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On the Security of Smart Home Systems:A Survey

Journal of computer science & technology 2023年第2期38卷 228-247页

作者：袁斌万俊吴宇晗邹德清金海 School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Distributed System Security Huazhong University of Science and Technology Wuhan 430074China Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan 430074China National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan 430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts to investigate and enhance the security of smart home *** a more secure smart home ecosystem,we present a detailed literature review on the security of smart home ***,we categorize smart home systems’security issues into the platform,device,and communication *** exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security ***,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

关键词： IoT(Internet of Things) smart home IoT security smart home security

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Exploring Agile Methodologies in Educational Software Development- A Comparative Analysis and Project Management Insights

Exploring Agile Methodologies in Educational Software Develo...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Vandana Kate Shilpa Bhalerao Vanshika Sharma Dept. of Computer Science and Information Technology Acropolis Institute of Technology and Research Indore India Dept. of Computer Science and Technology Acropolis Institute of Technology and Research Indore India

In the realm of academic research, the task of determining the most suitable Software Development Life Cycle (SDLC) becomes complex, owing to the diverse project requirements, rapidly changing technology, team skills and experience, organizational culture and readiness, flexibility and adaptability needs, and decision-making complexity. It requires a thorough analysis of various factors to make a wise decision that aligns with the project's goals and constraints. A variety of models, including Waterfall, Prototyping, and Spiral, offer distinct sets of advantages and disadvantages. Agile modeling is a flexible and iterative software development strategy that emphasizes collaborative modeling and rapid feedback. The agile philosophy promotes transparency, effective communication, and embracing change to meet evolving project needs efficiently and effectively. An In-Depth Examination of Agile Methodologies versus Conventional Approaches gives a deeper understanding of their respective strengths, weaknesses, and applicability. Also as the educational institutes increasingly recognize the importance of providing industry-relevant skills and preparing students for dynamic work environments, agile methodologies are likely to play a vital role in shaping the future of education and fostering the success of students in their careers. In this paper, we have explored the practical application of well-established Agile methodologies including Scrum, Kanban, Lean, Extreme Programming (XP), Feature Driven Development, and Dynamic Systems Development Method (DSDM) within the context of educational institutions and project management.

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A HIGH-PRECISION 3D LASER MEASUREMENT METHOD BASED ON ESTIMATION OF TRUE VALUES BY HIGH-DENSITY MEASUREMENTS AND CALCULATION OF DIFFERENCES BETWEEN MEASUREMENT DATA OF TWO TIMES

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AIJ Journal of technology and Design 2022年第68期28卷 515-520页

作者： Kakizaki, Ken'ichi Mukai, Tomohisa Araki, Shunsuke Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Dept. of Structural Engineering Building Research Institute

3D laser measurement has a problem that the measurement precision is not sufficient for damage analysis of buildings. In addition, when there is an unevenness in the measurement target, it is difficult to distinguish whether it is an initial shape or damage. In this paper, we propose a method to reduce the measurement error by processing the high-density laser measurement data with statistical methods. In addition, we propose a method to detect the damaged area by comparing the initial shape data of the measured object with the newly measured data. © 2022 Architectural institute of Japan. All rights reserved.

关键词： Measurement errors

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A Comparative Study of RESNET-Pretrained Models for computer Vision 2023

A Comparative Study of RESNET-Pretrained Models for Computer...

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15th International Conference on Contemporary Computing, IC3 2023

作者： Prajwal, Thode Sai Ilavarasi, A.K. School of Computer Science and Engineering Vellore Institute of Technology-Chennai India School of Computer Science and Engineering Centre for Healthcare Advancement Innovation and Research Vellore Institute of Technology-Chennai India

ISBN: (纸本)9798400700224

The science of computer vision has recently seen a revolution with deep learning models, and Residual Networks (ResNets) have become one of the most popular and efficient designs. ResNets have outperformed conventional convolutional neural networks in several image classification tasks, demonstrating outstanding results. An overview of ResNets and their variants, including Res2Net, COVID-ResNet, ResNeSt, SENet, and others, is presented in this review. We assess the performance of the ResNet design variants using benchmark datasets, highlight the major ideas and technological advancements underlying them, and their pros and cons. We also discuss some recent developments in ResNets research and applications, including object identification, picture segmentation, and transfer learning. This review would be beneficial to the research community in selecting the appropriate models based on the intended usage. © 2023 ACM.

