V. Nguyen, Van Bay Hoang, C. My, Le Minh Kien, Xuan-Tung Truong
{"title":"Toward socially aware trajectory planning system for autonomous mobile robots in complex environments","authors":"V. Nguyen, Van Bay Hoang, C. My, Le Minh Kien, Xuan-Tung Truong","doi":"10.1109/NICS51282.2020.9335845","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335845","url":null,"abstract":"This paper proposes a socio-spatio-temporal human characteristics-based socially aware navigation framework that enables mobile service robots to both approach and avoid humans in dynamic social environments. The proposed framework consists of two major stages. In the first stage, the robots estimate the approaching poses of the robot to the human or human group. In the second stage, the proposed framework will estimate an optimal robot's trajectory using the online trajectory planning technique. The control command extracted from the optimal trajectory is then utilized to drive the mobile robot to approach the individual humans or human groups, while avoiding regular obstacles, humans and human groups during the robot's navigation. The proposed framework is verified in the Gazebo-based simulation environment. The simulation results illustrate that, the mobile robots equipped with our proposed framework are able to safely and socially approach and avoid individual humans and human groups, providing socially acceptable behavior for the robots.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117163107","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}
Son Nguyen Truong Le, Vo Nguyen Quoc Bao, L. Nguyen, V. Huynh, H. Ta
{"title":"Power Control in Energy-Efficient Secure Transmission under QoS and Transmitter's Imperfect CSI","authors":"Son Nguyen Truong Le, Vo Nguyen Quoc Bao, L. Nguyen, V. Huynh, H. Ta","doi":"10.1109/NICS51282.2020.9335851","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335851","url":null,"abstract":"Under the constraints of quality of service and average transmission power, this paper studies the power control to maximize the energy efficiency of secure transmission. Assuming that the transmitter has imperfect channel state information, the derivation of the optimum power control rule and comparison of its resulting energy efficiency with various power control strategies are presented. Then, the impact of estimation error and number of antennas on the average secrecy energy efficiency (SEE) as well as the trade-off between the average SEE and quality of service will be characterized. The results show that the average SEE with the optimum power control increases significantly for small number of antennas or low transmit power. The results also show that the loss of average SEE is remarkably at high estimation error or extremely-high quality of service.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115406340","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}
{"title":"An Investigation of Ensemble Methods to Classify Electroencephalogram Signaling Modes","authors":"Hoang-Thuy-Tien Vo, V. Q. Huynh, Tuan Van Huynh","doi":"10.1109/NICS51282.2020.9335883","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335883","url":null,"abstract":"This research focuses on the feasibility of synthetic algorithms, including Boosted Trees, Bagged Trees, Subspace KNN, Subspace Discriminant, RUSBoosted Trees for identifying brain wave signal patterns. With two datasets used, it is the one that measures the four types of human emotions (valence, arousal, dominance, like). The receiver consists of 11 states composed of the groups of facial, normal, and thinking signals. The research focuses on researching the above algorithms, using the wavelet transform to determine the signal's characteristics, then classifying, comparing the results, improving, and reaching a conclusion.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131385714","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}
Duy Hoang Anh Do, Huy Van Quang Nguyen, Hy Dinh Ngo
{"title":"Supersonic SNARKs and Applications","authors":"Duy Hoang Anh Do, Huy Van Quang Nguyen, Hy Dinh Ngo","doi":"10.1109/NICS51282.2020.9335838","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335838","url":null,"abstract":"Supersonic is a transparent SNARKs proposed by Bünz et al. It has a significant improvement in proof size over previous constructions, while maintaining fast verification time. We summarize and implement Supersonic. The implementation is parallel and has good verifier performance. We run Supersonic on SHA-256 circuit and exponentiation-by-squaring circuit and provide experimentation results.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125415772","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}
{"title":"Keynote Talk #1: Intelligent Program Analysis for Automated Software Defect Detection and Fixing","authors":"T. Nguyen","doi":"10.1109/nics51282.2020.9335884","DOIUrl":"https://doi.org/10.1109/nics51282.2020.9335884","url":null,"abstract":"Detecting and fixing software defects are important in developing reliable and high-quality software systems. Software defects are so prevalent and detrimental that they cost billion dollars annually. In this talk, I will present the latest, advanced research in leveraging intelligent program analysis and combining with machine learning to automatically detect and fix software defects and vulnerabilities. I also present the challenges that the current state-of-the-art approaches in the field has been facing and the solutions in the new directions of intelligent program analysis and program representation learning. Finally, I will conclude the talk with the open challenges in the next steps in that direction.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114463545","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}
