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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Xiao Meng, Shunmei Li, Qianmu Xu, Xiaolong Qi, Lianyong Dou, Wanchun Zhang, Jing Zhang, Xuyun Nanjing University of Science and Technology China Nanjing University State Key Lab. for Novel Software Technology China Nanjing University of Information Science and Technology School of Computer and Software China College of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Macquarie University School of Computing Australia

ISBN: (纸本)9798350304855

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collaborative filtering, to disentangle the representations of users and items at the granularity of social users' intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user's intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores. © 2023 IEEE.

关键词： Collaborative filtering

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Utilizing Support Vector Machines for Signal Processing in Telecommunications 5th

Utilizing Support Vector Machines for Signal Processing in T...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Mehta, Deepak Arya, Rakesh Prasad, P.S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

SVMs had been effectively applied to various signal processing tasks in Telecommunications, along with goal detection and category in cognitive radio networks, channel estimation, exploiting spatial variety for multipath wireless conversation channels, interference cancellation, and era of feature vectors for sign type. In goal detection and category in Wi-Fi conversation networks, SVMs may be used to come across and classify specific types of wireless signals with an excessive diploma of accuracy. The input characteristic vectors are comprised of the signal’s spectrum and temporal features, and they are used for the education of the SVM classifier. In channel estimation, SVMs may be used to estimate the unknown channel parameters in Wi-Fi conversation channels with a low but suitable suggest-squared-blunders charge. That is viable via exploiting the diversity of more than one received signal replicas and the usage of the predicted channel parameters to improve signal reception. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wi-Fi

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Detecting Electromagnetic Vulnerabilities in Networking Topologies 5th

Detecting Electromagnetic Vulnerabilities in Networking Topo...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Preethi, D. Verma, Upendra K. Arumugam, Jayachandran Alam, Intekhab Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper describes a technique of detecting electromagnetic vulnerabilities in networking topologies, consisting of Wi-Fi communications. A green approach is offered which entails the mixture of electromagnetic measurements from multiple antennae, and the utility of parameterization, optimization and clustering algorithms. Effects are generated to offer a complete breakdown of the electromagnetic residences of a community topology, permitting identity of weaknesses or regions of vulnerability. The resulting vulnerabilities may be used to guide layout changes, or as a first indicator of potential security problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Topology

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Real-Time Classification of Defects During Automated Valve Testing and Inspection 5th

Real-Time Classification of Defects During Automated Valve T...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Singh, Shweta Agarwal, Trapty Haripriya Hemlata School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (纸本)9789819780426

This paper offers a singular method for actual-time type of defects at some point of computerized valve testing and inspection. Mainly, an image processing gadget is developed to discover and classify various defects which includes cracks, dents, foreign items, and different floor irregularities. The machine includes a high-decision digital camera, LED lighting fixtures, processing unit, and defect reputation algorithm. The image processing system captures the picture and methods it to pick out the illness regions. The gadget then extracts the features from every of the detected regions and classifies them the usage of a guide Vector device. The model is demonstrated with manually received pics, and the outcomes show true overall performance in terms of accuracy and speed. Similarly, the system can alert the consumer whilst a potential disorder is identified and the defects can then be in addition inspected and repaired. This approach has the ability to lessen inspection time, enhance manufacturing efficiency, and reduce charges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lighting fixtures

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Reinforcement Learning with Token-level Feedback for Controllable Text Generation

Reinforcement Learning with Token-level Feedback for Control...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Li, Wendi Wei, Wei Xu, Kaihe Xie, Wenfeng Chen, Dangyang Cheng, Yu Cognitive Computing and Intelligent Information Processing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China China Ping An Property & Casualty Insurance Company of China China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798891761193

To meet the requirements of real-world applications, it is essential to control generations of large language models (LLMs). Prior research has tried to introduce reinforcement learning (RL) into controllable text generation while most existing methods suffer from overfitting issues (finetuning-based methods) or semantic collapse (post-processing methods). However, current RL methods are generally guided by coarse-grained (sentence/paragraph-level) feedback, which may lead to suboptimal performance owing to semantic twists or progressions within sentences. To tackle that, we propose a novel reinforcement learning algorithm named TOLE which formulates TOken-LEvel rewards for controllable text generation, and employs a "first-quantize-then-noise" paradigm to enhance the robustness of the RL algorithm. Furthermore, TOLE can be flexibly extended to multiple constraints with little computational expense. Experimental results show that our algorithm can achieve superior performance on both single-attribute and multi-attribute control tasks. We have released our codes at https://***/WindyLee0822/CTG. © 2024 Association for Computational Linguistics.

