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Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Huadsri, Supika Mekruksavanich, Sakorn Jitpattanakul, Anuchit Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao56000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798350318241

This article is conducted with the primary aim of comparing various forecasting models to identify the optimal model for forecasting the growth of the life insurance industry in Thailand. The models under consideration include the SARIMAX model, the Recurrent Neural Network (RNN), and a hybrid model introduced as RNN-SARIMAX. The dataset utilized for this investigation is extensive, consisting of monthly direct premium data from the Thai life insurance sector spanning January 2003 to December 2022. The dataset is segregated into two distinct segments. Initially, data from January 2003 to December 2021 is employed for constructing the forecasting model. Subsequently, data from January 2022 to December 2022 is utilized for a comparative analysis of the forecasting models, to determine the most suitable and accurate model for predicting the growth of the life insurance industry in Thailand. The evaluation criteria for comparing models include the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R-squared). The consistent findings from our analysis indicate that the SARIMAX model consistently outperforms both the RNN and RNN-SARIMAX models. © 2024 IEEE.

关键词： Recurrent neural networks

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QUALYST: Data Quality Assessment System for Thailand Open Government Data

QUALYST: Data Quality Assessment System for Thailand Open Go...

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Tanapat Samakit Chutiporn Anutariya Marut Buranarach ICT Department Computer Science & Information Management School of Engineering and Technology Asian Institute of Technology Thailand Data Science and Analytics Research Group National Electronics and Computer Technology Center Thailand

Open Government Data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all; thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format.

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The Content Based Misinformation Detection for Gujarati Language 2nd

The Content Based Misinformation Detection for Gujarati Lan...

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2nd International Conference on Sustainable Smart Cities and Territories, SSCT 2023

作者： Chauhan, Uttam Sheth, Vinay Trivedi, Vishvesh Bhatt, Chintan Corchado, Juan Manuel Vishwakarma Government Engineering College Ahmedabad India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India BISITE Research Group University of Salamanca Salamanca37007 Spain Air Institute IoT Digital Innovation Hub Salamanca37188 Spain Faculty of Engineering Department of Electronics Information and Communication Osaka Institute of Technology Osaka535-8585 Japan

ISBN: (纸本)9783031369568

Misinformation detection (MID) has recently gained attention as a research question. We discover that many efforts have been made in response to the innovative research issues and MID research approaches as an empowering and developing rapidly research field. Misinformation typically spreads faster, profound, and wider in social networks. It is crucial to identify disinformation on social media since people are less able to distinguish between true information and false information due to an abundance of information and limited attention. Exploration of misinformation detection received little attention from the computational NLP research community. In this paper, we propose a novel architecture for detecting misinformation in Gujarati text. In the architecture, the significance of domain experts, crowd intelligence, and fact-checking website interaction has been depicted. Additionally, we define certain special Gujarati language traits for the early detection of counterfeit news. We have built a classifier incorporating domain experts to identify fake news and present outcomes for the automatic fake news detection and its class. We showed the findings of the experiments performed on the news article corpus that we built by fetching the articles from newspaper websites and applications. We found that stochastic Gradient Descent and NuSVC achieved the accuracy 88% using TFIDF and CV respectively, which is the highest among all models we experimented. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Stochastic systems

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Data Partition Optimization for High Energy Efficiency by Decoupling Local Dependence in Holographic Video Decoder 30

Data Partition Optimization for High Energy Efficiency by De...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Liu, Xinzhe Luo, Jianwen Blinder, David Chen, Fupeng Yu, Heng Schelkens, Peter Catthoor, Francky Ha, Yajun ShanghaiTech University School of Information Science and Technology Shanghai China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Belgium Leuven Belgium Silicon Realization Group Synopsys Shanghai China University of Nottingham Ningbo China School of Computer Science Ningbo China Leuven Belgium Shanghai Engineering Research Center of Energy Efficient and Custom AI IC Shanghai China

ISBN: (纸本)9798350326499

Holography has attracted considerable attention from researchers due to its ability to store and recreate the wavefront emanating from a three-dimensional object. However, holographic video requires enormous resolution (128 k × 128 k) at the same frame rates(60fps) as normal video to achieve acceptable visual effects. Data compression is thus essential for its storage/transmission. When implementing its decompression pipeline on hardware for mobile scenarios, data dependency and energy consumption must be handled carefully. In this work, we present a novel design framework and a data partition optimization approach to optimize the overall energy consumption by tackling local dependence in the motion compensation module for holographic video codec, and exploring the design space of data partition layout. First, we propose a local data dependency propagation (LDDP) method that transforms one holographic frame with strong local dependence into multiple mutually independent virtual blocks without local dependence at all. Second, we formulate a model for the data partitioning problem, allowing us to analyze and optimize energy consumption by adjusting the layout of data partitions. Third, we provide a heuristic and efficient solution to the formulated model taking advantage of the target application scenarios. Experiment results in various scenarios show that our proposed optimization method achieves 2.94 3.91 × energy efficiency and 46.37% 63.63% area efficiency compared to baseline approaches. © 2023 IEEE.

