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Sentiment analysis of Canadian maritime case law: a sentiment case law and deep learning approach

international Journal of Information Technology (Singapore) 2024年第6期16卷 3401-3409页

作者： Abimbola, Bola Tan, Qing De La Cal Marín, Enrique A. Computer Science Department University of Oviedo Oviedo Spain School of Computing and Information Systems Athabasca University Athabasca Canada Department of Computer Science University of Oviedo Oviedo Spain

Historical information in the Canadian Maritime Judiciary increases with time because of the need to archive data to be utilized in case references and for later application when determining verdicts for similar cases. However, such data are typically stored in multiple systems, making its reachability technical. Utilizing technologies like deep learning and sentiment analysis provides chances to facilitate faster access to court records. Such practice enhances impartial verdicts, minimizing workloads for court employees, and decreases the time used in legal proceedings for claims during maritime contracts such as shipping disputes between parties. This paper seeks to develop a sentiment analysis framework that uses deep learning, distributed learning, and machine learning to improve access to statutes, laws, and cases used by maritime judges in making judgments to back their claims. The suggested approach uses deep learning models, including convolutional neural networks (CNNs), deep neural networks, long short-term memory (LSTM), and recurrent neural networks. It extracts court records having crucial sentiments or statements for maritime court verdicts. The suggested approach has been used successfully during sentiment analysis by emphasizing feature selection from a legal repository. The LSTM + CNN model has shown promising results in obtaining sentiments and records from multiple devices and sufficiently proposing practical guidance to judicial personnel regarding the regulations applicable to various situations. © The Author(s) 2024.

关键词： Convolutional neural networks Deep learning Deep neural networks Long short-term memory Sentiment analysis

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Heterogeneous Network Embedding Method Based on Hyperbolic Space 5

Heterogeneous Network Embedding Method Based on Hyperbolic S...

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5th international Conference on Information Science, Parallel and distributed systems, ISPDS 2024

作者： Yang, Jie Wu, Yu Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing China Chongqing University of Posts and Telecommunications Key Laboratory of Web Intelligence and Technology Chongqing China

ISBN: (纸本)9798350352719

The core of heterogeneous network embedding is to transform nodes and edges into low and dense vector representations while preserving the structural features and semantic information of the original network. The existing embedding methods based on European space can not store the hierarchical structure of the network, nor can capture the rich semantic information contained in the heterogeneous network. To improve the embedding quality of heterogeneous networks and reduce information loss, a hyperbolic space-based embedding method for heterogeneous networks is proposed. Firstly, a random walk guided by meta-path is used to obtain the node neighborhood information and semantic relation of the network. Then the heterogeneous network is embedded into hyperbolic space based on Poincare sphere model, and the Riemann stochastic gradient descent method is used to optimize the model. Experiments on multiple datasets show that the proposed method has better performance than other baseline methods, and preserves more complete network information with fewer dimensions of storage space. © 2024 IEEE.

关键词： Network embeddings

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The Synergetic Effect in the Management of Active System with distributed Control 18

The Synergetic Effect in the Management of Active System wit...

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18th IEEE international Conference on computer Science and Information Technologies, CSIT 2023

作者： Chernov, Serhii Chernova, Liudmyla Chernova, Liubava Kunanets, Nataliia Piterska, Varvara Admiral Makarov National University of Shipbuilding Department of Project Management Mykolaiv Ukraine Admiral Makarov National University of Shipbuilding Information Management Systems & Technologies Department Mykolaiv Ukraine Lviv Polytechnic National University Information Systems and Networks Department Lviv Ukraine Odesa National Maritime University Dept. of Port Operation & Cargo Handling Technology Odesa Ukraine

ISBN: (纸本)9798350360462

The synergetic reasonability of joining the efforts of the centers of competence in the management of certain object participating in the game has been proved based on a theory-and-game model. An active system with two centers of competence has been researched as a two-level hierarchic game. The game equilibrium conditions have been analyzed and the efficiency of player management centers cooperation strategies in the distributed-control system confirmed. © 2023 IEEE.

关键词： distributed parameter control systems

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE international Conference on Parallel and distributed systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysing Mechanisms for Virtual Channel Management in Low-Diameter networks 35

Analysing Mechanisms for Virtual Channel Management in Low-D...

