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Contextual Text Mining Framework for Unstructured Textual Judicial Corpora through Ontologies

computer Systems science & engineering 2022年第12期43卷 1357-1374页

作者： Zubair Nabi Ramzan Talib Muhammad Kashif Hanif Muhammad Awais Department of Computer Science Government College UniversityFaisalabad38000Pakistan Department of Software Engineering Government College UniversityFaisalabad38000Pakistan

Digitalization has changed the way of information processing, and newtechniques of legal data processing are evolving. Text mining helps to analyze andsearch different court cases available in the form of digital text documents toextract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reducedtime. The rapid development of judicial ontologies seems to deliver interestingproblem solving to legal knowledge formalization. Mining context informationthrough ontologies from corpora is a challenging and interesting field. Thisresearch paper presents a three tier contextual text mining framework throughontologies for judicial corpora. This framework comprises on the judicial corpus,text mining processing resources and ontologies for mining contextual text fromcorpora to make text and data mining more reliable and fast. A top-down ontologyconstruction approach has been adopted in this paper. The judicial corpus hasbeen selected with a sufficient dataset to process and evaluate the *** experimental results and evaluations show significant improvements incomparison with the available techniques.

关键词： Natural language processing judicial corpora contextual text mining ontologies information extraction information retrieval

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A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

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computer Modeling in engineering & sciences 2023年第5期135卷 1209-1228页

作者： Chuan Wang Ruoyu Zhu Yi Jiang Weili Liu Sang-Woon Jeon Lin Sun Hua Wang College of Software Henan Normal UniversityXinxiang453007China College of Computer and Information Engineering Henan Normal UniversityXinxiang453007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou510665China Department of Military Information Engineering Hanyang UniversityAnsan15588South Korea College of Engineering and Science Institute for Sustainable Industries and Liveable CitiesVictoria UniversityMelbourneVIC 8001Australia

The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to *** paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP *** work is novel and contributes to three aspects:problemmodel,optimization framework,and ***,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over ***,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic *** framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous *** way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme *** the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational ***,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search *** evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic *** experimental results show that ACO with 2-opt can solve the DTSPs effectively.

关键词： Dynamic traveling salesman problem(DTSP) offline optimization and online application ant colony optimization(ACO) two-optimization(2-opt)strategy

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Evolutionary Optimization with a Simplified Helper Task for High-Dimensional Expensive Multiobjective Problems

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-32页

作者： Wu, Xunfeng Lin, Qiuzhen Zhou, Junwei Liu, Songbai Coello Coello, Carlos A. Leung, Victor C. M. College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Mexico City07300 Mexico Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In recent years, surrogate-Assisted evolutionary algorithms (SAEAs) have been sufficiently studied for tackling computationally expensive multiobjective optimization problems (EMOPs), as they can quickly estimate the qualities of solutions by using surrogate models to substitute for expensive evaluations. However, most existing SAEAs only show promising performance for solving EMOPs with no more than 10 dimensions, and become less efficient for tackling EMOPs with higher dimensionality. Thus, this article proposes a new SAEA with a simplified helper task for tackling high-dimensional EMOPs. In each generation, one simplified task will be generated artificially by using random dimension reduction on the target task (i.e., the target EMOPs). Then, two surrogate models are trained for the helper task and the target task, respectively. Based on the trained surrogate models, evolutionary multitasking optimization is run to solve these two tasks so that the experiences of solving the helper task can be transferred to speed up the convergence of tackling the target task. Moreover, an effective model management strategy is designed to select new promising samples for training the surrogate models. When compared to five competitive SAEAs on four well-known benchmark suites, the experiments validate the advantages of the proposed algorithm on most test cases. © 2024 held by the owner/author(s).

关键词： Multitasking

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Tidal-Tree-Mem: Toward Read-Intensive Key-Value Stores With Tidal Structure Based on LSM-Tree

Tidal-Tree-Mem: Toward Read-Intensive Key-Value Stores With ...

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作者： Ma, Chenlin Yang, Hao Wu, Shangyu Wang, Yi Mao, Rui Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China City University of Hong Kong Department of Computer Science Hong Kong Shenzhen University College of Computer Science and Software Engineering The Shenzhen Institute of Computing Sciences Shenzhen518060 China

The log-structured merge-tree (LSM-tree)-based key-value store has been widely adopted by many large-scale data storage applications for its excellent write performance. However, such write performance gains mainly come from scarifying read performance due to the leveled and log-structured intrinsic characteristics of the LSM-tree. Therefore, the critical challenge of the existing LSM-tree is how to improve the read efficiency by reducing read amplification. This article for the first time proposes Tidal-tree-Mem, a novel data structure where data flow inside the LSM-tree-like Tidal waves. First, a floating strategy is proposed to allow frequently accessed files at the bottom of the LSM-tree to move to higher positions, reducing read amplification. Second, a stretching strategy is proposed to vary the shape of the LSM-tree to adapt to workloads with different characteristics. To evaluate the performance of Tidal-tree-Mem, we conduct a series of experiments using standard benchmarks from YCSB. The experimental results show that Tidal-tree-Mem can effectively reduce read amplification and the overall latency by over 71.94% and 47.34%, respectively, compared with representative schemes. © 1982-2012 IEEE.

