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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jun Zhang Zhiqiang Yan Hao Zeng Zexuan Zhu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Aptamers are single-stranded DNA or RNA oligonucleotides that selectively bind to specific targets, making them valuable for drug design and diagnostic applications. Identifying the interactions between aptamers and target proteins is crucial for these applications. The systematic evolution of ligands by exponential enrichment process, traditionally used for this purpose, is challenging and time-consuming. The resulting aptamers often suffer from limitations in stability and diversity. Computational approaches have shown promise in aiding the discovery of high-performance aptamers, but existing methods are usually constrained by insufficient training data and limited generalizability. Recently, advancements in pre-training large language models have offered a new avenue to mitigate the dependency on large datasets. In this study, we propose a novel method to predict aptamer-protein interactions using large language models within a contrastive learning framework. Experimental results demonstrate that our method exhibits superior generalization and outperforms existing approaches. This method holds promise as a powerful tool for predicting aptamer-protein interactions.

关键词： Proteins Drugs Protein engineering Biological system modeling Large language models Computational modeling Training data Contrastive learning Predictive models Few shot learning

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Video-Based Jump Height Estimation in Athletic Performance Testing

Video-Based Jump Height Estimation in Athletic Performance T...

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2023 IEEE International Workshop on Sport, Technology and Research, STAR 2023

作者： Lokas, Ivan Vasilj, Matea Skender, Sven Vucetic, Vlatko Mihaldinec, Hrvoje Dzapo, Hrvoje Ultrax Technologies Software Development Zagreb Croatia Human Performance Laboratory Faculty of Kinesiology Zagreb Croatia Hardware Development Ultrax Technologies Zagreb Croatia Faculty of Electrical Engineering and Computing Department of Electronic Systems and Information Processing Zagreb Croatia

ISBN: (纸本)9798350316056

Jump height estimation is well-studied problem in sports science with a variety of methods proposed over the years. This paper introduces a portable, simple-to-setup and a cost-effective video-based jump height estimation method, and compares it with existing traditional methods that use expensive and specialized equipment. To enable more accurate comparison between different systems, a custom-designed Inertial Measurement Unit (IMU) was used as an additional sensor input. The analysis was performed on eleven female physically active handball players. The dataset consists of three most common vertical jump variations: squat jump, counter-movement jump and counter-movement jump with free arms. The analysis show that our video-based approach achieves comparable results to the traditional methods. © 2023 IEEE.

关键词： Cost effectiveness

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Emergency Transportation and Structural Prediction and Optimization of Goods Based on ARIMA and Multi-objective E-commerce Logistics Network Model 1

Emergency Transportation and Structural Prediction and Optim...

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1st IEEE International Conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Nie, Yiyang Chen, Xiang Wu, Xuanli Deng, Zihao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology Shcool of Mathematics and Computational Science Xiangtan China

ISBN: (纸本)9798350323795

With the development of the internet, online shopping has gradually become a popular way of shopping among the general public, which in turn poses a greater challenge to the e-commerce logistics network. Predicting the volume of goods for each logistics site and route, and establishing a model for quickly adjusting the network in response to sudden situations can save logistics network costs and improve its efficiency. Therefore, this article has done the following work: first, it establishes an effective logistics network volume prediction model;second, for the two scenarios of network structure that cannot be modified and can be modified, it provides a model that can effectively guide the adjustment of the volume of goods on each route when a logistics site is closed;finally, it establishes a logistics network evaluation model to evaluate the importance of logistics sites and routes. © 2023 IEEE.

关键词： Multiobjective optimization

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Node Coupling for Inferring Networks from Short Time Series 5th

Node Coupling for Inferring Networks from Short Time Series

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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Li, Hang Wang, Li Xia, Chengyi Tianjin University of Technology Tianjin300382 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Beijing China

ISBN: (纸本)9789811939976

Commonly abstracted as a set of units, the complex systems in the real world are interconnected with each other using the specific dynamical methods. However, in practice, there is nothing else but the performances of the system known, nor are the network structure and the dynamical rules observed. In order to solve this problem, a research has been conducted on network reconstruction, so as to recover the potential network structure and network dynamics from the observed time series. In this paper, a new method is proposed, namely, slope difference similarity (SDS), which is a way of thinking in reverse. Based on the trend of change in nodes, the coupling degree between nodes is judged and the network is reconstructed. By comparing the SDS and Kendall rank correlation method with experiments, the experimental results are obtained in this paper to demonstrate the effectiveness of SDS network reconstruction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time series

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Multi-granularity Information Flow Enhanced Skeleton-based Infant Seizure Detection

Multi-granularity Information Flow Enhanced Skeleton-based I...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Huiyuan Chen, Weiting Zhang, Yiqing Li, Na East China Normal University Moe Research Center of Software/Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing Shanghai China Shanghai Jiao Tong University School of Medicine Children's Hospital Shanghai China

ISBN: (纸本)9798350337488

Automatic video seizure detection can assist in the diagnosis of seizures. Existing works usually rely on the information of single granularity for learning seizure patterns and the performance of these works is often constrained by the limited annotated data. To overcome these problems, we introduce a multi-granularity information flow method to enhance skeleton-based infant seizure detection from different perspectives. A forward-backward consistency constraint is proposed to use the reverse temporal sequences as augmented data, helping make better predictions. Besides, an importance-aware module is proposed to adaptively pay more attention to important frames. Experimental results indicate that our method can improve the performance of seizure detection and achieve high performance with a relatively low amount of parameters. © 2023 IEEE.

