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Bayesian Network Reconstruction and Iterative Divergence Problem Solving Method Based on Norm Minimization

Computer Modeling in engineering & Sciences 2025年第4期143卷 617-637页

作者： Kuo Li Aimin Wang Limin Wang Yuetan Zhao Xinyu Zhu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China College of Software Jilin UniversityChangchun130012China

A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing *** method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable *** the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 ***,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent *** the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 *** mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.

关键词： Bayesian norm minimization network reconstruction iterative divergence sparsity

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Integrating Ontology-Based Approaches with Deep Learning Models for Fine-Grained Sentiment Analysis

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Computers, Materials & Continua 2024年第10期81卷 1855-1877页

作者： Longgang Zhao Seok-Won Lee The Knowledge-Intensive Software Engineering(NiSE)Research Group Department of Artificial IntelligenceAjou UniversitySuwon City16499Republic of Korea Department of Software and Computer Engineering Ajou UniversitySuwon City16499Republic of Korea

Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced *** study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant *** framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment *** the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word ***,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment *** evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification *** incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis *** results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.

关键词： Deep learning ontology fine-grained sentiment analysis online reviews

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A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

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Computers, Materials & Continua 2024年第6期79卷 4349-4370页

作者： Jiajie Shen Yan Wang Dongxu Zhang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of EducationColleague of Computer Science and TechnologyJilin UniversityChangchun130012China College of Software and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun130012China

Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ***,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion *** to the similarities between the information of the transitions and their adjacent steady ***,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as *** address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor *** classifier in these levels is composed of multiple-LSTM layers and an attention *** introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating *** method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions *** results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,*** is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.

关键词： Lower-limb prosthetics deep neural networks motion classification

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Contract-Driven Design of Scientific Data Analysis Workflows 19

Contract-Driven Design of Scientific Data Analysis Workflows

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19th IEEE International Conference on e-Science, e-Science 2023

作者： Vu, Anh Duc Sparka, Jan Arne De Mecquenem, Ninon Kehrer, Timo Leser, Ulf Grunske, Lars Humboldt-Universität zu Berlin Software Engineering Germany Humboldt-Universität zu Berlin Knowledge Management in Bioinformatics Germany University of Bern Software Engineering Group Switzerland

ISBN: (纸本)9798350322231

software systems enabling large-scale data analysis workflows (DAWs) are a key technology for many scientific disciplines, as they allow extracting new insights from experimental results. DAWs are (non-)linear pipelines composed of multiple interdependent tasks that are executed in a distributed fashion on large compute clusters. In science, the individual task implementations are developed by research groups all over the world and usually not tested outside a narrow scope of possible inputs, parameters, and infrastructures. As a result, the operations' correctness depends on many implicit assumptions. Among others this includes the completeness and suitability of input data, infrastructure properties such as available cores or main memory, etc. This combination of complexity, distribution and untested components makes quality assurance of DAWs a critical issue. In this paper, we propose to address this problem by introducing a contract-driven approach to DAW design and implementation. Following this method, DAW developers specify contracts in the form of requirements and promises for each task of a DAW. These contracts serve as guards to ensure that tasks run in a proper environment and produce correct results. We provide the first formal definition of contracts for DAWs and show how they are connected to DAW scheduling and execution. As a proof of concept, we extended Nextflow, a popular scientific workflow system, with contracts and defined a light-weight DSL for their specification. We exemplify the power of a contract-driven approach to DAW development by enhancing several real-world DAWs from Bioinformatics to capture typical problems during their execution and show how the specific notifications issued by broken contracts help debugging the DAWs. © 2023 IEEE.

关键词： Quality assurance

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Design by Contract Revisited in the Context of Scientific Data Analysis Workflows 19

Design by Contract Revisited in the Context of Scientific Da...

