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On the impossibility of increasing the MSY in a multisite Schaefer fishing model

Mathematical Biosciences and engineering 2025年第2期22卷 415-430页

作者： Auger, Pierre Nguyen-Huu, Tri Nguyen-Ngoc, Doanh IRD Sorbonne Université Unité de Modélisation Mathématique et Informatique des Systèmes Complexes UMMISCO BondyF-93143 France IXXI ENS Lyon 46 Allée d’Italie Lyon69007 France College of Engineering and Computer Science Center for Environmental Intelligence Vin University Viet Nam

Here, we consider a multisite Schaefer fishing model. The fishery resource grows logistically on each site and is exploited with different fishing efforts. We showed that the Maximum Sustainable Yield (MSY) of the multisite network, when the sites are connected, is always less than or equal to the sum of the MSY of the isolated sites. Equality occurred when the fish population is spatially distributed according to the ideal free distribution (IFD). In this case, the fish had the same access to the resource at each site. We generalized the known result for two sites and the same fishing effort to any number of sites and different fishing efforts. We also discussed how the creation of Marine Protected Areas impacts the fishing efforts. We showed that to minimize the fishing effort to reach the MSY, it is necessary to deploy the entire fishing fleet to the site where the fish is most abundant, the other sites being Marine Protected Areas. © 2025 the Author(s)

关键词： aggregation of variables fish habitat connectivity ideal free distribution marine protected area maximum sustainable yield multi-site fishery

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BOB-YOLO: Balancing Optimization Binarized YOLO via Module-Wise Latency 27

BOB-YOLO: Balancing Optimization Binarized YOLO via Module-W...

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27th European Conference on Artificial intelligence, ECAI 2024

作者： Liu, Xinyu Zhou, Wenqiang Yu, Zhendong Yang, Jiaming Wang, Tao Tang, Chenwei Lv, Jiancheng College of Computer Science Sichuan University Chengdu610065 China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education Chengdu610065 China

ISBN: (纸本)9781643685489

When it comes to object detection tasks, YOLO stands out for its impressive speed and efficiency. Nonetheless, deploying YOLO on resource-constrained devices remains a challenge due to its substantial model size and memory requirements. The direct application of conventional binary quantization strategies to YOLO can result in significant accuracy degradation. A prevalent solution is to introduce floating-point shortcuts. However, the increased computational demand and parameter complexity associated with these shortcuts limit their practical deployment on hardware platforms for optimal acceleration. To solve this problem, we propose a binary neural network (BNN) for object detection called BOB-YOLO to achieve a balanced performance in terms of computational speed, model size, and detection accuracy. Our BOB-YOLO fully leverages module-wise latency (MWL) to supervise the latency of floating-point shortcut branches by that of 1-bit trunk branches. This supervision maximizes the information carried by the floating-point data flow in shortcuts while maintaining latency within the limits set by the 1-bit convolution branch, thereby improving parallel computational efficiency. We also introduce the Roofline Model to address these limitations by considering both computational complexity and parameter compression, ensuring high computational intensity. Additionally, we propose a performance evaluation metric Pd, which provides an intuitive description of the trade-off between speed and accuracy, aligning closely with the practical requirements of binary quantization strategies. Extensive experiments on the VOC and COCO datasets demonstrate the significant advantages of our method over state-of-the-art BNN methods. © 2024 The Authors.

关键词： Digital storage

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Multi-View Clustering by Inter-cluster Connectivity Guided Reward 41

Multi-View Clustering by Inter-cluster Connectivity Guided R...

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41st International Conference on Machine Learning, ICML 2024

作者： Dai, Hao Liu, Yang Su, Peng Cai, Hecheng Huang, Shudong Lv, Jiancheng College of Computer Science Sichuan University Chengdu610065 China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education Chengdu610065 China

Multi-view clustering has been widely explored for its effectiveness in harmonizing heterogeneity along with consistency in different views of data. Despite the significant progress made by recent works, the performance of most existing methods is heavily reliant on strong priori information regarding the true number of clusters k, which is rarely feasible in real-world scenarios. In this paper, we propose a novel graph-based multi-view clustering algorithm to infer unknown k through a graph consistency reward mechanism. To be specific, we evaluate the cluster indicator matrix during each iteration with respect to diverse k. We formulate the inference process of unknown k as a parsimonious reinforcement learning paradigm, where the reward is measured by inter-cluster connectivity. As a result, our approach is capable of independently producing the final clustering result, free from the input of a predefined cluster number. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed approach in comparison to existing state-of-the-art methods. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Energy and Bandwidth Based Link Stability Routing Algorithm for IoT

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computers, Materials & Continua 2022年第2期70卷 3875-3890页

作者： D.Kothandaraman A.Balasundaram R.Dhanalakshmi Arun Kumar Sivaraman S.Ashokkumar Rajiv Vincent M.Rajesh School of Computer Science and Artificial Intelligence SR UniversityWarangalTSIndia School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)ChennaiIndia Department of Computer Science and Engineering KCG College of TechnologyChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiIndia Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennaiIndia

Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet ***,some of the nodes in IoT are mobile and dynamic in *** maintaining the link among the nodes,efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data *** such networks,many routing-problems arise due to difficulties in energy and bandwidth based quality of *** to the mobility and finite nature of the nodes,transmission links between intermediary nodes may fail frequently,thus affecting the routing-performance of the network and the accessibility of the *** existing protocols do not focus on the transmission links and energy,bandwidth and link stability of the nodes,but node links are significant factors for enhancing the quality of the *** stability helps us to define whether the node is within or out of a coverage *** paper proposed an Optimal Energy and bandwidth based Link Stability Routing(OEBLS)algorithm,to improve the link stable route with minimized error rate and *** this paper,the optimal route from the source to the sink is determined based on the energy and bandwidth,link stability *** the existing routes,the sink node will choose the optimal route which is having less link stability *** stable link is determined by evaluating link stability value using distance and ***-energy of the node is estimated using the current energy and the consumed *** energy is estimated using transmitted power and the received *** bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision.

