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2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)最新文献

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Context and Short Term User Intention Aware Hybrid Session Based Recommendation System 基于上下文和短期用户意图感知的混合会话推荐系统
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-29 DOI: 10.1109/INISTA.2019.8778352
Ramazan Esmeli, M. Bader-El-Den, Alaa Mohasseb
{"title":"Context and Short Term User Intention Aware Hybrid Session Based Recommendation System","authors":"Ramazan Esmeli, M. Bader-El-Den, Alaa Mohasseb","doi":"10.1109/INISTA.2019.8778352","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778352","url":null,"abstract":"Information overloading in e-commerce hinders the consumers' ability to make the right decisions. Customers visiting e-commerce websites can have specific goals in an individual session. However, using sessions that are based on the last item viewed or purchased is not enough to exploit the sessions specific intention or predict users' next actions in the sessions. In this paper, we proposed context and short term user intention aware (CSUI) framework which is based on item similarity collaborative filtering and Association Rule Session-based recommendation systems, the proposed model combines context factor of users' session and users' short term intentions. The developed model has been evaluated on two real-world datasets. Experimental results showed that using session context and users' short term intentions during the recommendation process could help in improving the accuracy of the next item prerlietion.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123947101","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
DOS and Brute Force Attacks Faults Detection Using an Optimised Fuzzy C-Means 基于优化模糊c均值的DOS和暴力破解攻击故障检测
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-29 DOI: 10.1109/INISTA.2019.8778238
Karwan Qader, M. Adda
{"title":"DOS and Brute Force Attacks Faults Detection Using an Optimised Fuzzy C-Means","authors":"Karwan Qader, M. Adda","doi":"10.1109/INISTA.2019.8778238","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778238","url":null,"abstract":"This paper explains how the commonly occurring DOS and Brute Force attacks on computer networks can be efficiently detected and network performance improved, which reduces costs and time. Therefore, network administrators attempt to instantly diagnose any network issues. The experimental work used the SNMP-MIB parameter datasets, which are collected via a specialised MIB dataset consisting of seven types of attack as noted in section three. To resolves such issues, this researched carried out several important contributions which are related to fault management concerns in computer network systems. A central task in the detection of the attacks relies on MIB feature behaviours using the suggested SFCM method. It was concluded that the DOS and Brute Force fault detection results for three different clustering methods demonstrated that the proposed SFCM detected every data point in the related group. Consequently, the FPC approached 1.0, its highest record, and an improved performance solution better than the EM methods and K-means are based on SNMP-MIB variables.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132559807","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
Malicious Loop Detection Using Support Vector Machine 基于支持向量机的恶意环路检测
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-29 DOI: 10.1109/INISTA.2019.8778291
Zirak Allaf, M. Adda, A. Gegov
{"title":"Malicious Loop Detection Using Support Vector Machine","authors":"Zirak Allaf, M. Adda, A. Gegov","doi":"10.1109/INISTA.2019.8778291","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778291","url":null,"abstract":"Existing Side-channel attack techniques, such as meltdown attacks, show that attackers can exploit the microarchitecture and OS vulnerabilities to achieve their goals. In this paper, we present the development of our real-time system for detecting side-channel attacks. Unlike previous works, our proposed detection system does not rely on synchronisation between the attackers and victims. Instead, it uses processors' performance indicators to capture malicious Flush+ Reload activities with an accuracy of up to 99%. Moreover, the detection activities can be achieved with minimum time delay in both native and cloud systems with a low overhead performance of approximately less than 1% in the host system.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115981890","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 Dialogue manager for task-oriented agents based on dialogue building-blocks and generic cognitive processing 基于对话构建块和通用认知处理的面向任务的代理的对话管理器
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778354
M. Wahde
