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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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A Study of Vehicular Desynchronization for Platooning Application

A Study of Vehicular Desynchronization for Platooning Applic...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Visatouch Deeying Kiattikun Kawila Kulit Na Nakorn Kultida Rojviboonchai Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Department of Computer Engineering Faculty of Engineering Chulalongkorn University

Platooning is a challenging application because it requires a frequent rate of beaconing. Performance of traditional Carrier Sense Multiple Access with Collision Avoidance method(CSMA/CA) on IEEE 802.11 p is unable to satisfy for platooning needs due to unbounded delay especially under high channel load. Several researches about platooning are involved with medium access control(MAC) layer modification in a protocol stack and/or does not support variable number of vehicles and/or have a single point of failure. In this paper, we propose to use a vehicular desynchronization TDMA-based protocol over off-the-shelf IEEE 802.11 p, while supporting vehicles joining and leaving a platoon and having no single point of failure. We recommend a range of parameters that are suitable for the platooning application. Our evaluation shows that using our suggested parameters, the vehicular desynchronization protocol could support a high-frequency beaconing with up to 16 vehicles at 100 Hz beaconing.

关键词： platooning Vehicular Ad Hoc Network(VANET) IEEE 802.11p medium access control performance evaluation desynchronization beaconing network simulation

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CSLP: Collaborative Solution to Long-Tail Problem and Popularity Bias in Sequential Recommendation

CSLP: Collaborative Solution to Long-Tail Problem and Popula...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Huang, Yan Yang, Zhenyu Hu, Wenyue Xu, Baojie Zhang, Zhibo Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9781665410205

Sequential Recommender Systems (SRS), leveraging the temporal information from users' behaviors, have noticeably improved user experience against traditional systems. However, these behaviors often follow long-tail distribution, making the systems biased towards popular items (i.e., popularity bias). Moreover, popularity bias would amplify the neglect of long-tail recommendations, thereby sharpening the long-tail problem. Previous researches usually address these challenges independently, focusing on reducing the over-recommendation of popular items or enhancing the representation quality of tail items. Indeed, it is possible to incorporate their merits to achieve the best of both worlds. Thus, we propose a novel and unified framework, named Collaborative Solution to Long tailed problem and Popularity bias (CSLP), to tackle both the long-tail problem and popularity bias simultaneously. To achieve this, we first introduce a representation enhancement module featuring dual generators to enhance user and item representations, particularly for those in the tail. On the other hand, a debiasing module incorporating an Inverse Propensity Score (IPS) with a clipping strategy is introduced to further alleviate the popularity bias. Specifically, this clipping strategy demonstrates a clear decrease in the original IPS method's variance, effectively improving the recommendation for stability and accuracy. Experiments on three widely-used datasets show CSLP's effectiveness in solving both issues. CSLP surpasses all baselines (traditional, popularity bias, and long-tail problem) in overall performance, significantly enhancing recommendation accuracy for both tail users and items, and achieving a more balanced ratio of recommendations between popular and tail items. Code is available at https://***/Echohuangyan/CSLP. © 2024 IEEE.

关键词： Inverse problems

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Driver Identification Using Histogram and Neural Network from Acceleration data

Driver Identification Using Histogram and Neural Network fro...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Nuttun Virojboonkiate Peerapon Vateekul Kultida Rojviboonchai Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Department of Computer Engineering Faculty of Engineering Chulalongkorn University

Sensor technology has continuously improved in term of size and cost. It encourages car companies to embed various types of sensors in their cars. The most common sensors that can be found are location sensor and acceleration sensor. The sensors generate a lot of useful data which could be applied for many future applications such as car tracking or emergency warning. One of the most interesting applications among others is analyzing the driver behavior. There are some previous works using this information to define types of drivers that are related to levels of accident risk. However, the driver behavior not only can separate the type of drivers but also can identify driving characteristic of an individual driver. The driver identification benefits a security application and it can eliminate the global car theft issue. Because the driver behavior cannot be repeated or copied like other anti-theft systems so this is an essential solution to stop increasing the number of stolen cars every year. In this paper, we propose a methodology to identify each driver and classify group of drivers using only accelerating behavior with machine learning technique. In order to identify an individual driver, the acceleration data have been transformed to a histogram in order to represent the accelerating behavior then we utilize a neural network model. We evaluate our methodology with data that have been collected from the acceleration sensors installed on 13 shuttle buses in our campus for more than 10 months. The results show the accuracy of the proposed methodology up to 88.3% for driver identification and up to 92% for driver classification.

