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2024 IEEE International Conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Gautam, Deepika Thakur, Garima Obaidat, Mohammad S. Hsiao, Kuei-Fang Kumar, Pankaj Central University of Himachal Pradesh Srinivasa Ramanujan Department of Mathematics HP Dharamshala176215 India School of Computer and Communication Engineering University of Science and Technology Beijing China School of Computing SRM University Department of Computational Intelligence TN Kattankulathur India School of Engineering The Amity University UP 201301 India

ISBN: (纸本)9798350349832

The Internet of Medical Things (IoMT) is a network of medical devices, hardware infrastructure, and software that accumulates real-Time data through remote monitoring and minimizes doctor-patient interaction, enhancing the quality of remedies. However, the facile availability and accessibility of data over the web have given rise to serious information security and credibility issues in the IoMT. Recently, Chen et al. [1] proposed a provably secure authenticated key agreement protocol for remote patient monitoring in IoMT. However, after revisiting their scheme, we found security flaws such as user impersonation, ephemeral secret leakage attacks, denial-of-service attacks, and session key disclosure attacks. Therefore, this paper presents cryptanalysis as well as an improvement of Chen et al. 's scheme that strives to furnish protocols with robust security in opposition to the mentioned attacks. The security and effectiveness are displayed through informal analysis and evaluation with other existing state-of-The-Art metrics like computational and commu-nication cost. At last, the findings project the superiority of the proposed improved scheme. © 2024 IEEE.

关键词： Authentication Protocol

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Not All Pairs are Equal: Hierarchical Learning for Average-Precision-Oriented Video Retrieval 24

Not All Pairs are Equal: Hierarchical Learning for Average-P...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Yang Xu, Qianqian Wen, Peisong Dai, Siran Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences BDKM University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

The rapid growth of online video resources has significantly promoted the development of video retrieval methods. As a standard evaluation metric for video retrieval, Average Precision (AP) assesses the overall rankings of relevant videos at the top list, making the predicted scores a reliable reference for the users. However, recent video retrieval methods utilize pair-wise losses that treat all sample pairs equally, leading to an evident gap between the training objective and evaluation metric. To effectively bridge this gap, in this work, we aim to address two primary challenges: a) The current similarity measure and AP-based loss are suboptimal for video retrieval;b) The noticeable noise from frame-to-frame matching introduces ambiguity in estimating the AP loss. In response to these challenges, we propose the Hierarchical learning framework for Average-Precision-oriented Video Retrieval (HAP-VR). For the former challenge, we develop the TopK-Chamfer Similarity and QuadLinear-AP loss to measure and optimize video-level similarities in terms of AP. For the latter challenge, we suggest constraining the frame-level similarities to achieve an accurate AP loss estimation. Experimental results present that HAP-VR outperforms existing methods on several benchmark datasets, providing a feasible solution for video retrieval tasks and thus offering potential benefits for the multi-media application. © 2024 Owner/Author.

关键词： Self-supervised learning

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"Socially Assistive Robot Privacy Model": A Multi-model Approach to Evaluating Socially Assistive Robot Privacy Concerns 16th

"Socially Assistive Robot Privacy Model": A Multi-model Appr...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Collins, Sawyer Stanojević, Čedomir Bennett, Casey Henkel, Zachary Henkel, Kenna Baugus Abbott, Nikki M. Bethel, Cindy L. Śabanović, Selma Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States College of Behavioral Social and Health Sciences Clemson University ClemsonSC United States College of Computing and Digital Media DePaul University ChicagoIL United States Department of Computer Science and Engineering Mississippi State University StarkvilleMS United States

ISBN: (纸本)9789819635214

As socially assistive robots (SARs) enter more diverse care settings, including users’ homes, it is critical to identify the shifting privacy risks and dimensions of privacy this technology and its data collection capabilities may affect. We propose a new model of privacy to address the complex nature of SARs as a multimodal technology within the healthcare space. To construct this new model, we combine a previous three-dimensional model from the healthcare literature and synthesize it with a seven-dimension technology-related model. We then use this new combined model to analyze self-report data from several workshops with prospective users of the dog-like robot Therabot to map out the dimensions of privacy identified as future concerns for clinicians and those living with depression. Finally, we suggest this model can be used in future studies to support the in-depth exploration of privacy implications of SARs within healthcare through discussions about privacy among clinicians, those receiving care, and robot designers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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An Enhanced Convolutional Neural Network Based on L1 Regularization for Segmentation of Coronary Arteries in X-Ray Angiograms 5

