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DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

3rd International Conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, IITCEE 2025

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

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KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News Detection 31

KGFakeNet: A Knowledge Graph-Enhanced Model for Fake News De...

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2025 Workshop on Generative AI and Knowledge Graphs, GenAIK 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Kumar, Anuj Kumar, Pardeep Yadav, Abhishek Ahlawat, Satyadev Prasad, Yamuna Department of Computer Science & Engineering Indian Institute of Technology Jammu India Department of Electrical Engineering Indian Institute of Technology Jammu India

ISBN: (纸本)9798891762121

The proliferation of fake news on social media has intensified the spread of misinformation, promoting societal biases, hate, and violence. While recent advancements in Generative AI (GenAI), particularly large language models (LLMs), have shown promise, these models often need more structured representation for accurate verification, as they rely on pre-trained data patterns without access to real-time or validated information. This study presents a framework that utilizes Open Information Extractor 6 (OpenIE6) to extract triplet relationships (subject-predicate-object) from statements and justifications to compute the cosine similarity between the Knowledge Graphs (KGs) of the statements and their supporting justification to precisely measure the relevance and alignment between them. This similarity feature is integrated with an attention mechanism over GenAI-generated embeddings to enhance the model’s ability to capture semantic features accurately. In addition, a Multi-Layer Perceptron (MLP) classifier is employed to integrate all features, resulting in a 4% improvement in accuracy and a 5% increase in F1-score over state-of-the-art LLM-based approaches. ©2025 International Committee on Computational Linguistics (ICCL)

关键词： Knowledge graph

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Securing MIMO Wiretap Channel With Learning-Based Friendly Jamming Under Imperfect CSI

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IEEE Internet of Things Journal 2025年第11期12卷 16009-16022页

作者： Minh Tuan, Bui Nguyen, Diep N. Linh Trung, Nguyen Nguyen, Van-Dinh Van Huynh, Nguyen Thai Hoang, Dinh Krunz, Marwan Dutkiewicz, Eryk University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia University of Engineering and Technology Vietnam National University Faculty of Electronics and Telecommunications Hanoi100000 Viet Nam College of Engineering and Computer Science VinUniversity Hanoi100000 Viet Nam University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom The University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems. © 2014 IEEE.

关键词： Jamming

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Deep learning and vision transformers-based framework for breast cancer and subtype identification

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Neural Computing and Applications 2025年 1-20页

作者： Jahan, Ishrat Chowdhury, Muhammad E. H. Vranic, Semir Al Saady, Rafif Mahmood Kabir, Saidul Pranto, Zahid Hasan Mim, Sabiha Jahan Nobi, Sadia Farhana Department of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet Bangladesh Department of Electrical Engineering Qatar University 2713 Doha Qatar College of Medicine QU Health Qatar University Doha Qatar Department of Electrical and Electronic Engineering University of Dhaka Dhaka1000 Bangladesh

Breast cancer, marked by uncontrolled cell growth in breast tissue, is the most common cancer among women and a second-leading cause of cancer-related deaths. Among its types, ductal and lobular carcinomas are the most prevalent, with invasive ductal carcinoma accounting for about 70–80% of cases and invasive lobular carcinoma for about 10–15%. Accurate identification is crucial for effective treatment but can be time-consuming and prone to interobserver variability. AI can rapidly analyze pathological images, providing precise, cost-effective identification, thus reducing the pathologists’ workload. This study utilizes a deep learning framework for advanced, automatic breast cancer detection and subtype identification. The framework comprises three key components: detecting cancerous patches, identifying cancer subtypes (ductal and lobular carcinoma), and predicting patient-level outcomes from whole slide images (WSI). The validation process includes visualization using Score-CAM to highlight cancer-affected areas prominently. Datasets include 111 WSIs (85 malignant from the Warwick HER2 dataset and 26 benign from pathologists). For subtype detection, there are 57 ductal and 8 lobular carcinoma cases. A total of 28,428 annotated patches were reviewed by two expert pathologists. Four pre-trained models—DenseNet-201, MobileNetV2, an ensemble of these two, and a Vision Transformer-based model—were fine-tuned and tested on the patches. Patient-level results were predicted using a majority voting technique based on the percentage of each patch type in the WSI. The Vision Transformer-based model outperformed other models in patch classification, achieving an accuracy of 96.74% for cancerous patch detection and 89.78% for cancer subtype classification. For WSI-based cancer classification, the majority voting method attained an F1-score of 99.06 and 96.13% for WSI-based cancer subtype classification. The proposed deep learning-based framework for advanced breast cancer det

