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31st International Conference on Computational Linguistics, COLING 2025

作者： Das, Souvik Jin, Lifeng Song, Linfeng Mi, Haitao Peng, Baolin Yu, Dong Department of Computer Science and Engineering University at Buffalo NY United States Tencent AI Lab BellevueWA United States

ISBN: (纸本)9798891761964

Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination - generating content ungrounded in the realities of training data. Recent work has focused on decoding techniques to improve factuality during inference by leveraging LLMs' hierarchical representation of factual knowledge, manipulating the predicted distributions at inference time. Current state-of-the-art approaches refine decoding by contrasting early-exit distributions from a lower layer with the final layer to exploit information related to factuality within the model forward procedure. However, such methods often assume that the final layer is the most reliable and the lower-layer selection process depends on it. In this work, we first propose the extrapolation of critical token probabilities beyond the last layer for more accurate contrasting. We additionally employ layer-wise entropy-guided lower-layer selection, decoupling the selection process from the final layer. Experiments demonstrate strong performance, surpassing state-of-the-art on multiple different datasets by large margins. The analyzes show that different kinds of prompt respond to different selection strategies. Our source code will be available on GitHub. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

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computers, Materials & Continua 2023年第5期75卷 2741-2757页

作者： Muhammad Usman Sana Zhanli Li Tayybah Kiren Hannan Bin Liaqat Shahid Naseem Atif Saeed College of Computer Science and Technology Xi’an University of Science and TechnologyXi’anShaanxi710054China Department of Computer Science(RCET Campus) University of Engineering and TechnologyLahore39161Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54700Pakistan Department of Computer Science COMSATS UniversityIslamabadLahore54700Pakistan

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being *** cloud computation,data processing,storage,and transmission can be done through laptops andmobile *** Storing in cloud facilities is expanding each day and data is the most significant asset of *** important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s *** have to be dependent on cloud service providers for assurance of the platform’s *** security and privacy issues reduce the progression of cloud computing and add ***;most of the data that is stored on cloud servers is in the form of images and photographs,which is a very confidential form of data that requires secured *** this research work,a public key cryptosystem is being implemented to store,retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman(RSA)algorithm for the encryption and decryption of *** implementation of a modified RSA algorithm results guaranteed the security of data in the cloud *** enhance the user data security level,a neural network is used for user authentication and ***;the proposed technique develops the performance of detection as a loss function of the bounding *** Faster Region-Based Convolutional Neural Network(Faster R-CNN)gets trained on images to identify authorized users with an accuracy of 99.9%on training.

关键词： Cloud computing data security RSA algorithm Faster R-CNN

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Parameter Estimation of PEM Fuel-Cell Using Brownian Butterfly Algorithm

Parameter Estimation of PEM Fuel-Cell Using Brownian Butterf...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Rajinikanth, Venkatesan Mohan, Ramya Narayanan, Mathiyazhagan Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India

ISBN: (纸本)9798331510022

The growing demand for electrical-energy (EE) has driven the development of varied power generation methods to meet global energy needs. The traditional Energy Generation Scheme (EGS) has greater impact on various environmental concerns and hence cleaner and sustainable energy sources are invented. Fuel-cell (FC) is one of the modern EGS, which converts the chemical-energy (CE) into EE with better efficiency. Further, the performance of the FC depends mainly on its parameters and hence it is necessary to identify the finest parameters. This research aims to develop a procedure to optimize the parameters of Nedstack-PS6;a commercially available Proton Exchange Membrane (PEM) FC (PEM-FC). The Nedstack-PS6 is a portable EE-source and its efficiency can be improved by assigning the finest possible value to its parameters. This research proposes a Brownian-Butterfly-Algorithm (BBA) based optimization process using a chosen cost-function for the chosen PEM-FC model. The BBA based search is implemented to solve a 7-dimensional optimization task using a bounded-search methodology and the achieved result with the BBA is compared against few chosen results by the optimization-algorithms. The experimental results confirms that the proposed scheme works well on the chosen PEM-FC model and help to identify the optimal values for all the 7-parametters. © 2024 IEEE.

关键词： Cost functions

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An Enhanced Ensemble-based Approach for Network Anomaly Detection using Voting Classifier 8

An Enhanced Ensemble-based Approach for Network Anomaly Dete...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Rafi, Rayees Anusha Bamini, A.M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

Anomaly detection systems are vital for identifying malicious activities within network traffic, but they often face challenges such as class imbalance, high dimensionality, and feature correlation in datasets like NSL-KDD. This work tackles these issues by creating a robust anomaly detection system using a hybrid approach that combines Synthetic Minority Over-sampling Technique, feature engineering, and ensemble learning. SMOTE is used to balance the dataset, while feature engineering reduces dimensionality and eliminates redundant features. The ensemble learning model, which integrates Support Vector Machines, Logistic Regression, and Decision Trees, achieved a high accuracy of 98.71% using a Voting Classifier. Additionally, the model shows a macro average precision of 0.89, a recall of 0.82, an F1-score of 0.85, and an overall robust ROC AUC score of 0.9871, indicating its effectiveness in distinguishing between normal and anomalous network behavior. © 2024 IEEE.