关键词： Link stability internet of things optimal energy optimal bandwidth residual energy

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GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching 38

GoMatching: A Simple Baseline for Video Text Spotting via Lo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： He, Haibin Ye, Maoyuan Zhang, Jing Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science Nanyang Technological University Singapore

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets. © 2024 Neural information processing systems foundation. All rights reserved.

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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

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computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel Doaa Sami Khafaga Ahmed Mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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A Graph with Adaptive AdjacencyMatrix for Relation Extraction

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computers, Materials & Continua 2024年第9期80卷 4129-4147页

作者： Run Yang YanpingChen Jiaxin Yan Yongbin Qin Engineering Research Center of Text Computing&Cognitive Intelligence Ministry of EducationGuizhou UniversityGuiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China College of Computer Science and Technology Guizhou UniversityGuiyang550025China

The relation is a semantic expression relevant to two named entities in a *** a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency information specific to the two named *** related work,graph convolutional neural networks are widely adopted to learn semantic dependencies,where a dependency tree initializes the adjacency ***,this approach has two main ***,parsing a sentence heavily relies on external toolkits,which can be ***,the dependency tree only encodes the syntactical structure of a sentence,which may not align with the relational semantic *** this paper,we propose an automatic graph learningmethod to autonomously learn a sentence’s structural *** of using a fixed adjacency matrix initialized by a dependency tree,we introduce an Adaptive Adjacency Matrix to encode the semantic dependency between *** elements of thismatrix are dynamically learned during the training process and optimized by task-relevant learning objectives,enabling the construction of task-relevant semantic dependencies within a *** model demonstrates superior performance on the TACRED and SemEval 2010 datasets,surpassing previous works by 1.3%and 0.8%,*** experimental results show that our model excels in the relation extraction task,outperforming prior models.

关键词： Relation extraction graph convolutional neural network adaptive adjacency matrix

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Schema-Driven Information Extraction from Heterogeneous Tables

Schema-Driven Information Extraction from Heterogeneous Tabl...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Bai, Fan Kang, Junmo Stanovsky, Gabriel Freitag, Dayne Dredze, Mark Ritter, Alan College of Computing Georgia Institute of Technology United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Artificial Intelligence Center SRI International Department of Computer Science Johns Hopkins University United States

ISBN: (纸本)9798891761681

In this paper, we explore the question of whether large language models can support cost-efficient information extraction from *** introduce schema-driven information extraction, a new task that transforms tabular data into structured records following a human-authored *** assess various LLM's capabilities on this task, we present a benchmark comprised of tables from four diverse domains: machine learning papers, chemistry literature, material science journals, and *** use this collection of annotated tables to evaluate the ability of open-source and API-based language models to extract information from tables covering diverse domains and data *** experiments demonstrate that surprisingly competitive performance can be achieved without requiring task-specific pipelines or labels, achieving F1 scores ranging from 74.2 to 96.1, while maintaining cost ***, through detailed ablation studies and analyses, we investigate the factors contributing to model success and validate the practicality of distilling compact models to reduce API reliance. © 2024 Association for Computational Linguistics.

关键词： Data mining

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Mapping the Knowledge Structure and Research Evolution of Deep Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第10期51卷 1404-1412页

作者： Yuan, Ke Zhao, Chenmeng Chen, Yayun Shen, Lin Tang, Qian Jia, Chunfu School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Spatial Information Processing Engineering Research Center Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China Yayun Chen is an undergraduate School of Computer and Information Engineering Henan University Kaifeng475004 China College of Geography and Environmental Science Henan University Kaifeng475004 China College of Cybersecurity Nankai University Tianjin 300350 China

Deep learning is a significant research field in today’s society, especially in the field of artificial intelligence. To gain a more comprehensive understanding of the current state of research in the field of deep learning and to identify areas of intense interest, this paper uses Citespace for research and learning, samples 1863 articles from the Web of science (WoS) core dataset, which spans the years 2017 to 2023, and adopts the methods of co-occurrence analysis, keyword burst analysis, and co-citation analysis. Information visualization is used to conduct bibliometric analysis, examining keywords, research institutions, authors, and countries. The analysis demonstrates a significant increase in the literature record from 2017 to 2022, followed by a decline from 2022 to 2023. The research on deep learning spans various fields, including computer science, engineering, and telecommunications, which are considered crucial areas of study. China, the USA, and England are leading the way in the field of deep learning research. These countries have established extensive cooperative relationships with many countries, with research institutions and authors playing a vital role in the cooperative network. The current theoretical research hotspots of deep learning mainly include "convolutional neural networks", "neural networks", "models", "classification", etc. The focus of research in deep learning has shifted from theoretical research to practical applications. It is now being applied in various areas, such as deep reinforcement learning, bearing fault, and semi-supervised learning. In the future, deep learning technology will be applied to more cutting-edge technologies. © (2024), (International Association of Engineers). All rights reserved.

关键词： Deep reinforcement learning

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