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Evaluating Privacy Policies and User Agreements of Smart Home Devices from the Perspective of the Aging Population 25th

Evaluating Privacy Policies and User Agreements of Smart Hom...

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25th international conference on Human-computer Interaction, HCII 2023

作者： Vaidya, Manasi Lee, Chaiwoo D’Ambrosio, Lisa Coughlin, Joseph F. AgeLab Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9783031359910

Smart home technology has the potential to support several tasks in the home, especially for older adults who are likely to increasingly experience difficulties with everyday activities. Trade-offs between privacy and autonomy, security, and comfort have been an ongoing discussion for over a decade. Although there have been significant advances in the technology available today compared to a few years ago, privacy communications to users accompanying these technologies have changed little. Much existing research in the smart home technology space focuses on the design of the technology itself;less has been done to ensure that older adults know where their data is going so that adoption of the technology is better informed, and so they feel more secure about using it. In this work a total of 30 documentations including 24 privacy policies of individual smart devices and 6 privacy terms and policies from platform providers were reviewed to understand their commonalities and shared limitations. Our evaluation revealed that the length of each ranged between 9 to 49 pages, and that such policies are rarely displayed to maximize user access and legibility, contributing to user confusion and concern. the results suggest a need for user agreements and privacy policies to be more easily accessible. Companies publishing privacy documents should be more mindful of how these are presented to the user, whether online or printed. this study recommends that companies should adhere to usability and readability guidelines and follow accessibility standards published by the Web Content Accessibility Guidelines. For older adults to successfully utilize smart home technology, adoption should happen in an informed manner. Sustained usage may also be encouraged with practices that make users feel confident and in control of their data. It is important that companies build their trust ethically, and ensure that older adults are not required to trade off their privacy for autonomy and secu

关键词： Economic and social effects

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Analysis of the Impact of White Box Adversarial Attacks in ResNet While Classifying Retinal Fundus Images 1

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5th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2022

作者： Bharath Kumar, D.P. Kumar, Nanda Dunston, Snofy D. Rajam, V. Mary Anita Department of Computer Science and Engineering College of Engineering Guindy Anna University Chennai India

ISBN: (数字)9783031163647

ISBN: (纸本)9783031163630

Medical image analysis with deep learning techniques has been widely recognized to provide support in medical diagnosis. Among the several attacks on the deep learning (DL) models that aim to decrease the reliability of the models, this paper deals with the adversarial attacks. Adversarial attacks and the ways to defend the attacks or make the DL models robust towards these attacks have been an increasingly important research topic with a surge of work carried out on both sides. the adversarial attacks of the white box category, namely Fast Gradient Sign Method (FGSM), the Box-constrained Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS-B) attack and a variant of the L-BFGS-B attack are studied in this paper. In this work, we have used two defense mechanisms, namely, Adversarial Training and Defensive distillation-Gradient masking. the reliability of these defense mechanisms against the attacks are studied. the effect of noise in FGSM is studied in detail. Retinal fundus images for the diabetic retinopathy disease are used in the experimentation. the effect of the attack reveals the vulnerability of the Resnet model for these attacks. © 2022, IFIP international Federation for Information Processing.

关键词： Diagnosis

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Early Recognition and Ranking of Knee Osteoarthritis by the Assistance of Enhanced Deep Learning on Knee MR Image Data 8th

Early Recognition and Ranking of Knee Osteoarthritis by the ...

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8th international conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Murali, Molleti Bala Purnima, Varigala Sai Laxmi, Kodamanchili Venkata Sampath, Rajana Sai Mahanty, Mohan Department of Computer Science and Engineering Vignan’s Institute of Information Technology-Duvvada Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9783031511660