关键词： Decision trees

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Smart Farming with Improved Security using Ascon Encryption and Authentication 2

Smart Farming with Improved Security using Ascon Encryption ...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Rahul, R. Venkatesan, R. Jebaseeli, T. Jemima Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore641114 India

ISBN: (纸本)9798350327533

The evolution of smart farming through the integration of Internet of Things (IoT) technology has ushered in a new era of precision agriculture, offering increased efficiency, sustainability, and productivity. However, this technological advancement also brings forth a critical concern: The security of the data collected and transmitted by IoT devices in agricultural settings. In response to this concern, this research presents a comprehensive security implementation tailored for IoT-based smart farming systems. At its core, the system focuses on two key aspects: data authentication and secure transmission. To achieve these objectives, the Ascon encryption algorithm, known for its lightweight design and robust security features has been proposed. The implementation utilizes Raspberry Pi devices, powered by the Adafruit CircuitPython library, to collect real-Time sensor data from various agricultural sources. This data encompasses a wide range of vital parameters, including temperature, humidity, soil moisture, and livestock health. The Ascon algorithm is employed for device authentication, ensuring that only authorized devices gain access to the IoT network. The crux of the research lies in securing the data from its point of origin to its final destinations. The collected sensor data undergoes encryption using the ASCON algorithm before transmission. This encryption guarantees the confidentiality and integrity of the data, making it immune to interception or tampering during transit. AskSensors cloud platform acts as the secure repository for this encrypted data, while mobile integration provides users with real-Time access to critical agricultural insights. This research represents a vital stride in addressing the pressing security challenges that accompany 10T-based smart farming. By combining the robust authentication capabilities of the Ascon algorithm with the secure data transmission to AskSensors, establish a trust framework essential for the widespread adop

关键词： Ascon AskSensors Authentication Cloud Transmission Internet of Things Mobile Integration Security Smart Farming

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Temporal Fusion Transformer Based Temporal Convolutional Network Model for Sleep Apnea Detection Using Single-Lead ECG Signals 9

Temporal Fusion Transformer Based Temporal Convolutional Net...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Devashania, Ielin Daisy Paulraj, Getzi Jeba Leelipushpam Jebadurai, Immanuel Johnraja Santhiya, P. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350377972

Sleep apnea (SA) is a sleep disorder which is characterized by intermittent interruptions in breathing and airflow throughout a sleep cycle. Traditional techniques such as polysomnography (PSG), remain costly, labor-intensive, and require overnight monitoring in specialized labs, which limits its accessibility and scalability. For continuous sleep apnea diagnosis, deep learning models offer a scalable, non-invasive, and economical alternative to multi-sensor PSG monitoring which paves the way for integration into wearable IoT devices for real-time health monitoring. To address the limitations posed by traditional multi-sensor techniques, a novel framework based on Deep Learning techniques is proposed: Temporal Fusion Transformer-based Temporal Convolutional Network (TFT-TCN), designed for the detection of obstructive sleep apnea (OSA) with the use of single-lead ECG signals. The TCN module uses dilated convolutions to extract temporal features efficiently whereas the TFT module employs Gated Residual Network (GRN), Adaptive Fusion and Multi-Head Attention to compute attention scores and fuse the feature vectors. Overall, the proposed TCN-TFT model achieved a noteworthy Accuracy of 89.55 %, FI-Score of 89.3% and AUC score of 0.95. © 2024 IEEE.

