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IEEE International Conference on Communication and Information Systems (ICCIS)

作者： Yujia Hu Zhiwei Zhang Department of Artificial Intelligence College of Computer Science Sichuan University Chengdu China Department of Software Engineering College of Computer Science Xi'an Aeronautical Institute Xi'an China

Coal mine belt conveyors are commonly used equipment in coal mining enterprises which transport coal from the coal faces using belts. Currently, video image monitoring serves as the predominant approach to identify foreign objects within the transported coal, such as large gangue and rock bolts. In recent years, the utilization of Convolutional Neural Networks has become prevalent in automatically recognizing foreign objects within images. Nevertheless, existing models suffer from issues including low recognition accuracy and slow speed. In response to the aforementioned problems, this paper proposes an improved coal foreign object recognition model based on MobileNetV3. Firstly, the Coordinate Attention mechanism is introduced into MobileNetV3 to fully consider both channel and spatial features. Secondly, the ReLU6 activation function in MobileNetV3 is replaced with the ELU activation function to augment the model's capacity to express information within the negative value range. Finally, superfluous network layers are removed to reduce computational complexity and improve recognition speed. Experimental results demonstrate that the proposed improved MobileNetV3 model achieves an accuracy of 90.8% in recognizing foreign object images, which is 2.5 to 4.0 percentage points higher compared to commonly used neural networks including AlexNet and EfficientNet. Moreover, compared with MobileNetV3, the proposed model reduces the number of parameters and computational complexity by 40.3% and 12.0% respectively. These results indicate that the proposed model reaches higher recognition accuracy while reducing network size and computational load. As a result, it provides a feasible approach for enhancing safety in coal mining enterprises.

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Design Concept of a Mental Health Monitoring Application with Explainable Assessments 34

Design Concept of a Mental Health Monitoring Application wit...

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34th Australasian Conference on Information Systems, ACIS 2023

作者： De Silva, Upeka Madanian, Samaneh Templeton, John Michael Poellabauer, Christian Schneider, Sandra L. Narayanan, Ajit Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Department of Computer Science and Engineering University of South Florida TampaFL33620 United States School of Computing and Information Sciences Florida International University MiamiFL33199 United States Department of Communicative Sciences and Disorders Saint Mary's College Notre DameIN46556 United States

Mental health has become a global issue with growing numbers of cases. Digital phenotyping in mental healthcare provides a highly effective, scaled, cost-effective approach to handling global mental health problems. Monitoring and diagnosis of mental health through mobile technologies are at an early stage. This paper proposes a novel architecture for a mobile application design based on speech and behavioural analysis that can lead to evidence-based monitoring outside of clinical settings. The application is designed to monitor the overall mental health status of individuals based on mood, stress, behaviour, and personality. It proposes to integrate objective mental health assessment from smartphone data with subjective assessments via periodic, short, self-reported, standardized questionnaires. The solution proposes explainable assessments for individual users and clinicians to build user trust and system credibility. This research provides insight into how digitally assisted mental health monitoring can be implemented in future mobile digital technologies using passive sensing. Copyright © 2023 De Silva, Madanian, Templeton, Poellabauer, Schneider and Narayanan.

关键词： Smartphones

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Optimizing Keyphrase Ranking for Relevance and Diversity Using Submodular Function Optimization (SFO)

arXiv

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arXiv 2024年

作者： Umair, Muhammad Hashmi, Syed Jalaluddin Lee, Young-Koo Department of Computer Science and Engineering Kyung Hee University Korea Republic of College of Software Kyung Hee University Korea Republic of

Keyphrase ranking plays a crucial role in information retrieval and summarization by indexing and retrieving relevant information efficiently. Advances in natural language processing, especially large language models (LLMs), have improved keyphrase extraction and ranking. However, traditional methods often overlook diversity, resulting in redundant keyphrases. We propose a novel approach using Submodular Function Optimization (SFO) to balance relevance and diversity in keyphrase ranking. By framing the task as submodular maximization, our method selects diverse and representative keyphrases. Experiments on benchmark datasets show that our approach outperforms existing methods in both relevance and diversity metrics, achieving SOTA performance in execution time. Our code is available online. © 2024, CC BY.

关键词： Information retrieval

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia computer science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Symmetrical Supervision with Transformer for Few-shot Medical Image Segmentation

Symmetrical Supervision with Transformer for Few-shot Medica...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Niu, Yao Luo, Zhiming Lian, Sheng Li, Lei Li, Shaozi Song, Haixin Xiamen University Department of Artificial Intelligence Fujian China Fuzhou University College of Computer and Data Science Fujian China Xiamen University Department of Software Engineering Fujian China

ISBN: (纸本)9781665468190

Few-shot learning can potentially learn the target knowledge in extremely few data regimes. Existing few-shot medical image segmentation methods fail to consider the global anatomy correlation between the support and query sets. They generally adopt a weak one-way information transmission that can not fully explore the knowledge to segment query data. To address this problem, we propose a novel Symmetrical Supervision network based on traditional two-branch methods. We raise two main contributions: (1) The Symmetrical Supervision Mechanism is leveraged to strengthen the supervision of network training;(2) A transformer-based Global Feature Alignment module is introduced to increase the global consistency between the two branches. Experimental results on two challenging datasets (abdominal segmentation dataset CHAOS and cardiac segmentation dataset MS-CMRSeg) show a remarkable performance compared to other comparing methods. © 2022 IEEE.