关键词： Benchmarking

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BPREC: A Bayesian Personalized Recommendation Based on User Preferences for Mining 21

BPREC: A Bayesian Personalized Recommendation Based on User ...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Qiu, Laixiang Feng, Jianping Zhou, Wang College of Computer Science Chongqing College of Mobile Communication Chongqing401420 China Mechanical and Electrica Engineering College Hainan Vocational University of Science and Technology Hainan571126 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798331519254

Faced with overwhelming product information, users often struggle to make choices, impacting their shopping experience and time. To address this, recommendation systems provide precise suggestions that streamline decision-making. The BPRec model, based on user preferences, enhances recommendation relevance and quality. Experiments on realworld datasets show that BPRec significantly improves recommendation effectiveness, thus enhancing the shopping experience. © 2024 IEEE.

关键词： Matrix factorization

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Deep learning based water leakage detection for shield tunnel lining

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Frontiers of Structural and Civil engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

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An Ingenious IoT Based Crop Prediction System Using ML and EL

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computers, Materials & Continua 2024年第4期79卷 183-199页

作者： Shabana Ramzan Yazeed Yasin Ghadi Hanan Aljuaid Aqsa Mahmood Basharat Ali Department of Computer Science&IT Government Sadiq CollegeWomen UniversityBahawalpur63100Pakistan Department of Computer Science/Software Engineering Al Ain UniversityAl Ain64141UAE Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Agronomic Research Station Bahawalpur63100Pakistan

Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the *** is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable *** implemented the proposed system in the *** efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.

关键词： Machine learning Internet of Things sensors ensemble learning

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A Hypergraph Transformer With Adaptive EdgeConv for Skeleton-based Action Recognition 7

A Hypergraph Transformer With Adaptive EdgeConv for Skeleton...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhou, Fengyu Liu, Jun Zhang, Dazhi Huang, Zengxi School of Computer Science and Software Engineering Xihua University Chengdu China Sichuan Police College Criminal Science and Technolog Department Luzhou China

ISBN: (纸本)9798350350890

Skeleton-based action recognition is a crucial task in computer vision, aiming to perform pattern recognition on a skeleton sequence to identify the actions represented by the skeleton's semantics. However, current mainstream skeleton-based methods primarily rely on global edge information. Unimportant edges may influence this approach, limiting the model's performance in action recognition tasks. Additionally, because these methods treat all information equally, they may lead to resource wastage. This paper proposes a Hypergraph Transformer-based action recognition method using Adaptive EdgeConv Module(AEM) to address these issues effectively. In this method, we introduce Adaptive Edge Graph Convolution Networks(AEGCNs), which utilize the AEM to adaptive obtain edge sets and edge set weights, thereby adaptive identifying key edge sets. With this information, the model can dynamically adjust the importance of different edge sets based on various samples. This approach improves the accuracy of action recognition and reduces the loss of computational resources. Our experimental results on two challenging benchmark datasets (i.e., NTU RGB+D and NTU RGB+D 120) show that the proposed method is comparable to state-of-the-art methods. © 2024 IEEE.

关键词： Musculoskeletal system

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DeepOCL:A deep neural network for Object Constraint Language generation from unrestricted nature language

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 250-263页

作者： Yilong Yang Yibo Liu Tianshu Bao Weiru Wang Nan Niu Yongfeng Yin School of Software Beihang UniversityBeijingChina College of Computer Science and Technology Guizhou UniversityGuiyangGuizhouChina Faculty of Information Technology Beijing University of TechnologyBeijingChina Department of Electrical Engineering and Computer Sciences University of CincinnatiCincinnatiOhioUSA

Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.

关键词： deep learning OCL software engineering

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Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

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computer Modeling in engineering & sciences 2024年第8期140卷 1997-2017页

作者： Yunye Wan Peng Chen Yunni Xia Yong Ma Dongge Zhu Xu Wang Hui Liu Weiling Li Xianhua Niu Lei Xu Yumin Dong College of Computer Science Chongqing UniversityChongqing400044China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer and Information Engineering Jiangxi Normal UniversityNanchang330022China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd.Yinchuan750002China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Computer Science and Technology Beijing Institute of TechnologyBeijing100083China School of Computer Science and Technology Dongguan University of TechnologyDongguan523808China School of Emergency Management Xihua UniversityChengdu610039China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as *** an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user ***,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of ***,techniques for caching content at the edge are becoming popular for addressing the above *** is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud ***,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity *** address this challenge,we introduce a novel method for content caching over MEC,i.e.,*** synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching *** results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

关键词： Mobile edge computing content caching system average cost deep reinforcement learning collaborative mechanism

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