An Enhanced Convolutional Neural Network Based on L1 Regular...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Wahid, Fazli Ma, Yingliang Sheikh, Ramla Aamir, Muhammad University of East Anglia School of Computing Sciences Norwich United Kingdom University of Haripur Department of Information Technology Khyber Pakhtunkhwa Pakistan University of Derby Data Science Research Centre Colloge of Science and Engineering Derby United Kingdom

ISBN: (纸本)9798350394764

The automatic segmentation of coronary arteries in X-Ray angiograms has a significant role in identification and detection of various abnormalities in vessels which can be adequately helpful in computer aided diagnosis (CAD) for supporting in analyzing the coronary heart diseases. Previously many machine learning (ML) algorithms and deep learning (DL) models have been applied for this purpose but all of them are suffered from some serious drawbacks when applied in their standardized working formats which leads to low performance of these models. Therefore, in this work an enhanced convolutional neural network named eCNN which is based on L1 regularization has been applied for automatic segmentation of coronary arteries. It has been observed that incorporating L1 regularization with different coefficient values has distinct effects on the working mechanism of CNN architecture resulting in improving its performance. In the proposed method, firstly, Gaussian Matched Filters (GMF) have been applied to enhance the quality of X-Ray angiograms. In segmentation stages, the eCNN has been applied for the segmentation. In this stage, dual L1 regularization (coefficient: 0.001 for convolutional and 0.01 for dense layers) improves model interpretability and generalization, facilitating effective segmentation of arteries. Finally, standard performance evaluation parameters have been used to evaluate the performance of the proposed model and compared with state-of-the-art techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Sensor-Based Authentication on Smartphones via Integrating Auxiliary Information

Sensor-Based Authentication on Smartphones via Integrating A...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Yingjie Hu, Ruimin Wuhan University National Engineering Research Center for Multimedia Software Whuhan430072 China School of Cyber Science and Engineering Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Whuhan430072 China

ISBN: (纸本)9781665410205

With the widespread use of smartphones, more and more private information is stored on the phone, and the loss or theft of the phone can lead to data leakage, theft of property, and other problems. Traditional active authentication methods based on passwords, faces, fingerprints, etc. authenticate only once at login, which still has some hidden dangers. Authentication methods based on behavioral biometrics such as walking gait, touch screen, keystroke, etc. can provide implicit and continuous authentication services, and thus have been widely studied recently. Considering the limited computational resources of smart devices, we first introduce the state-space model with linear model complexity, i.e., Mamba, to extract deep feature representations from raw signal sequences in the sensor-based authentication task. Then a series of auxiliary information from different domains are proposed and fused to the deep features to enhance the consistent semantic information and ultimately improve the discriminative ability of the model. Our model A-Mamba, i.e., Auxiliary-Mamba, is experimented on two public datasets. The experimental results show that our model outperforms existing approaches. © 2024 IEEE.

关键词： Information leakage

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Brain Tumor detection Using Machine Learning and Deep Learning Approaches

Brain Tumor detection Using Machine Learning and Deep Learni...

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2022 IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Khan, Oneza Tehreem Rajeswari, D. School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665495295

Recent years has provided extremely interesting and exciting developments and applications of Machine Learning (ML) and Deep Learning (DL) to automize healthcare delivery. ML techniques are surpassing human performances in detecting, predicting, diagnosing any disease and even recommending treatments for the same. ML along with Deep Learning has revolutionized Medical Imaging Technology. Brain Tumor detection is one of the many healthcare area Artificial Intelligence has marveled at. A tumor is basically an area or part of the body suffering by swelling caused by distorted tissues where the cells start multiplying uncontrollably. Magnetic Resonance Imaging (MRI) is the technique used to detect the tumor. MRI is quite commonly used in medical science to detect abnormalities in any part of the body. Tumor in the brain is far more perilous and different to treat than in any other part of the body which makes the early prediction and monitoring of brain tumor extremely expedient. This paper discusses the various algorithms, techniques, new approaches and their comparison with the existing ones and the general process of brain tumor detection with the help of MRI. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Chronic Kidney Disease Analysis using Various Biological Parameters under Machine Learning Techniques

Chronic Kidney Disease Analysis using Various Biological Par...

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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： M. Suresh E. Nirmala M. Maragatharajan Manish Yadav School of Computing Science and Engineering VIT Bhopal University Kotri Kalan Madhya Pradesh School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Kotri Kalan Madhya Pradesh

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Kidney chronic disease (KCD) has become one of the most critical illnesses which should be diagnosed properly within a certain limit of time for better treatment. Nowadays different algorithms being used for treatment of patients. By using the best predictory model, doctor can easily predict the disease on time. For this verdict, Kidney Chronic Disease Prediction has been discussed in this article. In this article various datasets have been used by the different article authors to predict the disease which are pre-processed with different methods like Univariate Feature Selection (UFS) and Recursive Feature Elimination (RFE), Data mining techniques, etc. which are further used to train and test various ML models get the accuracy by predictory models. The scores gained by the predictory models is then validated to check the precision of the output accuracy of by the model using methods like performance matrices, various cross validation techniques, Mathew Correlation Coefficient (MCC). This whole article is all around the analyses of different articles based on the machine learning and validation techniques with respect to the accuracy. Most of the models attained the accuracy of 99 percent and above. In this article, along with machine learning models it also discussed about two neural networks, which attained the accuracy between 93 to 98% with a good precision value.

