检索结果-内蒙古大学图书馆

13th International Conference on Information Technology in Asia, CITA 2023

作者： Abdulsalam, Nana Hauwa Muhammad, Sufri Mohamed, Raihani Universiti Putra Malaysia Department of Software Engineering and Information System Selangor Serdang43400 Malaysia Universiti Putra Malaysia Department of Computer Science Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350326208

Technology has increased the interest and demand for pervasive systems which require contextual information to function at optimal capacity. There have been numbers of research in context-aware systems that has limited focused on the semantic-based approach in the crowdsourcing domain. Thus, it promotes challenges in the context and service acquisition and representation for reasoning control mechanism. This paper aims to review semantic-based reasoning framework with a focus on the mobile crowdsourcing domain. Different domains acquire different contextual information, either extrinsic or intrinsic. The review of the frameworks has help to formulate a process framework that applied semantic approach that has the important component for context-aware reasoning process. The framework can be used in the context-aware mobile crowdsourcing domain or can be generalized to other domain to aid reasoning control. Its advantage over other crowdsourcing frameworks which is focuses not only on context and service acquisition but also the representation on the acquired information. © 2023 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Memetic Algorithm Based Feature Weighting for Metabolomics Data Classification

引用

Chinese Journal of Electronics 2025年第4期23卷 706-711页

作者： Jiarui Zhou Zexuan Zhu Zhen Ji College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China

A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCMA regards both global evolution and local search as equivalent elemental metaheuristics, and searches with a chain of metaheuristics performed alternatively on the target problem. A hidden Markov model based scheduling mechanism is employed in MCMA for the selection of metaheuristics. By using MCMA for optimizing the linkage weight vector, a feature weighting algorithm for metabolomics data is formed to identify relevant metabolite features and reveal their exact relationships with the given physiological states. An extreme learning machine based classifier is utilized in predicting the physiological states according to the weighted metabolite features. Experimental results on real metabolomics data of clinical liver transplantation demonstrate that the proposed feature weighting and classification method obtains better performance than the other compared algorithms.

关键词： Memetics Metabolomics Metaheuristics Urban areas Liver Prediction algorithms Search problems Physiology Vectors Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Distributed safe resource allocation using barrier functions

引用

Automatica 2023年 153卷

作者： Wu, Xuyang Magnússon, Sindri Johansson, Mikael School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Computer and System Science Stockholm University Sweden

Resource allocation plays a central role in networked systems such as smart grids, communication networks, and urban transportation systems. In these systems, many constraints have physical meaning and infeasible allocations can result in a system breakdown. Hence, algorithms with asymptotic feasibility guarantees can be insufficient since they cannot ensure that an implementable solution is found in finite time. This paper proposes a distributed feasible method (DFM) for resource allocation based on barrier functions. In DFM, every iterate is feasible and safe to implement since it does not violate the physical constraints. We prove that, under mild conditions, DFM converges to an arbitrarily small neighborhood of the optimum. Numerical experiments demonstrate the competitive performance of DFM. © 2023 The Author(s)

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Liao, Wenbin Wen, Du Li, Jianqiang Wang, Fangzhong Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Tianjin University Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology China

ISBN: (纸本)9798350337488

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model's robustness. © 2023 IEEE.

关键词： Class Imbalance Data Generation Gene Annotation Positive-Unlabeled Learning

来源：评论

学校读者我要写书评

暂无评论

RSP-gcForest: A Distributed Deep Forest via Random Sample Partition

RSP-gcForest: A Distributed Deep Forest via Random Sample Pa...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Li, Mark Junjie Cai, Wenzhu Lin, Yigang Huang, Sunjie Huang, Joshua Zhexue Peng, Patrick Xiaogang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen Academy of Inspection and Quarantine China

