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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 14-24页

作者： Kumar, Raghvendra Goddu, Bhargav Saha, Sriparna Jatowt, Adam Indian Institute of Technology Department of Computer Science and Engineering Patna801106 India University of Innsbruck Department of Computer Science Innsbruck6020 Austria

In the times of advanced generative artificial intelligence, distinguishing truth from fallacy and deception has become a critical societal challenge. This research attempts to analyze the capabilities of large language models (LLMs) for detecting misinformation. Our study employs a versatile approach, covering multiple LLMs with few- and zero-shot prompting. These models are rigorously evaluated across various fake news and rumor detection datasets. Introducing a novel dimension, we additionally incorporate sentiment and emotion annotations to understand the emotional influence on misinformation detection using LLMs. Moreover, to extend our inquiry, we employ ChatGPT to intentionally distort authentic news as well as human-written fake news, utilizing zero-shot and iterative prompts. This deliberate corruption allows for a detailed examination of various parameters such as abstractness, concreteness, and named entity density, providing insights into differentiating between unaltered news, human-written fake news, and its LLM-corrupted counterpart. Our findings aspire to furnish a refined framework for discerning authentic news, human-generated misinformation, and LLM-induced distortions. This multifaceted approach, utilizing various prompt techniques, contributes to a comprehensive understanding of the subtle variations shaping misinformation sources. © 2024 IEEE.

关键词： Iterative methods

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Influence of MMF Space Harmonic on PM Eddy- Current Losses in a Modular Fault-Tolerant In-Wheel Motor under Open-Circuit Faulty Operations 7

Influence of MMF Space Harmonic on PM Eddy- Current Losses i...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Tang, Yue Cai, William Chai, Feng Xie, Ying Gao, Junshan Harbin University of Science and Technology Department of Electrical Engineering Harbin China Harbin Institute of Technology Department of Electrical Engineering Harbin China Rongcheng College Harbin University of Science and Technology Harbin China

ISBN: (纸本)9798350359558

This paper investigates the influence of magneto motive force (MMF) space harmonic on permanent magnet (PM) eddy-current losses in a modular fault-tolerant in-wheel motor under open-circuit faulty operations. First, reconfiguring maximum round MMF with constant amplitude of current is taken as the fault-tolerant (FT) strategy for the open-circuit of one phase in one module. Second, the harmonic components of MMF under the faults and corresponding FT control are analyzed respectively. Then, an analytical model based on multi-loop method for calculating PM eddy-current losses induced by MMF harmonics is proposed. The PM losses induced by each MMF harmonic can be obtained through the model. The total PM losses named Ppm consist of P_mmf and P_slot. The former is induced by spatial harmonics of MMF. P_slot, the most important losses in PMs, are those generated by the fundamental frequency magnetic flux due to the stator slot openings. The influences of P_mmf and P_slot on the total PM losses under various operations are analyzed respectively. Finally, the analytical results are verified through finite element method (FEM). © 2024 IEEE.

关键词： Permanent magnets

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Memristive effects on an improved discrete Rulkov neuron model

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science China(Technological sciences) 2023年第11期66卷 3153-3163页

作者： BAO Han LI KeXin MA Jun HUA ZhongYun XU Quan BAO BoCheng School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213159China Department of Physics Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China

A change in neuronal-action potential can generate a magnetically induced current during the release and propagation of intracellular *** better characterize the electromagnetic-induction effect,this paper presents an improved discrete Rulkov(ID-Rulkov)neuron model by coupling a discrete model of a memristor with sine memductance into a discrete Rulkov neuron *** ID-Rulkov neuron model possesses infinite invariant points,and its memristor-induced stability effect is evaluated by detecting the routes of period-doubling and Neimark-Sacker *** investigated the memristor-induced dynamic effects on the neuron model using bifurcation plots and firing ***,we theoretically expounded the memristor initial-boosting mechanism of infinite coexisting *** results show that the ID-Rulkov neuron model can realize diverse neuron firing patterns and produce hyperchaotic attractors that are nondestructively boosted by the initial value of the memristor,indicating that the introduced memristor greatly benefits the original neuron *** hyperchaotic attractors initially boosted by the memristor were verified by hardware experiments based on a hardware *** addition,pseudorandom number generators are designed using the ID-Rulkov neuron model,and their high randomness is demonstrated based onstrict test results.

