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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Xu, Ronghai Zhao, Haijun Li, Zhi-Yuan Wang, Chang-Dong School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Beijing China

ISBN: (纸本)9783031306716

Recently, Graph Convolutional Network (GCN) has been widely applied in the field of collaborative filtering (CF) with tremendous success, since its message-passing mechanism can efficiently aggregate neighborhood information between users and items. However, most of the existing GCN-based CF models suffer from low convergence rates during training, mainly because they follow the design of standard GCN using a simple uniform average to aggregate the neighborhood information. We also find that the scale of embedding across different layers oscillates. We argue that these issues can be alleviated by our proposed graph convolution framework, namely Accelerated Light Graph Convolutional Network (ALGCN). ALGCN mainly contains two components: influence-aware graph convolution operation and augmentation-free in-batch contrastive loss on the unit sphere. Empirical evaluations on three large and public datasets demonstrate that the proposed method achieves remarkable training speedups over LightGCN and substantially outperforms the state-of-the-art GCN-based CF models. Our method also shows a great improvement in long-tail recommendation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph neural networks

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Very Short-Term Solar Irradiance Forecasting using Multilayered Long-Short Term Memory 7

Very Short-Term Solar Irradiance Forecasting using Multilaye...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Thirunavukkarasu, Gokul Sidarth Kalair, Ali Raza Seyedmahmoudian, Mehdi Jamei, Elmira Horan, Ben Mekhilef, Saad Stojcevski, Alex School of Science Computing and Engineering Technologies Swinburne University of Technology VIC3122 Australia College of Engineering and Science Victoria University VIC3011 Australia School of Engineering Deakin University Waurnponds VIC3216 Australia

ISBN: (纸本)9789532901160

The intermittent nature of the resources (inputs: sunlight) results in intermittent energy output (power) from the photovoltaic energy systems. Therefore, accurate prediction of the photovoltaic output is a key area of research across the globe. Direct normal irradiance $DNI$) forecasts are essential for the planning of a distributed energy $DE$) powered facility or concentrated solar power systems. Some areas are more prone to have random weather conditions, one such city is Melbourne, Australia. This makes the forecasting a challenging task for conventional approaches and ther is a need for more sophisticated algorithms to makes accurate predictions. Accurate forecasting aids in optimally scheduling of HVAC loads, demand side management, trading of transactive energy, asset optimization and data analytics. In this manuscript a faster, efficient and less exhaustive algorithm for quarter-hour (15 minutes) forecasts for sites prone to weather irregularities is presented. This study discusses a case study using a machine learning based multi-layered long-short term memory neural networks $(LSTM-NN$) with Adam optimizer are used to forecast $DNI$. This research addresses the best forecasting for subtropical conditions specifically and evaluates its performance by comparing with the most used methods including $ARIMA,\ ARMA,\ SVR$, Persistence, and $AR$. The proposed nested $LSTM$ model was assessed by comparing $RMSE(27.35),\ MAE(6.879)$ and $MBE(0.42)$ values with other aforementioned methods. The results reveal the optimal performance of $LSTM$ with reduced operational time, improved precision, and accuracy. © 2022 University of Split, FESB.

关键词： Long short-term memory

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Sentinel: An Integrated Framework for News Sentiment Analysis, Bias Detection, and Coverage Comparison Using LLMs

Sentinel: An Integrated Framework for News Sentiment Analysi...

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Smart Technologies, Communication and Robotics (STCR)

作者： Prasanna Kumar R Bharathi Mohan G Akilesh Rao S Jaikanth Y Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

The sheer proliferation of news sources online has resulted in a splintered media space, where readers are more often than not exposed to skewed reporting and limited perspectives. We present Sentinel, an AI-powered framework that relies on Large Language Models (LLMs) to facilitate sentiment analysis, bias detection, and coverage comparison. This would integrate an understanding of the emotional undertone, if biased at all, and the depth of coverage within all these news stories for the consumers. Our system incorporates web scraping, advanced NLP techniques, and custom algorithms to provide a comprehensive view on news articles. Going through the architecture, methodology, and real-world applications of the Sentinel, that is a product which has in it all the potential to empower its users in making ever better-informed decisions in the increasingly polarized media environment.

