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Data Science and Network Security (ICDSNS), International Conference on

作者： Aman Kataria Vikram Puri Piyush Kumar Pareek Sita Rani Amity Institute of Defence Technology Amity University Noida India Center of Visualization and Simulation Duy Tan University Da Nang Vietnam Dept. of Artificial Intelligence and Machine Learning and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India

Wearable body sensors have recently gained popularity because they monitor user behaviors and vital indicators. These sensors are designed to monitor various physiological factors, such as motion, temperature, and heart rate, among others. Making sense of the data gathered by these sensors requires separating the dependent and independent features. The dependent parameters are those that body-worn sensors can read in this scenario. Examples of such elements include blood oxygen levels, heart rate, temperature, mobility, and airflow. These sensors capture data about a person's activity and health. The independent parameters in wearable body sensors are related to the dependent parameters, whereas the dependent parameters are those that the sensors can measure directly. Such characteristics include age, sex, weight, and environmental influences. For example, humidity and airflow might impair a temperature sensor's accuracy. The user's age or level of physical fitness may also influence the accuracy of a heart rate monitor. Activity monitors are used in this study to capture the actions of various people. Static and dynamic properties are derived from the activity monitor recording. The performance evaluations of the eight distinct machine-learning models that were created using the retrieved features are discussed in this paper. The Grid Search method is used to enhance the performance of the XGB model.

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DLT Based Smart Medical Ecosystem

DLT Based Smart Medical Ecosystem

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Vikram Puri Aman Kataria Sita Rani Piyush Kumar Pareek Center of Visualization and Simulation Duy Tan University Da Nang Vietnam Amity Institute of Defence Technology Amity University Noida Uttar Pradesh India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Dept. of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

The healthcare system has become more reliant on the data collection capabilities of fast evolving IoT devices. Patient healthcare records (PHR) now contain additional information as a result. However, securing identities and guaranteeing data security have emerged as critical challenges in the healthcare system. When it comes to regulating PHR, many of the centralized and decentralized methods now in use struggle difficulties such as single point of failure, high transaction fees, slow throughput, and excessive latency. To address these concerns, a distributed ledger technology (DLT)-based framework named IOTA rather than a blockchain-based platform such as Ethereum or Polygon is proposed. The proposed framework considered the example of a hospital with several patients on the same and various floors. Furthermore, the suggested approach employs a real-time experimental setup for calculating the time and power consumption of the devices.

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Inconsistency Distillation For Consistency:Enhancing Multi-View Clustering via Mutual Contrastive Teacher-Student Leaning

Inconsistency Distillation For Consistency:Enhancing Multi-V...

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IEEE International Conference on Data Mining (ICDM)

作者： Dunqiang Liu Shu-Juan Peng Xin Liu Lei Zhu Zhen Cui Taihao Li Dept. of Comput. Sci. & Fujian Key Lab. of Big Data Intelligence and Security Huaqiao University Xiamen China Zhejiang Lab Hangzhou China Xiamen Key Lab. of Computer Vision and Pattern Recognition Huaqiao University Xiamen China Key Lab. of Computer Vision and Machine Learning (Huaqiao University) Fujian Province University Xiamen China School of Information Sci. and Eng. Shandong Normal University Jinan China School of Computer Sci. and Eng. Nanjing University of Science and Technology Nanjing China

Multi-view clustering has attracted more attention recently since many real-world data are comprised of different representations or views. Recent multi-view clustering works mainly exploit the instance consistency to obtain the shared representations across different views, and apply a single-view clustering method to perform data partitions. However, these existing methods often ignore the inconsistency of instance associations within the views, which may enlarge the intra-class diversity among the views and therefore degrade the clustering performance. To address this issue, this paper proposes an efficient mutual contrastive teacher-student leaning (MC-TSL) model to enhance the multi-view clustering, which is the first attempt to study the inconsistency distillation for consistency learning. First, the proposed MC-TSL approach exploits a view-specific encoder with two heads, an instance encoding head and a semantic distillation head, respectively, for capturing the consistent and discriminative feature representations. To be specific, the former head exploits a cross-view contrastive learning method to obtain a redundancy-free consistent representation at the instance level, while the latter head designs a mutual teacher-student learning module to capture the intra-view information at semantic level. By training these two heads in an end-to-end manner, the discriminative multi-view embeddings are efficiently obtained and refined by minimizing the weighted sum of the reconstruction loss, contrastive loss and contrast distillation loss. Extensive experiments verify the superiorities of the proposed MC-TSL framework and show its competitive clustering performances.