Osteoarthritis (OA) of the knee is an inflammation that impacts the knee bone due to the significant weight-bearing of the body. the disease results in degeneration and rupture of the cartilage elements in the knee joint, causing severe pain. Unfortunately, the prevalence of OA has been increasing globally, with a 113.25% increase in cases from 1990 to 2019. Currently, more than 350 million people globally suffer from arthritis, and it is estimated that by 2040, about 78 million US adults will have this condition. the diagnosis of OA is primarily carried out by evaluating symptoms and comparing plain radiographs, which can be subjective. However, Convolution Neural Networks (CNNs), one of the best deep learning technological advances, are currently attracting interest as a potential remedy for healthcare problems. Consequently, this study’s objective aims to design and implement a categorization scheme that can aid doctors in reducing their workload and assist rheumatologists in assessing the severity of the pain accurately. Furthermore, this will enable them to make the best diagnosis and recommend the most appropriate treatment. By using our proposed model, i.e., Enhanced MobileNet-V2, rheumatologists can make informed decisions on the severity of the condition, which can lead to better treatment outcomes for the patients. the proposed model Enhanced MobileNet-V2 was trained on the digital knee X-ray image dataset and achieved 91% accuracy. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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Developing a Multi-site Hyperscanning Procedure for Investigating Interbrain Synchrony Underlying Remote Social Interaction 17th

Developing a Multi-site Hyperscanning Procedure for Investig...

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17th international conference on Brain Informatics, BI 2024

作者： Limtragooltongchai, Sirapakit Yamtuan, Kawin Gracia, Javier O. Chunharas, Chaipat Sriravanasan, Ramesh Chaisilprungraung, thitaporn Itthipuripat, Sirawaj Neuroscience Center for Research and Innovation King Mongkut’s University of Technology Thonburi Bangkok Thailand Institute of Field Robotics King Mongkut’s University of Technology Thonburi Thonburi Thailand DEVCOM Army Research Laboratory Humans in Complex Systems Division AdelphiMD United States Chula Neuroscience Center King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok10330 Thailand Cognitive Clinical and Computational Neuroscience Department of Internal Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Department of Cognitive Sciences University of California IrvineCA United States Department of Biomedical Engineering University of California IrvineCA United States Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok10600 Thailand

ISBN: (纸本)9789819632930

EEG hyperscanning provides novel insights into inter-brain connectivity during social interactions by enabling the simultaneous recording of neural activity from multiple individuals. this research aims to develop and validate a new protocol in which dyads engage in a synchronized button-pressing task across both same-location and multi-location setups. Our findings reveal significantly higher interpersonal neural synchronization during the interaction period compared to the baseline. Notably, the increased neural synchronization is comparable across the same- and multi-location setups, spanning frontal, central, and posterior electrodes. Interestingly, the level of synchronization in the multi-location setup is greater than in the same-location setup over electrodes positioned above the right superior temporal gyrus (STG), a region associated with social cognition and the mirror neuron system (MNS). these findings validate our methodology for studies across different locations and open new avenues for research into inter-brain synchronization in remote environments. Our experimental protocol could enhance the design of experiments and applications involving social interaction, particularly in remote settings, which have gained popularity due to COVID-19. It also advances our understanding of the neural mechanisms that support social communication in the new normal era. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Economic and social effects

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Multi-Modal Learning with Joint Image-Text Embeddings and Decoder Networks

Multi-Modal Learning with Joint Image-Text Embeddings and De...

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Industrial Cyber-Physical Systems (ICPS)

作者： Ajai John Chemmanam Bijoy A Jose Asif Moopan Department of Electronics CPS Lab Cochin University of Science and Technology Kerala India Department of Computer Science CPS Lab Cochin University of Science and Technology Kerala India Vuelogix Technologies Pvt. Ltd Kerala India

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

advances in machine learning and neural networks have transformed natural language processing (NLP) and computer vision (CV) applications. Recent research efforts have begun to bridge the gap between the two domains. In this work, we propose a semi supervised Multi-Modal Encoder Decoder Network (MMEDN) to capture the relationship between images and textual descriptions, allowing us to generate meaningful descriptions of images and retrieve images from a database using cross-modality search. the semi-supervised training approach, which combines ground truth text descriptions and pseudotext generated by the text decoder within the model, requires far fewer image-text pairs in the training data and can directly add new raw images without manual text labelling for training. this approach is particularly useful for active learning environments, where labels are expensive and hard to obtain. We show that our model performs well with qualitative evaluations. We applied our model for finding images of a person from large databases and generating descriptions of people involved in an event for adding to an automatically generated report. the model was able to retrieve relevant images and generate accurate descriptions, demonstrating its applicability to more practical use cases.

