2017 Intelligent Systems Conference (IntelliSys)最新文献

{"title":"An in-the-wild and synthetic mobile notification dataset evaluation","authors":"Kieran Fraser, Bilal Yousuf, Owen Conlan","doi":"10.1109/INTELLISYS.2017.8324343","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324343","url":null,"abstract":"Managing the vast amounts of information being pushed at mobile users is a challenge that is becoming increasingly difficult as the number of connected devices and users continues to expand. In order to overcome this challenge, a Notification Management System (NMS), needs a number of detailed data resources in order to decide what to do with an incoming notification in-the-wild. Explicit data contained within the notification and contextual information regarding the user and immediate environment are both necessary in order for a system to accurately infer a user's preferred delivery time for a given notification. Due to the sensitive nature of notifications and contextual data, it is difficult to acquire the explicit notification datasets which sufficiently describe the incoming notifications as well as the current contextual states of the user. This poses a problem for prospective research in the domain of Notification Management as arduous and time-consuming data collection is necessary if a hypothesis depends on unique notification/user features not previously collected. Without a number of rich notification datasets, either experimentation is limited to synthetic, vague or incomplete data, or time must be invested in developing a system to capture the required features. This paper evaluates a notification dataset previously collected in-the-wild and subsequently used in an evaluation of a NMS. The necessary features of the collected dataset are outlined as well as its limitations. As a comparison, the process of creating a synthetic notification dataset derived from a mobile usage study carried out by the MIT Media lab is also evaluated. The synthetic dataset is henceforth used to optimize a previous set of knowledge base rules and membership functions used within the Fuzzy Inference System (FIS) of an NMS. The resulting optimized rules can be presented to the user as a means of throttling notifications based on their goals.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131621052","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":"Comprehensive study of complete graph and Perfect Difference Network (PDN)","authors":"R. Katare, T. A. Shiekh, Fayaz Naikoo, Gh. Hassan Ganaie","doi":"10.1109/INTELLISYS.2017.8324341","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324341","url":null,"abstract":"In this paper, we have explored the graph properties of PDN and Complete Graph of (δ²+ δ+l) node networks to make the complete traversal of the perfect different network (PDN). We have also showed the diagonal links of PDN through its adjacency matrix. In this paper, we have also calculated the density for Hypercube, PDN, and Complete Graph architectures by manipulating the edges of these architectures. The symmetry in Complete Graph of (δ²+ δ+1) nodes is seen on the repetition of edges in different circuits. In this paper, we have also seen that on the removal of chordal ring links in Complete Graph the circuits removed are getting successively decreased.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131742472","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":"Temporal segmentation of human actions in video sequences","authors":"J. Carmona, J. Climent","doi":"10.1109/intellisys.2017.8324220","DOIUrl":"https://doi.org/10.1109/intellisys.2017.8324220","url":null,"abstract":"Most of the published works concerning action recognition, usually assume that the action sequences have been previously segmented in time, that is, the action to be recognized starts with the first sequence frame and ends with the last one. However, temporal segmentation of actions in sequences is not an easy task, and is always prone to errors. In this paper, we present a new technique to automatically extract human actions from a video sequence. Our approach presents several contributions. First of all, we use a projection template scheme and find spatio-temporal features and descriptors within the projected surface, rather than extracting them in the whole sequence. For projecting the sequence we use a variant of the R transform, which has never been used before for temporal action segmentation. Instead of projecting the original video sequence, we project its optical flow components, preserving important information about action motion. We test our method on a publicly available action dataset, and the results show that it performs very well segmenting human actions compared with the state-of-the-art methods.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126656651","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":"Predicting all star player in the national basketball association using random forest","authors":"Ghada M. A. Soliman, Ala'a El-Nabawy, A. Misbah, S. Eldawlatly","doi":"10.1109/INTELLISYS.2017.8324371","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324371","url":null,"abstract":"National Basketball Association (NBA) All Star Game is a demonstration game played between the selected Western and Eastern conference players. The selection of players for the NBA All Star game purely depends on votes. The fans and coaches vote for the players and decide who is going to make the All Star roster. A player who continues to receive enough votes in following years will play more All Star games. The selection of All Star players in NBA is subjective based on voting and there are no selection criteria that take out the human bias and opinion. Analyzing data from previous sports leagues can provide insight into the factors that lead to winning games and titles. This study aims to classify the players into regular or All Star players from the National Basketball Association and identify the most important characteristics that make a player an All Star player. To accomplish this, the performance per minute of play and per average of total minutes of player were analyzed using Random Forest supported in Apache Spark's scalable machine learning library to identify which variables best predict the regular and All Star players categories. The NBA men basketball dataset is used that is publically available at open source sports in the period 1937 till 2011. This study showed that Random Forest predicts All Star players with an accuracy of 92.5% when studying the performance per average of total minutes of player, whereas an accuracy of 92.48% is obtained for the performance per minute of play. The results identified the features of importance that contribute significantly to scoring and performance index rating of player. In this study, the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology is implemented to address the data mining problem in consistent and professional way. CRISP-DM presents a hierarchical and iterative process model, and provides an extendable framework with generic-to-specific approach, starting from six phases, which are further detailed by generic and then specialized tasks.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125941958","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":"Discriminant learning for hybrid HMM/MLP speech recognition system using a fuzzy genetic clustering","authors":"L. Lazli, M. Laskri, R. Boudour","doi":"10.1109/INTELLISYS.2017.8324351","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324351","url":null,"abstract":"We suggest for this study a fuzzy-genetic process for speech clustering, in the framework where the result of fuzzy c-means (FCM) clustering was used as initial population for genetic algorithms (GA). The approach is used in a hybrid HMM/ANN system using an Artificial Neural Network (ANN) to compute the observation probabilities in the states of the Hidden Markov Models (HMM). Experimental results obtained with continuous databases of various sizes in two languages (Arabic and French) show a significantly improved recognition accuracy with respect to the discrete HMM and regular hybrid HMM/ANN model using traditional clustering approaches.