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35th IEEE international symposium on computer Architecture and High Performance Computing (SBAC-PAD)

作者： Cano, Alejandro Camarero, Cristobal Martinez, Carmen Beivide, Ramon Univ Cantabria Comp & Elect Dept Santander Spain

ISBN: (纸本)9798350305487

To interconnect their growing number of servers, current supercomputers and data centers are starting to adopt low-diameter networks, such as HyperX, Dragonfly and Dragon-fly+. These emergent topologies require balancing the load over their links and finding suitable non-minimal routing mechanisms for them becomes particularly challenging. The Valiant load balancing scheme is a very popular choice for non-minimal routing. Evolved adaptive routing mechanisms implemented in real systems are based on this Valiant scheme. All these low-diameter networks are deadlock-prone when non-minimal routing is employed. Routing deadlocks occur when packets cannot progress due to cyclic dependencies. Therefore, developing efficient deadlock-free packet routing mechanisms is critical for the progress of these emergent networks. The routing function includes the routing algorithm for path selection and the buffers management policy that dictates how packets allocate the buffers of the switches on their paths. For the same routing algorithm, a different buffer management mechanism can lead to a very different performance. Moreover, certain mechanisms considered efficient for avoiding deadlocks, may still suffer from hard to pinpoint instabilities that make erratic the network response. This paper focuses on exploring the impact of these buffers management policies on the performance of current interconnection networks, showing a 90% of performance drop if an incorrect buffers management policy is used. Moreover, this study not only characterizes some of these undesirable scenarios but also proposes practicable solutions.

关键词： Interconnection network routing mechanism buffer management Valiant load balancing

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CHIIoT 2021 - Proceedings of the 2021 Workshops on computer Human Interaction in IoT Applications, co-located with the international Conference on Embedded Wireless systems and networks, EWSN 2021 and the 13th ACM SIGCHI symposium on Engineering Interactive Computing systems, EICS 2021

CHIIoT 2021 - Proceedings of the 2021 Workshops on Computer ...

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2021 Workshops on computer Human Interaction in IoT Applications, CHIIoT 2021

The proceedings contain 13 papers. The topics discussed include: low-cost millimeter-wave interactive sensing through origami reflectors;field lab sleep and energy: a system for longitudinal remote sleep tracking and prototyping;cooperation and competition in the IoT sandbox;personal hygiene monitoring under the shower using Wi-Fi channel state information;ESPBoost: a rapid prototyping toolkit for helping designers create the Internet of tangible things;human computer interaction aspects of low-power wide area networks for wearable applications;JoyTilt: between autonomy and control of a robot vacuum cleaner;and designing micro-intelligences for situated affective computing.

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Deep Learning with IoT-Based Solar Energy System for Future Smart Agriculture System

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international JOURNAL OF ADVANCED computer SCIENCE AND APPLICATIONS 2024年第9期15卷 441-449页

作者： Vidya, M. S. Kumar, B. N. Ravi Anil, G. N. Ambika, G. N. BMS Inst Technol Dept Comp Sci & Engn Bangalore India

Agriculture has a considerable contribution to the economy. Agriculture automation is a serious issue that is becoming more prevalent around the world. Farmers' traditional practices were insufficient to achieve these objectives. Artificial Intelligence (A1) and the Internet of Things (IoTs) are being used in agriculture to improve crop yield and quality. distributed solar energy resources can now be remotely operated, monitored, and controlled through the IoT and deep learning technology. The development of an IoT-based solar energy system for intelligent irrigation is critical for water- and energy-stressed areas around the world. The qualitative design focuses on secondary data collection techniques. The deep learning model Radial Basis Function networks (RBFN) is used in conjunction with the Elephant Search Algorithm (ESA) in this IoT-based solar energy system for future smart agriculture. Sensor systems help farmers understand their crops better, reduce their environmental impact and conserve resources. These advanced systems enable effective soil and weather monitoring, as well as water management. To provide the required operating power, the proposed system, RBFN-ESA, employs an IoT-based solar cell forecasting process. The proposed model RBFN-ESA will collect these data to predict the required parameter values for solar energy systems in future smart agriculture systems. The results of the RBFN-ESA model are effective and efficient. According to the findings, RBFN-ESA outperforms CNN, ANN, SVM, RF, and LSTM in terms of energy consumption (56.764J for 100 data points from the dataset), accuracy achieved (97.467% for 600 nodes), and soil moisture level (94.41% for 600 data).

