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Medical knowledge graph question answering for drug-drug interaction prediction based on multi-hop machine reading comprehension

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CAAI Transactions on Intelligence technology 2024年第5期9卷 1217-1228页

作者： Peng Gao Feng Gao Jian-Cheng Ni Yu Wang Fei Wang Qiquan Zhang School of Cyber Science and Engineering Qufu Normal UniversityQufuChina School of Computer Science and Technology East China Normal UniversityShanghaiChina Network and Information Center Qufu Normal UniversityQufuChina School of Electronics and Information Engineering Harbin Institute of TechnologyShenzhenChina School of Electrical Engineering and Telecommunications University of New South WalesSydneyNSWAustralia

Drug-drug interaction(DDI)prediction is a crucial issue in molecular *** methods of observing drug-drug interactions through medical experiments require significant resources and *** authors present a Medical Knowledge Graph Question Answering(MedKGQA)model,dubbed MedKGQA,that predicts DDI by employing machine reading comprehension(MRC)from closed-domain literature and constructing a knowledge graph of“drug-protein”triplets from open-domain *** model vectorises the drug-protein target attributes in the graph using entity embeddings and establishes directed connections between drug and protein entities based on the metabolic interaction pathways of protein targets in the human *** aligns multiple external knowledge and applies it to learn the graph neural *** bells and whistles,the proposed model achieved a 4.5%improvement in terms of DDI prediction accuracy compared to previous state-of-the-art models on the QAngaroo MedHop *** results demonstrate the efficiency and effectiveness of the model and verify the feasibility of integrating external knowledge in MRC tasks.

关键词： artificial neural network data analysis document handling natural language processing

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Secure State Estimation for A Class of Nonlinear Systems Over Sensor networks With Sensor Resolution: Tackling Replay Attacks

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IEEE Internet of Things Journal 2025年第13期12卷 25333-25345页

作者： Fu, Haijing Wang, Zidong Zhao, Di Shen, Bo Donghua University College of Information Science and Technology Shanghai201620 China Ministry of Education Engineering Research Center of Digitalized Textile and Fashion Technology Shanghai201620 China Brunel University London Department of Computer Science Middlesex UxbridgeUB8 3PH United Kingdom University of Shanghai for Science and Technology Business School Shanghai200093 China

This paper addresses the problem of distributed state estimation for nonlinear systems over sensor networks that are subject to replay attacks. Sensor resolution, recognized as a crucial indicator of measurement accuracy and its influence on estimation performance, is taken into account to reflect practical engineering scenarios. An upper-bounding technique is employed to handle the uncertainty introduced by sensor resolution. Replay attacks, executed by adversaries on communication channels between sensor nodes, are characterized by the replacement of current innovations with previously recorded innovations. The dynamic behavior of these replay attacks is described by two factors: one dependent on a stochastic variable and the other on a time-varying parameter. The objective of this study is to design distributed state estimators to ensure that the estimation error dynamics are exponentially ultimately bounded in the mean-square sense while the desired security level is maintained. Furthermore, the gain parameters of the estimators are determined by solving specific matrix inequalities. Finally, the feasibility and effectiveness of the proposed estimation approach are validated through numerical simulations. © 2014 IEEE.

关键词： Stochastic systems

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A Unified Deep Reinforcement Learning Approach for Constructing Rectilinear and Octilinear Steiner Minimum Tree

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第7期44卷 2711-2724页

作者： Lin, Zhenkun Liu, Genggeng Huang, Xing Lin, Yibo Zhang, Jixin Liu, Wen-Hao Wang, Ting-Chi Fuzhou University College of Computer and Data Science Fuzhou350100 China Northwestern Polytechnical University School of Computer Science Xi'an710072 China Peking University Beijing Advanced Innovation Center for Integrated Circuits School of Integrated Circuits Beijing China Peking University Institute of Electronic Design Automation Wuxi China Hubei University of Technology School of Computer Science Wuhan China Nvidia Taiwan National Tsing Hua University Department of Computer Science Hsinchu30013 Taiwan