{"title":"Bit-Error Rate Analysis of Relay-Based DF Cooperative Diversity Systems Considering Multipath Fading Channels Along with Non-Identical Interferers","authors":"Rami Mohaisen, M. Al-Mistarihi, Khalid A. Darabkh","doi":"10.1109/NICS51282.2020.9335855","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335855","url":null,"abstract":"Incremental relaying, as a form of adaptive relaying, was introduced to compensate for the limitations of fixed relaying schemes which attributed to the improper channel utilization bearing in mind that the relay nodes forwards the sources' signal to the destination in a predetermined manner without taking into considerations the channel conditions between the source-destination links. In this article, a study of a two-hop decode-and-forward cooperative incremental relaying performance considering the existence of $L$ interfering signals nearby the destination over different types of fading channels is presented. Interestingly, a mathematical bit error rate derivation is exhibited and ultimately plotted.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123352741","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}
{"title":"Stereo Domain Translation for Denoising and Super-Resolution Using Correlation Loss","authors":"V. Q. Dinh, T. Nguyen, Phuc Hong Nguyen","doi":"10.1109/NICS51282.2020.9335830","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335830","url":null,"abstract":"This paper proposes a GAN-based denoising and super-resolution network for stereo images. The proposed network solves the two problems separately in an end-to-end training fashion. A matchability attention module are introduced to compute matching cost spaces and provide the stereo information between generated stereo images. In addition, the correlation loss is proposed to preserve the correspondence between a stereo pair. We evaluate the proposed network using the KITTI 2012 and KITTI 2015 datasets. In addition, we compare with state-of-the-art denoising and super-resolution methods. Experimental results show that the proposed method significantly outperforms the existing method both in terms of qualitative and quantitative analysis.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124733879","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}
{"title":"Saliency Detection by Superpixel Ranking for Person Re-identification","authors":"Chau Dang-Nguyen, Tien Ho Phuoc, Nghi Truong","doi":"10.1109/NICS51282.2020.9335858","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335858","url":null,"abstract":"The research described in this paper consists in developing a person re-identification framework for multiple non-overlapping camera system. The proposed approach consists of three main steps. Firstly, human images are segmented into atomic regions using the concept of the superpixel. A saliency detection framework based on the manifold ranking is then carried out to estimate a saliency score map, which emphasizes the perceived important regions of an image. Finally, a flexible matching procedure is introduced to estimate the similarity between two images and to make the final decision of person re-identification. The performance of our system is evaluated on the well-known VIPeR dataset. The experimental results show that the proposed system leads to satisfactory results.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117220787","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}
{"title":"Vietnamese Speech Synthesis with End-to-End Model and Text Normalization","authors":"D. Nhan, Nguyen Minh Tri, Cao Xuan Nam","doi":"10.1109/NICS51282.2020.9335905","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335905","url":null,"abstract":"Speech synthesis systems are now getting smarter and more natural thanks to the power of deep neural networks. However, each language has a different phonological and contextual characteristics, we have conducted experiments, statistics, and applied Vietnamese phonetics to improve speech synthesis systems based on Tacotron2 neural networks. Our methods achieve the accuracy of 97% in text normalization task, and the synthesized speeches with a MOS score of 3.97, asymptotic to 4.43 of the voices that are directly recorded. We also provide a library for standardizing Vietnamese text called Vinorm and a package that converts text into a phonetic format called Viphoneme, which is used as an input for end-to-end neural networks, make the synthesis process faster, more intelligent and natural than using character inputs.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124115864","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}
Hoang-Phong La, Minh-Thao Ha, Hai-Long Nguyen, Manh-Thien Nguyen
{"title":"Vehicle Counting: Survey and Experiments","authors":"Hoang-Phong La, Minh-Thao Ha, Hai-Long Nguyen, Manh-Thien Nguyen","doi":"10.1109/NICS51282.2020.9335840","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335840","url":null,"abstract":"Traffic management needs information about traffic to control the flow of transports. With millions of traffic video cameras acting as sensors around the world, collecting the information about traffic flow in real-time is quite easy, but using that information to process and control the traffic flow is a challenge. For detecting vehicles, old methods like inductive loop detectors (ILD), infrared detectors (IRDs), laser sensors, etc. have problems with high cost, efficiency, difficulty, etc. The methods we use in this paper are detection-based counting, regression-based counting. The authors propose a new method that is the combination of two methods above to achieve better results. We also evaluate the viability of using Deep Learning pre-trained models include Faster R-CNN, SSD, YOLO for detection-based. We experiment on 2018 AI CITY CHALLENGE datasets and Vehicles Nepal datasets. Our results show the effectiveness of the combining method in accuracy compares to using each of the methods separately.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127298133","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}