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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Anirudh, S. Kartik, Rejeti Nishant, Potula Radha Malkhed, Mahadevi E. Kumar, K. Dinesh Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331504403

Modeling efficient energy in the design of buildings is an important step for sustainability. Current methodologies are unable to accurately predict the energy requirements. The paper introduces a novel approach 'Differential Geometry-Based Regression' (DGR) in order to address this problem. DGR combines both conventional regression analysis and manifold learning techniques in order to establish a system where the correlation among the energy utilization and the building parameters are being explained. The DGR model was trained using the dataset sourced from UCI Machine Learning Repository, the precision and accountability are significantly improved. Thus, it may be used in various applications such as optimization of design towards improved levels of energy efficiency within buildings which shows the way forward for sustainable architecture and construction practices. © 2024 IEEE.

关键词： Sustainable building

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Graph Contrastive Learning for Truth Inference 40

Graph Contrastive Learning for Truth Inference

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Liu, Hao Liu, Jiacheng Tang, Feilong Li, Peng Chen, Long Yu, Jiadi Zhu, Yanmin Gao, Min Yang, Yanqin Hou, Xiaofeng Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong East China Normal University School of Data Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Aizu School of Computer Science and Engineering Japan Simon Fraser University School of Computing Science Canada

ISBN: (纸本)9798350317152

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques. © 2024 IEEE.

关键词： Crowdsourcing

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An Optimized Intrusion Detection Model Using ML and Explainable AI 15

An Optimized Intrusion Detection Model Using ML and Explaina...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Jain, Nikunj Hawari, Jawed Jha, Priyanshu Vishwas, H.N. Jain, Manish Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350370249

In the maintenance of network security, Intrusion Detection Systems (IDS) are very crucial in identifying unauthorized access and malicious activities. This research assesses how well different machine learning algorithms enhance IDS functionality, highlighting model interpretability through explainable AI techniques. Eight machine learning algorithms were assessed using a dataset from Kaggle that reflects a military network environment with 41 features and 22,544 records. Local Interpretable Model-agnostic Explanations (LIME) was used to analyze the top five models for their decision-making procedures. In several metrics, Random Forest algorithm appeared as the best performing among them all. LIME application provided useful insights into the main features that impact on the predictions being made by the model. The outcomes demonstrate the effectiveness of Random Forest for IDS and indicate how vital explainable AI methods such as LIME are in ensuring that IDS deployment can achieve transparency and reliability concerning its predictions for this model's purpose. © 2024 IEEE.

关键词： ADA Attack Ensemble Methods Intrusion Detection System Local Interpretable Model (LIME) Network Security Time series Analysis XG Boost

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Resonant Beam Communications:A New Design Paradigm and Challenges

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Journal of Communications and Information Networks 2024年第1期9卷 80-87页

作者： Yuanming Tian Dongxu Li Chuan Huang Qingwen Liu Shengli Zhou Future Network of Intelligence Institute and School of Science and Engineering the Chinese University of Hong KongShenzhen 518172China School of Science and Engineering and the Future Network of Intelligence Institute the Chinese University of Hong KongShenzhen 518172China College of Electronics and Information Engineering Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering University of ConnecticutStorrsCT 06250USA

Resonant beam communications (RBCom), which adopts oscillating photons between two separate retroreflectors for information transmission, exhibits potential advantages over other types of wireless optical communications (WOC). However, echo interference generated by the modulated beam reflected from the receiver affects the transmission of the desired information. To tackle this challenge, a synchronization-based point-to-point RBCom system is proposed to eliminate the echo interference, and the design for the transmitter and receiver is discussed. Subsequently,the performance of the proposed RBCom is evaluated and compared with that of visible light communications(VLC)and free space optical communications (FOC). Finally, future research directions are outlined and several implementation challenges of RBCom systems are highlighted.

关键词： resonant beam communications(RBCom) wireless optical communications(WOC) echo interference elimination mobility multiple access

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Sarcasm Detection in Newspaper Headlines 4

Sarcasm Detection in Newspaper Headlines

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Sreenivas Prasad, B. Akash Babu, N. Harithikeswar Reddy, T. Vishnu Vardhan Reddy, A. Vekkot, Susmitha Nair, Priyanka C. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Electronics & Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

In the current age of information saturation, where headlines serve as primary points of engagement, the pervasive use of sarcasm in news headlines poses a challenge for readers' comprehension, failing to discern sarcasm may lead to misunderstandings, misinformation dissemination, and harm journalistic integrity. This study addresses this critical issue and enhances the credibility of news sources by employing various ensemble learning algorithms and deep learning models with different types of word embedding, the evaluation includes various possible performance metrics and a complete correlation of the outcomes. XGBoost consistently outperforms other models with higher accuracy, precision, recall, and F 1-score in sarcasm detection. Among deep learning models, BiLSTM emerges as the top performer, achieving 0.86 accuracy, capturing nuanced language cues effectively and appears as the optimal model for sarcasm detection. BERT stands out with superior F1 scores, showcasing its exceptional ability in deciphering sarcastic language nuances. © 2024 IEEE.