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127501744","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":"A comparative study of the optimal control design using evolutionary algorithms: Application on a close-loop system","authors":"S. Mohseni, Vincent Duchaine, T. Wong","doi":"10.1109/INTELLISYS.2017.8324243","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324243","url":null,"abstract":"This paper considers the design of two controllers for a non-linear system, both of which are optimized by evolutionary algorithms (EAs). The first controller is an optimal non-linear state feedback control. This controller is optimized using EAs that aim to improve system stability and minimize the cost function. The second controller is a PID controller that is tuned using EAs. The two controllers are evaluated based on their performances when applied to a four-bar linkage mechanism. The experimental results show that the optimal non-linear state feedback controller enhances the performance of the closed loop system under specific conditions.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125093987","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":"Assessment of Digital Elevation Model (DEM) using onboard GPS and ground control points in UAV image processing","authors":"Ahmad Lukman Muji, K. N. Tahar","doi":"10.1109/INTELLISYS.2017.8324226","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324226","url":null,"abstract":"The Unmanned Aerial Vehicle (UAV) technology has evolved dramatically in the 21st century in the military and general public for the recreational purposes and mapping work. The UAV operating costs are much cheaper compared to the normal aircraft and do not require a large workforce. There are other systems that have the same function like the Light Detection and Ranging (LIDAR) and Satellite Images. These systems require a huge cost and more labors and are time consuming to produce the Digital Elevation Model (DEM). The objective of this study is to investigate the accuracy of the DEM by using commercial software such as Agisoft and Pix4D with the Ground Control Point (GCP) and onboard Global Positioning System (GPS) processing. The aim of the study is to assess the DEM based on the UAV image processing. In this study, a hexacopter UAV was used to capture aerial images from a certain altitude. The Ground Control Point and Control Point were established using the GPS static technique. There are two types of analyses in this study which are the DEM accuracy based on Residual Mean Square Error and slope percentage. As a conclusion, the UAV images have the potential to be used for updating the DEM at a specific area.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122597456","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":"Context classification in energy resource management of residential buildings using Artificial Neural Network","authors":"Bruno Madureira, T. Pinto, F. Fernandes, Z. Vale, C. Ramos","doi":"10.1109/INTELLISYS.2017.8324297","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324297","url":null,"abstract":"This paper proposes an Artificial Neural Network (ANN) based approach to classify different contexts, with the goal of enhancing the management of residential energy resources. The increasing penetration of renewable based generation has completely changed the paradigm of the power and energy sector. The intermittent nature of these resources requires the system to incentivize the adaptability of consumers in order to guarantee the balance between generation and consumption. This leads to the emergence of several incentives with the objective of increasing the flexibility from the consumer's side. This, allied to the increasing price of electricity, leads to an increasing need for consumers to adapt their consumption in order to improve energy efficiency, decrease energy bills, and achieve a better use of their own generation resources. With this, several House Management Systems (HMS), and Building Energy Management Systems (BEMS) have emerged. These systems allow adapting the consumption (or suggesting changes in consumers' habits) according to several factors. However, in order to make this management truly smart, there is a need for adaptation to different contexts, so that changes can be done accordingly to the different situations that are faced at each time. This paper addresses this problem by proposing a novel methodology that enables classifying different situations in different contexts, according to different contextual variables.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122803991","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":"Sequential evidence accumulation VIA integrating dempster-shafer reasoning and estimation theory","authors":"M. Farmer","doi":"10.1109/INTELLISYS.2017.8324373","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324373","url":null,"abstract":"Integrating evidence from multiple sources has been heavily researched with various approaches such as Bayes and Dempster-Shafer (D-S) being widely adopted. Integrating evidence from a single sensor over time is becoming more common due to the Internet of Things (IoT). Researchers have often adopted these same mechanisms used for multi-source integration for integrating evidence over time. There are many issues with this approach, however, including the facts that time series are order dependent and that changes in the environment, due to a dynamic environment or due to assignable errors in the sensor measurements may cause significant evidential conflict. While methods such as D-S theory are suitable for multi-source evidence accumulation, we propose an alternate approach for sequential evidence accumulation. Our approach integrates the set theoretic nature of Dempster-Shafer theory with an estimation structure based on Kalman filtering. This approach is motivated both from traditional signal processing as well as from research in human psychology where a very similar filtering structure has been proposed for modeling human evidence accumulation. The approach is demonstrated to be effective using a smart airbag deployment system application.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122139196","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":"Simulated validation of an intelligent traffic control system","authors":"Kaiyu Wan, V. Alagar, Nhat-Hai Nguyen","doi":"10.1109/INTELLISYS.2017.8324310","DOIUrl":"https://doi.org/10.1109/INTELLISYS.2017.8324310","url":null,"abstract":"Driverless cars and robot-driven autonomous vehicles are being promoted as safer than ordinary human-driver. Although it is clear that the software-hardware components embedded in such vehicles may react in a timely manner to reduce errors attributed to human drivers, their timely adaptation to dynamic changes in their environment for safe navigation needs a convincing proof or at least a simulated validation. This paper proposes the use of Adaptive Traffic Control System (ATCS), and explains through a simulation platform how it can enforce safety and promote optimized travel on the road populated by vehicles with and without human drivers. A mathematical models for vehicle behavior near road intersections is given as the foundation of ATCS simulator. The simulation results on a variety of traffic patterns is presented followed by the discussion on the limitation of the current work and challenges in extending the simulator.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131424804","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}