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Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural Computing and Applications, (2025), 10.1007/s00521-025-10991-3)

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Neural Computing and Applications 2025年 1-2页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘department of Electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘department of Electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and Faculty of Information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘department of computer science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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Bluetooth Vulnerabilities and Security

Bluetooth Vulnerabilities and Security

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Mareeswari V Vijayan R Ashish Shukla Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Master of Computer Applications School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices.

关键词： Wireless communication Ethics Protocols Ecosystems Denial-of-service attack Security Internet of Things Bluetooth Low Energy Microprogramming Guidelines

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Generalized Simultaneous Perturbation-Based Gradient Search With Reduced Estimator Bias

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IEEE Transactions on Automatic control 2025年

作者： Pachalyl, Soumen Bhatnagarz, Shalabh Prashanth, L.A. TCS Research Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India Indian Institute of Science Department of Computer Science and Automation The Robert Bosch Centre for Cyber Physical Systems Bangalore India Indian Institute of Technology Madras Department of Computer Science and Engineering Chennai India

We present a family of generalized simultaneous perturbation-based gradient search (GSPGS) estimators that use noisy function measurements. The number of function measurements required by each estimator is guided by the desired level of accuracy. We first present in detail unbalanced generalized simultaneous perturbation stochastic approximation (GSPSA) estimators and later present the balanced versions (B-GSPSA) of these. We extend this idea further and present the generalized smoothed functional (GSF) and generalized random directions stochastic approximation (GRDSA) estimators, respectively, as well as their balanced variants. We show that estimators within any specified class requiring more number of function measurements result in lower estimator bias. We present a detailed analysis of both the asymptotic and non-asymptotic convergence of the resulting stochastic approximation schemes. We further present a series of experimental results with the various GSPGS estimators on the Rastrigin and quadratic function objectives. Our experiments are seen to validate our theoretical findings. © 1963-2012 IEEE.

关键词： Stochastic systems

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Multi-Point Positional Insertion Tuning for Small Object Detection

Multi-Point Positional Insertion Tuning for Small Object Det...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kanoko Goto Takumi Karasawa Takumi Hirose Rei Kawakami Nakamasa Inoue Dept. of Computer Science Institute of Science Tokyo Tokyo Japan Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this issue, this paper introduces multi-point positional insertion (MPI) tuning, a parameter-efficient finetuning (PEFT) method for small object detection. Specifically, MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling the efficient detection of small objects by providing precise positional information to latent features. Through experiments, we demonstrated the effectiveness of the proposed method on the SODA-D dataset. MPI performed comparably to conventional PEFT methods, including CoOp and VPT, while significantly reducing the number of parameters that need to be tuned.

关键词： Computational modeling Object detection Signal processing Feature extraction Encoding Acoustics Speech processing Tuning

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A Globally Convergent Method for Computing B-stationary Points of Mathematical Programs with Equilibrium Constraints

arXiv

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arXiv 2025年

作者： Nurkanovic, Armin Leyffer, Sven Systems Control and Optimization Laboratory Department of Microsystems Engineering University of Freiburg Germany Mathematics and Computer Science Division Argonne National Laboratory United States

This paper introduces a method that globally converges to Bstationary points of mathematical programs with equilibrium constraints (MPECs) in a finite number of iterations. B-stationarity is necessary for optimality and means that no feasible first-order direction improves the objective. Given a feasible point of an MPEC, B-stationarity can be certified by solving a linear program with equilibrium constraints (LPEC) constructed at this point. The proposed method solves a sequence of LPECs, which either certify B-stationarity or provide an active-set estimate for the complementarity constraints, and nonlinear programs (NLPs) - referred to as branch NLPs (BNLPs) - obtained by fixing the active set in the MPEC. A BNLP is more regular than the MPEC, easier to solve, and with the correct active set, its solution coincides with the solution of the MPEC. The method has two phases: the first phase identifies a feasible BNLP or certifies local infeasibility, and the second phase solves a finite sequence of BNLPs until a B-stationary point of the MPECis found. The paper provides a detailed convergence analysis and discusses implementation details. In addition, extensive numerical experiments and an open-source software implementation are provided. The experiments demonstrate that the proposed method is more robust and faster than relaxation-based methods, while also providing a certificate of B-stationarity without requiring the usual too-restrictive *** Codes 90C30, 90C33, 49M37, 65K10, 90C11 © 2025, CC BY.

