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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Lu, Wenpeng Zhang, Guobiao Peng, Xueping Guan, Hongjiao Wang, Shoujin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Data Science Institute University of Technology Sydney Sydney Australia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9782493814104

Medical entity disambiguation (MED) plays a crucial role in natural language processing and biomedical domains, which is the task of mapping ambiguous medical mentions to structured candidate medical entities from knowledge bases (KBs). However, existing methods for MED often fail to fully utilize the knowledge within medical KBs and overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguation performance. To address these limitations, this paper proposes a novel approach, MED with Medical Mention Relation and Fine-grained Entity Knowledge (MMR-FEK). Specifically, MMR-FEK incorporates a mention relation fusion module and an entity knowledge fusion module, followed by an interaction module. The former employs a relation graph convolutional network to fuse mention relation information between medical mentions to enhance mention representations, while the latter leverages an attention mechanism to fuse synonym and type information of candidate entities to enhance entity representations. Afterwards, an interaction module is designed to employ a bidirectional attention mechanism to capture interactions between medical mentions and entities to generate the matching representation. Extensive experiments on two publicly available real-world datasets demonstrate MMR-FEK's superiority over state-of-the-art(SOTA) MED baselines across all metrics. Our source code is publicly available. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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Efficient distributed algorithms for holistic aggregation functions on random regular graphs

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science China(information sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph \({\cal C}{\cal O}{\cal N}{\cal G}{\cal E}{\cal S}{\cal T}\) model communication complexity round complexity

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

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2nd International Conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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Enhancing Blockchain Storage Efficiency in Mobile IoT through Multidimensional Structures and Collective Signing

Enhancing Blockchain Storage Efficiency in Mobile IoT throug...

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IEEE International Conference on Blockchain (Blockchain)

作者： Hussein Zangoti Alex Pissinou Makki Wazir Zada Khan Niki Pissinou School of Computing and Information Sciences Florida International University Miami FL USA Faculty of Computer Science & Information Technology Jazan University Jazan Saudi Arabia Frost Institute of Data Science and Computing University of Miami Miami FL USA University of Wah Wah Cantt Pakistan

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

The rapidly evolving mobile IoT landscape, driven by mobile financial applications, self-driving cars, wearables, and health monitoring devices, confronts substantial blockchain storage challenges due to decentralized networks, limited band-width, and high storage overhead. To tackle these issues, we propose the Collective Signing-Based Blockchain Storage Optimization (CSBSO) algorithm. CSBSO mitigates storage over-head by leveraging the Collective Signing (CoSi) protocol and employing a multidimensional blockchain structure for efficient block management and retrieval. It focuses on identifying and pruning irrelevant blocks and implementing streamlined data management. Evaluations with Ethereum Classic Blockchain and Facebook Users datasets show CSBSO achieves up to 92% storage optimization, surpassing current models. These results underscore the effectiveness of CoSi-based strategies in reducing blockchain storage overhead in resource-constrained environments.

关键词： Social networking (online) Scalability Smart contracts Blockchains Cryptocurrency Autonomous automobiles Wearable devices

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Detecting entanglement and nonlocality with minimum observable length

arXiv

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arXiv 2024年

作者： Chen, Zhuo Shi, Fei Zhao, Qi Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China QICI Quantum Information and Computation Initiative School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong

Quantum entanglement and nonlocality are foundational to quantum technologies, driving quantum computation, communication, and cryptography innovations. To benchmark the capabilities of these quantum techniques, efficient detection and accurate quantification methods are indispensable. This paper focuses on the concept of "detection length"–—a metric that quantifies the extent of measurement globality required to verify entanglement or nonlocality. We extend the detection length framework to encompass various entanglement categories and nonlocality phenomena, providing a comprehensive analytical model to determine detection lengths for specified forms of entanglement. Furthermore, we exploit semidefinite programming techniques to construct entanglement witnesses and Bell’s inequalities tailored to specific minimal detection lengths, offering an upper bound for detection lengths in given states. By assessing the noise robustness of these witnesses,we demonstrate that witnesses with shorter detection lengths can exhibit superior performance under certain conditions. © 2024, CC BY.