关键词： big data neural network driver behavior driver identification data mining

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Cannabinoid overrides triggers of GABAergic plasticity in vestibular circuits and distorts the development of navigation

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iScience 2025年第6期28卷

作者： Wei Shi Kenneth Lap-Kei Wu Mengliu Yang Francisco Paulo De Nogueira Botelho Oscar Wing-Ho Chua Hui-Jing Hu Ka-Pak Ng Ulysses Tsz-Fung Lam Kin-Wai Tam Chun-Wai Ma Daisy Kwok-Yan Shum Ying-Shing Chan School of Biomedical Sciences Li Ka Shing Faculty of Medicine Pokfulam Hong Kong SAR PR China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering and Medicine Beihang University Beijing PR China Neuroscience Research Centre The University of Hong Kong Pokfulam Hong Kong SAR PR China State Key Laboratory of Brain and Cognitive Sciences Pokfulam Hong Kong SAR PR China

Early life exposure to cannabis can result in long-lasting deficits in spatial navigation. We ask if development of this behavior is subject to early life activity of type I cannabinoid receptor (CB1R) in the vestibular nucleus. In rodents, we found that local exposure to CB1R agonist within the first postnatal week, but not thereafter, led to decline in induction efficacy of long-term depression at GABAergic synapses (LTD GABA ), a key step in the hard-wiring of vestibular circuits. Within this critical period, endocannabinoid-mediated LTD GABA at inhibitory neurons was selectively triggered by cholecystokinin whereas that at excitatory neurons was by serotonin. Neonatal exposure to cannabinoid extended the phase of high GABAergic synaptic plasticity and overrode the synapse-specific, modulatory mechanism for plasticity. Such treatment delayed postnatal emergence of vestibular-dependent reflexes and deranged adult navigational behavior. Deficits in higher functions are thus attributable to maldevelopment of sensory processing circuits resulting from early cannabis exposure.

关键词： endocannabinoids vestibular circuitry GABAergic transmission neuron-specific plasticity spatial cognition

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The role of usability on e-learning user interactions and satisfaction: a literature review

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Journal of Systems and Information Technology 2019年第3期21卷 368-394页

作者： Gunesekera, Asela Indunil Bao, Yukun Kibelloh, Mboni Center for Modern Information Managment Huazhong University of Science and Technology Wuhan China Center for Big Data Analytics Jiangxi University of Engineering Xinyu China Faculty of Arts and Social Science Tumaini University Makumira – Dar es Salaam College Dar es Salaam Tanzania United Republic of Department of Computer Science General Sir John Kotelawala Defence University Ratmalana Sri Lanka

Purpose: The purpose of this study is to review the effect of usability factors on e-learning user relationships, namely, student–student interaction (SSI), student–instructor interaction (SII) and student–content interaction (SCI), in the existing e-learning literature. Further, this study intended to identify whether usability contributes to the satisfaction of e-learners. Design/methodology/approach: This study has undertaken a systematic review using the PRISMA methodology to filter the literature in the domain of e-learning with respect to usability concerns using six databases. An analytical framework has been formulated to evaluate the literature against different dimensions of interactions and usability. Findings: Results reveal that while SSI has grabbed 71.4 per cent research attention with respect to usability factors of e-learning systems, SCI has been given the least focus, i.e. 26.6 per cent. According to the results, e-learning systems’ usability issues influence the user relationships and affect the user satisfaction, which will lead to lack of user continuity. Practical implications: The findings of this review will provide insights to instructional designers to construct more satisfied learning content for the users. The analysis framework of this study will encourage researchers to drive future research in e-learning along with the concern of usability. Originality/value: This research emphasizes on the importance of SCI to focus future e-learning research on a different angle, in addition to SSI and SII. The analysis framework of this study will provide different dimensions, specifically for the empirical research in the domain of e-learning. © 2019, Emerald Publishing Limited.

关键词： Students

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Computer Game-Based Social Skill Intervention for Children with Autism Spectrum Disorder

Computer Game-Based Social Skill Intervention for Children w...

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2023 International Conference on Intelligent Education and Intelligent Research, IEIR 2023

作者： Wang, Guangshuai Hu, Julin Zheng, Shiying Zhang, Kun Central China Normal University National Engineering Research Center of Educational Big Data Faculty Artificial Intelligence in Education Wuhan China Central China Normal University Wollongong Joint Institute Central China Normal University Wuhan China

ISBN: (纸本)9798350342895

Autism spectrum disorder (ASD) is a psychiatric disorder with a high incidence nowadays, which is incurable with medication in the whole world. Many researchers have tried to design computer games to help children with ASD improve their social skills. However, it is difficult to generalize the skill which is learned in most games. In this paper, a computer game based on real-life scenarios is designed for children with ASD to train their social skills. The virtual game can help children learn how to communicate and express their social language and behavior in corresponding social occasions. By constructing vivid game scenarios and their own 3D model, children can establish the connection between the digital world and real life, encouraging them to apply the social skills studied in real-world situations. The case intervention studies demonstrate that this computer game can improve the skills of interaction and communication with peers for children with ASD by phased intervention. © 2023 IEEE.