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19th IEEE International Conference on e-Science, e-Science 2023

ISBN: (纸本)9798350322231

software systems enabling large-scale data analysis workflows (DAWs) are a key technology for modern science as they allow extracting new insights from experimental results. DAWs are pipelines composed of interdependent tasks that are executed in a distributed fashion on large compute clusters. Typically, the individual task implementations are developed by research groups all over the world and usually not tested outside a narrow scope of possible inputs, parameters, and infrastructures. As a result, the operations' correctness depends on many implicit assumptions, such as the completeness and suitability of input data, infrastructure properties such as available cores, etc. This makes quality assurance of DAWs a critical issue. We propose to address this problem by introducing a contract-driven approach to DAW design and implementation. Following the well-known principle of Design by Contract, DAW developers specify contracts in the form of requirements and promises for each task of a DAW. These contracts serve as guards to ensure that tasks run in a proper environment and produce correct results. The detection of contract violations allows to halt the execution of a DAW and to identify the culprit that caused the violation. Thus, the integration of contracts into DAW design provides opportunities for efficiency improvements by reducing computation and debugging time in case of errors. © 2023 IEEE.

关键词： Data handling

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A Systematic Literature Review on Smart Contract Vulnerability Detection by Symbolic Execution 5th

A Systematic Literature Review on Smart Contract Vulnerabil...

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5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Wang, Yanli Sheng, Sifei Wang, Yanlin School of Software Engineering Sun Yat-sen University Zhuhai519082 China Knowledge-First Empowerment Academy HoustonTX77036 United States

ISBN: (纸本)9789819981007

Symbolic execution emerges as a potent method for software testing, progressively tackling the unique complexities associated with smart contract testing. Leveraging path exploration and constraint-solving mechanisms, symbolic execution uncovers potential vulnerabilities in smart contracts, ones that other testing methodologies might overlook. An expanding suite of tools and frameworks, including Oyente, Mythril, and Osiris, facilitate the symbolic execution of smart contracts. This paper delves into the theoretical underpinnings of smart contracts and symbolic execution. Subsequently, we provide a comprehensive review of the prevailing smart contract vulnerability detection tools reliant on symbolic execution, assessing their performance and testing scope, among other aspects. Lastly, we deliberate on the prospective challenges and the trajectory of this field in the future. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Lessons Learned from the Smart City Concept Implementation in West Lombok Regency, West Nusa Tenggara Province 10

Lessons Learned from the Smart City Concept Implementation i...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunindyo, Wikan Danar Alfrojems School of Electrical Engineering and Informatics Bandung Institute of Technology Knowledge and Software Engineering Research Group Bandung Indonesia Indonesia Business Coalition for Women Empowerment South Jakarta Indonesia

ISBN: (纸本)9798350339543

Accelerating regional economic growth is needed to be able to catch up with a region, especially if the region has economic potential, especially in terms of natural capital, such as a beautiful environment. However, in practice, a development process will experience obstacles as an inseparable part. This condition certainly requires appropriate treatment efforts based on empirical evidence data. This also happened to West Lombok. Where in fact the regions that get the opportunity to become Smart City pilots certainly have a positive impact, but the challenges that must be carried out also go hand in hand with the process of implementing these efforts. Referring to this background, the purpose of this research is to be able to explore and analyze the challenges experienced by West Lombok in implementing the Smart City program in its region. The method used is qualitative with data collection techniques in the form of documentation studies, observations, interviews, and focus group discussions. This study involved 50 informants with various backgrounds but still in accordance with the characteristics of the informants determined by the researcher through the use of purposive sampling technique. The results of this study are that there are four challenges that need to get more attention from the local government of West Lombok and its stakeholders as an effort to realize West Lombok as a smart city. The four challenges include policy, infrastructure, human capital, and environment. The need for strategic efforts carried out by West Lombok to be able to encourage the achievement of the target of becoming a smart city in Indonesia, especially in West Nusa Tenggara Province. © 2023 IEEE.

关键词： Smart city

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More Flexible Proximity Wildcards Path Planning with Compressed Path Databases 34

More Flexible Proximity Wildcards Path Planning with Compres...