关键词： Precision agriculture smart monitoring Internet of Things Radial Basis Function networks Elephant Search Algorithm (ESA)

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GenesisRM: : A state-driven approach to resource management for distributed JVM web applications

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FUTURE GENERATION computer systems-THE international JOURNAL OF ESCIENCE 2025年 163卷

作者： Xu, Dishi Liu, Fagui Wang, Bin Tang, Xuhao Zeng, Dinghao Gao, Huaiji Chen, Runbin Wu, Qingbo South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Dept New Networks Shenzhen 518066 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

Reducing resource waste while maintaining end-to-end latency service-level objective (SLO) by simultaneously managing CPU bandwidth, memory allocation, and pod number of web applications running on Java virtual machine (JVM) is challenging. The challenges stem from the complexity of the multi-type resource allocation optimization problem, the high sensitivity of JVM performance to resource scaling actions, and the lack of low-level resource scaling mechanisms. We present GenesisRM, a resource management framework with a novel state-driven architecture. Specifically, we design a state control model for JVM web applications that encompasses seven pod states. This model serves as an abstraction layer, decoupling the centralized resource management system into a global state manager and distributed pod managers. The state manager controls the state transitions of the pods based on the overall workload, while the pod managers dynamically allocate resources for each pod according to the state and local workload. Then, we propose a multi-frequency control model with two predictive state controllers and a reactive state controller to manage the state of pods based on the state control model. In addition, GenesisRM brings new mechanisms to scale JVM pods' low-level resources without damaging their performance. We evaluate our work using a real-world JVM web application benchmark in three different scale server clusters of Pengcheng Laboratory Developer Cloud, and the 21- day experimental results show that GenesisRM saves 31.70% CPU and 17.60% memory compared to the best-performing state-of-the-art solutions while guaranteeing the SLO imposed on end-to-end latency.

关键词： Cloud computing Resource management Autoscaling Workload prediction Quality of service JVM

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Group-Agent Reinforcement Learning 1

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32nd international Conference on Artificial Neural networks (ICANN)

作者： Wu, Kaiyue Zeng, Xiao-Jun Univ Manchester Manchester M13 9PL England

ISBN: (数字)9783031442230

ISBN: (纸本)9783031442223;9783031442230

It can largely benefit the reinforcement learning (RL) process of each agent if multiple geographically distributed agents perform their separate RL tasks cooperatively. Different from multi-agent reinforcement learning (MARL) where multiple agents are in a common environment and should learn to cooperate or compete with each other, in this case each agent has its separate environment and only communicates with others to share knowledge without any cooperative or competitive behaviour as a learning outcome. In fact, this scenario exists widely in real life whose concept can be utilised in many applications, but is not well understood yet and not well formulated. As the first effort, we propose group-agent system for RL as a formulation of this scenario and the third type of RL system with respect to single-agent and multi-agent systems. We then propose a distributed RL framework called DDAL (Decentralised distributed Asynchronous Learning) designed for group-agent reinforcement learning (GARL). We show through experiments that DDAL achieved desirable performance with very stable training and has good scalability.

关键词： Group-agent system Reinforcement learning distributed learning

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A distributed System for Optimization of Carbon Emitting Resource Consumption in Supply Chains

A Distributed System for Optimization of Carbon Emitting Res...

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2024 international symposium on networks, computers and Communications, ISNCC 2024

作者： Ali, Salman Gondro, Cedric Yan, Qiben Banzhaf, Wolfgang Michigan State University Department of Computer Science and Engineering East LansingMI United States

ISBN: (纸本)9798350364910

Industrialized supply chains significantly impact the environment by accelerated greenhouse gas emissions. As supply chains get complex, they suffer from fragmentation in terms of sharing knowledge among participants. Fragmented chains such as the meat business, encompasses sub-stages like feed production, processing, distribution and retail but incorporate bare minimum vertical integration. This hinders measurement of carbon footprint against products being shipped. Lack of in-frastructure to estimate emissions at different independent stages results in lost opportunity to minimize emissions from end-to-end. To address issues arising from isolated supply chain participants, we propose a decentralized framework leveraging blockchain functions, internet of things, and distributed databases to allow to capture fine-grained greenhouse gas emissions across supply chain for joint optimization of underlying resource consumption. The proposed framework facilitates formation of a mix of local and global collaboration groups for precise carbon emission tracing while ensuring privacy and transparency. Key frame-work features include system's extensibility and scalability for integration of diverse information sources, secure data capture mechanism, and propagation of data and policies via blockchain and internet of things infrastructure. Our proposed solution aims to offer a flexible, comprehensive, and collaborative approach to recording, monitoring and optimizing carbon footprint across complex disjoint supply chains, thereby promoting improved environmental oupnut management. © 2024 IEEE.

关键词： Greenhouse gas emissions

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