关键词： computer vision

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The Power of PDF-to-Audio Summaries in Modern Learning 4

The Power of PDF-to-Audio Summaries in Modern Learning

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kawde, Dhanshri Mendhe, Sameer Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

PDF -to-audio based summarization technology is designed to enhance information accessibility and efficiency by transforming long written reports into short, audible summaries. AI, NLP, and TTS technologies that have developed recently have made it possible to extract main points without losing context. Yet, current systems have various shortcomings such as inability to process intricate PDF layouts like tables, charts, and annotations, contextual summarization inaccuracies, robotic or unhuman speech synthesis, and limited customization. Furthermore, the absence of multilingual support and easy integration into various platforms hinders wider adoption. These issues are addressed in this paper with a better approach that employs sophisticated NLP methods, context-sensitive summarization algorithms, and adaptive TTS systems. Our goal is to create a strong, user-oriented solution that provides greater summarization accuracy, personalized speech output, and improved accessibility for a variety of users, such as professionals, students, and the disabled. © 2025 IEEE.

关键词： Web Design

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A KNN Algorithm Based Predictive Model for Heart Disease Progression

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Recent Advances in computer science and Communications 2023年第5期16卷 76-81页

作者： Jaffrin, Lijetha C. Srikanth, G. Umarani Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India

Background: Disease diagnosis is a useful phenomenon in healthcare. Machine learning classification methods would considerably improve the healthcare industry by providing a quick diagnosis of the disease. Thus, time could be saved for doctors. Nearly 17.9 million people expire due to heart disease every year. Objectives: World Health Organization (WHO) predicted that rate of death might increase by 24.5 million in 2030. Since heart illness was the major cause of death in comparison with other diseases today, it was the most challenging disease to diagnose. Methods: One of the reasons for death due to heart disease was due to the fact that risks were not identified in the earlier stage. Earlier diagnosis of disease was very much important. Machine Learning algorithms were used for predicting the prognosis of disease. Results: Here K-NN algorithm was used to predict the presence of heart disease in an individual. Thus, patients were classified as either positive or negative for heart disease and this model enhanced medical care and reduced the cost. This gave us significant knowledge that helps us to predict the patients with heart disease. Conclusion: The Python sci-kit library was used to implement this in Anaconda Navigator's Spyder Integrated Development Environment. Experiments revealed that technique worked well and was more accurate than before. © 2023 Bentham science Publishers.

关键词： Diseases

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GAIT based human Parkinson’s disease detection using fused features with multi-kernel support vector machine

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International Journal of Information technology (Singapore) 2025年第3期17卷 1387-1395页

作者： Jhapate, Arun Kumar Shrivastava, Hemang Department of Computer Science and Engineering Sagar Institute of Research and Technology Madhya Pradesh Bhopal India Institute of Advance Computing SAGE University Madhya Pradesh Indore India

Gait analysis plays a pivotal role in the detection and monitoring of Parkinson’s disease (PD), offering insights into motor impairments characteristic of the condition. This study proposes a novel approach employing advanced feature extraction and machine learning algorithms to enhance the classification of PD depends on gait features. In this study, both local as well as global features are extracted from the gait images to facilitate a classification process. Local feature extraction is achieved using an optimized convolutional neural network (OCNN), while global feature extraction is done by the Swin Transformer architecture. To further improve feature extraction, an Adaptive Glowworm Swarm Optimization (AGSO) algorithm is applied to improve the performance of the CNN. The fused local and global features are then fed into a Multi-Kernel Support Vector Machine (MK-SVM) classifier, which serves as the core of the proposed model. The performance of the model is evaluated using the GAIT-IT datasets, assessing metrics such as accuracy, precision, recall, and F-measure. As a result of the proposed model, performance is superior to that of traditional methods in terms of accuracy and reliability, offering a promising approach for PD detection based on gait analysis. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Gait analysis Global features Local features Machine learning Multi kernel SVM Optimized CNN Parkinson’s disease Swin transformer

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Multi-Class Breast Cancer Classification from Digital Mammograms Using Vision Transformers 2023

Multi-Class Breast Cancer Classification from Digital Mammog...