关键词： Quantum entanglement

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Defeasible-PROV: Conflict Resolution in Smart Building Devices

Defeasible-PROV: Conflict Resolution in Smart Building Devic...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Abdullah Al Farooq Zac Taylor Kyle Ruona Thomas Moyer School of Computing and Data Science Wentworth Institute of Technology Boston MA Department of Software and Information System The University of North Carolina Charlotte

Programmable Logic Controllers (PLCs) are an integral component for managing automation processes of smart buildings. PLCs use protocols which make these control systems vulnerable to many common attacks due to which it is possible to create conflicts on certain devices of smart buildings thereby disrupting functionality. In this paper, we propose DEFEASIBLE-PROV, a system for resolving conflicts in the system by detecting the conflict creating sensors and conflict impacted actuators. Our tool is capable of blocking conflict creating rules in the system. Our evaluation results show that our proposed methodology contributes significantly to conflict resolution in the system.

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Modeling Route Representation With Mixed-Scale Hierarchical Transformer

Modeling Route Representation With Mixed-Scale Hierarchical ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hanyuan Zhang Yuqi Chen Xinyu Zhang Qize Jiang Liang Li Baihua Zheng Weiwei Sun School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University Shanghai Institute of Intelligent Electronics & Systems School of Computing and Information Systems Singapore Management University

Modeling route representation aims to obtain contextual representations of an entire route for various traffic-related tasks. In reality, spatial-temporal data often exhibits multi-scale characteristics, which are utilized by many studies to enhance their performance. However, there is still a lack of in-depth research on how to effectively incorporate the multi-scale spatial-temporal information into transformer structure to adequately model route representation. In this paper, we propose a novel hierarchical route representation framework called RouteMT, which effectively captures multi-scale spatial-temporal characteristics of routes and leverages a mixed-scale transformer architecture to fuse intra and interroute features. Experiments on real data confirm RouteMT’s superior performance and versatility.

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A New Sentiment Analysis-based Approach for the Prediction of Users Beliefs

A New Sentiment Analysis-based Approach for the Prediction o...

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International Conference on Control, Decision and information Technologies (CoDIT)

作者： Sadia Abdulhalim Rakif Pathan Belkacem Chikhaoui Department of Data Science and Artificial Intelligence College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar Department of Science and Technology Institute of Applied Artificial Intelligence TELUQ University Montreal Canada

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper proposes a new approach based on sentiment analysis for predicting the relevance of religious beliefs in contemporary relationships within the context of online dating. The motivation for this research lies in understanding how shared values, common missions, and rituals in various religions might influence decision-making in relationships, the study aims to predict users’ religious beliefs based on OkCupid profiles. Our approach starts first by 1) extracting relevant features using sentiment analysis from text data, and 2) developing machine learning models based on state-of-the-art algorithms such as random forests, logistic regression, and decision trees. The results obtained demonstrated the effectiveness of our proposed approach in accurately predicting religious beliefs and its superiority compared to the state-of-the-art methods. Through this investigation, the paper seeks insights into the interplay between religious beliefs and romantic choices.

关键词： Sentiment analysis Logistic regression Machine learning algorithms Shape Predictive models Feature extraction Prediction algorithms Decision trees information technology Random forests

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Unconditional quantum magic advantage in shallow circuit computation

arXiv

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arXiv 2024年

作者： Zhang, Xingjian Pan, Zhaokai Liu, Guoding Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China QICI Quantum Information and Computation Initiative School of Computing and Data Science University of Hong Kong Pokfulam Road Hong Kong

Quantum theory promises computational speed-ups over classical approaches. The celebrated Gottesman-Knill Theorem implies that the full power of quantum computation resides in the specific resource of "magic" states — the secret sauce to establish universal quantum computation. However, it is still questionable whether magic indeed brings the believed quantum advantage, ridding unproven complexity assumptions or black-box oracles. In this work, we demonstrate the first unconditional magic advantage: a separation between the power of generic constant-depth or shallow quantum circuits and magic-free counterparts. For this purpose, we link the shallow circuit computation with the strongest form of quantum nonlocality — quantum pseudo-telepathy, where distant non-communicating observers generate perfectly synchronous statistics. We prove quantum magic is indispensable for such correlated statistics in a specific nonlocal game inspired by the linear binary constraint system. Then, we translate generating quantum pseudo-telepathy into computational tasks, where magic is necessary for a shallow circuit to meet the target. As a by-product, we provide an efficient algorithm to solve a general linear binary constraint system over the Pauli group, in contrast to the broad undecidability in constraint systems. We anticipate our results will enlighten the final establishment of the unconditional advantage of universal quantum computation. © 2024, CC BY.

关键词： Quantum computers

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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