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Extended Abstracts of the 2023 CHI Conference on Human Factors in computing Systems, CHI EA 2023

作者： Lu, Qi Zhang, Yuewei Zhang, Yuxin Ma, Shao-En Zhang, Yunfan Qin, Yuou Gao, Peizhong Zhang, Qiwei Xu, Yingqing The Future Laboratory Academy of Arts & Design Tsinghua University Beijing China The Future Laboratory Industiral Engineering Tsinghua University Beijing China The Future Laboratory School of Software Tsinghua University Beijing China The Future Laboratory Tsinghua University Beijing China The Future Laboratory Department of Automation Tsinghua University Beijing China College of Design and Innovation Tongji University Shanghai China Creative Computing Institute University of the Arts London London United Kingdom

ISBN: (纸本)9781450394222

Synchronous online classes have become increasingly common in recent years. However, students and instructors often face challenges when giving and receiving reactions and feedback. Existing solutions include audio and visual cues, which often act as distractions. A potential alternative is olfactory cues. This paper proposes Atmospheror, an interactive system that supports interaction and feedback between students and instructors through an ambient olfactory display. We performed a pilot study with two instructors and 13 students to evaluate the effectiveness of Atmospheror and its design and implementation, applying both quantitative and qualitative methods. Preliminary results indicate that Atmospheror can promote students' concentration and increase interactivity in online classes. User interviews provided suggestions for future enhancements of the selection and treatment of odor, hardware implementation, and interface system mechanisms. © 2023 Owner/Author.

关键词： Students

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TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinquan Yang Jinheng Xie Xuechen Li Xuguang Li Linlin Shen Yongqiang Deng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Stomatology Shenzhen University General Hospital Shenzhen China

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods.

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Defining Thin Objects in Computer Vision

Defining Thin Objects in Computer Vision

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ICT Conference (AICTC), Arab

作者： Rabeea Jaffari Syed Saad Ahmed Manzoor Ahmed Hashmani Syed Fahad Ahmed Software Engineering Department Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Universiti Teknologi Petronas Seri Iskander Perak Malaysia Electronics Engineering Department Universiti Tunku Abdul Rahman Kampar Perak Malaysia Computer and Information Sciences Department High Performance Cloud Computing Center (HPC3) Universiti Teknologi Petronas Perak Seri Iskander Malaysia UI/UX Designer Technoholic Advance Solutions Houston Texas USA

ISBN: (数字)9798350342086

ISBN: (纸本)9798350342093

Thin image objects detection in computer vision is a challenging task due to the compactness, slenderness, and extremely small coverage of these objects in the images. This makes these objects difficult to detect especially in the presence of background clutter. Efficient detection of thin image objects is of utmost importance in research areas including but not limited to electrical transmission and mooring fault detection systems, medical analysis, collision and obstacle detection in unmanned aerial and autonomous vehicles, plant stem detection in biological studies, road-lane-line detection and so on. Despite the significance of thin objects detection from images, there is no formal and accurate definition of what thin objects in computer vision are. In this research work, we propose and formalize the criteria for defining thin objects in computer vision. We conduct various experiments to identify the parameters and formalize the measures for describing thin objects in several computer vision research applications and other novel scenarios. The proposed research work can be utilized in the future research for the design and development of efficient thin objects detection and localization datasets, algorithms and other relevant tasks.

关键词： Location awareness Computer vision Plants (biology) Fault detection Object detection Biology Object recognition Clutter Autonomous vehicles Biomedical imaging

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Paddy Plant Disease Detection using Hybrid Deep Learning Algorithms

Paddy Plant Disease Detection using Hybrid Deep Learning Alg...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： R Suguna S Deepa T Kanimozhi M Chandru G Arun Harshini M Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Department of Computer Science VET-Institute of Arts and Science (Co-Education) College Erode Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai M.Sc (Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Agriculture is the backbone of India’s economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and ResNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial Leaf Blight, (iii) Sheath Blight, and (iv) Ufra Disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity.

关键词： Deep learning Productivity Plant diseases Microorganisms Accuracy Prediction algorithms Real-time systems Classification algorithms System analysis and design Residual neural networks

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Securing Multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure Multiparty Computation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： A. Ponmalar Rajkumar Pandiarajan I. Sudha P.S. Ramesh J. Jagannathan R.M.K. Engineering College Chennai Tamilnadu India Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The aim of this work is to examine the application of FHE-SMPC as a viable security model for protecting the tenant isolation in multi-tenant cloud model. In multi-tenant environments, where various customers use the same cloud system, controlling the data leakage and protecting privacy is a major issue. Actual safeguards fail to meet the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes must happen. This research work focuses on the use of FHE technique and SMPC such that data can undergo processing while the plaintext remains unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system-level architecture, and cryptographic algorithms using open-source libraries with cloud service providers. The results of such implementation indicate that despite the fact that the FHE-SMPC system has a considerable computational overhead in comparison with classical schemes it is very secure and protected against different types of cryptographic attacks. The problem of scalability is still an open issue, but the complex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in multitenant environment and will go along way in creating fertile ground for research and innovation in secure clouding.

