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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Helina Rajini Suresh R. Neelaveni Bonda Kiran Kumar R. Arunadevi Indhumathi G M Varun Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of CSE SRM institute of science and technology Chennai Tamilnadu India KL School of Architecture KL Deemed to be University Andhrapradesh India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamilnadu India Department of ECE Rajalakshmi Engineering College Chennai Tamilnadu India Department of ECE Saveetha Engineering College Chennai Tamilnadu India

The proliferation of IoT devices is primarily responsible for the data deluge that has engulfed multiple industries. Nevertheless, due to the high dimensionality and complexity of IoT data streams, anomaly detection remains a significant challenge. This suggests that an unsupervised deep learning strategy could be used to identify anomalies in IoT data streams. Instead of relying on labeled data, this method employs deep neural networks, which can be trained to recognize anomalies and comprehend complex patterns. Experiments are conducted on a real-world IoT dataset to evaluate the efficacy and accuracy of the proposed method. The results demonstrate its proficiency in detecting anomalies in IoT data streams, paving the way for a safer, more reliable Internet of Things. Using unsupervised deep learning techniques, the method provides a practicable solution to the age-old problem of IoT anomaly identification.

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Correction: Data Integration Method of Multi-source Feedback Evaluation for Remote Teaching Quality

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Mobile Networks and Applications 2023年第3期28卷 935-935页

作者： Chen, Lili Baza, Mohamed Alshahrani, Hani Development and Planning Office Jilin University of Architecture and Technology Changhun China Department of Computer Science College of Charleston Charleston USA Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia

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A cluster of FPAAs to recognize images using neural networks

TechRxiv

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TechRxiv 2021年

作者： Daniel, García Moreno Alberto, Del Barrio A. Guillermo, Botella Jennifer, Hasler The Department of Computer Architecture and Automation Complutense University of Madrid Madrid28040 Spain Georgia Institute of Technology GA Atlanta30332-0250 United States

Analog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network. I © 2021, CC BY-NC-SA.

关键词： computer programming

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EPRD: Exploiting prior knowledge for evidence-providing automatic rumor detection

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Neurocomputing 2024年 563卷

作者： Li, Jiawen Li, Ronghui Ni, Shiwen Kao, Hung-Yu Naval Architecture and Shipping College Guangdong Ocean University Guangdong Zhanjiang524000 China Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province Guangdong Zhanjiang524000 China Intelligent Knowledge Management Lab National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the prevalence of social media platforms, rumors have been a serious social problem. Notably, existing rumor detection methods simply provide detection labels while ignoring their explanation. However, illustrating the reasons why a suspicious statement is a rumor is essential. To address this realistic scenario, we propose a novel Evidence-Providing Rumor Detection model called EPRD. EPRD incorporates a wide variety of information from both prior knowledge sources and current comments. It also learns bilaterally friendly representations for interpretable rumor detection. EPRD first retrieves evidence from prior knowledge sources and checks the relationship between the given statement and its evidence. Our model then constructs two heterogeneous graph objects to simulate the propagation layout of the current comments and evidence relationships. Finally, EPRD integrates the GraphSAGE component and attention mechanism to detect rumors. To the best of our knowledge, we propose the first model that incorporates prior knowledge to verify rumors and boost credibility. Experiments on two real-world Twitter datasets demonstrate that EPRD consistently exhibits the best rumor detection performance. Moreover, EPRD outperforms other baselines in the early rumor detection task. © 2023

关键词： Graph neural networks

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Energy Mapping of Existing Building Stock in Cambridge using Energy Performance Certificates and Thermal Infrared Imagery

arXiv

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arXiv 2024年

作者： He, Yinglong Pan, Jiayu Debnath, Ramit Bardhan, Ronita Cullen, Luke Jenkins, Marco Gomez Mackie, Erik Hawker, George Parry, Ian Institute of Astronomy University of Cambridge CambridgeCB3 0HA United Kingdom Department of Architecture University of Cambridge CambridgeCB2 1PX United Kingdom EPRG Cambridge Judge Business School University of Cambridge CambridgeCB2 1AG United Kingdom Cambridge Zero Department of Computer Science and Technology Department of Architecture CambridgeCB3 OFD United Kingdom Division of Humanities and Social Science California Institute of Technology Pasadena91125 United States School of Mechanical Engineering Sciences University of Surrey SurreyGU2 7XH United Kingdom

