2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)最新文献_第8页

Hybrid NR Video Quality Metric with Decodable Payload 具有可解码负载的混合NR视频质量度量

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555510

C. Lee, G. Seo, H. Choi, S. Youn, K. Lee

引用次数: 0

A Teaching Assistant for Microelectronic Circuits Problems 微电子电路问题助教

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555515

Salam I. Nachawi, Vincent C. Gaudet, M. Elmasry

引用次数: 0

Stock Trend Prediction by Fusing Prices and Indices with LSTM Neural Networks 基于LSTM神经网络的价格与指数融合股票走势预测

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555506

Vasilis Karlis, Katerina Lepenioti, Alexandros Bousdekis, G. Mentzas

{"title":"Stock Trend Prediction by Fusing Prices and Indices with LSTM Neural Networks","authors":"Vasilis Karlis, Katerina Lepenioti, Alexandros Bousdekis, G. Mentzas","doi":"10.1109/IISA52424.2021.9555506","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555506","url":null,"abstract":"Forecasting stock market prices and trends is a promising area of machine learning. In the present paper we focus on the application of deep learning, a promising category of technical analysis, and in particular LSTM neural networks. In the context of this paper, stock market forecasts are estimated for large technology companies and the factors affecting their performance are studied. Stock market predictions can significantly benefit from the fusion of different information sources providing insights of diverse types and levels. Here we propose the use of LSTM neural networks in order to fuse different types and levels of information and generate predictions about the future prices and the future stock price trend. Specifically, the proposed LSTM model takes as input the price of the stock under examination along with related indices. Hence, it considers not only the historical price data but also the indices implying changes in the market due to several external influences. Our results demonstrate that the proposed approach outperforms other approaches which consider only the historical price data in terms of the price movement prediction.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115336861","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

Exploring the Effectiveness of Employing Limited Resources for Deep Neural Pairwise Evaluation of Machine Translation 探索利用有限资源对机器翻译进行深度神经两两评价的有效性

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555556

Despoina Mouratidis, Katia Lida Kermanidis

{"title":"Exploring the Effectiveness of Employing Limited Resources for Deep Neural Pairwise Evaluation of Machine Translation","authors":"Despoina Mouratidis, Katia Lida Kermanidis","doi":"10.1109/IISA52424.2021.9555556","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555556","url":null,"abstract":"In this paper, a light resource learning schema, i.e. a schema that depends on limited resources, is introduced, which aims to choose the better translation between two machine translation (MT) outputs, based on information regarding the source segments (SSE) and string-based features. A concatenation of vectors, including mathematically calculated embeddings from the SSE, statistical MT (SMT) and neural MT (NMT) segments (S1 and S2 respectively), are used as input to a neural network (NN). Experiments are run for two different forms of text structure (one that is a formal, well-structured corpus (C2) and one that is informal (C1)) for the English (EN) – Greek (EL) language pair. Instead of relying on high-level experts’ annotations, a novel automatic metric is proposed for determining the better translation, namely the quality estimation (QE) score. This score is based on string-based features derived from both the SSE and the MT segments. Experimental results demonstrate a quite good performance for the proposed feed-forward NN, comparable to the existing state of-the-art models for MT evaluation that require more sophisticated resources.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127003615","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

Dynamic k determination in k-NN classifier: A literature review k- nn分类器中动态k值的确定:文献综述

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555525

Merkourios Papanikolaou, Georgios Evangelidis, Stefanos Ougiaroglou

{"title":"Dynamic k determination in k-NN classifier: A literature review","authors":"Merkourios Papanikolaou, Georgios Evangelidis, Stefanos Ougiaroglou","doi":"10.1109/IISA52424.2021.9555525","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555525","url":null,"abstract":"One of the widely used classification algorithms is k-Nearest Neighbours (k-NN). Its popularity is mainly due to its simplicity, effectiveness, ease of implementation and ability to add new data in the training set at any time. However, one of its main drawbacks is the fact that its performance is highly dependent on the proper selection of parameter k, i.e. the number of nearest neighbours that the algorithm examines. The most frequently used technique for the “best” k determination is the cross validation as there is no general rule for choosing the k value due to its dependency on the training dataset. However, selecting a fixed k value throughout the dataset does not take into account its special features, like data distribution, class separation, imbalanced classes, sparse and dense neighborhoods and noisy subspaces. A lot of research has been done to date in the specific field, leading to many k-NN variations. In the present research, a thorough literature review is conducted in order to summarize all the achievements made to date in this field. Specifically, a pool of twenty eight (28) approaches with their experimental results are presented, all concerning methods and techniques for dynamic “best” k selection.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132797454","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

Attempting to reconstruct a 3D indoor space scene with a mobile device using ARCore 尝试用移动设备用ARCore重建一个3D室内空间场景

