2021 RIVF International Conference on Computing and Communication Technologies (RIVF)最新文献_第2页

Detection and tracking hand from FPV: benchmarks and challenges on rehabilitation exercises dataset 从FPV检测和跟踪手:康复训练数据集的基准和挑战

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642078

V. Pham, Thanh-Hai Tran, Hai Vu

{"title":"Detection and tracking hand from FPV: benchmarks and challenges on rehabilitation exercises dataset","authors":"V. Pham, Thanh-Hai Tran, Hai Vu","doi":"10.1109/RIVF51545.2021.9642078","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642078","url":null,"abstract":"Egocentric vision is an emerging field of computer vision characterized by the acquisition video from the first person perspective. Particularly, for evaluating upper extremity rehabilitation, egocentric vision offers the ability to quantitatively measure the function of hands used in physical-based exercises. For such applications, hand detection and tracking are the first requirement. In this work, we develop a fully automatic tracking by detection pipeline that firstly extracts hands positions and then tracks hands in consecutive frames. The proposed framework consists of state of the art detectors such as RCNN and YOLO family models coupled with advanced trackers (e.g., SORT and DeepSORT) for tracking task. This paper explores how performance of the stand alone object detection algorithms correlates with overall performance of a tracking by detection system. The experimental results show that detection highly impacts the overall performance. Moreover, this work also proves that the use of visual descriptors in the tracking stage can reduce the number of identity switches and thereby increase potential of the whole system. We also present challenges for new egocentric hand tracking dataset for future works.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"53 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89449248","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: 2

A hybrid kernel-based possibilistic fuzzy c-means clustering and cuckoo search algorithm 一种基于可能性模糊c均值聚类和布谷鸟搜索的混合核算法

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642080

V. D. Do, L. Ngo, D. Mai

{"title":"A hybrid kernel-based possibilistic fuzzy c-means clustering and cuckoo search algorithm","authors":"V. D. Do, L. Ngo, D. Mai","doi":"10.1109/RIVF51545.2021.9642080","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642080","url":null,"abstract":"Possibilistic Fuzzy c-means (PFCM) algorithm is a robustness clustering algorithm that combines two algorithms, Fuzzy c-means (FCM) and Possibilistic c-means (PCM). It addresses the weakness of FCM in handling noise sensitivity and the weakness of PCM within the case of coincidence clusters. However, PFCM works inefficiently when the input data is nonlinear separable. To solve this problem, kernel methods have been introduced into possibilistic fuzzy c-means clustering (KPFCM). KPFCM can address noises or outliers data better than PFCM. But KPFCM suffers from a common drawback of clustering algorithms that may be trapped in local minimum which results in not good results. Recently, Cuckoo search (CS) based clustering has proved to achieve fascinating results. It can achieve the best global solution compared to most other metaheuristics. In this paper, we propose a hybrid method encompassing KPFCM and Cuckoo search algorithm to form the proposed KPFCM-CSA. The experimental results indicate that the proposed method outperformed various well-known recent clustering algorithms in terms of clustering quality.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83654465","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

WEWD: A Combined Approach for Measuring Cross-lingual Semantic Word Similarity Based on Word Embeddings and Word Definitions 基于词嵌入和词定义的跨语言语义词相似度测量方法

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642084

Van-Tan Bui, Phuong-Thai Nguyen

{"title":"WEWD: A Combined Approach for Measuring Cross-lingual Semantic Word Similarity Based on Word Embeddings and Word Definitions","authors":"Van-Tan Bui, Phuong-Thai Nguyen","doi":"10.1109/RIVF51545.2021.9642084","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642084","url":null,"abstract":"Cross-lingual semantic word similarity (CLSW) ad- dresses the task of estimating the semantic distance between two words across languages. This task is an important component in many natural language processing applications. Recent studies have proposed several effective CLSW models for resource- rich language pairs such as English-German, English-French. However, This task has not been effectively addressed for language pairs consisting of Vietnamese and another one. In this paper, we propose a neural network model that exploits cross- lingual lexical resources to learn high-quality cross-lingual word embedding models. Since our neural network model is language- independent, it can learn a truly multilingual space. Furthermore, we introduce a novel cross-lingual semantic word similarity measurement method based on Word Embeddings and Word Definitions (WEWD). Last but not least, we introduce a standard Vietnamese-English dataset for the cross-lingual semantic word similarity measurement task (VESim-1000). The experimental results show that our proposed method is more robust and outperforms current state-of-the-art methods that are only based on word embeddings or lexical resources.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"94 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83914139","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

Automatically Estimate Clusters in Autoencoder-based Clustering Model for Anomaly Detection 基于自编码器的异常检测聚类模型中的自动估计聚类