关键词： Sentiment analysis Technological innovation Large language models Media Linguistics Market research Real-time systems Fake news Standards Robots

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Cybernated Watermarking Approach on Image with Cryptographic Encryption 2

Cybernated Watermarking Approach on Image with Cryptographic...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Sivasankari, R. Veeramakali, T. Mustafa Nawaz, S.M. Prakash, P. Umadevi, P. Alvin Ancy, A. B.S.Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering India Srm Institute of Science and Technology School of Computing Dept of Data Science and Business Systems Tamilnadu Kattankulathur India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Sri Sai Ram Engineering College Dept of Electronics and Communication Engineering India Kpr Institute of Engineering and Technology Department of S & H Coimbatore India Anna University Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350387933

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types;a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text. © 2024 IEEE.

关键词： Cryptography

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Closed-loop Multi-step Planning

arXiv

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arXiv 2024年

作者： Lafratta, Giulia Porr, Bernd Chandler, Christopher Miller, Alice School of Engineering University of Glasgow United Kingdom School of Computing Science University of Glasgow United Kingdom

Living organisms interact with their surroundings in a closed-loop fashion, where sensory inputs dictate the initiation and termination of behaviours. Even simple animals are able to develop and execute complex plans, which has not yet been replicated in robotics using pure closed-loop input control. We propose a solution to this problem by defining a set of discrete and temporary closed-loop controllers, called "Tasks", each representing a closed-loop behaviour. We further introduce a supervisory module which has an innate understanding of physics and causality, through which it can simulate the execution of Task sequences over time and store the results in a model of the environment. On the basis of this model, plans can be made by chaining temporary closed-loop controllers. Our proposed framework was implemented for a real robot and tested in two scenarios as proof of concept. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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AI-Driven Multi-Class Diagnosis of Parkinson’s Disease: Enhancing Accuracy and Differentiation from Movement Disorders

AI-Driven Multi-Class Diagnosis of Parkinson’s Disease: Enh...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Shubhika Verma Jasmeet Kaur Deepa Gupta Aarathi Rajagopalan Nair Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Parkinson’s disease is a progressive neurological disorder that is frequently misdiagnosed due to its symptomatic similarities with other movement-related conditions. Ensuring precise and early diagnosis is essential for effective treatment; however, current diagnostic approaches are often subjective and prone to errors. To overcome these limitations, this study introduces a multi-class classification framework designed to differentiate Parkinson’s disease from related disorders, including Essential Tremor, Atypical Parkinsonism, Secondary Parkinsonism, Multiple Sclerosis, and Healthy Controls. The classification process is structured into three tasks: distinguishing Parkinson’s disease from Healthy Controls, differentiating it from other movement disorders, and performing a multi-class classification that includes Parkinson’s disease, Healthy Controls, and additional disorders. Experimental evaluations indicate that XGBoost achieved an F1-score of 93% for Parkinson’s disease versus Healthy Controls, SVM attained 85% for Parkinson’s disease against other movement disorders, and Logistic Regression yielded 68% accuracy in the multi-class classification. These results highlight the potential of machine learning in providing accurate, scalable, and objective diagnostic solutions for Parkinson’s disease.

关键词： Support vector machines Neurological diseases Logistic regression Accuracy Multiple sclerosis Recurrent neural networks Machine learning algorithms Process control Predictive models Diseases

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Designing Multimodal Video Search by Examples (MVSE) user interfaces: UX requirements elicitation and insights from semi-structured interviews 23

Designing Multimodal Video Search by Examples (MVSE) user in...

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2023 European Conference on Cognitive Ergonomics, ECCE 2023

作者： Boyd, Kyle Mcallister, Patrick Mulvenna, Maurice Bond, Raymond Wang, Hui Spence, Ivor Wu, Guanfeng Haider, Abbas School of Computing Ulster University Belfast United Kingdom School of Electronics Electrical Engineering and Computer Science Queens University United Kingdom BBC Research and Development AI Research London United Kingdom