关键词： Training Visualization computer vision Databases Training data Vectors Natural language processing

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Generic and scalable multi-layered architecture for Digital Twin implementation in industrial processes: Mineral Processing case study 23

Generic and scalable multi-layered architecture for Digital ...

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Proceedings of the 2023 7th international conference on advances in Artificial Intelligence

作者： Oussama Hasidi El Hassan Abdelwahed Aimad Qazdar Moulay Abdellah El Alaoui-Chrifi Intissar Benzakour Rachida Chahid Ahmed Bendaouia François Bourzeix Computer Science Departement Computer Systems Engineering Laboratory Cadi Ayyad University Morocco and Analytics Lab Mohammed VI Polytechnic University Morocco Computer Science Departement Computer Systems Engineering Laboratory Cadi Ayyad University Morocco ESTIDMA Laboratory Ibn Zohr University Morocco R&D and Engineering center Reminex Managem Group Morocco Computer Science Departement Computer Systems Engineering Laboratory Cadi Ayyad University Morocco and SEIA Departement MAScIR Morocco Analytics Lab Mohammed VI Polytechnic University Morocco

ISBN: (纸本)9798400708985

Digitalization is crucial for achieving product quality, cost reduction, and timely delivery in manufacturing. With the rise of Industry 4.0, Digital Twin technology has gained popularity, enabling real-time monitoring, predictive maintenance, process optimization, and assumption testing. Manufacturing Digital Twins utilize digital models to represent physical processes, facilitating continuous simulation, interaction, correction, and optimization for autonomous control. However, implementing Digital Twins can be challenging due to diverse data requirements and multiple services. this paper proposes a scalable, service-oriented multi-layered architecture for developing Digital Twins in mineral processing, which can be adapted to other manufacturing systems. the paper provides an overview of Digital Twins, their data-centric workflow, and industrial applications. Additionally, a case study on implementing a Digital Twin for the froth flotation process in the mining industry is presented based on the proposed architecture.

关键词： Digital Twin Implementation Industry 4.0 Multi-layered Architecture Optimisation Predictive model Real time control and monitoring

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the Classification Method of Urban Architectural Styles Based on Deep Learning and Street View Imagery 7

The Classification Method of Urban Architectural Styles Base...

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7th international Technical conference on Frontiers of Hydraulic and Civil Engineering technology, HCET 2022

作者： Sun, Haozun Xu, Hong Wei, Quanfeng School of Urban Construction Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9781643683546

the task of identifying urban architectural styles occupies a very necessary position in the fields of construction of smart cities, sustainable urban development and community regeneration. the research method proposed in this paper can improve on the inconveniences of traditional methods of identifying urban architectural styles, such as: the community building is relatively old, and the integration of more periods of architectural style can significantly affect the test results. It is an established fact that data cannot be collected and processed efficiently by humans alone, and can not enter such qualitative and descriptive research methods into the computer for auxiliary research. this paper is based on the explosion of information data use in the 21st century, and use deep learning technology to process unstructured data with convolutional neural networks as the core to assist in the identification of urban architectural styles. With the rapid development of deep learning technology in recent years, its classification techniques for identification of street images of urban buildings can be used for urban management, and a new strong underpinning for the allocation of urban resources, urban diversification management, and the transformation of old communities in the later period has been provided by the proper classification of urban architectural styles. Notwithstanding its restrictions, the approach presented in this research has shown promise and the valuable value of deep learning-based techniques for the study of architectural styles, and this approach has universal significance. © 2022 the authors and IOS Press.

关键词： Deep learning

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Trend of the Use and Investment of Blockchain technology in the Banking Sector in Ecuador 8th

Trend of the Use and Investment of Blockchain Technology in ...