关键词： neuron model invariant point memristive effect complex dynamics pseudorandom number generators

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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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Surface engineering of highly ordered Bi_(2)S_(3) film with open channels toward high-performance broadband photodetection

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InfoMat 2024年第11期6卷 108-119页

作者： Ping Rong Shiyong Gao Lin Li Wen He Mingyi Zhang Shuai Ren Yajie Han Shujie Jiao Qing Chen Jinzhong Wang School of Materials Science and Engineering Harbin Institute of TechnologyHarbinthe People's Republic of China National Key Laboratory of Precision Hot Processing of Metals Harbin Institute of TechnologyHarbinthe People's Republic of China Key Laboratory for Photonic and Electronic Bandgap Materials Ministry of EducationSchool of Physics and Electronic EngineeringHarbin Normal UniversityHarbinthe People's Republic of China Sauvage Laboratory for Smart Materials School of Materials Science and EngineeringHarbin Institute of Technology(Shenzhen)Shenzhenthe People's Republic of China

The highly ordered film assembled by regularly 1D nanostructures has potentialprospects in electronic,photoelectronic and other fields because of its excellentlight-trapping effect and electronic transport ***,the controlledgrowth of highly ordered film remains a great ***,large-area andhighly ordered Bi_(2)S_(3) film is synthesized on fluorophlogopite mica substrate bychemical vapor deposition *** Bi2S3 film features hollowed-outcrosslinked network structure,assembled by 1D nanobelts that regularly distributein three orientations,which agrees well with the first principles *** on the as-grown Bi_(2)S_(3) film,the broadband photodetector with a responserange from 365 to 940 nm is fabricated,exhibiting a maximum responsivity upto 98.51 mA W^(–1),specific detectivity of 2.03×10^(10)Jones and fast response timeof 35.19 *** stable instantaneous on/off behavior for 500 cycles and reliablephotoresponse characteristics of the Bi_(2)S_(3) photodetector after storage in air for6 months confirm its excellent long-term stability and air ***,as sensing pixel and signal receiving terminal,the device successfully achieveshigh-resolution imaging of characters of“H”,“I”and“T”,and secure transmissionof confidential *** work shows a great potential of the largeareaand highly ordered Bi_(2)S_(3) film toward the development of future multiplefunctional photoelectronic applications.

关键词： broadband photodetection encrypted communication highly ordered Bi_(2)S_(3) film imaging open channel

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Question classification task based on deep learning models with self-attention mechanism

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Multimedia Tools and Applications 2025年第10期84卷 7777-7806页

作者： Mondal, Subhash Barman, Manas Nag, Amitava Computer Science and Engineering Central Institute of Technology Kokrajhar Assam Kokrajhar India Computer Science and Engineering [AI & ML Dayananda Sagar University Karnataka Bengaluru India

Question classification (QC) is a process that involves classifying questions based on their type to enable systems to provide accurate responses by matching the question type with relevant information. To understand and respond to natural language questions posed by humans, machine learning models or systems must comprehend the type of information requested, which can often be inferred from the structure and wording of the question. The high dimensionality and sparse nature of text data lead to challenges for text classification. These tasks can be improved using deep learning (DL) approaches to process complex patterns and features within input data. By training on large amounts of labeled data, deep learning algorithms can automatically extract relevant features and representations from text, resulting in more accurate and robust classification. This study utilizes a dataset comprising 5452 instances of questions and six output labels and uses two different word embedding techniques, like GloVe and Word2Vec, tested on the dataset using three deep learning models, LSTM, BiLSTM, and GRU, followed by a convolution layer. Additionally, a self-attention layer is included, which helps the model to focus on more relevant information when making predictions. Finally, an analytical discussion of the proposed models and their performance results provide insight into how GloVe and Word2Vec perform on the above-mentioned models. The GloVe embedding outperforms by achieving 97.68% accuracy and a moderate loss of 16.98 with the GRU model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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ResdenseNet: a lightweight dense ResNet enhanced with depthwise separable convolutions and its applications for early plant disease classification

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Neural Computing and Applications 2025年第8期37卷 6305-6326页

作者： Nagpal, Jyoti Goel, Lavika Department of Computer Science & Engineering The NorthCap University Gurugram122017 India Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India

