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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

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Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite Networks

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computers, Materials & Continua 2023年第5期75卷 3903-3922页

作者： Rui Xu Xiaoqiang Di Hao Luo Hui Qi Xiongwen He Wenping Lei School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China School of Information Engineering Henan Institute of Science and TechnologyXinxiang453003China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and TechnologyChangchun130022China Information Center Changchun University of Science and TechnologyChangchun130022China Beijing Institute of Spacecraft System Engineering Beijing100094China Beijing Institute of Space Mechanic and Electricity Beijing100094China

In information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite *** up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the ***,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache *** address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is *** two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground *** caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of *** cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached *** the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing *** results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.

关键词： information centric networking caching strategy two-layer caching model hierarchical division

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Towards Classifying File Segments in Memory Using Machine-Learning 9

Towards Classifying File Segments in Memory Using Machine-Le...

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9th International Conference on information Technology Trends, ITT 2023

作者： Al-Saleh, Mohammed I. Alkouz, Akram Alarabeyyat, Abdulsalam Bouchahma, Majed Jordan University of Science and Technology Computer Science Department Jordan Higher Colleges of Technology Computer Information System United Arab Emirates Al-Balqa Applied University Software Engineering Department Jordan-Salt Jordan

ISBN: (纸本)9798350327502

Digital media triage is a main challenge that faces a digital investigator. Knowing what might be useful during crime investigation could greatly save the investigator's time and enhance outcomes. Memory investigation can get potential benefit from the triage process because of its scattered and diverse contents. The Operating system's paging scheme might map the contents of a file into non-consecutive page frames in the physical memory, making the file-type classification and identification process harder. This paper tackles the memory content triage problem at the page level using a machine-learning methodology. © 2023 IEEE.

关键词： Digital storage

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A Review of Semantic-Based Reasoning Framework for Context-Aware Mobile-Crowdsourcing 13

A Review of Semantic-Based Reasoning Framework for Context-A...

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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

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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Concept of an Optical Distance and Speed Sensor Using Novel Offsetless Spatial Frequency Filters in the Area of Functional Safety 32

Concept of an Optical Distance and Speed Sensor Using Novel ...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Wasinski, Frank Bonath, Werner Ricklefs, Ubbo Borcsok, Josef Schwarz, Michael Hahn, Eike University of Applied Sciences THM Dept. of Electrical Engineering and Information Technology Giessen Germany Institute for Computer Architecture and System Programming University of Kassel ICAS Kassel Germany

ISBN: (纸本)9798350399714

Optical speed sensors based on the spatial frequency filter method are a proven technology that offer high measurement accuracy over a wide speed range. Newly developed variants also enable measurements at very low speeds and increase the robustness of the method [1]. This makes the sensor very suitable for use in the area of functional safety. A measurement chain up to safety integrity level 3 (SIL3) should be feasible via a combination with a safety system on a chip (SoC) for evaluating the measurement data and an extension of the sensor module to include safety-related elements [2], [3]. By integrating the sensor at IC level, a compact overall solution is to be made possible. After the first successful tests with the safety SoC, further examinations of the measuring method with regard to functional safety are now necessary. © 2023 IEEE.

关键词： system-on-chip

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

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

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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

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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

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computer systems Science & engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

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computers, Materials & Continua 2022年第11期73卷 3305-3318页

作者： Saud S.Alotaibi Amal Al-Rasheed Sami Althahabi Manar Ahmed Hamza Abdullah Mohamed Abu Sarwar Zamani Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)encompasses various domains such as Machine Learning(ML),Deep Learning(DL),and other cognitive technologies which have been widely applied in healthcare *** models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input *** this motivation,the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset,named MOKELM-CPED *** primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using epidemiology *** the proposed MOKELM-CPED model,the data first undergoes pre-processing to transform the medical data into useful *** by,data classification process is performed by following Kernel Extreme Learning Machine(KELM)***,Symbiotic Organism Search(SOS)optimization algorithm is utilized to fine tune the KELM parameters which consequently helps in achieving high detection *** order to investigate the improved classifier outcomes of MOKELM-CPED model in an effectual manner,a comprehensive experimental analysis was conducted and the results were inspected under diverse *** outcome of the experiments infer the enhanced performance of the proposed method over recent approaches under distinct measures.

关键词： COVID-19 epidemiology dataset machine learning artificial intelligence metaheuristics healthcare

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Internet of Things in Textile Sensors 5

Internet of Things in Textile Sensors

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5th National Conference of Saudi computers Colleges, NCCC 2022

作者： Ballaji, Hattan Khaled Umm Al-Qura University College of Computers and Information System Computer Engineering Department Makkah24381 Saudi Arabia

ISBN: (纸本)9798350333688

New integration options with conformable sensors have been made possible by the digitization of textiles (textronics), allowing for the continuous, unobtrusive, and noninvasive decoding of essential physiological signals. This article offers a thorough analysis of the Internet of Things' textile sensors and the fabrication techniques utilized for textronic sensors according to the form factors of fiber, yarn, fabric, and apparel. It also emphasizes the significance of pretreatment and conditioning reporting of the textile formfactors due to their influence on the mechanical and electrical performance of the textronic sensor. The study comes to the conclusion that in addition to being able to communicate information, textile-based sensors can be used as smart sensing devices to access the mobile internet through wearable computing devices that can be attached to or worn on the human body. The next generation of human-computer interfaces may be built on top of these sensors. One of the factors influencing the development of textile sensors is the ongoing discovery of novel conductive materials. © 2022 IEEE.

关键词： Smart textiles

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