The Steiner Minimum Tree (SMT) serves as an optimal connection model for multi-terminal nets in Very Large Scale Integration (VLSI). Constructing both Rectilinear Steiner Minimum Tree (RSMT) and Octilinear Steiner Minimum Tree (OSMT) are known to be NP-hard problems. Simultaneously, constructing multiple topologies of SMTs for a given net holds significant importance in alleviating routing constraints such as alleviating congestion and ensuring timing convergence. However, existing efforts predominantly focus on designing specialized methods to construct a specifically structured SMT for a given net, making it challenging to extend to different structures or topologies of SMTs, while also exhibiting insufficient optimization capabilities. In this work, we propose a unified approach based on Deep Reinforcement Learning (DRL) to address both RSMT and OSMT problems while generating diverse routing topologies. First, we design an Edge Point Sequence (EPS) that leverages the structural characteristics of SMT to connect the output of the deep learning model with the SMT structure. Second, we propose a deep learning model tailored for EPS, employing the negative wirelength of SMT as a reward to train the model using DRL. Third, we provide a corresponding rapid and accurate wirelength computation algorithm for evaluating the quality of the construction solution to expedite model training. Finally, we leverage the stochastic nature of machine learning to construct diverse SMT construction solutions. To the best of our knowledge, this is the first unified approach capable of simultaneously addressing both RSMT and OSMT problems while generating diverse solutions. The proposed unified approach demonstrates superior solution quality and higher efficiency compared to specifically designed algorithms. © 1982-2012 IEEE.

关键词： Stochastic systems

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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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JTEA: Implementing Semi-Supervised Entity Alignment Using Joint Teaching Strategies

JTEA: Implementing Semi-Supervised Entity Alignment Using Jo...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Kai Zhao, Jing Ding, Lichao Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Entity alignment(EA) aims to identify equivalent entities in different knowledge graphs or data sources and establish correspondences between them. In recent years, embedding-based methods have made significant progress in the field of entity alignment, yet the shortage of adequate training data (i.e., seed entity pairs) continues to be a primary challenge. Traditional semi-supervised methods typically address this issue by generating potential mappings (i.e., pseudo-mappings) for unlabeled entities. However, these methods often suffer from the influence of erroneous potential mappings or overlook the uncertainty of potential mappings. Therefore, we propose a novel semi-supervised entity alignment method called JTEA. This method combines seed entity pairs with potential mappings through end-to-end joint training, providing strong guidance for model training. In constructing potential mappings, we propose a bidirectional matching strategy (BMS), which generates more detailed potential mappings by jointly adjusting decisions in different directions and uses a joint matching confidence score to characterize its uncertainty. Furthermore, we also introduce a method of matching diversity alignment selection, aiming to screen out more reliable entity alignments to further improve the performance and stability of the model. We conducted a large number of experiments on the benchmark dataset to fully validate the effectiveness of the JTEA method. © 2024 IEEE.

关键词： Mapping

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TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge 41

TinyTrain: Resource-Aware Task-Adaptive Sparse Training of D...

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41st International Conference on Machine Learning, ICML 2024

作者： Kwon, Young D. Li, Rui Venieris, Stylianos I. Chauhan, Jagmohan Lane, Nicholas D. Mascolo, Cecilia Department of Computer Science and Technology University of Cambridge United Kingdom Samsung AI Center Cambridge United Kingdom School of Electronics and Computer Science University of Southampton United Kingdom

On-device training is essential for user personalisation and privacy. With the pervasiveness of IoT devices and microcontroller units (MCUs), this task becomes more challenging due to the constrained memory and compute resources, and the limited availability of labelled user data. Nonetheless, prior works neglect the data scarcity issue, require excessively long training time (e.g. a few hours), or induce substantial accuracy loss (≥10%). In this paper, we propose TinyTrain, an on-device training approach that drastically reduces training time by selectively updating parts of the model and explicitly coping with data scarcity. TinyTrain introduces a task-adaptive sparse-update method that dynamically selects the layer/channel to update based on a multi-objective criterion that jointly captures user data, the memory, and the compute capabilities of the target device, leading to high accuracy on unseen tasks with reduced computation and memory footprint. TinyTrain outperforms vanilla finetuning of the entire network by 3.6-5.0% in accuracy, while reducing the backward-pass memory and computation cost by up to 1,098× and 7.68×, respectively. Targeting broadly used real-world edge devices, TinyTrain achieves 9.5× faster and 3.5× more energy-efficient training over status-quo approaches, and 2.23× smaller memory footprint than SOTA methods, while remaining within the 1 MB memory envelope of MCU-grade platforms. Code is available at https://***/theyoungkwon/TinyTrain. Copyright 2024 by the author(s)