ISBN: (纸本)9798350324457

Deep Forest, a powerful alternative to deep neural networks, has gained much attention due to its advantages, such as low complexity, minimal hyperparameter requirements, and strong application performance. In the current big data environment, where data volumes and model complexities are growing rapidly, distributed computing is needed to increase computational efficiency. Recently, a distributed deep forest approach, called BLB-gcForest (Bag of Little Bootstraps-gcForest), has been successful in reducing training instances within cascade forests, combining BLB and granularity segmentation, thus improving the computational efficiency and scalability of distributed deep forests. However, it still transmits the entire data set between layers and requires double sampling with BLB, limiting the amount of data and the scalability of resource utilization. This paper introduces a novel algorithm, RSP-gcForest, based on Random Sample Partition (RSP) to improve distributed deep forests computational efficiency and scalability. RSP-gcForest uses block-level samples that replace the full dataset, significantly reducing interlayer instance transmission and prediction within cascade forests. Additionally, RSP blocks are integrated with the segmentation granularity of cascade forests for ensemble learning, effectively addressing computational efficiency and resource constraints. We conducted experiments on four extensive datasets using Spark and evaluated performance across five key metrics. The results clearly show that RSP-gcForest, while maintaining high classification quality, surpasses state-of-the-art methods in terms of computational efficiency and resource utilization. Furthermore, it achieves superior load balancing, demonstrating its potential as a powerful tool in big data and distributed computing. © 2023 IEEE.

关键词： Deep forest Distributed Computing Ensemble Learning Random Sample Partition

来源：评论

学校读者我要写书评

暂无评论

Honeypot Comparison of Attack Detection and Mitigation of SSH Attack 3

Honeypot Comparison of Attack Detection and Mitigation of SS...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ali, Wasif Sajid, Ahthasham Ghodke, Tanvi Ajit Malik, Rida Malik, Neeru Kaushik, Keshav Riphah Institute of System Engineering Riphah International University Department of Cyber Security Islamabad Pakistan Pimpri Chinchwad University Department of Computer Science and Engineering Maharashtra India School of Engineering & Technology Pimpri Chinchwad University Maharashtra Pune India Amity School of Engineering and Technology Amity University Mohali India

ISBN: (纸本)9798331518592

This research study addresses the importance of honeypots at multiple levels, which includes but not limited to placement in network or group of networks working alongside in an organization. This paper talks about the usage of multiple honeypots working alongside to log useful data for the mitigation of ongoing bot or actual attack this approach helps the organization better understand the threat they are dealing and the techniques which they can use to overcome the gap of the unseen security risks. The comparison will shed better light to the working and attack patterns of the ongoing attacks, gaining more confidence on using the honeypot for better protection. This allows strategic, optimized and critical insight to the evolving attack patterns. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Design of syntactic adaptive interactive system based on human perception state estimation within scenario context

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16933-16948页

作者： Jin, Dian Yu, Weiwei Srivastava, Gautam Yang, Xinliang School of Mechanical Engineering Northwestern Polytechnical University No. 127 West Youyi Road Shaanxi Xi’an710072 China Unmanned System Research Institute Northwestern Polytechnical University No. 127 West Youyi Road Shaanxi Xi’an710072 China Department of Math and Computer Science Brandon University Brandon Canada Department of Computer Science and Math Lebanese American University Beirut Lebanon

Adaptive interactive systems can be divided into two categories: Semantic adaptations affect the system’s function, and syntactic adaptations affect the presentation of information through the operator interface without modifying the system behavior. Although automation is becoming more and more important, in many situations, human operators still play an irreplaceable role, making the development of syntactic adaptations indispensable. However, existing syntactic adaptations usually do not relate to the task context, which is a critical factor to achieve syntactic adaptations. Besides, existing studies do not fully consider the operator’s information perception state under highly dynamic scenarios, thus cannot dynamically prompt accurate information to the operators. Aiming at the problems above, a syntactic adaptive interactive system architecture based on perception state estimation and context segmentation is proposed in this paper. To address the real-time context segmentation, a time series segmentation algorithm is proposed, which achieves real-time estimation of the scene and produces highly interpretable results. To address the problem of dynamic perception state estimation, for the first time, the concept of a perception stack is proposed, which can be used to estimate the perception bias in the operator’s working memory based on the eye movement data and generate proper prompt information according to the current context. The proposed design method of an adaptive syntactic interactive system was verified in a flight simulator. The system improves information prompts accuracy and reduces redundancy through these innovative mechanisms mentioned above. Comparative experiments confirm significant improvements in the operator’s control accuracy and control stability according to our evaluation criteria. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： State estimation

来源：评论

学校读者我要写书评

暂无评论

Track Slab Crack Detection Algorithm with Few Samples Based on Deep Learning 8

Track Slab Crack Detection Algorithm with Few Samples Based ...