关键词： Resistance Technological innovation Scalability Data protection Libraries Service-oriented architecture Encryption Cryptography Time complexity Secure storage

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Enhancing Healthcare Data Security in Cloud computing Systems through Advanced Homomorphic Encryption Techniques Integrated with Cuckoo Search Optimization Algorithm

Enhancing Healthcare Data Security in Cloud Computing System...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： I. Sudha P.S. Ramesh A. Ponmalar J. Jagannathan Rajkumar Pandiarajan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India R.M.K. Engineering College Chennai Tamilnadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Information and Technology School of Computing Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The studies in this research are focused on the mix of homomorphic encryption mechanisms and the Cuckoo Search Optimization Algorithm to promote the security of the healthcare data in the cloud computing system. I have observed that cloud solutions are now at the center of the contemporary health care systems, and security of patient’s data remains a top priority. Earlier used encryption techniques, though secure if implemented, have problems in the way that data is to be processed first it has to be decrypted and then made ready for use - it is in this phase that data can be easily hacked into. Homomorphic encryption provides solution to this issue since it enables one to perform computations on encrypted data while maintaining its confidentiality. However, it takes a lot of computation which presents a lot of problems even in terms of simple performance. The Cuckoo Search Algorithm, a new nature-inspired metaheuristic, is incorporated into the encryption process to provide further optimization, whilst preserving the security benefits of the algorithm. The use of the proposed methodology takes place employing Python and its deployment at the cloud platform with elaborated dataset containing anonymized records extracted from electronic health records. The outcome proves that there are great enhancements in encryption and decryption time, computational complexity, and capacity, and thereby making the system appropriate for real-world healthcare solutions. This research offers a strong base for protection of healthcare data in cloud and is useful for the enhancement of adaptive, comprehensive and secure cloud computing systems.

关键词： Cloud computing Machine learning algorithms Metaheuristics Distributed databases Medical services Organizations Machine learning Homomorphic encryption Protection Electronic medical records

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A Framework for Tele-rehabilitation Gaming System

A Framework for Tele-rehabilitation Gaming System

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Applied Computational Intelligence in Information Systems (ACIIS), International Conference on

作者： Ahmed M. S. Elaklouk Ratna Zuarni Ramli Ibrahim Edris Nena Padilla-Valdez Adel Al Jumaily School of Computing and Informatics Universiti Teknologi Brunei Bandar Seri Begawan Brunei Darussalam College of Computing Informatics and Mathematics Universiti Teknologi MARA Negeri Seremban Malaysia Centre for Communication Universiti Teknologi Brunei Bandar Seri Begawan Brunei Darussalam School of Computing University of Technology Brunei Department of Computer Science & Software Engineering University of Western Australia and ENSTA Bretagne University France

Brain damage resulting from stroke and traumatic brain injury is the primary cause of complicated disability and one of the major causes of mortality in most countries, burdening healthcare systems owing to the growth in the number of brain injury victims. Rehabilitation is required to deal with the brain damage. With fewer therapists available, it is very challenging to manage patients and even motivate them to participate actively in their rehabilitation. Patients have frequently complained that many traditional rehabilitation systems are monotonous and uninteresting. The study’s objective is to propose a telerehabilitation gaming system framework that can serve as guidance for developing an affordable rehabilitation gaming system that can motivate and engage patients and increase rehabilitation effectiveness. The research methodology is based on synthesizing related research and currently available technologies. The proposed framework includes the therapist and patient attached with vital constructs: tailoring tools, instructional content, game characteristics, tailored games, and performance. It is an internet-based communication structure that connects the Rehabilitation Gaming System to the hospital and other care provider networks. The proposed framework is evaluated by a panel of five experts. The results reveal that all experts believe that the proposed framework can serve as a useful guide for creating gaming interventions that can boost the patient’s motivation and engagement and increase the effectiveness of rehabilitation.

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Editorial:Special Topic on Federated Learning for IoT and Edge computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Advanced Multi-Spectral Image Processing Techniques for Enhanced Remote Sensing and Comprehensive Environmental Monitoring in Diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： J. Jagannathan A. Ponmalar Rajkumar Pandiarajan I. Sudha P.S. Ramesh Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India R.M.K. Engineering College Chennai Tamilnadu India Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study.

关键词： Accuracy Wildfires Multispectral imaging Disasters Ecosystems Vegetation mapping Water quality Soil Sensors Remote sensing

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Advanced Machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

Advanced Machine Learning Algorithms for 1Predictive Analyti...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： Rajkumar Pandiarajan J. Jagannathan P.S. Ramesh A. Ponmalar Sudha Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India R.M.K. Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image information, and genomic data the work proposes and assesses multiple machine learning models including deep learning, ensemble learning, and reinforcement learning. The main findings shown very high performance in disease prediction, in chronic illness and in the application of efficiency improvements in hospitals as a whole. Overall, such threats as data quality, data privacy, and model interpretability have been discussed in the research and emphasize the importance of the machine learning approach in healthcare. It is highlighted that further development and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and machine learning models can be urgent to increase the acceptance among the healthcare staff. This paper enhances the literature on applying machine learning in healthcare systems adding to the repertoire of knowledge for improved patient care.

关键词： Data privacy Machine learning algorithms Hospitals Data integrity Machine learning Reinforcement learning Predictive models Data models Predictive analytics Diseases

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