Energy performance certificate (EPC) and thermal infrared (TIR) images both play a key role in the energy performance mapping of the urban building stock. In this paper, we developed parametric building archetypes using an EPC database and conducted temperature clustering on TIR images acquired through drones and satellite datasets. We evaluated 1725 EPCs of existing building stock in Cambridge, UK to generate energy consumption profiles. Drone-based TIR images of individual buildings in two Cambridge University colleges were processed using a machine learning pipeline for thermal anomaly detection and investigated the influence of two specific factors that affect the reliability of TIR for energy management applications: ground sample distance (GSD) and angle of view (AOV). The EPC-level results suggest that the construction year of the buildings influences their energy consumption. For example, modern buildings were over 30% more energy-efficient than older ones. Parallelly, older buildings were found to show almost double the energy savings potential through retrofitting than newly constructed buildings. TIR imaging results showed that thermal anomalies can only be properly identified in images with a GSD of 1m/pixel or less. A GSD of 1-6m/pixel can detect hot areas of building surfaces. We found that a GSD > 6m/pixel cannot characterise individual buildings but does help identify urban heat island effects. Additional sensitivity analysis showed that building thermal anomaly detection is more sensitive to AOV than to GSD. Our study informs newer approaches to building energy diagnostics using thermography and supports decision-making for retrofitting at a large scale. Copyright © 2024, The Authors. All rights reserved.

关键词： Drones

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Assessing disruptive potential of retail central bank digital currency and influence of design considerations: An open innovation approach in Thailand

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Journal of Open Innovation: technology, Market, and Complexity 2025年第1期11卷

作者： Wathahong, Thanwa Ratanabanchuen, Roongkiat Israsena Na Ayudhya, Praima Tientanopajai, Kitt Technology Management and Innopreneurship Program Graduate School Chulalongkorn University Phayathai Road Bangkok 10330 Thailand Department of Banking and Finance Chulalongkorn Business School Chulalongkorn University Phayathai Road Bangkok 10330 Thailand Department of Industrial Design Faculty of Architecture Chulalongkorn University Phayathai Road Bangkok 10330 Thailand Department of Computer Engineering Faculty of Engineering Khon Kaen University Mittraphap Road Khon Kaen 40002 Thailand

Monetary authorities globally have studied central bank digital currency (CBDC) for wholesale and retail applications since the 1990s. Besides countering private cryptocurrencies' influence, central banks have also explored other benefits of CBDC, such as improving their financial oversight, enhancing digital payment systems, and reducing transaction fees and friction. For several years, the Bank of Thailand has also invested in research and development of CBDC, including retail use cases. However, an optimized design for an official launch of a retail CBDC in Thailand has yet to be determined, resembling fragmented progress observed among other central banks. This research has leveraged disruptive innovation theory to provide a fresh perspective on retail CBDC as potentially disruptive financial innovations. Additionally, it has proposed an open innovation-based methodology to determine industry preferences concerning twelve design considerations for retail CBDC and their impact on nine indicators of disruptive innovation. Data has been collected and analyzed from 98 respondents outside the Bank of Thailand, all possessing relevant expertise or experience in electronic payments. Crosstab and logistic regression analyses have indicated that retail CBDC in Thailand could embark on a disruptive innovation trajectory by adhering to defined design choices. Significantly, entering a new detached market has been identified as the most promising entry point, accompanied by explicit recommendations on nine out of twelve design considerations. © 2025 The Authors

关键词： CBDC Central bank digital Currency Disruptive innovation Open Innovation Retail

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TensorKMC: Kinetic Monte Carlo Simulation of 50 Trillion Atoms Driven by Deep Learning on a New Generation of Sunway Supercomputer

TensorKMC: Kinetic Monte Carlo Simulation of 50 Trillion Ato...

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Supercomputing Conference

作者： Honghui Shang Xin Chen Xingyu Gao Rongfen Lin Lifang Wang Fang Li Qian Xiao Lei Xu Qiang Sun Leilei Zhu Fei Wang Yunquan Zhang Haifeng Song SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Applied Physics and Computational Mathematics Beijing China Department of Computer Science and Technology Tsinghua University Wuxin China National Supercomputer Center in Wuxi Wuxi China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

The atomic kinetic Monte Carlo method plays an important role in multi-scale physical simulations because it bridges the micro and macro worlds. However, its accuracy is limited by empirical potentials. We therefore propose herein a triple-encoding algorithm and vacancy-cache mechanism to efficiently integrate ab initio neural network potentials (NNPs) with AKMC and implement them in our TensorKMC codes. We port our program to SW26010-pro and innovate a fast feature operator and a big fusion operator for the NNPs for fully utilizing the powerful heterogeneous computing units of the new-generation Sunway supercomputer. We further optimize memory usage. With these improvements, TensorKMC can simulate up to 54 trillions of atoms and achieve excellent strong and weak scaling performance up to 27,456,000 cores.