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34th International Conference on Automated Planning and Scheduling, ICAPS 2024

作者： Chen, Xi Zhang, Yue Zhang, Yonggang College of Software Jilin University China College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

ISBN: (纸本)9781577358893

Grid-based path planning is one of the classic problems in AI, and a popular topic in application areas such as computer games and robotics. Compressed Path Databases (CPDs) are recognized as a state-of-the-art method for grid-based path planning. It is able to find an optimal path extremely fast without state-space search. In recent years, researchers have tended to focus on improving CPDs by reducing CPD size or improving search performance. Among various methods, proximity wildcards are one of the most proven improvements in reducing the size of CPD. However, its proximity area is significantly restricted by complex terrain, which significantly affects the pathfinding efficiency and causes more additional costs. In this paper, we enhance CPDs from the perspective of improving search efficiency and reducing search costs. Our work focuses on using more flexible methods to obtain larger proximity areas, so that more heuristic information can be used to improve search performance. Experiments conducted on the Grid-Based Path Planning Competition (GPPC) benchmarks demonstrate that the two proposed methods can effectively improve search efficiency and reduce search costs by up to 3 orders of magnitude. Remarkably, our methods can further reduce the storage cost, and improve the compression capability of CPDs simultaneously. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Motion planning

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Effect of Solar Cell Interconnections on the Output of Solar UAV with 3D Curved Photovoltaics 2

Effect of Solar Cell Interconnections on the Output of Solar...

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2nd IEEE International Power Electronics and Application Symposium, PEAS 2023

作者： Jia, Wei Zhang, Xiaobin Lei, Tao Yu, Haoliang Yao, Wenli Qu, Yan School of Automation Northwestern Polytechnical University Xi'an China Suzhou Tongyuan Software and Control Technology Co. Ltd Knowledge Engineering Department Suzhou China

ISBN: (纸本)9798350313765

The solar-powered unmanned aerial vehicle (SUAV) utilizes a Curved Photovoltaic Array (CPVA) mounted on its wing surface to harness solar energy during daylight hours, storing any excess energy in the battery for nocturnal use. However, the irregularity of the wing surface pose challenges for accurately modeling the SUAV's photovoltaic energy yield and calculating the output of photovoltaic power. To maximize the photovoltaic output of the solar UAV, it is crucial to design a CPVA interconnection tailored to a specific airfoil and optimize the hardware to minimize the impact of the wing's curvature on photovoltaic performance. This paper presents a CPVA model and conducts an analysis of the Maximum Power Point Tracking (MPPT) output for different photovoltaic configurations, demonstrating more efficient configurations that yield higher outputs. The results show that the proposed Near Layout (NL) configuration achieves a significant increase of 6%10% in all-day energy for solar aircraft operating in mid-latitude regions compared to the conventional configuration of the ideal planar wing model. © 2023 IEEE.

关键词： curved PV MPPT photovoltaic array configuration photovoltaic modeling solar UAV

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Transformer-based multi-level attention integration network for video saliency prediction

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Multimedia Tools and Applications 2025年第13期84卷 11833-11854页

作者： Tan, Rui Sun, Minghui Liang, Yanhua Software College Jilin University Qianjin Street Jilin Changchun130012 China College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130012 China

Most existing models for video saliency prediction heavily rely on 3D convolutional operations to extract spatio-temporal features. However, it is worth noting that 3D convolution produces a local receptive field, which may struggle to capture long-range spatio-temporal dependencies effectively. To compensate for such shortage, this paper introduces a novel approach called the Transformer-based Multi-level Attention Integration Network (TMAI-Net) for video saliency prediction. TMAI-Net is designed as a two-stream encoder-decoder model, carefully integrating multi-level features of semantic information. Our model incorporates a Multi-level Interactive Attention(MLIA) module and a Transformer, both implemented based on self-attention mechanism, which are placed at different levels of the model to capture long-range spatio-temporal feature dependencies. Additionally, our model operates on input video frames and attentional patches, allowing the Transformer module to capture structural similarities between related objects in global features and attention features. This, in turn, enables the model to allocate increased attention to salient areas. The efficacy of our proposed approach is validated through extensive experiments conducted on three widely recognized benchmark datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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