关键词： Analytical models Renewable energy sources Accuracy Biological system modeling Machine learning Predictive models Feature extraction Data models Tuning Diseases

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Phase-field-based lattice Boltzmann method for containerless freezing

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Physical Review E 2024年第3期110卷 035301页

作者： Jiangxu Huang Lei Wang Zhenhua Chai Baochang Shi School of Mathematics and Statistics School of Mathematics and Physics Institute of Interdisciplinary Research for Mathematics and Applied Science Hubei Key Laboratory of Engineering Modeling and Scientific Computing

In this paper we first propose a phase-field model for the containerless freezing problems, in which the volume expansion or shrinkage of the liquid caused by the density change during the phase change process is considered by adding a mass source term to the continuum equation. Then a phase-field-based lattice Boltzmann (LB) method is further developed to simulate solid-liquid phase change phenomena in multiphase systems. We test the developed LB method by the problem of conduction-induced freezing in a semi-infinite space, the three-phase Stefan problem, and the droplet solidification on a cold surface, and the numerical results are in agreement with the analytical and experimental solutions. In addition, the LB method is also used to study the rising bubbles with solidification. The results of the present method not only accurately capture the effect of bubbles on the solidification process, but also are in agreement with the previous work. Finally, a parametric study is carried out to examine the influences of some physical parameters on the sessile droplet solidification, and it is found that the time of droplet solidification increases with the increase of droplet volume and contact angle.

关键词： Drops & bubbles Liquid-solid phase transition

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Empowering Semantic Segmentation with Selective Frequency Enhancement and Attention Mechanism for Tampering Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3270-3283页

作者： Xu, Xu Lv, Wenrui Wang, Wei Zhang, Yushu Chen, Junxin Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China

Nowadays, massive amounts of multimedia contents are exchanged in our daily life, while tampered images are also flooding the social networks. Tampering detection is therefore becoming increasingly important for multimedia integrity, and it is generally realized by designing specific convolutional neural networks. From a new perspective, this article proposes two pluggable modules for empowering existing semantic segmentation models for tampering detection. First, a selective frequency enhancement (SFE) module is developed to suppress the semantic information and selectively enhance the tamper information. Second, a boundary enhanced attention (BEA) module is designed to highlight the edge information of tempered area. Our SFE and BEA modules are combined with five mainstream semantic segmentation networks for performance evaluation. The experiment results demonstrate that our modules are able to empower the semantic segmentation networks for tampering detection, and their combinations even perform better than state-of-the-art algorithms in certain datasets. © 2020 IEEE.

关键词： Semantics

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Poster: Zero Trust Driven Architecture for Blockchain-Based Access Control Delegation

Poster: Zero Trust Driven Architecture for Blockchain-Based ...

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2024 SIGCOMM Poster and Demo Sessions, SIGCOMM Posters and Demos 2024

作者： Mukta, Rahma Pal, Shantanu Hitchens, Michael Paik, Hye-Young Kanhere, Salil S Department of CSE Chittagong University of Engineering and Technology Chittagong4349 Bangladesh School of Information Technology Deakin University MelbourneVIC3125 Australia School of Computing Macquarie University SydneyNSW2109 Australia School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia

ISBN: (纸本)9798400707179

Granting access and controlling access rights for online services is challenging in a highly dynamic and large-scale systems. In particular, the secure propagation of access right delegation is a major issue. Many proposals present access control issues for large-scale systems, however, little attention has been given to flexible and secure delegation in such dynamic systems. This article presents an approach to address such a delegation issue for large-scale systems using blockchain technology. We propose a blockchain-based delegation model that employs, i) self-sovereign identity (SSI) for user/device authentication, and ii) a restricted yet flexibly propagatable delegation process using the sanitizable signature scheme without the need for a centralized system. The goal of our primitive is to propose a Zero Trust architecture for access control delegation. To demonstrate the feasibility of our proposed architecture, we evaluate the system execution time using a sanitizable signature within a hyperledger fabric network. This work is in progress to further evaluate the system's efficiency in delegating access rights. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Large scale systems

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