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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Vivek, Yelleti Ravi, Vadlamani Mane, Abhay Naidu, Laveti Ramesh Institute for Development and Research in Banking Telangana Hyderabad India Center for Development of Advanced Computing Karnataka Bangalore India

ISBN: (纸本)9798331531195

Effective risk management and compliance adherence are paramount for the success of financial institutions and organizations. However, they often face significant challenges due to fraudulent activities, with ATM fraud, among others, emerging as a prevalent issue in today's banking landscape. We proposed a novel profiling-based one-class classification (OCC) method to solve this problem. Then training phase of our approach employs the K-Means clustering algorithm to cluster non-fraudulent transactions exhibiting similar characteristics and patterns. A rule is generated from each cluster, thereby in a rule set comprising K rules, each consisting of conditions based on the lower and upper bounds on all features. This rule set is employed to identify fraudulent transactions presented in the test phase because ours is an OCC method. One distinctive feature of our approach is its interpretability and explainability, which is crucial for understanding the model's predictions. Overall, our proposed approach demonstrates the best performance vis-à-vis that of various state-of-the-art OCC methods in terms of classification rate. Additionally, we provide sensitivity analysis by varying the number of conditions violated across the K rules. © 2025 IEEE.

关键词： Sensitivity analysis

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Hybrid forecasting of demand flexibility: A top-down approach for thermostatically controlled loads

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Energy and AI 2025年 20卷

作者： Massidda, Luca Marrocu, Marino CRS4 Center for Advanced Studies Research and Development in Sardinia Italy National Research Center in High Performance Computing Big Data and Quantum Computing Italy

Demand-side flexibility is crucial to balancing supply and demand, as renewable energy sources are increasingly integrated into the energy mix, and heating and transport systems are becoming more and more electrified. Historically, this balancing has been managed from the supply side. However, the shift towards renewable energy sources limits the controllability of traditional fossil fuel plants, increasing the importance of demand response (DR) techniques to achieve the required flexibility. Aggregators participating in flexibility markets need to accurately forecast the adaptability they can offer, a task complicated by numerous influencing variables. Based on a top-down approach, this study addresses the problem of forecasting electricity demand in the presence of flexibility from thermostatically controlled loads. We propose a hybrid model that combines data-driven techniques for probabilistic estimation of electricity consumption with a disaggregation of electricity consumption to identify the fraction of thermal loads, subject to flexibility, which is simulated by a virtual battery model. The technique is applied to a synthetic dataset that simulates the response of a European neighborhood to demand response interventions. The results demonstrate the model's ability to accurately predict both the reduction in electricity demand during DR events and the subsequent rebound in consumption. The model achieves a mean absolute percentage error (MAPE) lower than 17.0%, comparable to the accuracy without flexibility. The results obtained are compared with a direct data-driven approach, demonstrating the validity and effectiveness of our model. © 2025

关键词： Demand response

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Explainable One Class Classification for ATM Fraud Detection

Explainable One Class Classification for ATM Fraud Detection

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Yelleti Vivek Vadlamani Ravi Abhay Mane Laveti Ramesh Naidu Institute for Development and Research in Banking Hyderabad Telangana India Center for Development of Advanced Computing Bangalore Karnataka India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Effective risk management and compliance adherence are paramount for the success of financial institutions and organizations. However, they often face significant challenges due to fraudulent activities, with ATM fraud, among others, emerging as a prevalent issue in today’s banking landscape. We proposed a novel profiling-based one-class classification (OCC) method to solve this problem. Then training phase of our approach employs the K-Means clustering algorithm to cluster non-fraudulent transactions exhibiting similar characteristics and patterns. A rule is generated from each cluster, thereby in a rule set comprising K rules, each consisting of conditions based on the lower and upper bounds on all features. This rule set is employed to identify fraudulent transactions presented in the test phase because ours is an OCC method. One distinctive feature of our approach is its interpretability and explainability, which is crucial for understanding the model's predictions. Overall, our proposed approach demonstrates the best performance vis-à-vis that of various state-of-the-art OCC methods in terms of classification rate. Additionally, we provide sensitivity analysis by varying the number of conditions violated across the K rules.

关键词： Training Upper bound Sensitivity analysis Communication systems Clustering algorithms Organizations Predictive models Fraud Risk management Faces

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Correcting on-chip distortion of control pulses with silicon spin qubits

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Chinese Physics B 2025年第1期34卷 265-271页

作者： Ming Ni Rong-Long Ma Zhen-Zhen Kong Ning Chu Wei-Zhu Liao Sheng-Kai Zhu Chu Wang Gang Luo Di Liu Gang Cao Gui-Lei Wang Hai-Ou Li Guo-Ping Guo CAS Key Laboratory of Quantum Information University of Science and Technology of China(USTC)Hefei 230026China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China Integrated Circuit Advanced Process Research and Development Center Institute of MicroelectronicsChinese Academy of Sciences(CAS)Beijing 100029China Hefei National Laboratory Hefei 230088China Beijing Superstring Academy of Memory Technology Beijing 100176China Origin Quantum Computing Company Limited Hefei 230026China

In semiconductor quantum dot systems,pulse distortion is a significant source of coherent errors,which impedes qubit characterization and ***,we demonstrate two calibration methods using a two-qubit system as the detector to correct distortion and calibrate the transfer function of the control *** methods are straightforward to implement,robust against noise,and applicable to a wide range of qubit *** two methods differ in correction accuracy and *** first,coarse predistortion(CPD)method,partially mitigates *** second,all predistortion(APD)method,measures the transfer function and significantly enhances exchange oscillation *** methods use exchange oscillation homogeneity as the metric and are suitable for any qubit driven by a diabatic *** believe these methods will enhance qubit characterization accuracy and operation quality in future applications.