{"title":"A Dialogue manager for task-oriented agents based on dialogue building-blocks and generic cognitive processing","authors":"M. Wahde","doi":"10.1109/INISTA.2019.8778354","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778354","url":null,"abstract":"This paper introduces a novel dialogue manager, called DAISY, for intelligent virtual agents. The proposed approach is based on two central concepts: (i) dialogue building-blocks, which offer a systematic approach to the representation and implementation of human-agent dialogue, and (ii) cognitive processing in the form of sequences of simple, generic cognitive actions. The generic nature of the cognitive actions makes it possible to represent a large variety of cognitive processing (e.g, retrieving and manipulating memory content) by using a rather small set of such actions. DAISY is illustrated by means of a specific example, namely an agent acting as an information system for travel and tourist information. The example highlights the usefulness of the systematic approach offered by DAISY.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121373856","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: 4
Subject Classification of Learning Resources Using Word Embeddings and Semantic Thesauri 基于词嵌入和语义叙词表的学习资源主题分类
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778377
Dimitrios A. Koutsomitropoulos, Andreas D. Andriopoulos, S. Likothanassis
{"title":"Subject Classification of Learning Resources Using Word Embeddings and Semantic Thesauri","authors":"Dimitrios A. Koutsomitropoulos, Andreas D. Andriopoulos, S. Likothanassis","doi":"10.1109/INISTA.2019.8778377","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778377","url":null,"abstract":"Open Educational Resources (OERs) are often scattered among various sources and may follow different metadata schemata. In addition, they may not include exhaustive annotations; even worse, their subject characterization, if any, may be represented by arbitrary, ad-hoc keywords instead of standard, controlled vocabularies, a fact that stretches up the search space and hampers interoperability. To address this issue, in this paper we propose a twofold method based on two seemingly disjoint technology stacks: machine learning and the semantic web. First, OERs harvested from various repositories are assigned subject terms from a formal, standard thesaurus for a domain of interest, by discovering the semantic matches of the harvesting keyword within the thesaurus ontology. Then, we use word embeddings to represent an item's metadata and compute its similarity with the thesaurus keywords. These word embeddings are learned by a doc2vec model that has been trained with already annotated corpora from the biomedical domain. By combining both worlds, we show that it is possible to produce a reasonable set of thematic suggestions which exceed a certain similarity threshold.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129895946","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
A Case of Adaptive Nonlinear System Identification with Third Order Tensors in TensorFlow 基于TensorFlow的三阶张量自适应非线性系统辨识
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778406
G. Drakopoulos, Phivos Mylonas, S. Sioutas
{"title":"A Case of Adaptive Nonlinear System Identification with Third Order Tensors in TensorFlow","authors":"G. Drakopoulos, Phivos Mylonas, S. Sioutas","doi":"10.1109/INISTA.2019.8778406","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778406","url":null,"abstract":"Non-linear system identification is a challenging problem with a plethora of engineering applications including digital telecommunications, adaptive control of biological systems, assessing integrity of mechanical constructs, and geological surveys. Various approaches have been proposed in the scientific literature, including Volterra and multivariate Taylor series, fuzzy neural networks, state space models, and wavelets. This conference paper proposes a succinct model of a non-linear system with memory based on a third order tensor whose coefficients are trained in an LMS-like way. Moreover, two variants deriving from sign LMS and batch LMS algorithms respectively are also implemented in TensorFlow. The results of applying the three training algorithms to this system are compared in terms of the mean square error in validation phase, the convergence rate of the coefficients, and the convergence rate of the Euclidean norm of the local gradients of the system model.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114236312","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: 4
Diffused Label Propagation based Transductive Classification Algorithm for Word Sense Disambiguation 基于扩散标签传播的词义消歧转导分类算法
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778218
Gökhan Kocaman, Bilge Sipal, Aydın Gerek, B. Altinel, M. Ganiz
