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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Carvallo, Andrés Rojas, Matías Muñoz-Castro, Carlos Aracena, Claudio Guerra, Rodrigo Pizarro, Benjamín Dunstan, Jocelyn National Center for Artificial Intelligence Santiago Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Santiago Chile Department of Computer Science Pontificia Universidad Católica de Chile Santiago Chile Institute for Mathematical Computing Pontificia Universidad Católica de Chile Santiago Chile Center for Mathematical Modeling Santiago Chile Millenium Institute Foundational Research on Data Santiago Chile

The extraction and classification of important information from Spanish Electronic Clinical Narratives (ECNs) can be challenging due to the complexity of the clinical text and the limited availability of labeled data. In this paper, we introduce a chunked Named Entity Recognition model designed to parse and classify sections of ECNs into predefined categories. The model aims to improve section identification and classification accuracy within ECNs in the context of the IberLEF ClinAIS Task. Our system achieves a promising performance, obtaining a weighted B2 score of.6958, demonstrating its capability to accurately distinguish borders and boundaries between sections. The paper concludes with a comprehensive analysis of the results, discussing potential implications and suggesting directions for further improvements in clinical text analysis. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Classification (of information)

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Assortative and preferential attachment lead to core-periphery networks

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Physical Review research 2023年第4期5卷 043287-043287页

作者： Javier Ureña-Carrión Fariba Karimi Gerardo Íñiguez Mikko Kivelä Department of Computer Science Aalto University School of Science 00076 Espoo Finland Faculty of Computer Science and Biomedical Engineering Graz University of Technology 8010 Graz Austria Network Inequality Group Complexity Science Hub 1080 Vienna Austria Department of Network and Data Science Central European University 1100 Vienna Austria Faculty of Information Technology and Communication Sciences Tampere University 33720 Tampere Finland Centro de Ciencias de la Complejidad Universidad Nacional Autonóma de México 04510 Mexico City Mexico

Core-periphery is a key feature of large-scale networks underlying a wide range of social, biological, and transportation phenomena. Nodes in the core have an influential position in the network, and thus, the periphery can be under structural disadvantage if the groups are aligned with external attributes such as gender or economic status. Despite its prevalence in empirical data, it is unclear whether core-periphery is a consequence of fundamental network evolution processes. While preferential attachment can create degree heterogeneity indistinguishable from core-periphery, it does not explain why cores and peripheries are aligned with some external node attribute, i.e., why specific groups of nodes gain dominance and become cores. We show that even small amounts of assortative attachment, e.g., homophily in social networks, can break the symmetric effect of preferential attachment and that the interplay of the two mechanisms leads to one of the groups emerging as a prominent core. A systematic analysis of the phase space of the proposed model reveals the levels of assortative and preferential attachment necessary for a group to become either core or periphery. We find that relative group size is significant, with minority groups typically having a disadvantage on becoming the core for similar assortative attachment levels among groups. We also find that growing networks are less prone to develop core-periphery than dynamically evolving networks and that these two network evolution mechanisms lead to different types of core-periphery structures. Analyzing five empirical networks, our findings suggest that core nodes are highly assortative, illustrating the potential of our model as a tool for designing and analyzing interventions on evolving networks.

关键词： network evolution network formation & growth Preferential attachment Real world networks Social networks Transportation networks Block models Evolving network models Preferential attachment models

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DITE: Achieving Distributed Intelligent Traffic Engineering With Sparse network-Wide Guidance

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IEEE Transactions on network science and Engineering 2025年

作者： Guo, Yingya Zhou, Weihong Hu, Cheng Lin, Bin Deng, Yuhui Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou 350025 China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou 350025 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou 350025 China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou 510515 China Jinan University Department of Computer Science Guangzhou 510632 China University of Exeter Department of Computer Science College of Engineering Mathematics and Physical Sciences Exeter EX4 4PY United Kingdom