关键词： Energy utilization

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Radar Probing Optimization for Joint Beamforming and UAV Trajectory Design in UAV-Enabled Integrated Sensing and Communication

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IEEE Transactions on Communications 2024年

作者： Liu, Yaxi Mao, Wencan He, Boxin Huangfu, Wei Huang, Tianyao Zhang, Haijun Long, Keping University Of Science And Technology Beijing Beijing Advanced Innovation Center For Materials Genome Engineering Beijing Engineering And Technology Research Center For Convergence Networks And Ubiquitous Services School Of Computer And Communication Engineering Beijing100083 China University Of Science And Technology Shunde Innovation School Beijing China National Institute Of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task;ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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CERDL: Contextual Emotion Recognition Analysis Using Deep Learning 14th

CERDL: Contextual Emotion Recognition Analysis Using Deep Le...

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14th International Symposium on Ambient Intelligence, ISAmI 2023

作者： Chaudhari, Aayushi Bhatt, Chintan Krishna, Achyut Corchado, Juan M. Changa388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar382007 India BISITE Research Group University of Salamanca Salamanca37007 Spain Air Institute IoT Digital Innovation Hub Salamanca37188 Spain Department of Electronics Information and Communication Faculty of Engineering Osaka Institute of Technology Osaka535-8585 Japan

ISBN: (纸本)9783031434600

This paper delves into the critical importance of understanding emotions from a person’s perspective, and the potential for machines to improve human interaction by possessing this ability. While existing research on emotion recognition in computer vision has mainly focused on analyzing facial expressions and categorizing them into six basic emotions, it is important to recognize that contextual factors also play a crucial role in emotion perception. Emotions are not just limited to facial expressions but also include body language, the pitch of voice, and other nonverbal cues. We then trained a convolutional neural network model on this vast dataset and demonstrated the importance of incorporating context to recognize rich information about emotional states in images. Our model surpasses previous benchmarks and confirms the value of contextual information in emotion recognition. We have used the Emotions in Context (EMOTIC) [1] and Body Language Dataset (BoLD) [2] datasets for recognizing emotions by taking their contextual information into account. By incorporating contextual factors, machines can enhance human interaction by accurately recognizing emotional states in various situations. Based on the experiments, we recognized that the emotions of engagement (93.62%), confidence (92.41%), and excitement (95.93%) were predicted accurately. In contrast, the emotions of yearning, disapproval, and pain had low classification accuracy, with less than 40% accuracy. Lastly, this paper highlights the importance of understanding emotions beyond just facial expressions and provides a benchmark for emotion recognition in a contextual setting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Emotion Recognition

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Continuous Bijection Supervised Pyramid Diffeomorphic Deformation for Learning Tooth Meshes From CBCT Images

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Zechu Peng, Weilong Wen, Jinyu Tang, Keke Fang, Meie Feng, David Dagan Li, Ping Guangzhou University Metaverse Research Institute School of Computer Science and Cyber Engineering Guangzhou511400 China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou510006 China The University of Sydney Biomedical and Multimedia Information Technology Research Group School of Computer Science SydneyNSW2006 Australia The Hong Kong Polytechnic University Department of Computing School of Design and Research Institute for Sports Science and Technology Hong Kong Hong Kong

Accurate and high-quality tooth mesh generation from cone-beam computerized tomography (CBCT) is an essential computer-aided technology for digital dentistry. However, existing segmentation-based methods require complicated post-processing and significant manual correction to generate regular tooth meshes. In this paper, we propose a method of continuous bijection supervised pyramid diffeomorphic deformation (PDD) for learning tooth meshes, which could be used to directly generate high-quality tooth meshes from CBCT Images. Overall, we adopt a classic two-stage framework. In the first stage, we devise an enhanced detector to accurately locate and crop every tooth. In the second stage, a PDD network is designed to deform a sphere mesh from low resolution to high one according to pyramid flows based on diffeomorphic mesh deformations, so that the generated mesh approximates the ground truth infinitely and efficiently. To achieve that, a novel continuous bijection distance loss on the diffeomorphic sphere is also designed to supervise the deformation learning, which overcomes the shortcoming of loss based on nearest-neighbour mapping and improves the fitting precision. Experiments show that our method outperforms the state-of-the-art methods in terms of both different evaluation metrics and the geometry quality of reconstructed tooth surfaces. © 1999-2012 IEEE.

关键词： Mapping

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A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

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computer Systems Science & engineering 2023年第8期46卷 1789-1809页

作者： Hicham Moujahid Bouchaib Cherradi Oussama El Gannour Wamda Nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan EEIS Laboratory ENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE Team CRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Department of Computer Science University of Sheba RegionMarib14400Yemen DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information Technology College of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关键词： Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network

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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

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computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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A Spatio-Temporal Switchable Data Prefetcher for Convolutional Neural Networks

A Spatio-Temporal Switchable Data Prefetcher for Convolution...

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International SoC Design Conference, ISOCC

作者： Jihoon Jang Hyun Kim Hyokeun Lee Department of Electrical and Information Engineering Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul Korea Secure and Advanced Computer Architecture (SACA) Research Group North Carolina State University (NCSU) Raleigh NC USA

In this paper, we propose a spatio-temporal switchable data prefetcher that can adapt to the locality characteristics of CNN models. The proposed prefetcher records the recent delta history by leveraging two tables. The first table predicts spatial address patterns by comparing the delta score with the last delta, while the second table predicts temporal address patterns by recording and reordering the delta sequence from the delta history. Consequently, the proposed prefetcher is capable of appropriately switching between these two prediction methodologies based on spatial and temporal localities. The experimental results on CNN inference workloads show that we achieved high average accuracy of 83.8% and coverage of 81.6%, and hence the proposed prefetcher improves system performance by 33.8% over a baseline with no data prefetcher and 21% over the best-performing prior spatio-temporal prefetcher.

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