关键词： Nonlinear programming

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A Multilingual Mental Health Support Chatbot with Regional Language Capabilities Based On the AMER Model

A Multilingual Mental Health Support Chatbot with Regional L...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Amirthavarshini H Senthil Kumar G Vibhav Krashan Chaurasiya Kirubakaran N Balaraman Sundarambal Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Oriental Institute of Science and Technology Bhopal India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Despite that, access to mental health care is still limited in many areas, particularly where there are differences in language and cultural considerations prove challenging. Many available solutions poorly respond to the needs for inclusive and personalized support in local languages. We present the development of our advanced multilingual mental health support chatbot, AMER: Advanced Multilingual Emotional Response Model with Regional Language Capabilities. Using sophisticated NLP, emotion detection, and speech technologies, the chatbot aims to provide empathetic, relevant, and interactive support for the users in their desired languages. This model is responsible for the emotion prediction and classification; AMER runs through six advanced algorithms: sentiment analysis, emotion detection, multilingual support, emoji recognition, personalized recommendation, and speech emotion recognition. To make sure that it recognizes emotions and processes language accurately, the system is trained on datasets such as GoEmotions, Common Voice, and IndicCorp to accommodate ten different languages, including as many local languages (especially in Indian context). Thanks to its modular design, it can process user inputs, evaluate emotions, and produce tailored, context-sensitive outputs. Emotion detection accuracy at baseline testing was 87%, while language processing accuracy was greater than 92%, boosting user engagement by 76% in relation to standard text-based systems. Future improvements will center around multimodal features like expression and tone analysis, as well as integration with teletherapy platforms. The improvements follow the goal of making the chatbot more accessible and better performing for more users, specifically targeting underserved communities worldwide to broaden the accessibility of mental health support.

关键词： Emotion recognition Sentiment analysis Accuracy Mental health Speech recognition Predictive models Chatbots Multilingual Standards Testing

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A Comprehensive Review of Low-Rank Decomposition Techniques for Parameter Efficient Fine-Tuning

A Comprehensive Review of Low-Rank Decomposition Techniques ...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Senthil Kumar G Kiruthika M Vibhav Krashan Chaurasiya Balaraman Sundarambal Kirubakaran N Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Oriental Institute of Science and Technology Bhopal India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Recently, low-rank decomposition methods have brought about powerful methods for tuning deep learning models, reducing training parameters by orders of magnitude and maintaining performance. These methods, including LoRA, Delta LoRA, DyLoRA, WARP, KronA, as well as Intrinsic SAID, have seen widespread usage in many application domains, ranging from natural language processing (NLP) to computer vision, to multimodal tasks. Despite their success in reducing computational cost, these methods also suffer from some limitations including the risk of execution loss, especially for complex task, and the undesirable bipolarity of the model parameters. In addition, before most have addressed the problem, no pre-trained models optimised for low rank techniques have been made available and necessitate resource intensive fine tuning. This paper review the strengths and limitations of each method and point out the tradeoff between efficiency and performance. In addition, it presents possible improvements, including hybrid models, and developments in adaptability and scalability for use in operations of real-world applications like healthcare and finance. Future directions include: increasing efficiency; generalizing the use across many different architectures relevant to AI; and tackling the current limitations to make low rank decomposition techniques more widely usable in AI software.