关键词： Diseases

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Secured IIoT against trust deficit - A flexi cryptic approach

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Multimedia Tools and Applications 2025年第9期84卷 5625-5652页

作者： Padmapriya, V.M. Thenmozhi, K. Hemalatha, M. Thanikaiselvan, V. Lakshmi, C. Chidambaram, Nithya Rengarajan, Amirtharajan Department of Computer Sciences Marquette University MilwaukeeWI53233 United States School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613401 India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah22254 Saudi Arabia School of Electronics Engineering Vellore Institute of Technology Vellore632014 India

This research allows the secure surveillance approach for the Internet of Things (IoT) methodology to be developed by integrating wireless signalling and image encryption strategy. Since the Cloud Service Telco (CST) is a semi-trusted body in cloud services, user data is encrypted before uploading to a cloud server for data protection from disclosure. The flexibility of encrypted data sharing is essential for cloud storage users. This study investigates the Discrete Wavelet Transform (DWT) technique with modified Huffman compression and Elliptic Curve Cryptography (ECC). It encrypts and decrypts the data and enhances industrial security surveillance in transmission. It uses the wireless network’s next generation (5G or 6G) as uplink Single Carrier Frequency Division Multiple Access (SC-FDMA) strategies via the IoT. This study presented a novel approach to proposing hardware architecture for a secure web camera integrated with the Atmel in the mega AVR family (ATMEGA) microcontroller, suitable for IoT applications. The experimental results confirm the proposed model’s efficacy compared with existing robustness and security analysis algorithms. These systems are also used by implementing industry-standard protocols using IoTs to monitor industrial applications. The proposed framework can also minimise bandwidth, transmission cost, storage space, tracking data, and decisions about abnormal events such as potential fraud and extinguisher detection in surveilling applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： 5G mobile communication systems

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Joint V2V Group Association and Transmission Power Control in Federated Vehicular Networks

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IEEE Internet of Things Journal 2025年第11期12卷 18167-18182页

作者： Liu, Su Guan, Peiyuan Li, Yushuai Li, Tianyi Zolfagharian, Zolaikha Yu, Jiong Huang, Tingwen Xinjiang University School of Computer Science and Technology Urumqi830046 China University of Oslo Department of Informatics Oslo0316 Norway Aalborg University Department of Computer Science Aalborg9220 Denmark University of Tehran School of Electrical and Computer Engineering Iran Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

With growing awareness of privacy protection, Federated Learning (FL) in vehicular network scenarios effectively addresses privacy concerns, leading to the development of Federated Vehicular Networks (FVN). In FVN, vehicles maintain a global model by transmitting local models and iterative processing, resulting in significant communication overhead. In the Internet of Vehicles (IoV), as the majority of the spectrum resources are allocated to Vehicle-to-Vehicle (V2V) communication, vehicles engaged in FL encounter uplink interference when these resources are reused. This compromises the efficacy of FL vehicle communication. To address this issue, we propose FedCDC, a new Federated learning framework with adaptive weight Clustering with knowledge Distillation and Channel sharing-based resource allocation. First, we implement a communication compression strategy based on clustering and distillation to alleviate transmission load. Then, we develop a resource allocation strategy to maximize the Signal-to-Interference-plus-Noise Ratio (SINR) for both FL vehicles and V2V groups, which investigates two critical components: channel pairing and power coordination. We formulate the optimization issue as a multi-objective optimization problem that is solved offline using the MIDACO solver. Due to the dynamic characteristics of FVN, we employ a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enhance efficiency. Finally, extensive experiments demonstrate that our approach significantly reduces data transmission and offers an efficient resource coordination plan, thus improving FL communication efficiency and ensuring the Quality of Service (QoS) for both V2V groups and FL vehicles. © 2014 IEEE.

