{"title":"Scheduling the execution of tasks at the edge","authors":"Kostas Kolomvatsos, Thanasis Loukopoulos","doi":"10.1109/EAIS.2018.8397183","DOIUrl":"https://doi.org/10.1109/EAIS.2018.8397183","url":null,"abstract":"The Internet of Things provides a huge infrastructure where numerous devices produce, collect and process data. These data are the basis for offering analytics to support novel applications. The processing of huge volumes of data is a demanding process, thus, the power of Cloud is already utilized. However, latency, privacy and the drawbacks of this centralized approach became the motivation for the emerge of edge computing. In edge computing, data could be processed at the edge of the network; at the IoT nodes to deliver immediate results. Due to the limited resources of IoT nodes, it is not possible to have a high number of demanding tasks locally executed to support applications. In this paper, we propose a scheme for selecting the most significant tasks to be executed at the edge while the remaining are transferred into the Cloud. Our distributed scheme focuses on mobile IoT nodes and provides a decision making mechanism and an optimization module for delivering the tasks that will be executed locally. We take into consideration multiple characteristics of tasks and optimize the final decision. With our mechanism, IoT nodes can be adapted to, possibly, unknown environments evolving their decision making. We evaluate the proposed scheme through a high number of simulations and give numerical results.","PeriodicalId":368737,"journal":{"name":"2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"30 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120925997","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":"Active fuzzy rule induction","authors":"Aikaterini Ch. Karanikola, Stamatis Karlos, Vangjel Kazllarof, Eirini Kateri, S. Kotsiantis","doi":"10.1109/EAIS.2018.8397175","DOIUrl":"https://doi.org/10.1109/EAIS.2018.8397175","url":null,"abstract":"The use of rule based learners has been highly motivated all these years because of their inherent properties of interpretability and comprehensibility, leading to the construction of user friendly exported models by keeping pace with propositional logic. Besides this, their ability to operate under efficient time complexity allows us to occupy it under Active Learning schemes that integrate the human factor as an oracle into their learning kernel so as to tackle with the scarcity of existing labeled examples over several scientific fields. Upon this assumption, a recently proposed fuzzy rule based learner has been combined with a suitable query strategy for mining, with both robust and fast enough ability, unlabeled instances that facilitate the improvement of the learning behavior of the whole classification method. Rigorous experiments have been executed, proving the rightness of our ambition.","PeriodicalId":368737,"journal":{"name":"2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128463541","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}
G. Tsekouras, Stamatis Chatzistamatis, C. Anagnostopoulos, D. Makris
{"title":"Color adaptation for protanopia using differential evolution-based fuzzy clustering: A case study in digitized paintings","authors":"G. Tsekouras, Stamatis Chatzistamatis, C. Anagnostopoulos, D. Makris","doi":"10.1109/EAIS.2018.8397173","DOIUrl":"https://doi.org/10.1109/EAIS.2018.8397173","url":null,"abstract":"The daltonization process refers to the color adaptation of images in order to improve the perception of color-blind viewers. This paper proposes a modified clustering approach, which is applied to color adaptation of digitized art paintings and concerns a specific color vision deficiency called protanopia. To accomplish this task, the objective function of the fuzzy c-means is reformulated as to include only the cluster centers, and then it is minimized by the differential evolution. By using a standard technique, the original image is transformed to simulate the effect of the protanopia deficiency. Then, the above-mentioned clustering approach is separately applied to the original and the protanopia simulated images. By comparing the color clusters between these two cases, the colors in the original image are classified into two classes: (a) colors that must be corrected so that a protanope can easily distinguish them, and (b) colors that must remain intact. To this end, the colors belonging to the former class are adapted subject to the constraint that they must not be similar to the colors belonging to the latter class. Finally, the effectiveness of the proposed methodology is demonstrated through a number of experiments on color art paintings.","PeriodicalId":368737,"journal":{"name":"2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124558371","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":"Supporting semi-automatic marble thin-section image segmentation with machine learning","authors":"Á. Budai, K. Csorba","doi":"10.1109/EAIS.2018.8397181","DOIUrl":"https://doi.org/10.1109/EAIS.2018.8397181","url":null,"abstract":"For archaeologists knowing the provenance of mar-ble artifacts is important. The methodologies are based on finding the boundaries of the marble grains but only a few algorithms are available to do this instead of the expert. In this paper we propose an adaptive algorithm, called live-polyline, which is able to help the experts marking the grain boundaries and it is able to learn from user interactions as well. We investigate two different approaches. The first one is a heuristic based method, however the other one is a machine learning based solution. We define metrics for the performance, identify its key indicators, provide an algorithm to calculate it and determine the required values of the key indicators for sufficient performance. We also examined the heuristic and machine learning methods in terms of these indicators and measured their performance.","PeriodicalId":368737,"journal":{"name":"2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123320264","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":"Constructing fuzzy numbers from arbitrary statistical intervals","authors":"Kingsley Adjenughwure, B. Papadopoulos","doi":"10.1109/EAIS.2018.8397171","DOIUrl":"https://doi.org/10.1109/EAIS.2018.8397171","url":null,"abstract":"We show a simple method to construct a fuzzy number from an arbitrary statistical interval around the mean (or central value) of a sample while considering the uncertainty in the assumed distribution. Furthermore, we discuss its relationship with probability-possibility transformations and fuzzy estimators. Finally, we suggest some possible applications in evolving information systems of the proposed fuzzy number and other fuzzy numbers which can be constructed from well-known statistical intervals.","PeriodicalId":368737,"journal":{"name":"2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123383713","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}