关键词： Contrastive Learning

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A MultiModal Neural Network Architecture for Document Image Classification 15

A MultiModal Neural Network Architecture for Document Image ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Krithika, S. Priyadharshini, A.R. Bharathi Mohan, G. Gayathri, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350370249

Document classification is a crucial domain in natural language processing that involves classifying text documents into predefined categories. However, in multiple cases, the documents are heterogeneous with different modalities such as images and flow diagrams, giving rise to the need for handling multiple modalities. Thus, multimodal document classification enables a more comprehensive understanding of document content by incorporating multiple modalities such as text and images. This enhances information extraction and enables more accurate categorization of documents, benefiting various applications such as content organization, search, recommendation systems, and information retrieval. This work proposes a deep learning approach that leverages both textual and image information for improved classification of multimodal documents. Our multimodal deep neural network architecture utilizes two different channels in processing the multimodal features: text and document images. The textual model leverages BERT to generate embeddings of the text, while the image feature extraction channel employs a hybrid architecture consisting of the pre-trained convolutional neural network model VGG-19 as the backbone followed by a vision transformer. The two different channels are incorporated into a unified framework that performs the final classification. The training and testing of the proposed framework were conducted on the benchmark TOBACCO-3482 dataset, demonstrating the effectiveness and potential of our proposed multimodal classification approach. The TOBACCO-3482 dataset is a widely used benchmark dataset for document image analysis and classification. The effectiveness in performing the classification of the proposed framework is evaluated on the grounds of performance metrics such as confusion matrix, accuracy, precision, recall, and F1-score. © 2024 IEEE.

关键词： BERT CNN NLP OCR ViT

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Online Linear Regression in Dynamic Environments via Discounting 41

Online Linear Regression in Dynamic Environments via Discoun...

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41st International Conference on Machine Learning, ICML 2024

作者： Jacobsen, Andrew Cutkosky, Ashok Department of Computing Science University of Alberta Edmonton Canada Department of Electrical and Computer Engineering Boston University BostonMA United States

We develop algorithms for online linear regression which achieve optimal static and dynamic regret guarantees even in the complete absence of prior knowledge. We present a novel analysis showing that a discounted variant of the Vovk-Azoury-Warmuth forecaster achieves dynamic regret of the form RT(u) ≤ O (d log(T ) ∨ √dPγT (u)T ), where PγT (u) is a measure of variability of the comparator sequence, and show that the discount factor achieving this result can be learned on-the-fly. We show that this result is optimal by providing a matching lower bound. We also extend our results to strongly-adaptive guarantees which hold over every sub-interval [a, b] ⊆ [1, T ] simultaneously. Copyright 2024 by the author(s)

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Attention Based CNN-RNN Hybrid Model for Image Captioning 5

Attention Based CNN-RNN Hybrid Model for Image Captioning

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Raphael, Anisha Abisri, S. Anitha, E. Ritika, S. Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Image captioning enables computers understand and interpret visual content by bridging the gap between natural language processing and computer vision. The model proposed in this paper for image captioning that employs a hybrid deep learning method and includes an attention mechanism. Combining the strengths of convolutional neural networks (CNNs) for image interpretation and recurrent neural networks (RNNs) for caption generation allows machines to provide more accurate and contextually appropriate descriptions. The CNN+RNN with attention technique improves captioning by dynamically focusing on relevant image regions, and the CNN+RNN encoder-decoder structure serves as a robust foundation for textual descriptions. In contrast, the Vision Transformer (ViT) with GPU acceleration provides greater feature extraction and faster processing, using global context for potentially more accurate and efficient caption production. This work presents a detailed approach to image captioning using a well-chosen dataset from Kaggle. This dataset features images with human-written captions, which helps train the model more effectively. The models experimented were CNN+RNN with attention mechanism, CNN+RNN encoder-decoder and ViT+GPU2. The best results are reported from DenseNet201 which is based on CNN+RNN with attention mechanism achieving an average BLEU score of 0.668 and an average ROUGE-L score of 0.746. © 2024 IEEE.

关键词： Convolutional neural networks

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Novel Topology and Design for Wideband Bandpass Filter with Frequency-and Attenuation-Reconfigurable In-Band Notch

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IEEE Transactions on Microwave Theory and Techniques 2023年第4期71卷 1663-1676页

作者： Liu, Bin Yang, Tao Li, Kun Chi, Pei-Ling University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu610054 China National Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan

A novel topology and the design method are proposed for a wideband bandpass filter (BPF) with frequency-and attenuation-reconfigurable in-band notch by engineering the complex frequencies of transmission zeros. The proposed topology consists of a fifth-order wideband BPF and two coupled notch resonators. With the two coupled notch resonators, a pair of complex in-band transmission zeros are formed. By controlling the frequency difference between the two notch resonators, a frequency-and attenuation-reconfigurable notch can be obtained in the passband of the wideband BPF. A matrix analysis method is presented to characterize the frequency-and attenuation-tunable in-band notch precisely. The relationship between complex transmission zeros and in-band attenuation is also investigated. To demonstrate the proposed techniques, a prototype using microstrip and evanescent mode resonators is designed and fabricated. The proposed wideband BPF exhibits a reconfigurable in-band notch with frequency tuning from 1.494 to 2.004 GHz and attenuation tuning from 2 to 79 dB. The simulated and measured results are in good agreement with each other, validating the proposed design and method. © 1963-2012 IEEE.

关键词： Bandpass filters

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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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computer Modeling in engineering & sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

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