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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Kumar, Rajendra Mehta, Shashank K Rajasree, Mahesh Sreekumar Indian Institute of Technology Delhi Department of Computer Science & Engineering New Delhi India Indian Institute of Technology Kanpur Department of Computer Science & Engineering Uttar Pradesh India

ISBN: (纸本)9798331532833

In this paper, we introduce the Maximum Distance Sublattice Problem (MDSP). We observed that the problem of solving an instance of the Closest Vector Problem (CVP) in a lattice L is the same as solving an instance of MDSP in the dual lattice of L. We give an alternate reduction between the CVP and MDSP. This alternate reduction does not use the concept of dual lattice. © 2024 IEEE.

关键词： CVP geometry GSO Karp reduction lattice

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Efficient IRS-Aided Rate Optimizations for Dual Functional Radar and Communications

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IEEE Communications Letters 2024年第1期28卷 39-42页

作者： Sweta, S. Dubey, Ankit Nguyen, Van-Linh Indian Institute of Technology Jammu Department of Electrical Engineering Jammu and Kashmir Jammu181221 India National Chung Cheng University Department of Computer Science and Information Engineering Chiayi62102 Taiwan

This letter presents a novel method to enable beam steering optimization for secure communications and resilience against radio tracking in an intelligent reflecting surfaces-enabled multiple-input multiple-output dual-functional radar and communications system. The approach involves optimizing beam steering by solving two convex sub-problems: i) maximizing secrecy rate and ii) maximizing legitimate receiver rate, solved using Taylor series approximation. Evaluation results demonstrate that optimizing radar and beam steering enhances secure communications while maintaining quality of service. Furthermore, the utilization of IRS effectively prevents passive eavesdropping in both line-of-sight (LOS) and non-LOS scenarios. © 1997-2012 IEEE.

关键词： Convex optimization

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TRE-DSP:A traceable and revocable CP-ABE based data sharing scheme for IoV with partially hidden policy

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Digital Communications and Networks 2025年第2期11卷 455-464页

作者： Yousheng Zhou Rundong Peng Yuanni Liu Pandi Vijayakumar Brij Gupta College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Computer Science and Engineering University College of EngineeringTindivanamMelpakkamTamil nadu 604001India Department of Computer Engineering National Institute of Technology KurukshetraIndia

With the popularity of the Internet of Vehicles(IoV),a large amount of data is being generated every *** to securely share data between the IoV operator and various value-added service providers becomes one of the critical *** to its flexible and efficient fine-grained access control feature,Ciphertext-Policy Attribute-Based Encryption(CP-ABE)is suitable for data sharing in ***,there are many flaws in most existing CP-ABE schemes,such as attribute privacy leakage and key *** paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP).A partially hidden access structure is adopted to hide sensitive user attribute values,and attribute categories are sent along with the ciphertext to effectively avoid privacy *** addition,key tracking and malicious user revocation are introduced with broadcast encryption to prevent key *** the main computation task is outsourced to the cloud,the burden of the user side is relatively *** of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.

关键词： Privacy-preserving Attribute-based encryption Partially hidden policy Traceability User revocation Internet of vehicles

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Bridging the gap in biomedical information retrieval: Harnessing machine learning for enhanced search results and query semantics

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9491-9510页

作者： Madhubala, P. Ghanimi, Hayder M.A. Sengan, Sudhakar Abhishek, Kumar Department of Computer Science and Engineering Bharathiyar Institute of Engineering for Women Tamil Nadu Salem India Information Technology Department College of Science University of Warith Al-Anbiyaa Karbala Iraq Computer Science Department College of Computer Science and Information Technology University of Kerbala Karbala Iraq Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Mathematics VIT Bhopal Bhopal India

The medical domain faces unique challenges in information Retrieval (IR) due to the complexity of medical language and terminology discrepancies between user queries and documents. While traditional Keyword-Based Methods (KBM) have limitations, the integration of semantic knowledge bases and concept mapping techniques enhances data organization and retrieval. Addressing the growing demands in the biomedical field, a novel medical information Retrieval System (IRS) is proposed that employs Deep Learning (DL) and KBM. This system comprises five core steps: pre-processing of texts, document indexing using DL (ELMo) and KBM, advanced query processing, a BiLSTM-based retrieval network for contextual representation, and a KR-R re-ranking algorithm to refine document relevance. The purpose of the system is to give users improved biomedical search results through the integration of all of these techniques into a method that takes into consideration the semantic problems of medical records. An in-depth examination of the TREC-PM track samples from 2017 to 2019 observed an impressive leading MRR score of 0.605 in 2017 and a best-in-class rPrec score of 0.350 in 2019, proving how well able the system is to detect and rank relevant medical records accurately. © 2024 – IOS Press. All rights reserved.