关键词： forward-backward consistency multi-granularity information seizure detection skeleton-based action recognition

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Systematic analysis of artificial intelligence in the era of industry 4.0

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Journal of Management Analytics 2023年第1期10卷 89-108页

作者： Weiru Chen Wu He Jiayue Shen Xin Tian Xianping Wang Department of Information Technology&Decision Science Old Dominion UniversityNorfolkVAUSA Department of Computer&Information Science Arkansas Tech UniversityRussellvilleARUSA Department of Engineering Technology SUNY Polytechnic InstituteUticaNYUSA College of Computing and Software Engineering Kennesaw State UniversityMariettaGAUSA Computer Information Technology&Graphics Department Purdue University NorthwestHammondINUSA

Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily *** the increasing capabilities and accuracy of AI,the application of AI will have more impacts on manufacturing and service areas in the era of industry *** study conducts a systematic literature review to study the state-of-the-art on AI in industry *** paper describes the development of industries and the evolution of *** paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry *** findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of *** the era of industry 4.0,AI system will become an innovative and revolutionary assistance to the whole industry.

关键词： Industry 4.0 AI deep learning neural network supervised learning unsupervised learning reinforced learning

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Ontology Driven Testing Strategies for IoT Applications

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Computers, Materials & Continua 2022年第3期70卷 5855-5869页

作者： Muhammad Raza Naqvi Muhammad Waseem Iqbal Muhammad Usman Ashraf Shafiq Ahmad Ahmed T.Soliman Shahzada Khurram Muhammad Shafiq Jin-Ghoo Choi INP-ENIT University of ToulouseTarbes65000France Department of Software Engineering Superior UniversityLahore54000Pakistan University of Management and Technology Sialkot CampusPakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Internet-of-Things(IoT)has attained a major share in embedded software *** new era of specialized intelligent systems requires adaptation of customized software engineering ***,software engineering has merged the development phases with the technologies provided by industrial *** improvements are still required in testing phase for the software developed to IoT *** research aims to assist in developing the testing strategies for IoT applications,therein ontology has been adopted as a knowledge representation technique to different software engineering *** proposed ontological model renders 101 methodology by using Protégé.After completion,the ontology was evaluated in three-dimensional view by the domain experts of software testing,IoT and ontology *** results of the research are showed in interest of the specialists regarding proposed ontology development and suggestions for *** Proposed reasoning-based ontological model for development of testing strategies in IoT application contributes to increase the general understanding of tests in addition to assisting for the development of testing strategies for different IoT devices.

关键词： Ontology software testing internet of things automation

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FBI-Net: Frequency Band Integration Network for Infrared Small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Biqiao Xin Qiang Li Qianchen Mao Jinbao Wang Bingshu Wang School of Software Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches.

关键词： Wavelet transforms Frequency-domain analysis Semantics Signal processing Feature extraction Excavation Robustness Real-time systems Complexity theory Speech processing

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Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning

arXiv

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arXiv 2024年

作者： Gao, Xin Lin, Yang Li, Ruiqing Wang, Yasha Chu, Xu Ma, Xinyu Yu, Hailong Software Engineering Institution School of Computer Science Peking University Beijing China Key Lab of High Confidence Software Technologies Ministry of Education Beijing China Center on Frontiers of Computing Studies School of Computer Science Peking University China National Engineering Research Center of Software Engineering Peking University Beijing China

Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised tool in this field. However, the predominant objective in NTMs, which aims to discover topics maximizing data likelihood, often lacks alignment with the central goals of data mining and knowledge discovery which is to reveal interpretable insights from large data repositories. Overemphasizing likelihood maximization without incorporating topic regularization can lead to an overly expansive latent space for topic modeling. In this paper, we present an innovative approach to NTMs that addresses this misalignment by introducing contrastive learning measures to assess topic interpretability. We propose a novel NTM framework, named ContraTopic, that integrates a differentiable regularizer capable of evaluating multiple facets of topic interpretability throughout the training process. Our regularizer adopts a unique topic-wise contrastive methodology, fostering both internal coherence within topics and clear external distinctions among them. Comprehensive experiments conducted on three diverse datasets demonstrate that our approach consistently produces topics with superior interpretability compared to state-of-the-art NTMs. © 2024, CC BY.

关键词： Contrastive Learning

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FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training 24

FedCST: Federated Learning on Heterogeneous Resource-constra...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wang, Zhilu Lin, Haiyang Liu, Qi Zhang, Yonghong Liu, Xiaodong Nanjing University of Information Science and Technology School of Computer Science Jiangsu Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Nanjing China Nanjing University of Information Science and Technology School of Software Jiangsu Nanjing China Nanjing University of Information Science and Technology School of Automation Jiangsu Nanjing China Edinburgh Napier University School of Computing Edinburgh United Kingdom

ISBN: (纸本)9798350365658

With the rapid development of 5G and Internet of Things (IoT) technologies, edge devices such as sensors, smartphones, and wearable devices have become increasingly prevalent. The massive amount of distributed data generated by these devices offers unprecedented opportunities for deep learning, especially in fields like computer vision and edge computing. However, this data often exists in isolated data silos, and due to its privacy-sensitive nature, it cannot be directly processed in a centralized manner. Additionally, the data heterogeneity and devices heterogeneity further exacer-bates the challenges in federated learning (FL). Moreover, with the implementation of data privacy protection regulations, user demand for privacy protection is growing significantly. Therefore, how to effectively utilize dispersed data for distributed collaborative learning while ensuring data privacy is a pressing issue that needs to be addressed in the field of FL. In response to these challenges, this work has conducted research on clustering and segmentation FL algorithms for heterogeneous resource-constrained devices. A lightweight homogeneous device clustering strategy has been designed, which incorporates a split learning (SL) mechanism to enhance the accuracy and training efficiency of FL models. This approach reduces the load on resource-constrained devices and improves privacy security. Consequently, this method can meet the dual requirements of privacy protection and computational efficiency in real-world scenarios. © 2024 IEEE.

关键词： Federated learning

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