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8th international conference on Technologies and Innovation, CITI 2022

作者： Garzón-Goya, Mayra Delgado-Vera, Carlota Lagos-Ortiz, Katty Jurado-Larrea, Fabricio Facultad de Economía Agrícola Universidad Agraria del Ecuador Av. 25 de Julio Guayaquil Ecuador Facultad de Ciencias Agrarias Universidad Agraria del Ecuador Av. 25 de Julio Guayaquil Ecuador 7CargoCorp Guayaquil Ecuador

ISBN: (纸本)9783031199608

Technological advances used in the banking sector have allowed the coupling of various technological and computer tools, giving rise to Blockchain. this research presents a vision of the components, the architecture, the platforms, and the algorithms used and finally the frequency of their use. this research highlights the evolution of Fintech in Latin America, and links up these statistics to the reality of the banking sector in Ecuador;it is also described the performance indicator of the investment made in three of the main banks in this country and the amounts invested in technology are shown as evidence that they are also part of the digitalization process of the financial industry. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Deep Learning Approaches for Cyber threat Detection and Mitigation 23

Deep Learning Approaches for Cyber Threat Detection and Miti...

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Proceedings of the 2023 7th international conference on advances in Artificial Intelligence

作者： Aayush Juyal Bharat Bhushan Alaa Ali Hameed Akhtar Jamil Department of Computer Science and Engineering School of Engineering and Technology Sharda University India Department of Computer Engineering Istinye University Turkey Department of Computer Science National University of Computer and Engineering Sciences Pakistan

ISBN: (纸本)9798400708985

Cyberspace has inflated over the past decade, primarily driven by pervasive development and widespread usage of the internet. Prolonged cyber-attacks and security vulnerabilities have become more common as a consequence. A recent study conducted by Ponemon Institute revealed that 64% of businesses have suffered web-based attacks in 2023. thus, cybersecurity measures have become mandatory in every sector, that leverages technology, to protect confidential information from malignant threats. the majority of the sectors use conventional security measures like anti-virus products to defend their systems against outside attacks, however, these products fail to work on unseen and polymorphic threats. With cutting-edge deep learning (DL) processes getting adopted in nearly every field, it has been widely exploited in cybersecurity to detect and classify cyber-attacks. this study discusses the application of common deep learning models in cybersecurity to detect malware, intrusion, phishes, and spam. this study provides a description of deep learning models and their mathematical backgrounds that are common in cybersecurity. this study also reviews and analyses the work of other researchers who experimented with deep learning models in cybersecurity. this study delineates the challenges faced by deep learning models in cybersecurity. Finally, concluding remarks and future research directions are summarized to foresee the potential improvement of deep learning in cybersecurity.

关键词： Cybersecurity Deep Learning Intrusion Detection Machine Learning Malware Detection

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Digital Eye on Endangered Wildlife: Crafting Recognition Datasets through Semi-Automated Annotation 23

Digital Eye on Endangered Wildlife: Crafting Recognition Dat...

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Proceedings of the 2023 7th international conference on advances in Image Processing

作者： Sibusiso Reuben Bakana Yongfei Zhang Shan Yang School of Computer Science and Engineering Beihang University China School of Computer Science and Engineering Beihang University China and Beijing Key Laboratory of Digital Media Beihang University China School of Computer Science and Engineering Beihang University China and Beijing Key Laboratory of Digital Media Beihang Umniversity China

ISBN: (纸本)9798400708275

Wildlife conservation, especially in Africa, faces significant challenges from hunting and poaching, endangering species like rhinos and elephants. Despite concerted efforts, the lack of annotated wild animal datasets has hampered the development of effective monitoring algorithms for reserves and parks. Although advances in technology have enabled institutions to upload images for monitoring purposes, manual annotation remains time-consuming and inefficient. To address this issue, the paper proposes a semi-automatic annotation framework for wild animal recognition dataset construction, focusing on six classes of common animals in danger in Africa (including rhino, elephant, lion, giraffe, cheetah, and zebra). the framework involves training a YOLOv5 model on a small manually annotated dataset of 10%, and using this model to semi-automatically label the remaining large-scale dataset. the framework results are convincing when compared to a semi-auto annotation tool (Bylabel) with an 86.16% improvement in terms of time consumption in seconds, which enhances the time efficiency for annotation. this is the first wild animal dataset constructed with boundary box labels based on semi-automatic annotation. the proposed framework has the potential to expedite research on wildlife protection, serve as a guide for semi-automatic annotation dataset construction, and have a significant impact on conservation communities. the dataset is publicly available at http://***/datasets/vhmvfbgvxj.

关键词： Wild animal dataset boundary box labels deep learning algorithms semi-automatic annotation time efficient

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