关键词： Differential privacy

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Cross-Scenario Device-Free Wireless Sensing with A Free-Energy View

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IEEE Internet of Things Journal 2025年第11期12卷 16610-16621页

作者： Chen, Bo Wang, Jie Liu, Xiaokai Gao, Qinghua Pan, Miao Fang, Yuguang Dalian Maritime University School of Information Science and Technology Dalian116026 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116023 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Device-free wireless sensing (DFWS) has gained significant attention due to its high accuracy and privacy-preserving capabilities. DFWS systems work by analyzing the influence pattern of targets on the surrounding wireless signals. However, changes in the sensing scenario can alter signal propagation patterns, causing deep learning models to lose effectiveness in cross-scenario applications. To address this problem, we analyze the information content of different samples from a free-energy view, and provide a new idea to guide the alignment of target scenario samples with source scenario samples based on free-energy. We find that free-energy can measure the degree of scenario knowledge contribution of the samples. Based on this observation, we first perform unsupervised coarse alignment by minimizing the free-energy deviation between scenarios. Next, we iteratively select a few number of high free-energy samples near the decision boundary to fine-Tune the network, achieving scenario fine alignment with a small labeling effort. Extensive experiments on two public datasets and one self-collected dataset show that our proposed method achieves high accuracy in cross-scenario human activity and gesture recognition tasks. © 2014 IEEE.

关键词： Labeling Sensors Wireless sensor networks Wireless communication Feature extraction Costs Internet of Things Gesture recognition Metalearning Training

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ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN

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Journal of Biomedical Physics and engineering 2025年第1期15卷 77-92页

作者： Rahman, Tawfikur Ahommed, Rasel Deb, Nibedita Das, Utpal Kanti Moniruzzaman, Md. Bhuiyan, Md. Alamgir Sultana, Farzana Kausar, Md. Kamruzzaman Department of Electrical and Electronic Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Agriculture International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Computer Science and Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh

Background: Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. Objective: This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities along-side normal signals. Material and Methods: This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on di-verse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. Results: The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting ***: The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analy-sis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection. © Journal of Biomedical Physics and engineering.

关键词： Wavelet transforms

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Carrier Frequency Offset Compensation for OSDM in Underwater Acoustic Communications: Theory and Experiments Using a Vector MIMO Modem 59

Carrier Frequency Offset Compensation for OSDM in Underwater...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Qi, Zhuoran Rashid, Rami Abdi, Ali Pompili, Dario Rutgers University Department of Electrical and Computer Engineering New BrunswickNJ United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ United States

ISBN: (纸本)9798331513269

Underwater acoustic communications suffer from time-varying multipath delay and different Doppler frequency offsets at different paths. To address these issues, a Carrier Frequency Offset Compensated Orthogonal Signal Division Multiplexing (CFO-C-OSDM) is proposed based on the basis expansion model for the time-varying channel. The CFO-C-OSDM method performs CFO compensation and channel estimation jointly, which manages a dynamic Doppler shift range characterized by a nonzero main CFO and residual CFOs caused by multipath propagation effectively. To validate the efficacy of this proposal, several rounds of emulations and experiments were conducted. The results show that the proposed CFO-C-OSDM proves to be more robust against doubly spread channels than existing techniques, such as D-OSDM and OSDM. © 2025 IEEE.

关键词： Underwater acoustic communication

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Weibull probability distribution based on four years wind speed data using nine numerical methods

Weibull probability distribution based on four years wind sp...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Badawi, Ahmed Ali, Hassan Ismail, Nasser A. Zyoud, Alhareth Yusoff, Siti Hajar Al-Rimawi, Ashraf Ghanem, Wasel College of Engineering Technology University of Doha for Science and Technology Doha Qatar Department of Electrical and Computer Engineering Birzeit University Birzeit Ramallah Palestine Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781032803722

Wind energy potential can be estimated in a specific area using the Probability density function (PDF). PDF is used for the mean wind speed data on daily basis. This paper represents the analysis of the climatology of the mean wind speed data for Askalan city on the Mediterranean coastal plain of Palestine. A selection of the PDF had been implemented using nine numerical analysis methods to represent the cumulative distribution function and Weibull probability distribution. The two main parameters of the Weibull shape factor are k = 1.7848 and the scale factor parameter c = 4.3642 ms-1. The study assessment finds out the characteristics of wind energy conversion and results confirm that it can produce energy using small-scale wind turbines. Matlab software was used to implement the numerical methods and extract the wind characteristics. © 2025 The Author(s).

关键词： Windmill

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