关键词： Technological innovation Monte Carlo methods Codes Quantum chemistry High performance computing Optimization methods Artificial neural networks

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Efficient network management scheme for device-to-device communication using channel indicators

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Telecommunication Systems 2025年第2期88卷

作者： Rathod, Tejal Gupta, Rajesh Jadav, Nilesh Kumar Tanwar, Sudeep Pau, Giovanni Alblehaİ, Fahad Alfarraj, Osama Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad382481 India Department of Computer Engineering - AI ML & DS Marwadi University Rajkot360003 India Faculty of Engineering and Architecture Kore University of Enna Enna94100 Italy Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia

Device-to-Device (D2D) communication maneuvers the proximity services for flexible communication between two nearby devices. It provides high data rates and enhances the spectral and energy efficiency of the D2D communication systems. The base station plays an important role in allocating cellular resources to D2D users. However, it suffers from challenges, such as interference, inefficient resource allocation, security, device discovery, and many more, which may deteriorate the performance of D2D communication. To overcome the aforementioned issues, in this paper, we present an analytics-based efficient network selection scheme that utilizes the reference channel indicators, i.e., receive strength signal indicator, received signal received power, and reference signal received quality to select the best micro base station (MBS) between the D2D user. We evaluated the proposed scheme in five different simulated scenarios wherein each scenario, there are a different number of MBS and D2D pairs. The MBS broadcasts the reference channel indicators at a specific interval of time, and the D2D pairs select the MBS that has the most promising indicator. Finally, all the proposed scenarios are compared and evaluated against the sum rate to analyze the performance of D2D communication with the reference channel indicators. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Resource allocation

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Understanding power quality using iot-based smart analyzers and advanced software tools

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2021年 19卷 356-361页

作者： Alcayde, A. Montoya, F.G. Arrabal-Campos, F.M. González, Jesús Ortiz, Andrés Baños, R. Department of Engineering E.S.I. University of Almería Carretera de Sacramento s/n La Cañada de San Urbano Almería04120 Spain Department of Computer Architecture and Technology E.T.S.I.I. University of Granada C/ Periodista Daniel Saucedo Aranda s/n Granada18071 Spain Department of Signal Theory Networking and Communications E.T.S.I.T. University of Malaga Campus de Teatinos Málaga29071 Spain

Power Quality is an important topic for undergraduate electrical engineering students around the world. In addition to the theoretical contents prepared and explained by the lecturer to their students, this matter has an important practical focus. In this paper, a framework for teaching power quality in laboratories using IoT-based smart analyzers and advanced software tools is developed to provide the students the opportunity of studying real data with a high level of detail. In particular, practical lessons have been designed in such a way that the students are trained in the use of well-known commercial smart meters (like the Circutor MYeBOX 1500) or opensource systems (like the openZmeter) to acquire energy and power quality data from real world measurements and to analyze the data collected using advanced software tools (like PowerVision). The results obtained from several courses of electrical and electronic engineering show that the students acquire practical skills that allow them to reinforce their knowledge regarding power quality concepts, including harmonics, and power quality events such as voltage sag/swell, flicker, or waveform distortions. Therefore, this methodology can be applied for teaching power quality in undergraduate and graduate electrical engineering courses. © 2021, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Power quality

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Improving the prediction accuracy of cyanobacterial blooms using a hybrid model integrating dimensionality reduction and deep learning

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Water Biology and Security 2025年

作者： Chen, Jiwei Hou, Zhongbao Li, Yunxi Li, Jiexian Li, Lin School of Naval Architecture and Ocean Engineering Huazhong University of Science and Technology Wuhan 430074 China Department of Public Health Fu Jen Catholic University Taiwan 242062 China Khoury College of Computer Sciences Northeastern University Vancouver V6B1Z3 Canada Institute of Hydrobiology Chinese Academy of Sciences Wuhan 430072 China

Cyanobacteria Harmful Blooms have negatively impacted human socio-economic activities and the natural ecological environment. Predicting cyanobacterial blooms requires the application of advanced analytical models powered by artificial intelligence to accurately model bloom development. In this study, we developed a hybrid model that integrates the Temporal Convolution Network (TCN) with the Long Short-Term Memory Network (LSTM) to achieve highly accurate predictions of cyanobacterial bloom events. We used Principal Component Analysis (PCA) during the preprocessing phase to reduce data dimensionality of model input variables and enhance computational efficiency. We used a data set from the Darling River in Australia to conduct a comparative analysis with traditional deep learning models and comprehensively assess the proposed model performance. We also investigated the application of the correlation coefficient matrix method in variable selection. The results demonstrate that the proposed model outperforms conventional methods in predicting cyanobacterial blooms. When benchmarked against the Recurrent Neural Network (RNN) model, our model demonstrates a superior ability to capture the intricacies of time series data. Specifically, it achieves a 79.5 % reduction in Mean Square Error (MSE) and a 197 % increase in the Coefficient of Determination (R²). Furthermore, comparative experiments were carried out across several prediction time steps, indicating that the model achieves optimal performance in minimizing both the MSE and Mean Absolute Error (MAE) when predicting one day ahead. Notably, the error distribution is most condensed at this time step, outperforming models with prediction horizons ranging from two to seven days. We also undertook a thorough analysis of the significance of each principal component in model predictions. The results highlight that principal component 5 and principal component 2 exhibit the most substantial contribution to the p

关键词： Cyanobacteria bloom Deep learning Long short-term memory network Prediction Principal component analysis Temporal convolution network

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