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642120

Van Quan Nguyen, V. H. Nguyen, Nhien-An Le-Khac, V. Cao

{"title":"Automatically Estimate Clusters in Autoencoder-based Clustering Model for Anomaly Detection","authors":"Van Quan Nguyen, V. H. Nguyen, Nhien-An Le-Khac, V. Cao","doi":"10.1109/RIVF51545.2021.9642120","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642120","url":null,"abstract":"In a previous work, a clustering-based method had been incorporated with the latent feature space of an autoencoder to discover sub-classes of normal data for anomaly detection. However, the work has the limitation in manually setting up the numbers of clusters in the normal training data. Finding a proper number of clusters in datasets is often ambiguous and highly depends on the characteristics of datasets. This paper proposes a novel data-driven empirical approach for automatically identifying the number of normal sub-classes (clusters) without human intervention. This clustering-based method, afterward, is co-trained with an autoencoder to automatically discover the appreciated number of clusters of normal training data in the middle hidden layer of the autoencoder. The resulting clustering centers are then used to identify anomalies in querying data. Our approach is tested on four scenarios from the CTU13 datasets, and the experimental results show that the proposed model often perform better than those of the model in the previous work on almost scenarios.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79946184","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

Semi-Supervised GAN for Road Structure Recognition of Automotive FMCW Radar Systems 半监督GAN用于汽车FMCW雷达系统道路结构识别

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642101

The-Duong Do, Hong Nhung-Nguyen, A. Pham, Yong-Hwa Kim

{"title":"Semi-Supervised GAN for Road Structure Recognition of Automotive FMCW Radar Systems","authors":"The-Duong Do, Hong Nhung-Nguyen, A. Pham, Yong-Hwa Kim","doi":"10.1109/RIVF51545.2021.9642101","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642101","url":null,"abstract":"Research in autonomous driving systems technology, which is considered as a leader of the fourth industrial revolution, is defining a new era of mobility. Due to its safety and reliability in real-time traffic environments, radar, one of the most important components utilized in driverless vehicles, is actively carried out. For automotive radar systems on the road, each road environment produces superfluous echoes known as clutter, and the magnitude distribution of received radar signal varies reliance on road structures, leading to an increasing requirement for classifying the road environment and adopting a suitable target detection algorithm for each road environment characteristic. However, the classification of road environments using super-vised algorithms such as feedforward neural networks (FNN) or convolutional neural networks (CNN) requires a massive amount of training data, which is a popular impediment in deep learning. In order to tackle the problem of shortage of labeled data, in this study, we propose a semi-supervised GAN approach to recognize different road environments with auto-motive frequency-modulated continuous-wave (FMCW) radar systems. The proposed model achieves a substantial performance improvement over other existing methods, especially when only a small proportion of the training data are labeled, demonstrating the potential of the proposed Semi-GAN-based method for the challenging task of various road environments recognition.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"82 3 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77526724","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

MC-OCR Challenge 2021: End-to-end system to extract key information from Vietnamese Receipts MC-OCR挑战2021:端到端系统从越南收据中提取关键信息

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642083

Duy Nguyen, Tuan-Anh Nguyen, Xuan-Chung Nguyen

{"title":"MC-OCR Challenge 2021: End-to-end system to extract key information from Vietnamese Receipts","authors":"Duy Nguyen, Tuan-Anh Nguyen, Xuan-Chung Nguyen","doi":"10.1109/RIVF51545.2021.9642083","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642083","url":null,"abstract":"In the information age, how to quickly obtain information and extract key information from massive and complex re-sources has become challenging. Extracting information from scanned or captured document is one of the most demanding process in many areas such as finance, accounting, and taxation. The current achievement in the computer vision field has shown a substantial improvement in the field of Optical Character Recognition (OCR), including text detection and recognition tasks. However, there are two challenges for current OCR. The first one is the quality of the input data which is captured by mobile phone. The quality is greatly affected by external factors like light condition, dynamic environment or blurry content. Secondly, Key Information Extraction (KIE) from documents, which is a downstream task of OCR, had been a largely under explored domain because the input documents have not only textual features extracting from OCR systems but also semantic visual features which are not fully utilized and play a critical role in KIE. In this paper, we propose an end-to-end system based on several state-of-the-art models from both computer vision and natural language processing areas to deal with the Mobile captured receipts OCR (MC-OCR) challenge, including two tasks: (1) evaluating the quality of the captured receipt, and (2) recognizing required fields of the receipt. Our code is publicly available at https://github.com/ndcuong9/MC_OCR","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"36 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87267721","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

Stack of Services for Context-Aware Systems: An Internet-Of-Things System Design Approach 上下文感知系统的服务栈:一种物联网系统设计方法

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642107

Quang-Duy Nguyen, C. Roussey, P. Bellot, J. Chanet

引用次数: 0

Multidomain Supervised Aspect-based Sentiment Analysis using CNN_Bidirectional LSTM model 基于cnn_双向LSTM模型的多域监督情感分析