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15th International Conference on Contemporary Computing, IC3 2023

作者： Salgarkar, Varun Sunil Ilavarasi, A.K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Centre for Healthcare Advancement Innovation and Research Vellore Institute of Technology Chennai India

ISBN: (纸本)9798400700224

Breast cancer stands as a prevalent form of neoplastic disease afflicting women worldwide, with timely detection playing a pivotal role in ensuring favorable treatment outcomes. In this paper, we propose a methodology for multi-class breast cancer classification using Vision Transformers, a powerful architecture for learning long-range dependencies. CNNs utilizing deep backbones have demonstrated remarkable performance in medical image analysis tasks, including disease classification, tumor segmentation, and lesion detection. Nevertheless, CNNs' inductive bias towards locality limits their ability to extract global feature information from breast cancer mammogram images, resulting in suboptimal classification results. To overcome this shortcoming, Vision Transformer (ViT) is a promising deep learning model for computer vision tasks, but its applicability to medical image classification is limited by the need for large-scale datasets and high computational power. Our results show that Vision Transformers have the potential to outperform convolutional neural networks in image-based computer-aided diagnosis (CAD) schemes. DeiT is more efficient for small-scale datasets, as it can effectively learn general features through self-supervised pre-training. On the other hand, Swin Transformer's hierarchical structure and linear computational complexity make it a computationally effective alternative. We applied the proposed methodology on two variants of two different Vision Transformer architectures, Swin and DeiT, and achieved high accuracy and AUC values for differentiating normal, benign, and malignant cases. The models are trained on Inbreast dataset obtained from Kaggle after which they are evaluated on accuracy, precision, recall, the F1-score and training time. The DeiT-B transformer model beats the Swin-S on accuracy by 11.29% achieving a highest accuracy of 88.50% with ROC of 90, 92 and 96 for normal, benign and malignant classes respectively. © 2023 ACM.

关键词： Image classification

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Intracranial Haemorrhage Detection Based on Deep Learning Using CT Images 2023

Intracranial Haemorrhage Detection Based on Deep Learning Us...

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15th International Conference on Contemporary Computing, IC3 2023

作者： Mishra, Pratyush Raj Ilavarasi, A.K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Centre for Healthcare Advancement Innovation and Research Vellore Institute of Technology Chennai India

ISBN: (纸本)9798400700224

Intracranial haemorrhage (ICH) is a medical condition that can have life-threatening consequences if it is not promptly diagnosed and treated. Diagnostic tools for medical imaging, such computed tomography (CT) scans, can help identify ICH. In this study, we investigated the application of deep learning methods to CT scan images used for ICH diagnosis. In particular, the authors made use of the Block Matching 3D (BM3D) algorithm for image pre-processing, the Densenet121 neural network design, and the Hough Transformation for image transformation. The objective was to create a model that could correctly identify and categorize ICH in CT scan pictures. The region of interest (ROI) in the CT scan pictures was then located and extracted using the Hough Transformation approach. Overall, the findings of this study show the potential of deep learning methods to support ICH diagnosis from CT scan pictures. The suggested approach was highly accurate and has the potential to help radiologists identify ICH. © 2023 ACM.

关键词： Diagnosis

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ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-OBJECTIVE LEARNING 12

ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-O...

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12th International Conference on Learning Representations, ICLR 2024

作者： Ye, Feiyang Lyu, Yueming Wang, Xuehao Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Shanghai Artificial Intelligence Laboratory China School of Computer Science and Engineering Nanyang Technological University Singapore

Multi-objective optimization (MOO) has become an influential framework for various machine learning problems, including reinforcement learning and multitask learning. In this paper, we study the black-box multi-objective optimization problem, where we aim to optimize multiple potentially conflicting objectives with function queries only. To address this challenging problem and find a Pareto optimal solution or the Pareto stationary solution, we propose a novel adaptive stochastic gradient algorithm for black-box MOO, called ASMG. Specifically, we use the stochastic gradient approximation method to obtain the gradient for the distribution parameters of the Gaussian smoothed MOO with function queries only. Subsequently, an adaptive weight is employed to aggregate all stochastic gradients to optimize all objective functions effectively. Theoretically, we explicitly provide the connection between the original MOO problem and the corresponding Gaussian smoothed MOO problem and prove the convergence rate for the proposed ASMG algorithm in both convex and non-convex scenarios. Empirically, the proposed ASMG method achieves competitive performance on multiple numerical benchmark problems. Additionally, the state-of-the-art performance on the black-box multi-task learning problem demonstrates the effectiveness of the proposed ASMG method. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Multiobjective optimization

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