关键词： quantum computation quantum dot pulse distortion

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Probability maximization of magnetization switching by spin-orbit torques generated by a ferromagnet

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Physical Review B 2025年第9期111卷 094420-094420页

作者： Tomohiro Taniguchi Shinji Isogami Shuji Okame Katsuyuki Nakada Tomoyuki Sasaki Seiji Mitani Masamitsu Hayashi National Institute of Advanced Industrial Science and Technology (AIST) Research Center for Emerging Computing Technologies Tsukuba Ibaraki 305-8568 Japan National Institute for Materials Science Tsukuba 305-0047 Japan Advanced Products Development Center Technology & Intellectual Property HQ TDK Corporation Ichikawa Chiba 272-8558 Japan Department of Physics The University of Tokyo Tokyo 113-8654 Japan

Spin-orbit torques (SOTs) caused by spin currents generated in a ferromagnetic electrode enable a fast and deterministic magnetization switching. One SOT (y-SOT), polarized orthogonal to both electric current flowing in the electrode and easy axis of a ferromagnetic free layer, causes fast magnetization instability. The other SOT (z-SOT) points to the easy-axis direction and leads to the deterministic switching. Here, we evaluate the magnetization switching probability by these SOTs for various values of electric current density and the ratio of two SOTs from numerical simulation of the Landau-Lifshitz-Gilbert (LLG) equation. It is found that the switching probability is maximized when the electric current density is close to a critical value for the magnetization destabilization solely by the y-SOT. The origin of such a current dependence is investigated by analyzing temporal dynamics, spectra of the magnetization distribution, and a steady-state solution of the LLG equation. We reveal that the maximization of the switching probability originates due to two different switching behaviors. In the low current region, the magnetization in some trials remains near the initial state because of the weak y-SOT, and thus, the switching error occurs. The number of such trials, which represents the switching error, decreases as the electric current density increases because both y and z-SOTs prompt the switching. In the large current region, the large y-SOT immediately tilts the magnetization toward the switched direction. However, since the y-SOT prefers an in-plane magnetized state, the switching error due to a probabilistic return to the initial state after turning off the electric current density occurs. The switching error in this region tends to increase with increasing current density. As a result, the switching probability is optimized when the electric current density is close to the critical value.

关键词： Ferromagnetism

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Building Privacy-Preserving Medical Text Models With a Pretrained Transformer

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IEEE Internet of Things Journal 2025年第9期12卷 11529-11538页

作者： Shafiq, Muhammad Ren, Lijing Srivastava, Gautam Zhang, Denghui Bourouis, Sami Gadekallu, Thippa Reddy Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou511370 China Institute of Artificial Intelligence Guangdong Mechanical & Electrical Polytechnic Guangzhou510515 China Department of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung40402 Taiwan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura140401 India Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan250013 China Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Division of Research and Development Lovely Professional University Phagwara144411 India Center of Research Impact and Outcome Chitkara University Rajpura140401 India

The rapid advancement of big data and artificial intelligence (AI) in healthcare heightens the urgency for accurate medical text sentiment analysis. The privacy protection of medical data has been a crucial concern due to its sensitivity. The Internet of Medical Things (IoMT) facilitates large-scale data collection at lower cost, enabling precision medicine. However, decentralized IoMT poses novel challenges to centralized standard encryption schemes. In this article, we propose a novel approach to building privacy-preserving sentiment models with a generative pretrained transformer (GPT). We first convert sensitive medical text data into noise-like and distributed one-hot images. Then, we introduce visual cryptography (VC) for lightweight and secure transmission of medical text across public networks in resource-limited IoMT devices. We adopt a cross-domain sentiment analysis framework that finetunes transformer-based language models for accurate sentiment analysis instead of training GPT in sentiment analysis from scratch. Experimental results show that the proposed approach improves the accuracy and effectiveness of sentiment analysis while maintaining privacy, thereby addressing a significant gap in biomedical text analysis. © 2014 IEEE.

关键词： Differential privacy

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Supporting Elderly Care Through an AI-Driven and FHIR-Based Remote Monitoring System 21st

Supporting Elderly Care Through an AI-Driven and FHIR-Based ...