关键词： Reinforcement learning

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Feature selection using modified chaotic satin bowerbird algorithm with deep transfer learning for Multispectral Image Classification

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International Journal of information technology (Singapore) 2024年 1-13页

作者： Rajakani, M. Kavitha, R.J. Rajesh, S. Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Electronics and Communication Engineering University College of Engineering Panruti Panruti 607106 India Department of Information Technology Mepco Schlenk Engineering College Sivakasi India

Multispectral image classification has received significant attention among research communities and academicians. Recently, several artificial intelligence (AI) models can be used for the extraction of prominent features. Besides, deep learning (DL) methods become more familiar and gained interest in the remote sensing community for the classification of multispectral and hyperspectral images. With this motivation, this paper presents an optimized feature learning with deep transfer learning enabled multispectral image classification (OFLDTL-MSIC) technique. The proposed OFLDTL-MSIC technique aims to categorize the different class labels of the multispectral images. Besides, the OFLDTL-MSIC technique applies multi-level discrete wavelet transform (DWT) based image decomposition technique. Moreover, the EfficientNet technique is applied as a feature extractor to generate a collection of features. Furthermore, selecting optimal features extracted by a EfficientNet involves identifying and retaining the most relevant and informative features The improved chaotic satin bowerbird optimization (CSBO) algorithm which uses dynamic population size is proposed for feature selection and kernel extreme learning machine (KELM) model is applied for the classification process. In order to ensure the enhanced performance of the OFLTDL-MSIC technique, a wide range of simulations take place using the Madurai LISS IV multispectral images and the results are examined under several aspects. The extensive comparative study highlighted the better performance of the OFLTDL-MSIC technique over the recent methods. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Deep learning Image classification Multispectral images Parameter tuning Remote sensing

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Deep Representation Learning for Electron Ionization Mass Spectra Retrieval

Deep Representation Learning for Electron Ionization Mass Sp...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Shibo Zhang, Xuan Jiao, Anlei Sun, Shiwei Dian, Longyang Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China Shandong University State Key Laboratory of Microbial Technology Qingdao China

ISBN: (纸本)9798350386226

The task of retrieving and analyzing mass spectra is indispensable for the identification of compounds in mass spectrometry (MS). This methodology is of critical importance as it enables researchers to correlate observed spectra with established databases, thereby precisely determining the chemical composition of samples. The primary challenges to its efficacy lie in optimizing the balance between retrieval accuracy and processing speed. Empirical studies have demonstrated that by converting mass spectra into embeddings via deep learning, it is possible to achieve both high accuracy and speed in retrieval. Nevertheless, there are complex challenges associated with employing deep learning for spectral embedding, particularly within the domain of electron ionization mass spectrometry (EI-MS). In this paper, we introduce a novel representation learning technique termed EI-MS2VEC for EI-MS retrieval. Our spectrum retrieval methodology surpasses current state-of-the-art techniques such as FastEI. For the in-silico library, we attain hit rate@1 and hit rate@10 of 43.6% and 84.5%, respectively, compared to FastEI's 36.7% and 80.4%. Moreover, our retrieval approach operates with an order of magnitude greater speed than FastEI. The source code is available on Github (https://***/xfcui/EI-MS2VEC). © 2024 IEEE.

关键词： Embeddings

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How to Train Your Neural Network for Molecular Structure Generation from Mass Spectra?

How to Train Your Neural Network for Molecular Structure Gen...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Kai Liu, Yanmin Dian, Longyang Sun, Shiwei Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Shandong University State Key Laboratory of Microbial Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9798350386226