关键词： Sleep research

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Eliciting In-Context Learning in Vision-Language Models for Videos Through Curated Data Distributional Properties

Eliciting In-Context Learning in Vision-Language Models for ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yu, Keunwoo Peter Zhang, Zheyuan Hu, Fengyuan Storks, Shane Chai, Joyce Computer Science and Engineering Division University of Michigan Ann ArborMI United States

ISBN: (纸本)9798891761643

A major reason behind the recent success of large language models (LLMs) is their in-context learning capability, which makes it possible to rapidly adapt them to downstream text-based tasks by prompting them with a small number of relevant *** large vision-language models (VLMs) have recently been developed for tasks requiring both text and images, they largely lack in-context learning over visual information, especially in understanding and generating text about *** this work, we implement Emergent In-context Learning on Videos (EILeV), a novel training paradigm that induces in-context learning over video and text by capturing key properties of pre-training data found by prior work to be essential for in-context learning in *** our experiments, we show that EILeV-trained models outperform other off-the-shelf VLMs in few-shot video narration for novel, rare ***, we demonstrate that these key properties of bursty distributions, skewed marginal distributions, and dynamic meaning each contribute to varying degrees to VLMs' in-context learning capability in narrating procedural *** results, analysis, and EILeV-trained models yield numerous insights about the emergence of in-context learning over video and text, creating a foundation for future work to optimize and scale VLMs for open-domain video understanding and reasoning. © 2024 Association for Computational Linguistics.

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Multi-orthogonal-oppositional enhanced African vultures optimization for combined heat and power economic dispatch under uncertainty

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Neural Computing and Applications 2025年第8期37卷 6097-6123页

作者： Rizk-Allah, Rizk M. Snášel, Václav Hassanien, Aboul Ella Basic Engineering Science Department Faculty of Engineering Menoufia University Shebin El-Kom32511 Egypt Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava70800 Czech Republic Faculty of Computers and AI Cairo University Giza Egypt

This paper attempts to conceptualize a potent methodology by combining the African vultures optimization algorithm (AVOA) with a multi-orthogonal-oppositional strategy (M2OS), named AVO-M2OS, to address the nonconvexity and multidimensional nature of the combined heat and power economic dispatch (CHPED) problem under both crisp and uncertainty aspects. The AVO-M2OS uses the M2OS to simultaneously explore the search region, improving solutions’ diversity as well as solution quality. Therefore, AVO-M2OS can perform deeper exploration and exploitation features and thus mitigate the trapping at local optima, especially when tackling the more complicated nature of the CHPED problem. A three-stage analysis is conducted to assess the effectiveness of the proposed AVO-M2OS algorithm. During the first stage, the algorithm’s performance is evaluated on benchmark problems such as CEC 2005 and CEC 2019, employing statistical verifications and convergence characteristics. In the second stage, the significance of the results is evaluated using the nonparametric Friedman test to demonstrate that the results did not occur by chance. The results indicate that the AVO-M2OS algorithm outperforms the best existing algorithm (AVOA) by an average rank of the Friedman test exceeding 26% for the CEC 2005 suite while outperforming the gray wolf optimization (GWO) by 60% for the CEC 2019 suite. Moreover, the AVO-M2OS demonstrates exceptional performance compared to existing state-of-the-art algorithms, surpassing the best algorithm available by an average rank of the Friedman test that exceeds 41%. Finally, the AVO-M2OS’s applicability is achieved by minimizing the operational costs by finding the optimal power and heat generation scheduling for the CHPED problem. The recorded results realize that the AVO-M2OS algorithm offers accurate performance compared to competing optimizers, where it saves the operational cost of the 48-unit system by 24% on the original AVO variant. Furthermore, the u

关键词： Quality control

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An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

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computer Modeling in engineering & sciences 2024年第3期138卷 2029-2059页

作者： Sheetal Sharma Kamali Gupta DeepaliGupta Shalli Rani Gaurav Dhiman Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India PG Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma CollegeChandigarh160030India Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Division of Research and Development Lovely Professional UniversityPunjab144001India

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart *** address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed *** analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

关键词： Error fault detection techniques sensor faults outliers Internet of Things

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Entanglement-Controlled Quantum Federated Learning

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IEEE Internet of Things Journal 2025年第11期12卷 18318-18330页

作者： Park, Soohyun Lee, Hyunsoo Jung, Soyi Park, Jihong Bennis, Mehdi Kim, Joongheon Sookmyung Women’s University Division of Computer Science Seoul04310 Korea Republic of Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of Pillar Tampines Singapore University of Oulu Centre for Wireless Communications Oulu90014 Finland

According to the advances in quantum computing and distributed learning, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. To successfully opeate QFL under various and dynamic channel conditions in Internet of Things (IoT) environments, this article develops a novel depth-controllable architecture of entangled slimmable QNNs (eSQNNs), and thus, proposes an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eSQNNs. Even though the proposed eSQNN-based eSQFL is superior, training the depth-controllable eSQNN architecture is challenging due to high-entanglement entropy and interdepth interference. Therefore, the proposed method in this article mitigates the interference using entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing interdepth quantum state differences, respectively. Furthermore, the proposed method optimizes the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. The novelty of this work is evaluated via extensive simulations in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions. © 2014 IEEE.

关键词： Federated learning

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