2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献_第9页

Optimal Partitioning of Distributed Neural Networks for Various Communication Environments 各种通信环境下分布式神经网络的最优划分

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415248

J. Jeong, Hoeseok Yang

{"title":"Optimal Partitioning of Distributed Neural Networks for Various Communication Environments","authors":"J. Jeong, Hoeseok Yang","doi":"10.1109/ICAIIC51459.2021.9415248","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415248","url":null,"abstract":"Recently, it is increasingly necessary to run high-end neural network applications on top of resource-constrained embedded systems, such as wearable or Internet-of-Things devices. To cope with their high computation overheads on low-end systems, the distributed neural network approach in which multiple small neural networks separately and cooperatively operate on multiple devices has been proposed. While the computational overhead could be effectively alleviated by this approach, the existing techniques still suffer from large traffics between the devices, making it vulnerable to communication failures. This drawback hinders the application of the distributed neural network techniques to wearable devices, which may be connected with each other through unstable and low data rate communication medium like human body communication. Therefore, in this paper, we propose to improve the distributed neural network by adopting a partitioning method that can adapt to given communication environments. To validate the effectiveness of the proposed portioning technique, we compare the inference accuracies of the distributed neural networks that are partitioned differently for various communication environments.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123873400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards an Adaptive Education through a Machine Learning Recommendation System 通过机器学习推荐系统实现适应性教育

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415211

Ossama H. Embarak

{"title":"Towards an Adaptive Education through a Machine Learning Recommendation System","authors":"Ossama H. Embarak","doi":"10.1109/ICAIIC51459.2021.9415211","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415211","url":null,"abstract":"Educational institutions have a tremendous burden of handling students with low academic performance (At-risk students). Many approaches support this group of pupils, such as psychological therapy, a proper timetable for vulnerable pupils, recall, personal training, mock-tests, direct private education, or success centres. However, these methods are not enough to solve the issue since other factors influence the learner’s success, which could be their family difficulties, cognitive style, prior performance, and college foundation level. This paper explores machine learning models for predicting at-risk students and then build a recommendation platform to provide direct system-based coaching to such students to remediate the fragmentation in their knowledge and skills. We use a dataset of 554 students from a computer program; the study aims to break down the curricula into a set of crumbs of knowledge and skills used to measure learners’ progress during their study. We used various machine learning algorithms, a decision tree with an accuracy of 82.68% (positive class: Good Standing), and high coverage of 87.69% (positive class: Good Standing). We completed the model optimization and used the ROC comparison to compare the classifier models. A remediation algorithm is used to support at-risk cases, leading to a sharp decline in the at-risk rate. The study finds that the current applied approaches to handle at-risk students exaggerate the problem, and students should not be treated in bulk.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117214843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Timing diagram for CNPC interleaver implementation on FPGA 中石油交织器FPGA实现时序图

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415266

Gwonhan Mun, K. Kang, Deaho Kim

引用次数: 0

Principal Component Analysis Implementation for Signal Processing of Electrochemical Impedance Spectroscopy in the Detection of Fake Fingerprints 电化学阻抗谱检测假指纹信号处理的主成分分析实现

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415216

Seongsu Park, Ki-Hyung Kim

{"title":"Principal Component Analysis Implementation for Signal Processing of Electrochemical Impedance Spectroscopy in the Detection of Fake Fingerprints","authors":"Seongsu Park, Ki-Hyung Kim","doi":"10.1109/ICAIIC51459.2021.9415216","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415216","url":null,"abstract":"Electrochemical impedance spectroscopy (EIS) is widely used to analyze biometric data such as medical and bio-health. This EIS method is intended to be used in the detection of human fingerprints and fake fingerprints. The most import factor in the detection of such fake fingerprints lies in the ability to increase the discrimination of fake fingerprints compared to the human fingerprints. In particular, the deviation of the EIS value varies depending on how the finger is in contact with the electrode. if the detection is not passed and retry is required. To solve this problem, principal component analysis (PCA) is applied. PCA is widely used as a method of extracting various data features. In order to effectively apply EIS signal data to PCA, the change of wave form according to 10 frequencies between 6K and 15K was generated and compared to input vs. output. After measuring the wave magnitude of the output signal and the time to reach 80% of the wave-max value, it was analyzed with PCA to determine the wave trend. And more higher discrimination was obtained than when using only the decision tree method in the detection.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117028007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Smart-Meter Data Monitoring System based on Optical Camera Communication 一种基于光学摄像机通信的智能电表数据监控系统

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415233

Md. Osman Ali, Md. Morshed Alam, Md. Faisal Ahmed, Y. Jang

引用次数: 0

Multi-Scale Proposal Regions Fusion Network for Detection and 3D Localization of the Infected Trees 多尺度建议区域融合网络检测和三维定位的感染树

