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A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

computers, Materials & Continua 2023年第11期77卷 1557-1573页

作者： Tanvir Fatima Naik Bukht Naif Al Mudawi Saud S.Alotaibi Abdulwahab Alazeb Mohammed Alonazi Aisha Ahmed AlArfaj Ahmad Jalal Jaekwang Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Information Systems Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Convergence Program for Social Innovation Sungkyunkwan UniversitySuwon03063Korea

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social *** enhances systems’ability to interpret and respond to human behavior *** research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse *** overall purpose of this study is to develop a robust and accurate system for human interaction *** research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out *** filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical *** extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)*** application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification *** ensures that the final features loaded into the HMM classifier accurately represent the relevant human *** impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed *** proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM *** enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

关键词： Human interaction recognition HMM classification quadratic discriminant analysis dimensionality reduction

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Automated classification of big X-ray diffraction data using deep learning models

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npj Computational Materials 2023年第1期9卷 169-180页

作者： Jerardo E.Salgado Samuel Lerman Zhaotong Du Chenliang Xu Niaz Abdolrahim Materials Science program University of RochesterRochesterNew York 14627USA Department of Computer Science University of RochesterRochesterNew York 14627USA Department of Mechanical Engineering University of RochesterRochesterNew York 14627USA Laboratory for Laser Energetics University of RochesterRochesterNew York 14627USA

In current in situ X-ray diffraction(XRD)techniques,data generation surpasses human analytical capabilities,potentially leading to the loss of *** techniques require human intervention,and lack the performance and adaptability required for material *** the critical need for high-throughput automated XRD pattern analysis,we present a generalized deep learning model to classify a diverse set of materials’crystal systems and space *** our approach,we generate training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal *** also employ an expedited learning technique to refine our model’s expertise to experimental *** addition,we optimize model architecture to elicit classification based on Bragg’s Law and use evaluation data to interpret our model’s *** evaluate our models using experimental data,materials unseen in training,and altered cubic crystals,where we observe state-of-the-art performance and even greater advances in space group classification.

关键词： Automate classify cubic

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Building Prediction Model for Detecting Cyberbullying using TikTok Comments 8

Building Prediction Model for Detecting Cyberbullying using ...

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8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Prameswari, Bunga Aura Syafa Oktaviani, Haliza Wicaksono, Titus Rangga Pieter Leonard, Biben Achmad, Said Sutoyo, Rhio Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Business School Undergraduate Program International Business Management Program Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315516

Easily accessible Internet and the need for communication led to the widespread use of social media platforms, e.g., TikTok. Social media platforms offer opportunities for social interaction and enter-Tainment but also introduce risks such as fake news, fraud, and cyberbullying. The latter severely threatens the victim's mental health and overall well-being. This research collects, annotates, and analyzes 1,508 TikTok comments concerning cyberbullying behavior. Then, the comments were used to build a Deep Learning BERT (Bidirectional Encoder Representations from Transformers) architecture prediction model. This experiment will use pretrained model BERT and fine-Tuning BERT. After fine-Tuning, the prediction model achieves 0.63 validation accuracy. This research high-lights the importance of building a prediction model to detect cyberbullying and contribute to a better understanding and prevention of this behavior on social media platforms. © 2023 IEEE.

关键词： Sentiment analysis

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Bilateral Masking with prompt for Knowledge Graph Completion

Bilateral Masking with prompt for Knowledge Graph Completion

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Kong, Yonghui Fan, Cunhang Chen, Yujie Zhang, Shuai Lv, Zhao Tao, Jianhua Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Department of Automation Tsinghua University China

ISBN: (纸本)9798891761193

The pre-trained language model (PLM) has achieved significant success in the field of knowledge graph completion (KGC) by effectively modeling entity and relation descriptions. In recent studies, the research in this field has been categorized into methods based on word matching and sentence matching, with the former significantly lags behind. However, there is a critical issue in word matching methods, which is that these methods fail to obtain satisfactory single embedding representations for *** address this issue and enhance entity representation, we propose the Bilateral Masking with prompt for Knowledge Graph Completion (BMKGC) *** methodology employs prompts to narrow the distance between the predicted entity and the known entity. Additionally, the BMKGC model incorporates a bi-encoder architecture, enabling simultaneous predictions at both the head and tail. Furthermore, we propose a straightforward technique to augment positive samples, mitigating the problem of degree bias present in knowledge graphs and thereby improving the model's robustness. Experimental results conclusively demonstrate that BMKGC achieves state-of-the-art performance on the WN18RR dataset. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Neuro-Symbolic Classification of Basic Human Values 2nd