Stamatis Karlos, Nikos Fazakis, Konstantinos Kaleris, V. G. Kanas, S. Kotsiantis
{"title":"An incremental self-trained ensemble algorithm","authors":"Stamatis Karlos, Nikos Fazakis, Konstantinos Kaleris, V. G. Kanas, S. Kotsiantis","doi":"10.1109/EAIS.2018.8397180","DOIUrl":"https://doi.org/10.1109/EAIS.2018.8397180","url":null,"abstract":"Incremental learning has boosted the speed of Data Mining algorithms without sacrificing much, or sometimes none, predictive accuracy. Instead, by saving computational resources, combination of such kind of algorithms with iterative procedures that improve the learned hypothesis utilizing vast amounts of available unlabeled data could be achieved efficiently, in contrast to supervised scenario where all this information is rejected because no exploitation mechanism exists. The scope of this work is to examine the ability of a learning scheme that operates under shortage of labeled data for classification tasks, based on an incrementally updated ensemble algorithm. Comparisons against 30 state-of-the art Semi-supervised methods over 50 publicly available datasets are provided, supporting our assumptions about the learning quality of the proposed algorithm.","PeriodicalId":368737,"journal":{"name":"2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128511133","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}
Vardis-Dimitris Anezakis, Georgios Mallinis, L. Iliadis, Konstantinos Demertzis
{"title":"Soft computing forecasting of cardiovascular and respiratory incidents based on climate change scenarios","authors":"Vardis-Dimitris Anezakis, Georgios Mallinis, L. Iliadis, Konstantinos Demertzis","doi":"10.1109/EAIS.2018.8397174","DOIUrl":"https://doi.org/10.1109/EAIS.2018.8397174","url":null,"abstract":"Climate change is one of the most serious threats for modern societies. It contributes to the fluctuation of air pollutants' concentrations which affects the number of respiratory and cardiovascular incidents. This research initially determines the contributing meteorological features for the maximization of air pollutants on a seasonal basis. In the second stage it employs Fuzzy Cognitive Maps (FCMs) to model and forecast the level of morbidity and mortality due to the above health problems, which are intensified from the changes in minimum and maximum meteorological values. This research effort takes into consideration the climate change scenarios for the period up to 2100. The assessment of the proposed model is done on historical meteorological, pollution and nursing data from the prefecture of Thessaloniki, for the period 2000–2013.","PeriodicalId":368737,"journal":{"name":"2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125835732","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":"Deep reinforcement learning for frontal view person shooting using drones","authors":"N. Passalis, A. Tefas","doi":"10.1109/EAIS.2018.8397177","DOIUrl":"https://doi.org/10.1109/EAIS.2018.8397177","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs), also known as drones, are increasingly used for a wide variety of novel tasks, including drone-based cinematography. However, flying drones in such setting requires the coordination of several people, increasing the cost of using drones for aerial cinematography and limiting the shooting flexibility by putting a significant cognitive load on the director and drone/camera operators. To overcome some of these limitation, this paper proposes a deep reinforcement learning (RL) method for performing autonomous frontal view shooting. To this end, a realistic simulation environment is developed, which ensures that the learned agent can be directly deployed on a drone. Then, a deep RL algorithm, tailored to the needs of the specific application, is derived building upon the well known deep Q-learning approach. The effectiveness of the proposed technique is experimentally demonstrated using several quantitative and qualitative experiments.","PeriodicalId":368737,"journal":{"name":"2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117216615","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":"Interval-valued intuitionistic fuzzy cognitive maps for stock index forecasting","authors":"P. Hájek, Ondřej Procházka, Wojciech Froelich","doi":"10.1109/EAIS.2018.8397170","DOIUrl":"https://doi.org/10.1109/EAIS.2018.8397170","url":null,"abstract":"There are several applications of time series fore-casting for which accurate knowledge of it is not required. In those cases it is enough to deal with the approximation of time series by intervals i.e. interval-valued time series (ITS). In this paper we propose a new method for the forecasting of univariate ITS. A part of the theoretical contribution of the paper is the development of the forecasting model which is based on fuzzy cognitive maps (FCMs). Instead of fuzzy sets used in standard FCMs we apply interval-valued intuitionistic fuzzy sets as their concepts. In this way we get interval-valued intuitionistic fuzzy cognitive maps (IVI-FCMs) which we use for the forecasting of ITS. To validate IVI-FCMs we apply them for the forecasting of the ITS made up by the daily minima and maxima of Nasdaq-100 stock index. Experimental evaluation proved high efficiency of the proposed approach.","PeriodicalId":368737,"journal":{"name":"2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133618295","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}
Salvador Hinojosa, D. Oliva, E. V. C. Jiménez, M. A. P. Cisneros, G. Pajares
{"title":"Real-time video thresholding using evolutionary techniques and cross entropy","authors":"Salvador Hinojosa, D. Oliva, E. V. C. Jiménez, M. A. P. Cisneros, G. Pajares","doi":"10.1109/EAIS.2018.8397184","DOIUrl":"https://doi.org/10.1109/EAIS.2018.8397184","url":null,"abstract":"Evolutionary Algorithms (EAs) are present in most areas of science and engineering where difficult problems arise. However, EAs are often applied to design problems where the speed is not a crucial factor. This tendency has lead EAs to be excluded from real-time applications due to its iterative nature. Image processing has benefited from EAs on many off-line applications, but little research has been made for real-time image processing problems. This paper presents the evaluation of EAs applied to the thresholding of a stream of images in real-time. Results indicate that Differential Evolution (DE) can be modified to achieve real-time performance on a single core implementation without any form of parallelization. These circumstances indicate that the performance can be further improved with multi-core implementations or GPU parallelization.","PeriodicalId":368737,"journal":{"name":"2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129105731","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}