关键词： Training learning systems Clustering methods Semantics Encoding Data mining

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AI auditing: The Broken Bus on the Road to AI Accountability

arXiv

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arXiv 2024年

作者： Birhane, Abeba Steed, Ryan Ojewale, Victor Vecchione, Briana Raji, Inioluwa Deborah Mozilla Foundation Sch. of Computer Science and Statistics Trinity College Dublin Dublin Ireland Heinz College Machine Learning Dept. Carnegie Mellon University Pittsburgh United States Ctr. for Technological Responsibility Reimagination & Redesign Brown University Providence United States Data & Society New York City United States Mozilla Foundation University of California Berkeley Berkeley United States

One of the most concrete measures to take towards meaningful AI accountability is to consequentially assess and report the systems' performance and impact. However, the practical nature of the "AI audit" ecosystem is muddled and imprecise, making it difficult to work through various concepts and map out the stakeholders involved in the practice. First, we taxonomize current AI audit practices as completed by regulators, law firms, civil society, journalism, academia, consulting agencies. Next, we assess the impact of audits done by stakeholders within each domain. We find that only a subset of AI audit studies translate to desired accountability outcomes. We thus assess and isolate practices necessary for effective AI audit results, articulating the observed connections between AI audit design, methodology and institutional context on its effectiveness as a meaningful mechanism for accountability. © 2024, CC BY.

关键词： machine learning

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learning weakly convex sets in metric spaces

arXiv

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arXiv 2021年

作者： Stadtländer, Eike Horváth, Tamás Wrobel, Stefan Dept. of Computer Science University of Bonn Germany Fraunhofer IAIS Schloss Birlinghoven Sankt Augustin Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

One of the central problems studied in the theory of machine learning is the question of whether, for a given class of hypotheses, it is possible to efficiently find a consistent hypothesis, i.e., which has zero training error. While problems involving convex hypotheses have been extensively studied, the question of whether efficient learning is possible for non-convex hypotheses composed of possibly several disconnected regions is still less understood. Although it has been shown quite a while ago that efficient learning of weakly convex hypotheses, a parameterized relaxation of convex hypotheses, is possible for the special case of Boolean functions, the question of whether this idea can be developed into a generic paradigm has not been studied yet. In this paper, we provide a positive answer and show that the consistent hypothesis finding problem can indeed be solved in polynomial time for a broad class of weakly convex hypotheses over metric spaces. To this end, we propose a general domain-independent algorithm for finding consistent weakly convex hypotheses and prove sufficient conditions for its efficiency that characterize the corresponding hypothesis classes. To illustrate our general algorithm and its properties, we discuss several non-trivial learning examples to demonstrate how it can be used to efficiently solve the corresponding consistent hypothesis finding problem. Without the weak convexity constraint, these problems are known to be computationally intractable. We then proceed to show that the general idea of our algorithm can even be extended to the case of extensional weakly convex hypotheses, as it naturally arise, e.g., when performing vertex classification in graphs. We prove that using our extended algorithm, the problem can be solved in polynomial time provided the distances in the domain can be computed efficiently. Copyright © 2021, The Authors. All rights reserved.