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555529

Dimitrios Tsoukalos, Vangelis Drosos, D. Tsolis

引用次数: 1

A FIWARE-based IoT Framework for Smart Water Distribution Management 基于fiwa的智能配水管理物联网框架

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555509

T. Panagiotakopoulos, D. Vlachos, Thanasis V. Bakalakos, Andreas Kanavos, A. Kameas

{"title":"A FIWARE-based IoT Framework for Smart Water Distribution Management","authors":"T. Panagiotakopoulos, D. Vlachos, Thanasis V. Bakalakos, Andreas Kanavos, A. Kameas","doi":"10.1109/IISA52424.2021.9555509","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555509","url":null,"abstract":"A key axis of design and implementation of sustainable water management solutions is based on the application of IoT, which enable the deployment of monitoring systems across the water distribution network, facilitate data acquisition in an automated manner and provide a rich set of data. Distributed software architectures are acknowledged as vital to realize smart water management systems, especially in large-scale and complex deployments with heterogeneous interacting entities. Current smart water systems mainly use commercial IoT platforms with only a few researches rooting for open source software solutions. However, both approaches have limitations as the closed APIs of commercial IoT platforms have been accused of hindering open market evolution, while open source solutions lack interoperability and data integration/sharing mechanisms. This paper presents an IoT framework based on FIWARE that aims to realize a highly flexible standards-based open source software solution for the development of smart water systems. We designed an architecture consisting of various FIWARE software components and two dashboard applications. We have also constructed a digital model of a part of a water distribution network and used a simulation dataset to showcase the framework’s functionality and data visualization aspects.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130904554","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: 8

Preliminary research on the design of an ambient intelligence application utilizing the exploitation of landmarks for the promotion of ancient greek technology exhibits. 利用地标性建筑促进古希腊科技展览的环境智能应用设计初探

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555571

Konstantinos I. Kotsopoulos, Konstantinos G. Kotsanas, Asimina N. Mitsomponou, Theofanis Alexandridis

引用次数: 0

An improved CNN model based on fused time-frequency features for mental fatigue detection in BCIs 基于融合时频特征的脑机接口精神疲劳检测改进CNN模型

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555518

Kun Chen, Zhilei Li, Qingsong Ai, Quan Liu, Lei Wang

{"title":"An improved CNN model based on fused time-frequency features for mental fatigue detection in BCIs","authors":"Kun Chen, Zhilei Li, Qingsong Ai, Quan Liu, Lei Wang","doi":"10.1109/IISA52424.2021.9555518","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555518","url":null,"abstract":"Mental fatigue detection is important in brain-computer interface systems (BCIs). However, owing to the time-variability and nonlinear characteristics of Electroencephalogram (EEG) signals, it is difficult to extract corresponding fatigue features of EEG signals. This paper proposes an improved CNN model based on time-frequency domain fused features which can detect mental state in BCIs. To be specific, a private EEG dataset of 8 subjects with two mental states (alertness and fatigue) induced by the 2-back task was applied. Then, 3 kinds of time domain feature-Hjorth Parameter-activity, Hjorth-mobility, Hjorth-complexity and 4 kinds of frequency domain feature: power spectral density (PSD)-a, PSD-β, PSD-θ, PSD-γ were extracted and fused together. Finally, the extracted fused features were fed into a 3 convolution layers’ convolutional neural network (CNN) model to distinguish mental states automatically. Compared with other methods for mental state classification, the proposed method achieved an average accuracy of 92.8% (max 97.8%, min 88.3%), outperforming 11 conventional methods, which indicated that the proposed method is effective for mental fatigue detection in BCIs.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131355271","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

A Factoid based Question Answering System based on Dependency Analysis and Wikidata 基于依赖分析和维基数据的Factoid问答系统

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555551

T. Ploumis, I. Perikos, F. Grivokostopoulou, I. Hatzilygeroudis

{"title":"A Factoid based Question Answering System based on Dependency Analysis and Wikidata","authors":"T. Ploumis, I. Perikos, F. Grivokostopoulou, I. Hatzilygeroudis","doi":"10.1109/IISA52424.2021.9555551","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555551","url":null,"abstract":"Over the last years, the use and the need for automated question answering systems have become more important than ever. The main reasons for this relate to the constant increase of the information that is available in textual form as well as the need to facilitate users in getting information they seek in a precise, fast, and easy way. In this work, we deal with the problem of the open domain factoid-based answering of questions, where the answer to a question is in the form of a word or a small phrase. We present an efficient methodology for analyzing questions and specifying proper answers with the use of knowledge bases. Initially, given a specific question that a user sets, a high-quality linguistic analysis of the question is performed. The dependencies of the question are specified, and the main triplets of the question are created. The system creates a SPARQL query to get the right answer for the question via the API from the Wikidata Query Service. The results are quite encouraging and highlight the quite good performance of our methodology.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117142105","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