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642146

T. Tran, H. Hoang, Phuong Hoai Dang, M. Riveill

{"title":"Multidomain Supervised Aspect-based Sentiment Analysis using CNN_Bidirectional LSTM model","authors":"T. Tran, H. Hoang, Phuong Hoai Dang, M. Riveill","doi":"10.1109/RIVF51545.2021.9642146","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642146","url":null,"abstract":"Sentiment analysis or opinion mining used to capture the community’s attitude who have experienced the specific service/product. Sentiment analysis usually concentrates to classify the opinion of whole document or sentence. However, in most comments, users often express their opinions on different aspects of the mentioned entity rather than express general sentiments on entire document. In this case, using aspect-based sentiment analysis (ABSA) is a solution. ABSA emphases on extracting and synthesizing sentiments on particular aspects of entities in opinion text. The previous studies have difficulty working with aspect extraction and sentiment polarity classification in multiple domains of review. We offer an innovative deep learning approach with the integrated construction of bidirectional Long Short Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for multidomain ABSA in this article. Our system finished the following tasks: domain classification, aspect extraction and opinion determination of aspect in the document. Besides applying GloVe word embedding for input sentences from mixed Laptop_Restaurant domain of the SemEval 2016 dataset, we also use the additional layer of POS to pick out the word morphological attributes before feeding to the CNN_BiLSTM architecture to enhance the flexibility and precision of our suggested model. Through experiment, we found that our proposed model has performed the above mentioned tasks of domain classification, aspect and sentiment extraction concurrently on a mixed domain dataset and achieved the positive results compared to previous models that were performed only on separated domain dataset.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"3 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89326040","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 land-use change model to study climate change adaptation strategies in the Mekong Delta 基于土地利用变化模型的湄公河三角洲气候变化适应策略研究

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642072

Q. C. Truong, B. Gaudou, Minh Van Danh, N. Huynh, A. Drogoul, P. Taillandier

{"title":"A land-use change model to study climate change adaptation strategies in the Mekong Delta","authors":"Q. C. Truong, B. Gaudou, Minh Van Danh, N. Huynh, A. Drogoul, P. Taillandier","doi":"10.1109/RIVF51545.2021.9642072","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642072","url":null,"abstract":"The rice-shrimp farming system is considered as a sustainable and beneficial model for the environment. However, the area of rice-shrimp was increasingly narrowed due to the trend of converting from rice to aquaculture by economic reasons. This paper aims to propose a medium scale land use change model for understanding the land use decision of farmers in adaptation to the environment and climate change. The model integrates a land-use decision making process based on multi-criteria selection where the main factors are land suitability, land convertibility, land use situation of neighbors, and profitability of land use patterns. Concerning the land use data, we used historical land use map in 2005, 2015 and 2019. Shrimp cultivation regions was completed by Landsat satellite image processing. The model has been calibrated by rice-shrimp map in 2015 and has been verified with the rice – shrimp map in 2019 of the My Xuyen district, Soc Trang province, Vietnam. The simulated results show that the rice-shrimp area was increasingly narrowed and has been converted to aquaculture land. In addition, the model tends to show that in a scenario of sea level rise of 15 cm in 2030, the share of rice-shrimp and shrimp tends to rise sharply, which is an important lesson for developing complex adaptive strategies of farmers.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"102 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80437663","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

MalDuoNet: A DualNet Framework to Detect Android Malware MalDuoNet:一个检测Android恶意软件的双重网络框架

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642094

Aayasha Palikhe, Longzhuang Li, Feng Tian, Dulal C. Kar, Ning Zhang, Wen Zhang

{"title":"MalDuoNet: A DualNet Framework to Detect Android Malware","authors":"Aayasha Palikhe, Longzhuang Li, Feng Tian, Dulal C. Kar, Ning Zhang, Wen Zhang","doi":"10.1109/RIVF51545.2021.9642094","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642094","url":null,"abstract":"Today mobile phones provide a wide range of applications that make our daily life easy. With popularity, smartphones have become a target for cybercrime where malicious apps are developed to acquire sensitive information or corrupt data. To mitigate this issue and to improve the security in mobile devices, different techniques have been used. These techniques can be broadly classified as static, dynamic and hybrid approaches. In this paper, a static-based model MalDuoNet is proposed to detect Android malwares, which uses a DualNet framework to analyze the features from the API calls. In the MalDuoNet model, one sub-network is focused to learn the features relevant to malicious behavior and the other sub-network is focused to learn the features in general. Thus it enables the model to learn complementary features which in turn helps get richer features for analysis. Then the features from the two sub-networks are combined in the final fused classifier for the final classification. In addition, each of the feature extractors has a separate classifier so that each sub-network can optimize its performance separately. The experimental results demonstrate that the MalDuoNet model outperforms the two baseline models with single network.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"80 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80031441","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