Mass spectrometry serves as a pivotal tool for the analysis of small molecules through an examination of their mass-to-charge ratios. Recent advancements in deep learning have markedly enhanced the analysis of mass spectrometric data, facilitating the prediction of novel small molecule structures without the necessity of extensive databases. Nonetheless, the paucity of annotated datasets impedes the efficacious training of molecular generation models predicated on MS2 spectra. To mitigate this limitation, we introduce ctMSNovelist, an avant-garde method that amalgamates pre-training, fine-tuning, and co-training techniques to construct a more precise model for the generation of molecular structures from tandem mass spectrometry data. This novel approach augments both the training regimen and the predictive accuracy of the MSNovelist model, thereby surmounting the obstacle of limited data. The methodology commences with the pretraining of a Variational Autoencoder (VAE) to generate molecular fingerprints derived from SMILES strings. Subsequently, it undergoes fine-tuning to emulate noisy fingerprints originating from mass spectrometry (MS) data. Concurrently, MSNovelist is co-trained utilizing these simulated fingerprints, inclusive of the highly noisy variants produced in the early stages of VAE training. The incorporation of a substantial volume of noisy data serves to enhance model accuracy and avert overfitting. We evaluated ctMSNovelist using the GNPS dataset and attained a SMILES prediction accuracy of 48.8%, representing a 4.1% enhancement over MSNovelist. It is pertinent to note that the sole distinction between ctMSNovelist and MSNovelist in this experiment was the training process. The code and models are publicly available at https://***/xfcui/ctMSNovelist. © 2024 IEEE.

关键词： Neural networks

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Improved Honey Encryption to Protect the Internet users from Various Attacks 3

Improved Honey Encryption to Protect the Internet users from...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Nirmalraj, T. Jebathangam, J. Vistas School of Computing Science Department of Computer Science Chennai India Vistas School of Computing Science Department of Information Technology Chennai India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

In recent months, there has been a rise in security issues as well as data breaches, which has led to increased anxiety regarding the safety of online banking. The existing authentication method for internet banking, which is entirely dependent on a password, is not robust enough to withstand the recent wave of successful password guessing and cracking attacks due to the fact that it is solely dependent on a password. As a potential remedy to this problem, the authors of this paper proposed a method known as Honey Encryption (HE), which adds another layer of protection on top of the user authentication measures that are already in place. When a malicious user guesses the password for a cloud account, the HE algorithm immediately generates false bank data that is impossible to distinguish and then directs the attacker to an account that has a fake password. This prevents the attack from being able to determine whether or not the password that was guessed is valid. As a direct consequence of this, cracking attacks and guessing passwords have become increasingly challenging. In addition to that, this research investigates different attack models and potential defences for password-based authentication schemes. © 2022 IEEE.

关键词： Cryptography

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An Accurate Blood Pressure Prediction Based on Clinical and Physiological Data Using Machine Learning

An Accurate Blood Pressure Prediction Based on Clinical and ...

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2023 IEEE Global Conference on information Technologies and Communications, GCITC 2023

作者： Sivaji, U. Chatrapathy, K. Kiran, Ajmeera Shaker Reddy, Pundru Chandra Rao, P Varaprasada Partheeban, N. Institute of Aeronautical Engineering Department of Information Technology Dundigal Hyderabad India Reva University School of Computing & Information Technology Karnataka Bengaluru India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Gokaraju Rangaraju Institute of Engineering and Technology Computing Science and Engineering Telangana Hyderabad India Galgotias University School of Computing Science and Engineering Utter Pradesh Greater Noida India

ISBN: (纸本)9798350308167

When attempting to diagnose hypertension or cardiovascular illness, blood pressure (BP) is an essential biological feature. Taking a reading is interrupted and can be painful when done with cuff-based equipment like a sphygmomanometer. Recently, a cuff-free approach has been presented that uses electrocardiogram (ECG) and photoplethysmography (PPG) signals. Due to the need for extensive multi-sensor collections, however, this approach is time-consuming and expensive. The current research uses a machine-learning(ML) strategy called classification-tree to examine the relationship between BMI, waist-circumference(WC), hip-circumference(HC), and waist-hip-ratio(WHR) in predicting hypertension(IBP). 400 college students, ranging in age from 16 to 63 (with women making up 56.3% of the sample), provided the data. In the training set, fifteen trees were generated for each sex by varying the number and order of predictors. Due to their low variance (89.56), low misclassification (.18), and high pseudo R2 (.43), BMI, WC, and WHR are the combination that yields the best anticipation for women. In the validation set, the model showed sensitivity of 82.54% and specificity of 83.68%, whereas in the test set, those numbers dropped to 45.65% and 65.15%, respectively. The lowest deviation (54.79), misclassification (.17), and highest pseudo R2 (.46) were found for BMI, WC, HC, and WHC when predicting male health. In the training set, this model showed sensitivity of 69% and specificity of 87.54%, whereas in the test set, these figures dropped to 58.38% and 69.70%, respectively. In the end, the classification-tree examination result was contrasted to the standard logistic-regression(LR) method, showing that the former was more accurate. © 2023 IEEE.

关键词： Blood Pressure Clinical data Hypertension Machine learning Physiological data prediction

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