Resolving pronominal anaphora using commonsense knowledge

Coreference resolution is the task of resolving all expressions in a text that refer to the same entity. Such expressions are often used in writing and speech as shortcuts to avoid repetition. The most frequent form of coreference is the anaphor. To resolve anaphora not only grammatical and syntactical strategies are required, but also semantic approaches should be taken into consideration. This dissertation presents a framework for automatically resolving pronominal anaphora by integrating recent findings from the field of linguistics with new semantic features. Commonsense knowledge is the routine knowledge people have of the everyday world. Because such knowledge is widely used it is frequently omitted from social communications such as texts. It is understandable that without this knowledge computers will have difficulty making sense of textual information. In this dissertation a new set of computational and linguistic features are used in a supervised learning approach to resolve the pronominal anaphora in document. Commonsense knowledge sources such as ConceptNet and WordNet are used and similarity measures are extracted to uncover the elaborative information embedded in the words that can help in the process of anaphora resolution. The anaphoric system is tested on 350 Wall Street Journal articles from the BBN corpus. When compared with other systems available such as BART (Versley et al. 2008) and Charniak and Elsner 2009, our system performed better and also resolved a much wider range of anaphora. We were able to achieve a 92% F-measure on the BBN corpus and an average of 85% F-measure when tested on other genres of documents such as children stories and short stories selected from the web

Image Segmentation from RGBD Images by 3D Point Cloud Attributes and High-Level Features

In this paper, an approach is developed for segmenting an image into major surfaces and potential objects using RGBD images and 3D point cloud data retrieved from a Kinect sensor. In the proposed segmentation algorithm, depth and RGB data are mapped together. Color, texture, XYZ world coordinates, and normal-, surface-, and graph-based segmentation index features are then generated for each pixel point. These attributes are used to cluster similar points together and segment the image. The inclusion of new depth-related features provided improved segmentation performance over RGB-only algorithms by resolving illumination and occlusion problems that cannot be handled using graph-based segmentation algorithms, as well as accurately identifying pixels associated with the main structure components of rooms (walls, ceilings, floors). Since each segment is a potential object or structure, the output of this algorithm is intended to be used for object recognition. The algorithm has been tested on commercial building images and results show the usability of the algorithm in real time applications

Gesture based Human Computer Interaction for Athletic Training

The invention of depth sensors for mobile devices, has led to availability of relatively inexpensive high-resolution depth and visual (RGB) sensing for a wide range of applications. The complementary nature of the depth and visual information opens up new opportunities to solve fundamental problems in object and activity recognition, people tracking, 3D mapping and localization, etc. One of the most interesting challenges that can be tackled by using these sensors is tracking the body movements of athletes and providing natural interaction as a result. In this study depth sensors and gesture recognition tools will be used to analyze the position and angle of an athlete’s body parts thought out an exercise. The goal is to assess the training performance of an athlete and decrease injury risk by giving warnings when the trainer is performing a high risk activity

Assessment of Levee Erosion using Image Processing and Contextual Cueing

Soil erosion is one of the most severe land degradation problems afflicting many parts of the world where topography of the land is relatively steep. Due to inaccessibility to steep terrain, such as slopes in levees and forested mountains, advanced data processing techniques can be used to identify and assess high risk erosion zones. Unlike existing methods that require human observations, which can be expensive and error-prone, the proposed approach uses a fully automated algorithm to indicate when an area is at risk of erosion; this is accomplished by processing Landsat and aerial images taken using drones. In this paper the image processing algorithm is presented, which can be used to identify the scene of an image by classifying it in one of six categories: levee, mountain, forest, degraded forest, cropland, grassland or orchard. This paper focuses on automatic scene detection using global features with local representations to show the gradient structure of an image. The output of this work counts as a contextual cueing and can be used in erosion assessment, which can be used to predict erosion risks in levees. We also discuss the environmental implications of deferred erosion control in levees

Optimizing the best play in basketball using deep learning

In a close game of basketball, victory or defeat can depend on a single shot. Being able to identify the best player and play scenario for a given opponent’s defense can increase the likelihood of victory. Progress in technology has resulted in an increase in the popularity of sports analytics over the last two decades, where data can be used by teams and individuals to their advantage. A popular data analytic technique in sports is deep learning. Deep learning is a branch of machine learning that finds patterns within big data and can predict future decisions. The process relies on a raw dataset for training purposes. It can be utilized in sports by using deep learning to read the data and provide a better understanding of where players can be the most successful. In this study the data used were on division I women’s basketball games of a private university in a conference featuring top 25 teams. Deep learning was applied to optimize the best offensive play in a game scenario for a given set of features. The system is used to predict the play that would lead to the highest probability of a made shot

Using RapidMiner for executing queries and visualization in a traditional database course

In this article we discuss the use of RapidMiner, a data science software platform, in a Database Management Systems course. For further understanding of the database and the skill learned, students are given an assignment to complete, to not only use another software beside SQL Server Management Studio but also translate their findings in a more visualized format. In this assignment students will use RapidMiner to execute queries and then perform visualization in order to answer business questions. Survey shows that after completing the assignment, students felt more confident when faced with data analytics problems. In conclusion and analysis of the project, the use of RapidMiner technique, proves significant

Business Intelligence in the Real Estate Industry and Effect of BIM Adoption

Rapid changes in technology are impacting the real estate brokerages and made it possible for emerging new systems and strategies. Valuable real estate industry data has been transferred from the exclusive domain of professionals to the general public. In this paper the changing role of the stakeholders in real estate industry is forecasted and the effect of electronic marketplace in changing traditional brokerage models is studied. For this purpose 26 real estate agents are surveyed to examine how they transform ordinary data into value-added expert knowledge. The goal of this paper is to explore the future need and demands of marketplace and bind data analytics to this industry. Data analytics can serve for developing an essential marketing and analysis tool and be used as a valuable strategy to change the culture of real estate industry

Attention Detection using Kinect

After the invention of the Microsoft Kinect in 2009, many developers began researching possible applications of Kinect that go beyond the system’s original intended use in playing video games. The Kinect is an input device that helps researchers develop immersive applications that harness voice, movement and gesture recognition. The appeal of the Kinect to researchers stems from its affordability and the extensive built-in image processing capabilities of the device. In this research Microsoft Kinect is used to collect facial and posture information from the users and implement a machine learning algorithm to detect the level of attention in students

ERP adoption in enterprises with emerging Big Data

Communication and information technologies are reshaping enterprises worldwide. Tremendous amounts of data are being harvested about our browsing and shopping habits and social networks. Data is being aggregated not just within organizations but also across industrial and governmental sectors, such as in healthcare, education, and supply chains. And more and more devices in consumer use, manufacturing, and logistics are equipped with data acquisition capabilities, increasing the pace of data accumulation. Many organizations now have data stores in the petabyte range, with world-wide data now in the Exabyte range. There\u27s no way to analyze this data using traditional statistical techniques, because even at very low sampling rates, data set sizes are in the billions or more observations. To solve this problem, we need to understand the underlying business relationships and bring intuition and judgment into identifying patterns in the data. After identifying the problem, the computer studies the different data and builds a model from a generalization of the samples. These patterns can be used to forecast the system\u27s behavior and this prediction increases the productivity and opens new business opportunities. In this paper we\u27ll show the potential of big data and how analytics and big data can transform industrial engineering and illustrate an implementation of ERP cloud and big data solution in an organization