Neuro-Symbolic Classification of Basic Human Values

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2nd International Workshop on Value Engineering in Artificial Intelligence, VALE 2024

作者： De Giorgis, Stefano Lazzari, Nicolas Institute of Cognitive Sciences and Technologies National Research Council Rome Italy Department of Computer Science University of Pisa Pisa Italy Department of Modern Languages Literature and Cultures University of Bologna Bologna Italy

ISBN: (纸本)9783031854620

This work explores the integration of ontology-based reasoning and Machine Learning techniques for explainable classification in the domain of moral and cultural values. By relying on an ontological formalization of moral values as in the Basic Human Values theory, which is based on the Description and Situation Ontology Design Pattern, the sandra neuro-symbolic reasoner is used to infer values (formalized as descriptions) that are satisfied by a certain sentence. Sentences, alongside their structured representation, are automatically generated using an open-source Large Language Model. The inferred descriptions are used to automatically detect the value associated with a sentence. We show that only relying on the reasoner’s inference results in explainable classification comparable to other more complex approaches. Moreover, we test how the LLMs tacit knowledge can be exploited to obtain novel formalizations of the domain. We show that combining the reasoner’s inferences with simple distributional semantics methods largely outperforms all the baselines, including complex models based on neural network architectures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge graph

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Performance Assessment of Classical Machine Learning Models Used for Entity Relationship Extraction on Online Philippine News Articles 6

Performance Assessment of Classical Machine Learning Models ...

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6th International Conference on Electrical, Control and Instrumentation Engineering, ICECIE 2024

作者： Ogbinar, Michel Onasis S. Ignacio, Michael Q. Penas, Reynaldo Ted L. Naval, Prospero C. University of the Philippines - Diliman Artificial Intelligence Program Quezon Philippines University of the Philippines - Diliman Department of Computer Science Quezon Philippines

ISBN: (纸本)9798350380040

Content from legitimate online news articles is used to determine relationships of objects or entities to enable a more efficient way of extracting information. It could also be used to establish ground truths on news stories or current events to provide a reference against the emergence of fake news articles. This study involved the entity relationship extraction in an online news source, the Philippine Daily Inquirer online site ***. Classical machine learning methods were designed to perform pairwise entity relationships via Semantic Web, and the performance of the methods was assessed and compared with one another in terms of training time and F1 scores. A 90-10 train-test split of data was used for each of the methods. A total of 12 machine learning methods were configured to accomplish the task of entity relationship extraction, and a total of 27 top entity relationships were obtained and ranked. The use of support vector machines (SVM) yielded the highest F1 score of 60.73% but took 592.54 seconds to complete the training. The fastest method to train was the multinomial naïve Bayes (MNB) at 0.63 seconds but only obtained an F1 score of 50.14%. © 2024 IEEE.

关键词： Adversarial machine learning

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Boost DC-DC converter with regenerative snubber

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Energy Reports 2024年 12卷 798-819页

作者： Yau, Yeu-Torng Department of Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung Taiwan

To accommodate the wide range of input voltages supplied by redundant batteries and ensure an adequate hold-up time for communication systems during utility power failures, power supplies used in 5 G base stations typically include a front-end boost converter as the first stage and a second-stage isolated converter. In this paper, a new topology for a regenerative snubber in boost converters is proposed. Compared to traditional active or passive snubber topologies, the proposed snubber offers the advantages of having fewer components and occupying less space. Moreover, it does not require additional active devices and can be driven by widely-used commercial PWM control integrated circuits, achieving a higher level of cost-effectiveness and offering increased industrial applications. The final prototype of a power supply for a networking switch hub (as used in 5 G base stations) is fabricated to demonstrate the feasibility of the proposed method. © 2024 The Authors

关键词： Topology

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Classification of Rupiah Banknote for the Visually Impaired using Convolutional Neural Network with Custom Architecture and RMSProp Optimizer 11

Classification of Rupiah Banknote for the Visually Impaired ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Kurniadi, Dede Rahmi, Murni Lestari Mulyani, Asri Nashrulloh, Muhammad Rikza Septiana, Yosep Tri Julianto, Indri Institut Teknologi Garut Department of Computer Science Garut Indonesia Institut Teknologi Garut Informatics Study Program Garut Indonesia