关键词： information retrieval

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A Novel Music Recommendation System Using Filtering Techniques

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Informatica (Slovenia) 2024年第4期48卷 595-610页

作者： Vashishtha, Srishti Varshney, Deepika Sarin, Eva Kaur, Simran Computer Science and Engineering Dept. Bharati Vidyapeeth’s College of Engineering GGSIPU New Delhi India Computer Science and Engineering Dept. Jaypee Institute of Information Technology Noida India

With the growth of the World Wide Web, a large amount of music data is available on the Internet. A large number of people listen to music online rather than downloading and listening offline. But only some sites provide personalized and accurately recommended songs while they listen to an auto-playing playlist. Hence the need for recommendation systems arises. Two approaches can be applied to the recommendation system: content based filtering and collaborative filtering. While in content-based filtering approach, analysis on the songs’ content which has been preferred by the user in history is done and the songs with relative similarity are recommended. While latter suggests songs that certain users of similar listening pattern have preferred. But collaborative filtering-based recommendation systems not only requires time to attain stability but also might recommend unsuitable music because it is not personalized to each user’s preference. Also, the latter requires the songs to be listened by a few users already to recommend it any other user. Hence for overall analysis, answers to few questions need to be implemented in recommendation systems: the very first one is how the properties should be analyzed, next one is how the analysis should be done and last is how the songs related to the user’s preference should be chosen. So, for suggesting the better system which solves these three questions as well as provides better and personalized recommendations, In this paper we build a collaborative filtering as well as content based filtering recommendation system, where various factors determine the essence of the songs, i.e. liveliness, keys used, loudness, pitch, valence, etc., are analyzed for comparison. From the experimental analysis in has been identified that content-based filtering technique performed best on KNN machine learning classifier with accuracy of 85%. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Collaborative filtering

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Deep Reinforcement Learning for Portfolio Management with Dependency Factor 9

Deep Reinforcement Learning for Portfolio Management with De...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Kanakaian, Shaji Martin, John Paul Indian Institute of Information Technology Department of Computer Science and Engineering Kerala Kottayam India

ISBN: (纸本)9798350314090

Portfolio Management is an asset management technique where funds are continuously allocated and reallocated to obtain maximum gain with minimal risk. Constructing an efficient portfolio model is very challenging since the model should reduce the investment risk and improve returns and profit. A portfolio management system is proposed based on deep reinforcement learning strategy that incorporates Holding companies performance and % of shares holding in other stocks(Holdings) as an additional feature to the reward system to dynamically analyze the perceived market condition from the real world. In order to utilize the correlation of shares effectively, we introduce a new feature, dependency factor that is modeled based on price of the holdings, % of the shares held on holdings and the total share value of holding and the price change on each trading day. This deep reinforcement learning framework uses Deterministic Policy Gradient Algorithm. Experimental analysis demonstrates that the proposed framework with dependency factor obtained better results. © 2023 IEEE.

关键词： Reinforcement learning

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A Reversible Data Hiding Scheme for ECG Signals Using CNN-PEE

A Reversible Data Hiding Scheme for ECG Signals Using CNN-PE...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Anjum, Mohammad Okasha Bhalerao, Siddharth Ansari, Irshad Ahmad ABV-Indian Institute of Information Technology and Management Department of Information Technology Gwalior India Gyan Ganga Institute of Technology and Sciences Department of Computer Science and Engineering Jabalpur India ABV-Indian Institute of Information Technology and Management Department of Electrical and Electronics Engineering Gwalior India

ISBN: (纸本)9798350379525

The security of biomedical signals is the highest priority of any medical or clinical setup. The electrocardiogram (ECG) signals are very crucial in assessing heart health and their security is of utmost importance. A reversible data hiding scheme based on prediction error expansion (PEE) has been proposed in this work. The patient's personal data acts as a watermark and is embedded in the ECG signal. A 1D CNN based predictor has been used to obtain the sample predictions. The signals obtained from the MIT-BIH arrhythmia dataset have been utilized for training and testing the CNN model. The CNN-based predictor ensures superior performance and a high value of performance parameters. © 2024 IEEE.