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21st International Symposium on Distributed computing and Artificial Intelligence, DCAI 2024

作者： Martins, Rafael Pereira, Hugo Corrente, Gustavo Xavier, William Conceição, Luís Freitas, Alberto Marreiros, Goreti GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering – Polytechnic of Porto Porto Portugal LASI – Intelligent Systems Associate Laboratory Guimarães Portugal Wiseware Solutions Gafanha da Encarnação Portugal MEDCIDS – Department of Community Medicine Information and Health Decision Sciences Faculty of Medicine University of Porto Porto Portugal CINTESIS@RISE – Center for Health Technology and Services Faculty of Medicine University of Porto Porto Portugal

ISBN: (纸本)9783031809453

An increase in the elderly population has led to a growing demand for constant health care. Remote monitoring of seniors aims to provide more effective care and promote patient independence, whether they live in residential homes, or their own homes supported by a domiciliary care service. It encompasses several issues present in their lives that may benefit from being assisted and monitored remotely. There are many successful studies and products focused on one of these issues, but there are no systems, to the best of our knowledge, that serve as a broad remote monitoring solution for multiple of these topics simultaneously. This paper proposes an architecture for an intelligent health monitoring ecosystem to interact with these various aspects of the elderly’s daily life, including monitoring of vital signs, movement and medication intake, detection of falls, sleep quality analysis, access control, and patient–caregiver communication. This solution will make use of wearables and medication dispensers, and it will securely store all patient-related information into an HL7 FHIR standardized database, from where machine learning models can import data for predicting important information and generating alerts when necessary. Next steps involve the development of this proposed system and testing it with elderly volunteers living in a residential home or subscribed to its domiciliary care services. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Remote patient monitoring

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Software-based, low-cost fault detection for microprocessors

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IEEE Potentials 2008年第1期27卷 37-41页

作者： Saha, Goutam Kumar Center for Development of Advanced Computing Kolkata India IEEE

An advanced error detection method known as PSW (Processor status word)-NOP (No operation) for the detection of operational errors in the microprocessors is presented. This method is very much cost effective than other approaches such as fingerprinting and masking redundancy. It is extremely suitable for improving the reliability of microprocessors based systems having low fault tolerance. The PSW-NOP method helps the programmer in the insertion of the detection program code at the critical parts of an application program, which are useful for the fault detection even if the faults are related to the programmer itself. It is a very much useful tool for transient susceptibility testing in a microprocessor system and in the supplementation of the error correcting codes. The PSW-NOP provides an economical solution to the error detection problem as in this multiple versions of code or hardware are not needed. A dependable software also can be created with the help of the PSW-NOP approach.

关键词： Microprocessor chips

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A PARALLEL ALGORITHM FOR ELIMINATION TREE COMPUTATION AND SYMBOLIC FACTORIZATION

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PARALLEL computing 1992年第8期18卷 849-856页

作者： KUMAR, PS KUMAR, MK BASU, A Center for Development of Advanced Computing 2/1 Brunton Road Bangalore 560 025 India

The notion of an elimination tree plays a very important role in the parallel algorithms for sparse Cholesky decomposition, symbolic factorization and in determining the mapping of columns of the matrix to processors. In this paper, we present a parallel algorithm to compute the elimination tree and simultaneously carry out symbolic factorization on a local memory multiprocessor. An existing parallel algorithm for symbolic factorization [5] requires the computation of elimination tree separately. In our algorithm, we use a tree defined on the given matrix, called false elimination tree, and convert it into the actual elimination tree. In the process, we also compute the structure of the columns of the factor matrix. Using the new parallel algorithm on grid problems, we found that it performs 2 to 3 times faster compared to the total time taken for sequential computation of the elimination tree and the parallel computation of symbolic factorization using [5]. Also, our algorithm is the first parallel algorithm for elimination tree computation that gives a speed-up.

关键词： SPARSE CHOLESKY DECOMPOSITION SYMBOLIC FACTORIZATION ELIMINATION TREE LOCAL MEMORY MULTIPROCESSOR

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ON THE PERFORMANCE OF TRANSPUTER ARRAYS FOR DENSE LINEAR-SYSTEMS

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PARALLEL computing 1990年第1-3期15卷 107-117页

作者： BOREDDY, J PAULRAJ, A Center for Development of Advanced Computing 2/1 Brunton Road Bangalore-560 025 India

In this paper, computation and communication performance is evaluated for single and multitransputer arrays. Performance models are proposed for Occam program execution, under Transputer development System TDS2. The performance features of normalised arithmetic, concurrent floating and integer arithmetic, logarithmic array indexing, and on-chip/off-chip RAM are studied. The startup time, byte transfer rate, asymptotic link bandwidth, and half performance message length are estimated for simultaneous operation of one, two, three, and four links at clock in unidirectional/bidirectional modes. The impact of various performance maximisation techniques on execution time is also addressed. The matrix factorisation algorithms for dense linear systems are chosen as the focus for this study. The implementations include LUD, Householder, Gauss-Jordan, Choleski, and Givens methods. Floating point operations count alone is inadequate to estimate computation time; many other factors such as array indexing, load/store overhead, and loop overhead play a significant role in the transouter performance for the dense linear systems. The reduction in array indexing overheads in multitransputer arrays may result in superlinear speedups.

关键词： Dense linear systems Matrix factorisation Performance characterisation Performance maximisation Superlinear speedup

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