Distributed Traffic Engineering (TE) adopts decentralized routing and offers natural merits over centralized TE in rapidly responding to dynamic network flows. However, based solely on local network observations, traditional distributed TE methods often lack sufficient routing evidence necessary for generating optimal policies. Furthermore, reinforcement learning, commonly used in distributed TE methods to avoid the need for labeled data, faces challenges in accumulating valuable routing experiences due to its trial-and-error exploration. To handle these challenges, we propose a novel Distributed Intelligent Traffic Engineering (DITE) framework that empowers geographically distributed agents to rapidly and adaptively optimize routing policies for dynamic network flows with low communication overhead. Specifically, to address the limitation of optimizing local routing based solely on partial network observations, we propose enriching the routing evidence for each agent with sparse network-wide information. To promote agents to learn cooperative routing polices efficiently, we explore a new paradigm for training routing agents by seamlessly integrating imitation learning with reinforcement learning. Additionally, to efficiently describe the implicit spatiotemporal features among the routing evidence, we customize a transformer-based agent to utilize the attention mechanism for establishing the relationships between agent states and routing policies. Extensive comparative evaluations on three real network topologies and traffic traces demonstrate the superior performance of DITE in achieving distributed TE. Compared to the existing distributed TE approaches, DITE achieves a notable reduction of 35.95%–42.35% in maximum link utilization and a reduction of 23.62%-68.39% in end-to-end latency. Moreover, the experimental results indicate DITE is robust to network failures and traffic changes. © 2013 IEEE.

关键词： Distributed traffic engineering imitation learning network-wide guidance reinforcement learning transformer

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Control of Continuous, High Purity Distillation Column Using Nonlinear Adaptive Control Based on Robust Fixed Point Transformation Phenomena

Control of Continuous, High Purity Distillation Column Using...

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International Conference on Computational Cybernetics (ICCC)

作者： Marianna Dimitrova Kucarov Miklos Kozlovszky Doctoral School of Applied Informatics and Applied Mathematics BioTech Óbuda University Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Bp Hun Medical Device Research Group LPDS Institute for Computer Science and Control (SZTAKI) Hungarian Research Network (HUN-REN)

ISBN: (数字)9798350350159

ISBN: (纸本)9798350350166

A nonlinear feedback control scheme for continuous, binary, tray, multiple-product, and high-purity distillation columns has been proposed. Vapor-liquid equilibrium curve of raw material is presented. Physical properties, kinematics, and dynamics of the distillation column are also calculated. Automatic process control technique is designed for the composition of product in order to follow a desired trajectory. Controlling is solved with a novel nonlinear, adaptive approach, called Robust Fixed Point Transformation (RFPT) as well as with the classical, linear PID controller so as to compare their performance. RFPT provides a remedy to disturbances that occur at linear process control.

关键词： Deformable models Trajectory tracking Distillation equipment Simulation Process control Kinematics Mathematical models

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GPU-based Parallel Technique for Solving the N-Similarity Problem in Textual data Mining

GPU-based Parallel Technique for Solving the N-Similarity Pr...

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Distributed Computing and High Performance Computing (DCHPC), International Conference on

作者： Mahmood Fazlali Mina Mirhosseini Mahyar Shahsavari Alex Shafarenko Mashallah Mashinchi Department of Computer Science Cyber Security and Computing Research Group University of Hertfordshire Hertfordshire UK Department of Computer and Data Sciences Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran AI Department Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen The Netherlands Department of Statistics Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran

An important issue in data mining and information retrieval is the problem of multiple similarity or n-similarity. This problem entails finding a group of n data points with the highest similarity within a large dataset. Exact methods to solve this problem exist but come with high time and space complexities. Additionally, various metaheuristic algorithms have been proposed, including genetic algorithms, gravitational search algorithms, particle swarm optimization, imperialist competitive algorithms, and fuzzy imperialist competitive algorithms. These metaheuristics are capable of finding near-optimal solutions within a reasonable timeframe, although there is no guarantee of achieving exact results. In this paper, we employ a parallelization technique using CUDA to expedite the exact method. We conduct experiments on textual datasets to identify a group of n textual documents with the highest similarity to each other. The experimental results demonstrate that the proposed parallel exact method significantly reduces execution time compared to the best sequential approach and CPU multi-core implementation. Furthermore, it is evident that the proposed method requires less memory space than the exact method.

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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An Early Detection Model of Prostate Cancer Based on MRI

An Early Detection Model of Prostate Cancer Based on MRI

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IEEE International Conference on data science in Cyberspace (DSC)

作者： Yuankun Liu Lumin Xing Di Wang Xin Nie Xueyan Zhang Di Wu Wenjian Liu Faculty of Data Science City University of Macau China Shandong Provincial Qianfoshan Hospital The First Affiliated Hospital of Shandong First Medical University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Chongqing Jianzhu College Chongqing China School of Information Work Office Shandong Youth University of Political Science Jinan China School of Mathematics Physics and Computing University of Southern Queensland Queensland Australia

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively.