关键词： CNN ECG PEE reversible data hiding watermarking

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Relevance of ISO/SAE 21434 in Vehicular Architecture Development

Relevance of ISO/SAE 21434 in Vehicular Architecture Develop...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Kethareswaran, V. Moulik, Sanjay Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798350337020

Research in terms of security in vehicular architecture has been a focal point of deliberation in the automotive sector. The Automotive industry development has continuously evolved the scenario of adding more integration to networked vehicles or semi-autonomous vehicles. The end product of such manufacturing practices is a vehicle becoming a networked computer on wheels. In spite of providing numerous advantages, these developments in the automotive industry are also bringing more challenges in terms of security. Hence, the industry has been forced to emphasise the principle of Security by Design. Efforts in this direction have resulted in the formulation of the ISO/SAE 21434 standard. In this work, we focus on summarising the ISO/SAE 21434 standard and analysing its relevance for automotive architecture development. Related works in the standard's approach of Security by Design are summarised. Additionally, we propose an application work of the standard in the context of automotive Architecture development as future work. © 2023 IEEE.

关键词： Architecture Development Automotive Industry ISO/SAE 21434 Security

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Learning Semantic Representations and Discriminative Features in Unsupervised Domain Adaptation 11

Learning Semantic Representations and Discriminative Feature...

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11th International Symposium on Electronic Systems Devices and Computing, ESDC 2023

作者： Sidibomma, Rushendra Sanodiya, Rakesh Kumar Indian Institute of Information Technology Department of Computer Science and Engineering Sri City India

ISBN: (纸本)9781665455725

In domain adaptation, the goal is to train a neural network on the source domain and obtain a good accuracy on the target domain. In such a scenario, it is important to transfer the knowledge from the labelled source domain to the unlabelled target domain due to the expensive cost of manual labelling. Following the trail of works in the recent time, feature level alignment seems to be the most promising direction in unsupervised domain adaptation. In most of the recent works using this feature alignment, the semantic information present in the labelled source domain has not been exploited. Among the works that have tried to learn this semantic representations, the discriminative features have not been taken into consideration which results in lower accuracy on target domain. In this paper, we present a novel approach, joint discriminative and semantic transfer network (JDSTN) that not only aligns the semantic representations of source and target domain, but also enhances the discriminative features and thereby improving the accuracy significantly. This is achieved by using pseudo-labels to align the feature centroids of source and target domains while introducing losses that promote the learning of discriminative features. © 2023 IEEE.

关键词： Semantics

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Identifying Insider Cyber Threats Using Behaviour Modelling and Analysis 21

Identifying Insider Cyber Threats Using Behaviour Modelling ...

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21st OITS International Conference on information technology, OCIT 2023

作者： Akash, Tej Jayan, Alaka Gaur, Nimit Reddy, Saddikuti Vishnu Vardhan Manjith, B.C. Indian Institute of Information Technology Kottayam Department of Computer Science & Engineering Kerala India

ISBN: (纸本)9798350358230

Prevention of insider threats is challenging as credible associates of the organisation are involved and have access to confidential information. User behaviour modelling is crucial for detecting these anomalies. The suggested approach leverages a GRU-based autoencoder to model user behaviour based on users' session activities and identify anomalous events based on day-wise data. The technique consists of two steps. It initially calculates the reconstruction error using the GRU-based autoencoder on the nonanomalous data to determine the threshold used to identify outliers apart from regular data points. Then, anomalies are allocated to the identified outliers. The model creates substantial reconstruction errors for any unseen behaviour or abnormal pattern. © 2023 IEEE.

关键词： Statistics

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