{"title":"Diffused Label Propagation based Transductive Classification Algorithm for Word Sense Disambiguation","authors":"Gökhan Kocaman, Bilge Sipal, Aydın Gerek, B. Altinel, M. Ganiz","doi":"10.1109/INISTA.2019.8778218","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778218","url":null,"abstract":"A major natural language processing problem, word sense disambiguation is the task of identifying the correct sense of a polysemous word based on its context. In terms of machine learning, this can be considered as a supervised classification problem. A better alternative can be the use of semi-supervised classifiers since labeled data is usually scarce yet we can access large quantities of unlabeled textual data. We propose an improvement to Label Propagation which is a well-known transductive classification algorithm for word sense disambiguation. Our approach make use of a semantic diffusion kernel. We name this new algorithm as diffused label propagation algorithm (DILP). We evaluate our proposed algorithm with experiments utilizing various sizes of training sets of disambiguated corpora. With these experiments we try to answer the following questions: 1. Does our algorithm with semantic kernel formulation yield higher classification performance than the popular kernels? 2. Under which conditions does a kernel design perform better than others? 3. What kind of regularization methods result with better performance? Our experiments demonstrate that our approach can outperform baseline in terms of accuracy in several conditions.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132148632","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
Aggressive Driving Detection Using Deep Learning-based Time Series Classification 基于深度学习的时间序列分类的主动驾驶检测
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778416
Youness Moukafih, H. Hafidi, M. Ghogho
{"title":"Aggressive Driving Detection Using Deep Learning-based Time Series Classification","authors":"Youness Moukafih, H. Hafidi, M. Ghogho","doi":"10.1109/INISTA.2019.8778416","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778416","url":null,"abstract":"Driver aggressiveness is a major cause of traffic accidents. Aggressive driving detection is an important application in the field of intelligent transportation systems (ITS). Developing systems capable of automatically detecting aggressive driving behavior should help improve traffic safety. In this paper we propose a novel solution to the problem of drivers' behavior classification based on a Long Short Term Memory Fully Convolutional Network (LTSM-FCN) to detect if a driving session involves aggressive behavior. We formulate the problem as a time series classification and test the validity of our approach on the UAH-DriveSet, a public dataset that provides a large amount of naturalistic driving data obtained from smartphones via a driving monitoring application. The proposed solution is compared to other deep learning and classical machine learning models for different processing time window sizes. It is shown that the proposed system outperforms the other methods in terms of the F-measure score, which reaches 95.88% for a 5 minutes window length.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116028244","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: 42
Word Embeddings Improvement via Echo State Networks 基于回声状态网络的词嵌入改进
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778297
K. Simov, P. Koprinkova-Hristova, Alexander Popov, P. Osenova
{"title":"Word Embeddings Improvement via Echo State Networks","authors":"K. Simov, P. Koprinkova-Hristova, Alexander Popov, P. Osenova","doi":"10.1109/INISTA.2019.8778297","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778297","url":null,"abstract":"The paper continues investigations on the application of bidirectional echo state networks (BiESN) to the task of word sense disambiguation (WSD). Motivated by observations that the quality of the embedding vectors used to train the models influences to a significant degree their accuracy, here we propose the application of a single ESN reservoir to generate new potentially better embedding vectors with different dimensions. BiESN models for WSD of various reservoir sizes were trained using various combinations of new and original embeddings models for the input and/or output steps; the achieved accuracy is reported here. The results demonstrate increased WSD accuracy in several cases of newly derived embedding sets.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123707740","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: 4
Decision Process of Autonomous Drones for Environmental Monitoring 自主无人机环境监测决策过程研究
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778341
Ömür Yildirim, K. Diepold, R. Vural
{"title":"Decision Process of Autonomous Drones for Environmental Monitoring","authors":"Ömür Yildirim, K. Diepold, R. Vural","doi":"10.1109/INISTA.2019.8778341","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778341","url":null,"abstract":"Environmental monitoring has a key role to reduce the human effect on nature and wildlife. Due to intense tracking and observation tasks, environmental monitoring is an expensive solution. Autonomous observation is an open discussion to raise the efficiency of environmental monitoring and reduce the cost of operation. Unmanned aerial vehicles (UAVs) are possible candidates with their proven success in monitoring and tracking. Thus, we are offering a decision process for the autonomy of drones in monitoring tasks. Our system is capable to fly autonomously, e.g., covering a given area, and able to perform certain tasks, e.g., identifying bottles. Our simulation results prove that autonomous drones can be used for a large variety of environmental monitoring tasks.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126950829","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
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