In recent years, artificial intelligence has undergone robust development, leading to the emergence of numerous autonomous AI applications. However, a crucial challenge lies in optimizing computational efficiency and reducing training time while maintaining high accuracy with limited hardware resources. This paper introduces ResdenseNet, a model built upon the MobileNet, DenseNet, and ResNet architectures. ResdenseNet combines dense blocks and residual blocks from the DenseNet and ResNet architectures. In these dense blocks, the standard convolutional units are replaced by depthwise separable convolutional units, a significant part of the MobileNet architecture. The experimental outcomes are contrasted with established models and their iterations, including ResNet-50, ResNet-101, MobileNet-V1, MobileNet-V2, DenseNet-121, and DenseNet-169. The proposed model is tested on benchmark and proposed datasets, showcasing its efficiency in reducing computations and accelerating the training process. Emphasizing hyperparameter importance, ResdenseNet, optimized with a growth rate of 64, 6 layers, and ReLU activation, achieves an accuracy of (98.73%) and a F1-score of (98.20%) on the wheat and barley dataset. The results indicate that ResdenseNet significantly decreases the number of parameters to 0.72M and efficiently shortens training time to 5983.54 s. Particularly noteworthy is ResdenseNet’s superiority over other models in terms of having the fewest parameters, the shortest training time, and the highest accuracy, especially when dealing with wheat, barley, and maize datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Plant diseases

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Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

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computers, Materials & Continua 2023年第5期75卷 2957-2979页

作者： Zibo Wang Chaobin Huo Yaofang Zhang Shengtao Cheng Yilu Chen Xiaojie Wei Chao Li Bailing Wang School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China National Computer System Engineering Research Institute of China Beijing100083China School of Cyber Science and Technology Harbin Institute of TechnologyHarbin150001China Weihai Cyberguard Technologies Co.Ltd. Weihai264209China Faculty of Science Vrije Universiteit AmsterdamAmsterdam1081HVNetherlands

With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecast potential ***,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis *** address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the *** reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical *** proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the *** emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate *** order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related *** critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation *** particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning ***,the proposed algorithm avoids redundant paths between the given pairs of *** results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.

关键词： Path-based reasoning representation learning attention mechanism vulnerability knowledge graph industrial control component

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Toward stable and highly reversible zinc anodes for aqueous batteries via electrolyte engineering

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Journal of Energy Chemistry 2023年第8期83卷 209-228,I0007页

作者： Ang Li Jiayi Li Yurong He Maochun Wu Department of Mechanical Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China Schoolof Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001HeilongjiangChina

Featuring low cost, high abundance, low electrochemical potential, and large specific capacity, zinc(Zn)metal holds great potential as an anode material for next-generation rechargeable aqueous ***, the poor reversibility resulting from dendrite formation and side reactions poses a major obstacle for its practical application. Electrolyte, which is regarded as the “blood” of batteries, has a direct impact on reaction kinetics, mass transport, and side reactions and thus plays a key role in determining the electrochemical performance of Zn electrodes. Therefore, considerable efforts have been devoted to modulating the electrolytes to improve the performance of Zn electrodes. Although significant progress has been made, achieving stable and highly reversible Zn electrodes remains a critical challenge. This review aims to provide a systematic summary and discussion on electrolyte strategies for highperformance aqueous Zn batteries. The(electro)-chemical behavior and fundamental challenges of Zn electrodes in aqueous electrolytes are first discussed. Electrolyte modulation strategies developed to address these issues are then classified and elaborated according to the underlying ***, remaining challenges and promising future research directions on aqueous electrolyte engineering are highlighted. This review offers insights into the design of highly efficient electrolytes for new generation of rechargeable Zn batteries.

关键词： Rechargeable aqueous zinc batteries Zinc anode Dendrite growth Side reactions Electrolyte engineering

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Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

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computer Modeling in engineering & sciences 2024年第6期139卷 3085-3099页

作者： Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie School of Computer Science and Technology Harbin University of Science and TechnologyHarbin150080China Shanghai Futures Information Technology Co. Ltd.Shanghai201201China Heilongjiang Province Cyberspace Research Center Harbin150001China Information Network Engineering and Research Center South China University of TechnologyGuangzhou510641China

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic *** applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be ***,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured ***,privacy of patients’data needs to be protected,as these data contain the most sensitive and private ***,considering the practicality of the model,the computational requirements should not be too *** address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage *** method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an *** also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in ***,this paper designs and conducts experiments to evaluate the proposed *** experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

关键词： Vertical federation homomorphic encryption deep neural network intelligent diagnosis machine learning and big data

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