关键词： Set theory

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A Provably Secure ECC-based Multi-factor 5G-AKA Authentication Protocol

A Provably Secure ECC-based Multi-factor 5G-AKA Authenticati...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Awaneesh Kumar Yadav Manoj Misra Pradumn Kumar Pandey Kuljeet Kaur Sahil Garg Xi Chen Dept. of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand India Electrical Engineering Department Ecole de Technologie Supérieure (ETS) Montreal QC Canada Resilient Machine Learning Institute (ReMI) Montreal QC Canada School of Computer Science McGill University Montreal QC Canada

ISBN: (纸本)9781665435413

Due to the constant penetration of various security attacks, it is highly important to secure the underlying communication networks between the IoT, Fog and Cloud in the next generation of mobile communication system (5G). Thus, secure authentication and key agreement protocol, namely 5G-AKA, has been proposed in the literature to safely and stably access the 5G mobile services. However, some recent findings reveal that 5G-AKA and its numerous versions based on symmetric or asymmetric encryption are either vulnerable to different attacks such as perfect forward secrecy violation, malicious Serving Network (SN), de-synchronization attack, privacy theft, stolen device, or are computationally intensive. Apart from that, these protocols use single-factor authentication. Considering the above demerits of these protocols and the necessity to provide enhanced security, we propose an Elliptic Curve-Cryptography (ECC)-based multi-factor 5G-AKA authentication protocol. It provides additional security and achieves cost-effectiveness in terms of computational, communication, storage costs and energy consumption. The formal security analysis using Real-Or-Random (ROR) logic has been done to confirm its security. Moreover, we evaluate the performance of the proposed protocol in terms of computational, communication, storage costs and energy consumption. The evaluation results show that the proposed protocol requires less cost than its counterparts, reducing computational cost by up to 57%, communication cost by up to 59%, storage cost by up to 52%, and energy consumption by up to 51%.

关键词： Energy consumption Privacy Protocols Costs Multi-factor authentication 5G mobile communication Robustness

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Multiple linear regression model for inflation in India 2

Multiple linear regression model for inflation in India

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2nd International Conference for Emerging Technology, INCET 2021

作者： Mohammed, Najeebuddin Sarma, A. Kusalava Dhamani, Shahid University of Waterloo Graduate Studies and Postdoctoral Affairs Electrical and Computer Engineering Dept. WaterlooONN2L 3G1 Canada Osmania University Department of Electrical Engineering Telangana Hyderabad500017 India Plaksha Tech Leaders Fellowship Artificial Intelligence and Machine Learning Haryana Gurugram122022 India

ISBN: (纸本)9781728170299

This paper examines the factors that effect the monthly inflation rate in India using monthly data from January 2010 to October 2020. Two multiple linear regression models, with and without interaction terms, using least squares method for coefficient estimation have been used to find out which factors have a direct effect on the monthly inflation rate. The results indicate that the Gross Domestic Product (GDP), the interaction between exchange rate and M1 money supply;interaction between bank rate and GDP, and interaction between bank rate and M1 money supply;have a significant influence on the monthly inflation rate in India. © 2021 IEEE.

关键词： Multiple linear regression

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The Frontier of Geospatial AI Deep learning Applications in Data Mining and Spatial Analysis

The Frontier of Geospatial AI Deep Learning Applications in ...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Ajit Kumar Mishra Vikash Verma R. Manivannan N. Krishna Kumar Mudunuru Srividya Sanskriti Sharma Department of Electrical & Electronics Engineering IES College of Technology Bhopal M.P India Dept of Artificial Intelligence & Machine Learning GGITS Jabalpur M.P India School of Computing Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai TN India Dept.of CSE Guru Nanak Institutions Technical Campus Ibrahimpatam Hyderabad Telangana India Dept. of CSE MLR Institute of Technology Hyderabad Telangana India Dept of Computer Science & Business System GGITS Jabalpur M.P India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Modern artificial intelligence uses geographical data mining and regional analysis to analyze large datasets. Deep learning is a new geographic AI method. Example: Geographic Deep Mining Network (GDMN). The Spatial Feature Extractor (SFE), Temporal Geospatial Attention Mechanism (TGAM), and Geospatial Data Fusion Module (GDFM) comprise this technique. We examined six popular approaches and found GDMN performed best. It worked successfully for several reasons. GDMN always outperforms its competitors in memory, F1 score, IoU, training time, and inference time. This is always true. These findings suggest that GDMN might boost geospatial AI’s data mining and location analysis abilities. After all the effort, we have developed a solid basis for location intelligence, making it useful in various subject areas. Because it’s versatile, mapping specialists may use GDMN for better environmental monitoring or city development. Deep learning helps GDMN make better space choices and uncover insights in large, challenging data sets that no one knew about. GDMN’s cutting-edge approaches make space and time research and prediction simpler. This wide perspective offers a more full and all-encompassing picture of today’s challenges than previous techniques. This makes it impossible to overstate how significant this work is to location intelligence. Its unmatched research and insights usher in a new era.