关键词： Training Deep learning Adaptation models computer vision Reviews Computational modeling Scalability computer architecture Natural language processing Tuning

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Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and Networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8141-8157页

作者： Li, Xuehan Jing, Tao Li, Ruinian Wang, Xiaoxuan Yan, Yu Fan, Xin Huo, Yan Yu, Fei Richard Beijing Jiaotong University School of Electronic and Information Engineering National Engineering Research Center of Advanced Network Technologies Beijing100044 China Bowling Green State University Department of CS Bowling GreenOH43403 United States Beijing Forestry University School of Information Science and Technology Beijing100083 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As vehicular edge computing and networks (VECONs) emerge, Internet of vehicles (IoVs) is gaining significant advantages. To provide users with dependable services, VECONs need to offer efficient and secure task offloading services. Unfortunately, little effort has been put into addressing security issues such as the risk of privacy exposure when offloading data. Modeling and optimizing offloading dependability are affected by these issues. To address these concerns, a dependable three-layer offloading framework involving blockchain technology is presented in this paper, with fully homomorphic encryption (FHE) algorithm incorporated in order to mitigate privacy exposure risks. The framework divides tasks reasonably and introduces a novel measure of offloading dependability called cost from energy consumption, delay, and privacy exposure risk (cEDP) for constructing dependable offloading optimization. Additionally, a self-learning based dependable privacy preservation offloading (SDPPO) algorithm is presented that is capable of solving the optimal offloading problem in a decentralized manner. Simulation results show that the SDPPO algorithm outperforms its counterparts in terms of faster convergence speed, better cEDP convergence results, and improved scalability and tunability characteristics. © 1967-2012 IEEE.

关键词： Differential privacy

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Traffic and Pollution control Using IoT-Enabled Smart Routing Algorithm

Traffic and Pollution Control Using IoT-Enabled Smart Routin...

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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： S Shahul Hammed C. Preethi K Haripriya S. Pavalarajan M.R. Gowtham N. Akshaay Krithick Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Urbanization has led to increased traffic congestion and air pollution, primarily from vehicle emissions, posing risks to public health and the environment. Existing traffic management systems are inefficient in integrating real-time pollution data, leading to reactive control measures. This project proposes an IoT-based smart traffic management system that integrates real-time air quality monitoring with dynamic traffic control. IoT sensors collect pollution and vehicle data, which is processed using cloud computing and machine learning to optimize traffic flow and reduce emissions. AI techniques provide transparency, helping city authorities understand and trust the system's decisions. The system also offers predictive analytics for proactive pollution management and a user-friendly dashboard for real-time visualization. The solution aims to reduce urban emissions, and enhance traffic efficiency, offering a sustainable approach for modern cities.

关键词： Urban areas Pollution control Machine learning Traffic control Prediction algorithms Real-time systems Internet of Things Public healthcare Vehicle dynamics Monitoring

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IoT Based Leakage Detection and Emergency Shutdown

IoT Based Leakage Detection and Emergency Shutdown

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： PS. Abarna S. Padma Priya S. Pavalarajan S. Sivatharani M. Manshi Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The risk of gas leaks has grown significantly as a life threatening issue in industrial activities, cooking, and heating. This system integrates automatic reaction mechanisms, real-time monitoring capabilities, and advanced detection technologies in a multifaceted manner to address various dangers. The comprehensive gas leak detection and automatic cut off system is intended to improve safety in a variety of situations, including industrial and residential ones. To precisely identify the existence of dangerous gases like methane and propane, the proposed system combines infrared, electrochemical, and semiconductor types of gas sensors integrated with a mobile application, users may remotely administer and monitor the system in real-time. The system initiates a multi-tiered response upon detecting a gas leak; it sounds an alarm, notifies user-specified The mobile devices about the leak, and turns on an exhaust fan to release concentrated gas. A solenoid valve is used by the system to quickly shut off the gas supply and electricity from any location. The need of user-friendly controls and prompt notifications in emergency situations is highlighted in this paper's discussion of software architecture, sensor integration, and user interface design. Experimental results show that the system is reliable and capable of responding quickly under different circumstances. In future the application of machine learning algorithms for predictive maintenance and the possibility of integrating IoT to further strengthen security protocols. The results emphasize importance to deploy these kinds of extensive safety systems widely in order to reduce the risks associated with gas exposure.

关键词： Gases Fans Machine learning algorithms Valves Real-time systems Mobile handsets Security Solenoids Leak detection Gas detectors

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