关键词： Image segmentation

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Dual-Cope: Category-Level Object Pose Estimation with Dual Sim2real Unsupervised Domain Adaptation Module

SSRN

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SSRN 2024年

作者： Ren, Xi Guo, Nan Zhu, Zichen Jiang, Xinbei Department of Computer Science and Engineering Northeastern University Shenyang110004 China Software College Northeastern University Shenyang110004 China

Category-level pose estimation offers the generalization ability to novel objects unseen during training, which has attracted increasing attention in recent years. Despite the advantage, annotating real-world data with pose label is intricate and laborious. Although using synthetic data with free annotations can greatly reduce training costs, the Synthetic-to-Real (Sim2Real) domain gap could result in a sharp performance decline on real-world test. In this paper, we propose Dual-COPE, a novel category-level object pose estimation method with dual Sim2Real domain adaptation to avoid expensive real pose annotations. First, we propose an estimation network featured with dual prior deformation and transformer-based matching to realize high-precision pose prediction. Upon that, an efficient dual Sim2Real domain adaptation module is further designed to reduce the feature distribution discrepancy between synthetic and real-world data both semantically and geometrically, thus maintaining superior performance on real-world test. Moreover, the adaptation module is loosely coupled with estimation network, allowing for easy integration with other methods without any additional inference overhead. Comprehensive experiments show that Dual-COPE achieves state-of-the-art performance on REAL275 dataset under both unsupervised and supervised settings. © 2024, The Authors. All rights reserved.

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Preventive measurement and Prediction of Covid-19 in India through Business Intelligence Tools 6

Preventive measurement and Prediction of Covid-19 in India t...

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6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022

作者： Kumar Dewangan, Bhupesh Alwan, Mohammed Hasan Mahdi, Hussain Falih Choudhury, Tanunriva Piyush, Asmit Rai, Ahhijeet Dubey, Alok Singh, Akshay School of Engineering OP Jindal University Department of Computer Science Engineering Raigarh496111 India Bilad Alrafidain University College Electrical Power Engineering Techniques Department Baqubah32001 Iraq University of Diyala Department of Computer and Software Engineering Baquba Iraq Dehradun248007 India

ISBN: (数字)9781665470131

ISBN: (纸本)9781665470131

From December 2019, a major outbreak called novel corona virus is infecting people all over the world now. It is believed to be a beta corona virus of SARS-CoV and MERS-CoV. Infected people are unable to detect this disease as they feel normal till 10-12 days. After that, the virus infects the whole body and starts to find another body to infect, multiplying it day by day. As per the media news and other sources, epidemic is spreading globally, especially in countries like China, Italy where its effect is at peak, killing thousands of people. Based on the data of infected Covid-19 people in India, we systematically discuss the outbreak of epidemic corona virus in India. Defining the structure of active cases day by day, we predict the future of Covid-19 in India. We also suggest important measures to help prevent the spread of Covid-19 in India. © 2022 IEEE.

关键词： Data Analytics

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年第6期29卷 1380-1384页

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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Crime Based Evaluation of GPS Network Using Machine Learning Techniques 4

Crime Based Evaluation of GPS Network Using Machine Learning...

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Rahul Monika Durgapal, Vartika Hari Department Of Software Engineering Delhi Technological University Delhi India Department of Computer Science Shaheed Rajguru College of Applied Sciences for Women University of Delhi Delhi110096 India

ISBN: (纸本)9781665401180

With the increased usage of data transmission, data leakage and privacy protection are becoming increasingly critical. Data comes in a variety of forms, and the amount of protection required for each one differs. With the rapid development and demand of Global Positioning S ystem (GPS) networks in every of big data technology, the vulnerability of the transportation system to unintentional and intentional GPS disruption is also increasing. The GPS signal is degraded and lost as a result of opposing interests' attacks. Potential assaults vary from GPS signal jamming and spoofing to GPS ground stations and satellite disruption. S ecurity issues with respect to data increases during data transmission. Data travelling across an organization's network is frequently sensitive and internet being the storehouse of every kind of data with methods for accessing the precise information serves as an auspicious medium for the hackers [19]. Network security solutions must deal with complicated data in this setting in order to detect and minimize any threats in real time. There are several techniques to the problem of data security such as cryptographic algorithms, classification-based models, and block-chain methods [1]-[6]. Through this research, crime rates are evaluated in a GPS network data with different other parameters using machine learning (ML) algorithms. Moreover, the inferred features effecting the crimes during data transmission are extracted. S ome of the techniques used includes techniques such as 'Decision Tree (DT)', 'K Nearest Neighbors (KNN)', 'Random Forest (RF)', 'Multivariable Regression (MR)' and 'Naïve Bayes (NB)'. It was seen that the best accuracy was given by RF - of approximately 84.25%. © 2022 IEEE

关键词： Crime

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Ensuring IoT Controller Reliability with Colored Generalized Stochastic Petri Net

Ensuring IoT Controller Reliability with Colored Generalized...

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International computer science and engineering Conference (ICSEC)

作者： Kruntarat Samngamnoi Sutinun Pomsiri Apipath Kamput Chanon Dechsupa Department of Computer Science Intelligent Software Engineering Research Group College of Computing Khon Kaen University Khon Kaen Thailand

ISBN: (数字)9798350366860

ISBN: (纸本)9798350366877

In IoT systems managing multiple devices simultaneously, errors in system controllers often undermine intended operations. Formal verification offers a method to assess system reliability. Colored Generalized Stochastic Petri Net (CGSPN), a formal language, facilitate correctness checks of such systems. This study proposes a verification approach by translating a C++-based system controller of a self-service machine into a CGSPN models and validating it using the Snoopy Tool. Mapping techniques employed to transform components in the controller into CGSPN models are provided. Results demonstrate the method’s efficacy in verifying system safety properties, simulating system events, and enabling quantitative verification.

关键词： Codes Petri nets Stochastic processes C++ languages Transforms Control systems Internet of Things Reliability Washing machines Self-service

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