Forecasting Enrollment Model Based on First-Order Fuzzy Time Series

This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical data are linguistic values. It is shown that proposed time-invariant method improves the performance of forecasting process. Further, the effect of using different number of fuzzy sets is tested as well. As with the most of cited papers, historical enrollment of the University of Alabama is used in this study to illustrate the forecasting process. Subsequently, the performance of the proposed method is compared with existing fuzzy time series time-invariant models based on forecasting accuracy. It reveals a certain performance superiority of the proposed method over methods described in the literature

SemWeB Semantic Web Browser – Improving Browsing Experience with Semantic and Personalized Information and Hyperlinks

Imagine a Web browser that can understand the context of a Web page and recommends related semantic hyperlinks in any Web domain. In addition, imagine this browser also understands your browsing needs and personalizes information for you. The aim of our research is to achieve this in open Web environment using Semantic Web technologies and adaptive hypermedia techniques. In this paper, we discuss a novel Semantic Web browser, SemWeB, which utilizes linked data for context-based hyperlink recommendation and uses a behavior-based and an ontology-driven user modeling architecture for personalization on Web documents. The aim of this research is to bring the gap between the technology and user needs using Semantic Web technologies in Web browsing

Developing Knowledge Models of Social Media: A Case Study on LinkedIn

User Generated Content (UGC) exchanged via large Social Network is considered a very important knowledge source about all aspects of the social engagements (e.g. interests, events, personal information, personal preferences, social experience, skills etc.). However this data is inherently unstructured or semi-structured. In this paper, we describe the results of a case study on LinkedIn Ireland public profiles. The study investigated how the available knowledge could be harvested from LinkedIn in a novel way by developing and applying a reusable knowledge model using linked open data vocabularies and semantic web. In addition, the paper discusses the crawling and data normalisation strategies that we developed, so that high quality metadata could be extracted from the LinkedIn public profiles. Apart from the search engine in LinkedIn.com itself, there are no well known publicly available endpoints that allow users to query knowledge concerning the interests of individuals on LinkedIn. In particular, we present a system that extracts and converts information from raw web pages of LinkedIn public profiles into a machine-readable, interoperable format using data mining and Semantic Web technologies. The outcomes of our research can be summarized as follows: (1) A reusable knowledge model which can represent LinkedIn public users and company profiles using linked data vocabularies and structured data, (2) a public SPARQL endpoint to access structured data about Irish industry and public profiles, (3) a scalable data crawling strategy and mashup based data normalisation approach. The proposed data mining and knowledge representation proposed in this paper are evaluated in four ways: (1) We evaluate metadata quality using automated techniques, such as data completeness and data linkage. (2) Data accuracy is evaluated via user studies. In particular, accuracy is evaluated by comparison of manually entered metadata fields and the metadata which was automatically extracted. (3) User perceived metadata quality is measured by asking users to rate the automatically extracted metadata in user studies. (4) Finally, the paper discusses how the extracted metadata suits for a user interface design. Overall, the evaluations show that the extracted metadata is of high quality and meets the requirements of a data visualisation user interface

Player detection in field sports

We describe a method for player detection in field sports with a fixed camera set-up based on a new player feature extraction strategy. The proposed method detects players in static images with a sliding window technique. First, we compute a binary edge image and then the detector window is shifted over the edge regions. Given a set of binary edges in a sliding window, we introduce and solve a particular diffusion equation to generate a shape information image. The proposed diffusion to generate a shape information image is the key stage and the main theoretical contribution in our new algorithm. It removes the appearance variations of an object while preserving the shape information. It also enables the use of polar and Fourier transforms in the next stage to achieve scale and rotation invariant feature extraction. A Support Vector Machine (SVM) classifier is used to assign either player or non-player class inside a detector window. We evaluate our approach on three different field hockey datasets. In general, results show that the proposed feature extraction is effective, and performs competitive results compared to the state-of-the-art methods

Abnormal crowd behavior detection using novel optical flow-based features

In this paper, we propose a novel optical flow based features for abnormal crowd behaviour detection. The proposed feature is mainly based on the angle difference computed between the optical flow vectors in the current frame and in the previous frame at each pixel location. The angle difference information is also combined with the optical flow magnitude to produce new, effective and direction invariant event features. A one-class SVM is utilized to learn normal crowd behavior. If a test sample deviates significantly from the normal behavior, it is detected as abnormal crowd behavior. Although there are many optical flow based features for crowd behaviour analysis, this is the first time the angle difference between optical flow vectors in the current frame and in the previous frame is considered as a anomaly feature. Evaluations on UMN and PETS2009 datasets show that the proposed method performs competitive results compared to the state-of-the-art methods

SemWeB: A Semantic Web Browser for Supporting the Browsing of Users using Semantic and Adaptive Links

Web browsing is a complex activity and in general, users are not guided during browsing. The aim of this research is to support the browsing of users using semantic and adaptive hyperlinks using Semantic Web technologies and personalization methods. In this paper, we propose a novel Semantic Web browser (SemWeB), which uses a behavior-based and an ontology-driven user modeling architecture. In our approach, semantic links and adaptive hypermedia can be achieved on different websites. In addition, user profiles can be easily updated with semantic metadata coming from the Semantic Web browser

SEMPort – A Personalized Semantic Portal

This paper presents an ontology-based semantic portal, SEMPort, which aims to support both content providers and the users of the portal during providing information, browsing and searching. The content is enriched with context-based semantic hyperlinks and personalized views. Distributed content editing/provision is supplied for the maintenance of the contents in real-time. As a case study, SEMPort is tested on the school’s Course Modules Web Page (CMWP) and evaluated using this domain

EXTRINSIC LOSS REMOVAL IN AES/XPS

This paper presents a semantic portal, SEMPort, which provides better user support with personalized views, semantic navigation, ontology-based search and three different kinds of semantic hyperlinks. Distributed content editing and provision is supplied for the maintenance of the contents in real-time. As a case study, SEMPort is tested on the Course Modules Web Page (CMWP) of the School of Electronics and Computer Science (ECS)

Semantic Annotation of Surveillance Videos for Abnormal Crowd Behaviour Search and Analysis

Monitoring videos captured by surveillance cameras is a very difficult and time consuming task. There is a need for automated analysis using computer vision methods in order to recognize abnormal human behaviors and assist authorities. On the other hand, crowd (group of people) behavior analysis is a new direction of research, which can be utilized for automatic detection of panic in crowds. Once, videos are processed using computer vision technologies, another problem is how this data is indexed for search and analysis, since cameras continuously capture videos resulting vast amounts of data. Oftentimes various techniques are used for indexing, storage and access of video surveillance information, which makes global analysis and search on this data very difficult. In this paper, we introduce a novel semantic metadata model based on multimedia standards to extract and annotate globally inter-operable data about abnormal crowd behaviors from surveillance videos. We demonstrate on UMN and PETS2009 datasets that generated semantic annotations enable detailed searching and analysis of abnormal crowd behaviors with complex queries