关键词： 3D modeling

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Revisiting the starting age of colorectal cancer screening for the average-risk Asian population: a cost-effectiveness analysis

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Gastrointestinal Endoscopy 2025年

作者： Huang, Junjie Chan, Victor C.W. Chen, Mingtao Liew, Jamie Jie Mei Liu, Xianjing Zhong, Chaoying Lin, Jianli Hang, Junjie Zhong, Claire Chenwen Yuan, Jinqiu Xu, Wanghong Withers, Mellissa Chan, Andrew T. Wong, Martin C.S. Jockey Club School of Public Health and Primary Care Faculty of Medicine The Chinese University of Hong Kong Hong Kong Centre for Health Education and Health Promotion Faculty of Medicine The Chinese University of Hong Kong Hong Kong Department of Radiology and Nuclear Medicine Erasmus MC University Medical Center Rotterdam Netherlands Department of Electrical Engineering and Automation Guangdong Ocean University Guangdong China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University Beijing China Cancer Hospital & Shenzhen Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Guangdong China Clinical Research Center Big Data Center The Seventh Affiliated Hospital Sun Yat-Sen University Guangdong China The School of Public Health Fudan University Shanghai China Department of Population and Health Sciences Institute for Global Health University of Southern California Los Angeles CA United States Clinical and Translational Epidemiology Unit Department of Medicine Massachusetts General Hospital Boston MA United States Division of Gastroenterology Department of Medicine Massachusetts General Hospital Boston MA United States The School of Public Health Peking University Beijing China The School of Public Health Chinese Academy of Medical Sciences and Peking Union Medical College Guangdong China

Background and Aims: One of the most prevalent and fatal tumors, colorectal cancer (CRC), has a significant impact on the use of healthcare services. Although Hong Kong's CRC screening program has been successful, it does not prioritize preventing early-onset CRC in people under age 50 years. This study aimed to assess the cost-effectiveness of different starting ages for CRC screening among an Asian population. Methods: We conducted a simulation study involving 100,000 individuals in Hong Kong who were screened using either the fecal immunochemical test (FIT) or colonoscopy as primary screening methods at ages 40, 45, and 50 until age 75. The performance of different strategies was evaluated based on life-years gained, and cost-effectiveness was measured using the incremental cost-effectiveness ratio (ICER). Results: The ICERs for initiating FIT screening at age 50, screening starting at age 45, and screening starting at age 40 were U.S. dollars (USD) 53,262, USD 67,892, and USD 86,554, respectively. For colonoscopy, the ICERs for initiating screening at ages 50, 45, and 40 were USD 267,669, USD 312,848, and USD 372,090, respectively. Overall, the FIT strategy was found to be less costly. At 70%, 80%, and 90% compliance rates, the FIT at age 45 gained 2135, 2296, and 2438 life-years, respectively, whereas colonoscopy at age 45 gained 2725, 2798, and 2855 life-years, respectively. With increased compliance rates, the FIT could save a similar number of life-years as colonoscopy with lower cost. Conclusions: Initiating CRC screening at age 45 using the FIT in Hong Kong was determined to be a well-balanced and cost-effective strategy. This approach demonstrated a cost advantage over starting screening at age 40 and resulted in more lives saved compared with screening at age 50. © 2025 American Society for Gastrointestinal Endoscopy

关键词： Starting age colorectal cancer cost-effectiveness screening

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Claim Stance Classification Optimized by data Augment

Claim Stance Classification Optimized by Data Augment

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Bai Wei Zhuang Yan Faculty of Compute Science and Engineering Big Data Center University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665413657

As online fora increasingly become the main media for argument and debate, the automatic processing of such data is rapidly becoming more and more important. Stance classification, which aims to classify the stance of the claims towards the given topic, can be applied in many application areas such as users' feelings about services and products. We propose a ensemble model for stance classification with data augment for small sample scenarios, multi-sample dropout for low training speed scenarios, focal loss for imbalance sample scenarios, pseudo labels for self-supervised training scenarios, adversarial training for low robustness scenarios, and all the above can be used in normal scenarios. Besides, the ensemble model is composed of task-specific RoBERTa and MacBERT, which can make more reasonable predictions. We used dataset from NLPCC to validate the model and it worked well.

关键词： Training Computational modeling Information processing Media Predictive models data models Robustness

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Deep Learning for Stock Market Prediction Using Event Embedding and Technical Indicators

Deep Learning for Stock Market Prediction Using Event Embedd...

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Pisut Oncharoen Peerapon Vateekul Big Data Analytics and IoT Center (CUBIC) Chulalongkorn University Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9781538648056

Recently, ability to handle tremendous amounts of information using increased computational capabilities has improved prediction of stock market behavior. Complex machine learning algorithms such as deep learning methods can analyze and detect complex data patterns. The recent prediction models use two types of inputs as (i) numerical information such as historical prices and technical indicators, and (ii) textual information including news contents or headlines. However, the use of textual data involves text representation construction. Traditional methods like word embedding may not be suitable for representing the semantics of financial news due to problems of word sparsity in datasets. In this paper, we aim to improve stock market predictions using a deep learning approach with event embedding vectors extracted from news headlines, historical price data, and a set of technical indicators as input. Our prediction model consists of Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) architectures. We use accuracy and annualized return based on trading simulation as performance metrics, and then perform experiments on three datasets obtained from different news sources namely Reuters, Reddit, and Intrinio. Results show that enhancing text representation vectors and considering both numerical and textual information as input to a deep neural network can improve prediction performance.

关键词： Stock markets Numerical models Predictive models Neural networks Biological system modeling data mining

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