关键词： computer vision Visualization Accuracy Magnetic resonance imaging Mortality Transformers Feature extraction Reliability Prostate cancer Medical diagnostic imaging

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Q3DE: A Fault-Tolerant Quantum computer Architecture for Multi-Bit Burst Errors by Cosmic Rays 22

Q3DE: A Fault-Tolerant Quantum Computer Architecture for Mul...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Yasunari Suzuki Takanori Sugiyama Tomochika Arai Wang Liao Koji Inoue Teruo Tanimoto NTT Computer and Data Science Laboratories NTT Corporation Musashino Tokyo Japan and JST PRESTO Honcho Kawaguchi Saitama Japan Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Meguro-ku Tokyo Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino Tokyo Japan and School of Science The University of Tokyo Hongo Bunkyo-ku Tokyo Japan Photon Science Center The University of Tokyo Yayoi Bunkyo-ku Tokyo Japan Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan JST PRESTO Honcho Kawaguchi Saitama Japan and Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (纸本)9781665462723

Demonstrating small error rates by integrating quantum error correction (QEC) into an architecture of quantum computing is the next milestone towards scalable fault-tolerant quantum computing (FTQC). Encoding logical qubits with superconducting qubits and surface codes is considered a promising candidate for FTQC architectures. In this paper, we propose an FTQC architecture, which we call Q3DE, that enhances the tolerance to multi-bit burst errors (MBBEs) by cosmic rays with moderate changes and overhead. There are three core components in Q3DE: in-situ anomaly DEtection, dynamic code DEformation, and optimized error DEcoding. In this architecture, MBBEs are detected only from syndrome values for error correction. The effect of MBBEs is immediately mitigated by dynamically increasing the encoding level of logical qubits and re-estimating probable recovery operation with the rollback of the decoding process. We investigate the performance and overhead of the Q3DE architecture with quantum-error simulators and demonstrate that Q3DE effectively reduces the period of MBBEs by 1000 times and halves the size of their region. Therefore, Q3DE significantly relaxes the requirement of qubit density and qubit chip size to realize FTQC. Our scheme is versatile for mitigating MBBEs, i.e., temporal variations of error properties, on a wide range of physical devices and FTQC architectures since it relies only on the standard features of topological stabilizer codes.

关键词： quantum computing quantum error correction fault-tolerant quantum computing

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Easy-access online social media metrics can effectively identify misinformation sharing users

arXiv

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arXiv 2024年

作者： Számely, Júlia Galeazzi, Alessandro Koltai, Júlia Omodei, Elisa Department of Network and Data Science Central European University Vienna Austria Department of Mathematics University of Padova Padova Italy MTA–TK Lendület "Momentum" Digital Social Science Research Group for Social Stratification HUN-REN Centre for Social Sciences Budapest Hungary Department of Social Research Methodology Faculty of Social Sciences Eötvös Loránd University Budapest Hungary

Misinformation poses a significant challenge studied extensively by researchers, yet acquiring data to identify primary sharers is costly and challenging. To address this, we propose a low-barrier approach to differentiate social media users who are more likely to share misinformation from those who are less likely. Leveraging insights from previous studies, we demonstrate that easy-access online social network metrics—average daily tweet count, and account age—can be leveraged to help identify potential low factuality content spreaders on X (previously known as Twitter). We find that higher tweet frequency is positively associated with low factuality in shared content, while account age is negatively associated with it. We also find that some of the effects, namely the effect of the number of accounts followed and the number of tweets produced, differ depending on the number of followers a user has. Our findings show that relying on these easy-access social network metrics could serve as a low-barrier approach for initial identification of users who are more likely to spread misinformation, and therefore contribute to combating misinformation effectively on social media platforms. © 2024, CC BY.

关键词： Tweets

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The Adaptive Fault-tolerant Routing Based on an Improved Local Security Information Model of the Exchanged Hypercube

The Adaptive Fault-tolerant Routing Based on an Improved Loc...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Yazhi Zhang Chuanwen Luo Guijuan Wang Li Zhang Mengjie Lv Jiguo Yu School of Computer Science and Technology Qilu University of Technology Jinan China School of Information Science and Technology Beijing Forestry University Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Nanjing University of Posts and Telecommunications Nanjing China Big Data Research Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length.

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