引用

8th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Liu, Zhongjie Li, Wenju Gao, Xiaosong Liu, Cui Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China Shanghai Crrc Ro-Intelligent System. Co. Ltd. Shanghai China

ISBN: (纸本)9798350369564

In order to solve the problem of track slab crack detection under few samples' conditions, this paper proposes a track slab crack detection algorithm with few samples based on deep learning. Firstly, after extracting multi-scale query and support features through the Swin Transformer backbone, the similarity between query pixels and support pixels is computed according to the scaled dot-product attention of the Transformer architecture, and the query masks are aggregated;in order to filter a large amount of irrelevant background information in the crack image, multi-scale prototypes are computed by using prototypical learning, highlighting the class information of the cracks, and introducing a lightweight attention module to generate class-representative attention feature maps using support features at different scales, which guides the network to focus its attention on the target region;finally, the final prediction of cracks is realized by the feature fusion module. The experimental results show that the track slab crack IoU reaches 66.94% and the FB-IoU reaches 83.02% under the few samples' condition and exhibits strong generalization performance on the crack set of other scenarios as well. © 2023 IEEE.

关键词： Crack detection

来源：评论

学校读者我要写书评

暂无评论

CLMSI: A Novel Image-Text Named Entity Recognition Method Based on Contrast Learning and Multimodal Semantic Interaction 11

CLMSI: A Novel Image-Text Named Entity Recognition Method Ba...

引用

11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Ma, Jiangtao Jin, Wenduo Chen, Yonggang Zhang, Fan Qian, Shenyi Qiao, Yaqiong College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China The State Information Center China China National Digital Switching System Engineering and Technology R&D Center Zhengzhou China College of Cyber Science Nankai University Tianjin300350 China

ISBN: (纸本)9798331531904

Most existing multimodal named entity recognition (MNER) methods cannot align image and text well, and fail to effectively fuse image-text semantic information, leading to suboptimal MNER accuracy. To this end, this paper proposes a novel named entity recognition model based on Contrastive Learning and Multimodal Semantic Interaction (named CLMSI). Specifically, to narrow the semantic gap between image-text modalities, making semantically similar image-text representations closer and pushing dissimilar ones further apart, we propose a contrastive learning-based cross-modal alignment method. Moreover, to integrate multimodal information effectively and filter out the noise introduced by image features as thoroughly as possible, we propose to incorporate image descriptions as additional features to enrich the multimodal representation and to reduce the semantic bias of the image information. Extensive experiments conducted on two MNER benchmark datasets, Twitter-2015 and Twitter-2017, demonstrate the superiority of the proposed method. Further ablation studies and case analyses also affirm the efficacy of CLMSI. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

NEONATE: a bi-directional search strategy for non-overlapping pattern matching with gap constraint

引用

Pattern Analysis and Applications 2025年第3期28卷 1-20页

作者： Shen, Cong Li, Yongyi School of Computer Science and Engineering Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Tianjin University of Technology Tianjin China

Pattern matching calculates support or occurrence numbers of a pattern in information retrieval and has derived kinds of constraints. Among the variety of constraint-matching conditions, plenty of research has illustrated the significant non-overlapping condition, which means that any two occurrences cannot use the identical character of the sequence in the same position of the pattern. Although a corresponding completeness proof has been given in state-of-the-art works, there is still room to improve the matching process. Inspired by the literature on strict matching under the non-overlapping condition, this study proposes a Nettree-based approach to matching given patterns called NEONATE. It adopts a bi-directional search strategy and pruning operation to realize the non-overlapping pattern matching under the gap and length constraints. NEONATE first locates the horizontal layer with the fewest available nodes in the Nettree. Then, it iteratively seeks the rightmost parent and rightmost child node until an occurrence is obtained in which the nodes in the Nettree path can strictly satisfy the pattern constraint. This study has not only demonstrated the completeness of NEONATE but also analyzed the complexity of the algorithm. The correctness and efficiency of NEONATE have been proved via extensive experiments on biological and musical sequences.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：