关键词： Deep learning Training Urban areas Transforms Feature extraction Solids Spatial databases Geospatial analysis Data mining Artificial intelligence

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Algorithmic Computability of the Capacity of Additive Colored Gaussian Noise Channels

Algorithmic Computability of the Capacity of Additive Colore...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Holger Boche Andrea Grigorescu Rafael F. Schaefer H. Vincent Poor Chair of Theoretical Information Technology Technical University of Munich BMBF Research Hub 6G-life CASA - Excellence Cluster Ruhr University Bochum Chair of Information Theory and Machine Learning Technische Universität Dresden BMBF Research Hub 6G-life CETI - Excellence Cluster Dept. of Electrical and Computer Engineering Princeton University

Designing capacity-achieving coding schemes for the band-limited additive colored Gaussian noise (ACGN) channel has been and is still a challenge. In this paper, the capacity of the band-limited ACGN channel is studied from a fundamental algorithmic point of view by addressing the question of whether or not the capacity can be algorithmically computed. For this purpose, the concept of Turing machines is used, which provides fundamental performance limits of digital computers. It is shown that there are band-limited ACGN channels having a computable continuous spectral density whose capacity is a non-computable number. Moreover, it is demonstrated that for these channels, it is impossible to find a computable sequence of asymptotically sharp upper bounds for their capacity.

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Vehicle Type Detection and Classification Using Improved Relief Feature Selection Algorithm and Bidirectional Gated Recurrent Unit

Vehicle Type Detection and Classification Using Improved Rel...

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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： K M Monica Mohammed Al-Farouni G K Madhura K. Ghamya M. Geethalakshmi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India Dept. of CSE(AI&ML) School of Computing Mohan Babu University Tirupati India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Nowadays, Traffic density estimation can be utilized to control traffic light signals for managing the traffic effectively. Vehicle Type Classification (VTC) is used in this traffic density. Because of the factors like minimal interclass variance and maximum intraclass variation, the VTC is challenging which is handled by proposed Improved Relief Feature Selection Algorithm and Bi-directional Gated Recurrent Unit (IRFSA-Bi-GRU) method. Bi-directional Gated Recurrent Unit (Bi-GRU) model is proposed for enhancing Vehicle Type Classification (VTC) within Intelligent Transport Systems. Multiview In Out Traffic Complexity Database (MIOTCD) is used for data collection then a camera response model is used for enhancing visual data quality. Kalman filtering and Gaussian mixture model techniques are utilized for vehicle recognition and tracking. Local Binary Pattern (LBP), Steerable Pyramid Transform (SPT), and Dual Tree Complex Wavelet Transform (DTCWT) are used for feature extraction and Improved Relief Feature Selection Algorithm (IRFSA) is used for feature selection. Finally, the Bi-directional Gated Recurrent Unit (Bi-GRU) model is used for classifying 11 vehicle categories on MIO-TCD. The obtained results exposed that the IRFSA-BiGRU method outperformed the existing methods by achieving an accuracy of 99.02%, 98.34% F-score, 98.78% MCC,99.93% specificity, 99.32% NPV, 98.75% precision, 98.14% recall on the MIO-TCD database.

关键词： Wavelet transforms Visualization Accuracy Data integrity Bidirectional control Logic gates Feature extraction

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