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415224

Junlin Hou, Weihong Li, W. Gong, Zixu Wang

{"title":"Multi-Scale Proposal Regions Fusion Network for Detection and 3D Localization of the Infected Trees","authors":"Junlin Hou, Weihong Li, W. Gong, Zixu Wang","doi":"10.1109/ICAIIC51459.2021.9415224","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415224","url":null,"abstract":"Forest surveillance towers have the advantages of long observation time, wide observation range, stable and real-time observation. In this paper, a multi-scale proposal regions fusion network (MFRPN) is proposed for detecting the infected trees automatically on the enhanced images from the forest surveillance towers, which can solve the problem that small and large targets can’t be effectively detected on a single scale. The proposed MFRPN includes multi-scale images, three CNNs, three different RPNs, and proposal regions fusion model. In the proposed method, we train and run the scale-specific detectors in a multi-task fashion. And, to obtain the accurate spatial level location information of the infected trees, we achieve the three-dimensional (3D) coordinates localization of the digital elevation model (DEM) by using the principle of forest surveillance towers imaging and terrain elevation data. The experimental results show the detection accuracy achieves 91.63%, the detection time of a single image is 0.46 second, and the 3D localization error is less than 50m. The proposed network can realize the real-time detection and 3D localization of the infected trees.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127129433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Facial Expression Recognition In The Wild Using Bidirectional Convolutional Neural Network 基于双向卷积神经网络的野外面部表情识别

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415198

Jiaxu Liu

{"title":"Facial Expression Recognition In The Wild Using Bidirectional Convolutional Neural Network","authors":"Jiaxu Liu","doi":"10.1109/ICAIIC51459.2021.9415198","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415198","url":null,"abstract":"The Static Facial Expressions In The Wild database (SFEW) contains unconstrained facial expressions close to the real world. In former research, current machine learning techniques are not robust enough for such an uncontrolled environment and it remains challenging nowadays. Coping with such task, we augment the state-of-art model which achieved the best performance for in the wild dataset and proposed two boosting algorithms of adding bidirectionality to convolution neural network based on the bidirectional neural network prototype, which is the first to integrate these two notions in literature. We also conducted experiments applying the decision fusion framework for classification, the proposed framework is trained simultaneously forward and backward, the final output is generated through voting mechanism. In this paper, two algorithms of adding bidirectionality to CNN are proposed, a framework for the facial expression recognition task (ensemble of HOG face detector and CNN with decision fusion and bidirectionality) is introduced and the classification result is listed, compared, and analyzed. The empirical results affirmed that the bidirectional boosting achieved good performance on the SFEW benchmark. Furthermore, some future works for precision improvement based on the existing deficiency of the current model are presented.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127318742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble Classifier Based Modulation Recognition for Beyond 5G Massive MIMO (mMIMO) Communication 基于集成分类器的超5G大规模MIMO (mMIMO)通信调制识别

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415269

Md. Habibur Rahman, M. Shahjalal, Y. Jang

引用次数: 3

Pre-trained Deep Convolutional Neural Network for Detecting Malaria on the Human Blood Smear Images 基于预训练深度卷积神经网络的人体血液涂片图像疟疾检测

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415183

I. G. S. M. Diyasa, Akhmad Fauzi, A. Setiawan, M. Idhom, Radical Rakhman Wahid, Alfath Daryl Alhajir

引用次数: 6

Globally Informative Thompson Sampling for Structured Bandit Problems with Application to CrowdTranscoding 结构化强盗问题的全局信息汤普森抽样及其在众转码中的应用

2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2021-04-13 DOI: 10.1109/ICAIIC51459.2021.9415255

Xingchi Liu, Mahsa Derakhshani, Ziming Zhu, S. Lambotharan

{"title":"Globally Informative Thompson Sampling for Structured Bandit Problems with Application to CrowdTranscoding","authors":"Xingchi Liu, Mahsa Derakhshani, Ziming Zhu, S. Lambotharan","doi":"10.1109/ICAIIC51459.2021.9415255","DOIUrl":"https://doi.org/10.1109/ICAIIC51459.2021.9415255","url":null,"abstract":"Multi-armed bandit is a widely-studied model for sequential decision-making problems. The most studied model in the literature is stochastic bandits wherein the reward of each arm follows an independent distribution. However, there is a wide range of applications where the rewards of different alternatives are correlated to some extent. In this paper, a class of structured bandit problems is studied in which rewards of different arms are functions of the same unknown parameter vector. To minimize the cumulative learning regret, we propose a globally-informative Thompson sampling algorithm to learn and leverage the correlation among arms, which can deal with unknown multi-dimensional parameter and non-monotonic reward functions. Our studies demonstrate that the proposed algorithm achieves significant improvement in the learning speed. In particular, the designed algorithm is used to solve an edge transcoder selection problem in crowdsourced live video streaming systems and shows superior performance as compared to the existing schemes.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131585347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0