ISBN: (纸本)9798350389654

Visual impairment is a condition that affects the sense of sight. In Indonesia, around 1.5% of the 4 million people are blind. In their daily activities, the visually impaired rely on their sense of touch to recognize the value of money by feeling the double lines on the right and left sides of the rupiah banknote. However, if the money is in bad condition, they are unable to recognize it, so they have to involve the help of others with the risk of fraud. Research is underway to develop a paper currency detection tool to assist the visually impaired in everyday transactions and reduce the risk of fraud. The research in this paper focuses on creating a classification model for detecting Indonesian rupiah banknotes. The method approach used in this research involves using a custom architecture Convolutional Neural Network model that combines elements from the AlexNet and VGGNet architectures, along with the RMSProp Optimizer. Before training the model, the private dataset that has been collected is augmented to increase the dataset and then split data. Next, the model is trained and tested using a private dataset of 800 images of rupiah banknotes for 2016, 2020, and 2022 emissions years, divided into eight classes of banknotes with denominations of IDR 1000, IDR 2000, IDR 5000, IDR 10,000, IDR 20,000, IDR 50,000, IDR 75,000, and IDR 100,000. Finally, the model's performance is evaluated using loss function, accuracy, precision, recall, and AUC-ROC evaluation metrics. The model evaluation results show a value loss function of 1.28, an accuracy of 95.31 %, a precision of 95.24%, a recall of 93.75%, and an AUC-ROC value of 0.99. This evaluation result indicates that the model is highly effective for use in the rupiah currency nominal detection system for the visually impaired. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Parallel Graph Applications 30

Analysis of Parallel Graph Applications

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Atik, Funda Yesil, Serif Ouarnoughi, Hamza Niar, Smail Ozturk, Ozcan Washington University in St. Louis Computer Science and Engineering Department DoimukhMI United States NVidia UrbanaIL United States Université Polytechnique Hauts-de-France Department of Computer Science Valenciennes France LAMIH/CNRS Université Polytechnique Hauts-de-France Valenciennes France Sabanci University Computer Science and Engineering Program Electronics Engineering Program Istanbul Turkey

ISBN: (纸本)9798331515966

Despite the increasing computing power of shared memory systems with high core counts, parallel graph processing frameworks cannot exploit it effectively. The reason behind this is the inherent challenges in parallel graph algorithms, which are efficient management of dynamically created tasks and irregular data access patterns. In this paper, we categorize several popular design choices into three design dimensions: (i) execution mode, (ii) data access pattern, and (iii) work activation. We provide their high-level parallel implementations and analyze various implementations of three representative iterative graph algorithms by considering these design dimensions. To gain a better understanding of design choices, we examine their impacts on performance, communication, scalability, and work efficiency. We also investigate the communication characteristics of the design choices on two state-of-the-art shared-memory platforms by performing micro-architectural analysis. Our microarchitectural analysis reveals that a topology-driven, pull-based model gives up to 20 x better performance. © 2024 IEEE.

关键词： Communication Graph Analytics Parallel Frameworks Performance Shared Memory Systems

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Secure and Energy-Efficient Framework for Internet of Medical Things (IoMT)-Based Healthcare System

Secure and Energy-Efficient Framework for Internet of Medica...

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International Conference on Data science and Network Engineering, ICDSNE 2023

作者： Dewan, Ritu Nagpal, Tapsi Ahmad, Sharik Rana, Arun Kumar Islam, Sardar M. N. Department of Computer Science and Engineering Lingaya’s Vidyapeeth Faridabad India Department of Computer Science and Engineering Galgotias College of Engg. and Technology Greater Noida India SILC and Decision Sciences and Modelling Program Victoria University Footscray Australia

ISBN: (纸本)9789819967544

Manufacturing, energy, finance, education, transportation, smart home, and medicine employ IoT technology. IoT solutions can efficiently manage hospital patients and mobile assets to provide high-quality medical services. The Internet of Medical Things (IoMT) integrates IoT with medical equipment to increase patient comfort, cost-effective medical solutions, hospital treatment speed, and personalized healthcare. This work uses Constrained Application Protocol to secure remote patient health data (CoAP). Nevertheless, the CoAP DTLS layer lacks key control, session establishment, and multicast message exchange. Hence, IoMT communication requires an efficient protocol for safe CoAP session formation. Consequently, to address key management and multicast security issues in CoAP, we presented an efficient and secure communication method to establish a secure session key between IoMT devices and distant servers utilizing lightweight, energy efficient, and Secure CoAP Elliptic Curve Cryptography (E2SCEC2). E2SCEC2 can use a smaller key size than Rivest-Shamir-Adleman (RSA) due to its tiny key size. To determine if these algorithms are compatible in limited contexts, the paper examines key creation, signature generation, and verification of E2SCEC2 and RSA algorithms, energy consumption, and radio duty cycle. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Hospitals

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