Erbil44001 Iraq School of Science Dept. General Athens Greece

To reflect the nonlinear characteristics of the building structural adjustment system, an active vibration control strategy based on the nonlinear is proposed. In this method, the size of the structural control force is determined by comparing the allowable energy with the actual energy, and its direction is determined by taking the derivative of the energy. Because there is no need to solve the linear matrix inequality, the calculation is simple and the operation is convenient. Moreover, when multiple actuators with the same capacity limit are used, the control output force is consistent, thus avoiding the design of multiple control systems and simplifying the controller design. The simulation results show that the maximum controlled displacements of floor 6 to floor 8 are 1.16cm, 1.12cm, and 0.97cm, respectively. The proposed nonlinear energy control method has the advantages of simple control process and avoiding actuator saturation. © 2023 CAD Solutions, LLC.

关键词： Floors

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