关键词： Data accuracy

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User story clustering in agile development:a framework and an empirical study

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Frontiers of computer science 2023年第6期17卷 43-59页

作者： Bo YANG Xiuyin MA Chunhui WANG Haoran GUO Huai LIU Zhi JIN School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry Oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijing 100083China School of Information Science and Technology North China University of TechnologyBeijing 100144China College of Computer Science and Technology Inner Mongolia Normal UniversityHohhot 010020China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthorn VIC 3122Australia Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationBeijing 100871China Institute of Software School of Computer SciencePeking UniversityBeijing 100871China

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development *** stories are the primary means of requirements collection and elicitation in the agile development.A project can involve a large amount of user stories,which should be clustered into different groups based on their functionality’s similarity for systematic requirements analysis,effective mapping to developed features,and efficient ***,the current user story clustering is mainly conducted in a manual manner,which is time-consuming and subjective to human *** this paper,we propose a novel approach for clustering the user stories automatically on the basis of natural language ***,the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story *** similarity of user stories is calculated,which can be used to generate the connected graph as the basis of automatic user story *** evaluate the approach based on thirteen datasets,compared against ten baseline *** results show that our clustering approach has higher accuracy,recall rate and F1-score than these *** is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile *** study also highlights promising research directions for more accurate requirements elicitation.

关键词： user story agile development user story mapping clustering

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security Data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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Quantifying the weakness of ties with hierarchy-based link centrality

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science China(Information sciences) 2025年

作者： Jianhong MOU Longyun WANG Kang WEN Bitao DAI Suoyi TAN Fredrik LILJEROS Petter HOLME Xin LU College of Systems Engineering National University of Defense Technology Department of Sociology Stockholm University Department of Computer Science Aalto University Center for Computational Social Science Kobe University

Quantifying the significance of ties in preserving network connectivity is crucial for identifying weak ties, which often serve as bridges between communities, and for detecting community structures. However, accurately characterizing network connectivity and formalizing the relationship between weak ties and communities remain challenging. In this study, we introduce hierarchy-based link centrality(HLC), a novel metric based on the dissimilarity between the original network and its contracted version, where the terminal nodes of links merge and connect to all their neighbors. This dissimilarity is quantified by variations in the network hierarchy, specifically the nodal distance distributions. In addition to the experiments on weak tie identification and link-based network disintegration, we develop a link-based community detection(LCD) approach that focuses on optimal link ranking to elucidate community structures. Experiments across various networks demonstrate that HLC excels in identifying weak ties, achieving a 2.9% higher accuracy than the second-best metric. It also outperforms others in detecting critical link combinations for network disintegration, reducing the average size of the giant connected component by 7.2% compared to the suboptimal counterpart. Furthermore, HLC enhances community detection, achieving optimal partitioning with an average 5.7% improvement in modularity over five other indices. These results highlight the effectiveness of HLC in quantifying weak ties and suggest broad applications for this innovative approach in network analysis.

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Enhance Performance of Ad-hoc Search via Prompt Learning 1

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28th China Conference on Information Retrieval, CCIR 2022

作者： Yang, Shenghao Liu, Yiqun Xie, Xiaohui Zhang, Min Ma, Shaoping Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (数字)9783031247552

ISBN: (纸本)9783031247545

Recently, pre-trained language models (PTM) have achieved great success on ad hoc search. However, the performance decline in low-resource scenarios demonstrates the capability of PTM has not been inspired fully. As a novel paradigm to apply PTM to downstream tasks, prompt learning is a feasible scheme to boost PTM’s performance by aligning the pre-training task and downstream task. This paper investigates the effectiveness of the standard prompt learning paradigm on the ad hoc search task. Based on various PTMs, two types of prompts are tailored for the ad hoc search task. Overall experimental results on the MS Marco dataset show the credible better performance of our prompt learning method than fine-tuning based methods and another previous prompt learning based model. Experiments conducted in various resource scenarios show the stability of prompt learning. RoBERTa and T5 deliver better results compared to BM25 using 100 training queries utilizing prompt learning, while fine-tuning based methods need more data. Further analysis shows the significance of the uniformity of tasks’ format and adding continuous tokens into training in our prompt learning method. © 2023, Springer Nature Switzerland AG.

关键词： Learning systems

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