ISBN: (纸本)9798400708756

In order to search for content from large video archives, it is typically undertaken via keyword queries using predefined metadata such as title and other tags. However, it is difficult to use keywords to search for specific moments in a video. Video search by examples is a desirable approach for this scenario as it allows users to search for content using one or more examples without having to specify a keyword. However, video search by examples is notoriously challenging, and performance is poor. To improve search performance, multiple modalities may be considered - image, sound, voice and text, multiple search cues could be used to identify more relevant content. This is multimodal video search by examples (MVSE), where users can search for content using multiple modalities. In this paper, typical end users - BBC archivists, programme support staff - are interviewed to identify how their search needs can be addressed with the technical capabilities of a MVSE tool. Such a search tool will be useful for organisations such as the BBC who maintain large collections of video archives and want to provide a search tool for their own staff as well as for the public. It will also be useful for companies such as Youtube who host videos from the public and want to enable video search by examples. The study's objectives explored in this paper were to inform the design and development of the UX workflows to gain a broader understanding of what opportunities and issues may arise from the proposed prototype tool. Results from the thematic analysis was highlighted 4 main themes: Opportunities, Time constraints, Activities, and Pain points. Further analysis highlighted key areas that should be considered for an MVSE-based system, such as scene recognition, face recognition, speed issues, and integration.. © 2023 Owner/Author.

关键词： User interfaces

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Grid Search Hyperparameters Tuning with Supervised Machine Learning for Awngi Language Named Entity Recognition 2

Grid Search Hyperparameters Tuning with Supervised Machine L...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Ayalew, Amogne Andualem Bitew, Gizachew Salau, Ayodeji Olalekan Tegegne, Melaku Lake Gupta, Shashi Kant Israr, Mohammad Tegegne, Tesfa Assegie, Tsehay Admassu Mizan-Tepi University School of Computing and Informatics Department of Information Technology Tepi Ethiopia Mizan Tepi University School of Computing Computer Science Department Mizan-Tepi Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Injibara University College of Engineering and Technology Department of Information Technology Injibara Ethiopia Eudoxia Research University Department of Computer Science and Engineering United States Maryam Abacha American University of Nigeria Hotoro Gra Kano State Nigeria Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology Bahir Dara Ethiopia Injibara University College of Engineering and Technology Department of Computer Science Injibara Ethiopia

ISBN: (纸本)9798350364699

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment. © 2024 IEEE.

关键词： Support vector machines

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Design and Analysis of TCP Connection States Using Finite Automata

Design and Analysis of TCP Connection States Using Finite Au...

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computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Rohan Gamidi M Hemasri Tejaswi Muppala Vinitha Chowdary A Niharika Panda Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Transmission Control Protocol (TCP) is a fundamental protocol in the Internet Protocol Suite, responsible for establishing reliable data transmission between devices in network communication. This study models TCP connection states using Non-Deterministic Finite Automata (NFA) and Pushdown Automata (PDA), specifically implementing the three-way hand- shake and data transfer processes through these formal methods. Each TCP state, from connection initiation to termination, is represented by corresponding automata states, demonstrating the applicability of Formal Language Automata (FLA) in illustrating TCP's state-driven structure. A side-by-side comparison of NFA and PDA, showing important distinctions in how each handles the complex flow of TCP—NFA provides the simplest solution because it is stateless. Still, the stack-based memory of PDA is better suited for the layered nature of TCP, where there are states for connection setup, data transfer, and connection teardown that can nest and recurse is provided. PDA, therefore, better meets these requirements for accurately capturing layered and memory- dependent processes of TCP communication.

关键词： TCP Analytical models Protocols Turing machines Automata Formal languages Data transfer Probabilistic logic Reliability Sustainable development

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IoT-based Environmental Monitoring System

IoT-based Environmental Monitoring System

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： D. Abhiram K. Dhanush Reddy P. Koushik Reddy Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This project meets the current important demand for the constant monitoring of environmental conditions because climate change and human activities affect ecosystems. Temperature and humidity are measured using the DHT11/DHT22 sensor connected to the ESP8266 microcontroller and the GPS module is used to obtaining the location information. In contrast to current approaches targeting the communication data or energy consumption only, the contaminants of this system are unified, location-based, and central. It provides immediate data transfer to the cloud through a comfortable low power interface for usage in farming, transportation, and more for environmental monitoring. The findings show that all the environmental conditions and location change can be monitored by the IoT-based system which use ESP32/Arduino microcontroller for sensing data by a Bluetooth or Wi-Fi through cloud platform or server and it gives alert message to web interface or mobile application when temperature increases or decreases more than threshold values. This practical system is especially valuable for those areas of interest that require a constant check, such as environmental control or the logistics and health safety of the transported goods or passengers.

关键词： Climate change Internet of Things Environmental monitoring Embedded systems Intelligent sensors Data processing Air quality

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