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People can now receive custom-made information through smartphones, tablets or wearable devices. However, people often tend to miss vital information, even reminders, in the flood of notifications. The problem of finding convenient moments for need-to-know information should be investigated. Because each person's message awareness pattern on a smart medium might be different, the necessity of personalized...
Context aware services provide functionalities which are personalized for their users' current situations. A main concern of context-aware services is a substantial consumption of resources while preserving a minimal level of accuracy. There is a tradeoff between efficiency and accuracy on context acquisition. Hence, we propose a cloud-based Context Acquisition Platform to provide a high-level of...
Detecting the text in natural scene images is often challenging due to the complexity and variety of text's appearance and its interaction with the scene context. In this paper, we present a novel hierarchical text detection method exploiting textual characteristics at both character and text line scales for improved accuracy. First, seed candidate characters are detected with discriminative deep...
Articulated hand pose recovery in egocentric vision is useful for in-air interaction with the wearable devices, such as the Google glasses. Despite the progress obtained with the depth camera, this task is still challenging with ordinary RGB cameras. In this paper we demonstrate the possibility to recover both the articulated hand pose and its distance from the camera with a single RGB camera in egocentric...
The natural language processing became one of the most important fields of artificial intelligence because is related to the area of human-computer interaction using human languages (natural language generation, question answering, machine translation, etc.) or speech understanding (language modeling).To model the relations between words it is necessary to find the syntactic and semantic relations...
Real-time data-driven systems often utilize discrete valued time series data and their functionality is highly dependent on the accuracy of such data. In order to improve the performance of these systems, an important pre-processing step is the denoising of data before performing any action (e.g. forecasting or control activities). Existing algorithms have primarily focused on the offline denoising...
In this paper we propose a method for identifying the semantic context of segments of text within a larger document. Our method is based on an extension ofChomsky’s x-bar theory. We adapt the xbar concept of headedness to a coarser granularity of text, such as paragraphs. Using this method, which we call P-bar, we map a set of vocabulary domains to a unique semantic context.Using a rule-based errordriven...
Hindi language is written and spoken by majority of people in India. Like other natural languages, Hindi is also an ambiguous language which creates obstacle in usage of information technology properly. To use Hindi language efficiently and effectively on web, we require a tool to remove ambiguity from a single word, or from all words, called word sense disambiguation (WSD). In this paper we introduce...
In order to develop provably safe human-in-the-loop systems, accurate and precise models of human behavior must be developed. Driving is a good example of such a system because the driver has full control of the vehicle, and her likely actions are highly dependent on her mental state and the context of the current situation. This paper presents a testbed for collecting driver data that allows us to...
n-gram statistical language model has been successfully applied to capture programming patterns to support code completion and suggestion. However, the approaches using n-gram face challenges in capturing the patterns at higher levels of abstraction due to the mismatch between the sequence nature in n-grams and the structure nature of syntax and semantics in source code. This paper presents GraLan,...
Developers work on parallel tasks and switch between them due to interruptions and dependencies. For each task, developers interact with artifacts that constitute the task context. The more dissimilar tasks are, the more time is needed for switches to restore the contexts and adjust the mindset. Organizing tasks by their similarity can increase the efficiency of task switches. Moreover, knowing similar...
Identification of implicit and explicit relationships in a data is a generic problem commonly encountered in many fields of science and engineering. In the case of explicit relations, one is interested in identifying a compact and an accurate predictor function i.e. y = ƒ(x), while in the implicit case, one is interested in identifying an equation of the form ƒ(x) = 0. In both these classes of problems,...
The widespread use of wireless communications, Internet, and mobile technology offers the opportunity of supplying new generation of decision support commonly known as Mobile Decision Support Systems (MDSS). This paper describes research towards evaluation of such systems. Our view is that the end user will benefit if provided with a better Quality of MDSS. We propose a quality model taking into account...
This paper investigates the development of a knowledge base (KB) of logical functions, that can be used to do reasoning, from the consolidation of training examples of those logical functions. The work is based on the L2R (Learning to Reason) framework. A L2R agent only needs to answer knowledge queries that are relevant to its environment in a Probably Approximately Correct sense. We develop an L2R...
We present a new method for shot boundaries detection and classification that operates directly on the MPEG compressed video. It is based only on the information about the macroblock coding mode in P and B frames. In order to maintain good accuracy while limiting complexity, the system follows a two-pass scheme and has a hybrid rule-based/neural structure. A rough scan over the P frames locates the...
There exist a wide variety of time sensitive contexts (e.g. image-guided neurosurgery (IGNS)), whereby image registration is required to be both fast and accurate if it is to be adopted clinically. Many sampling techniques have been proposed to speed up the registration process but these often come at the expense of accuracy (e.g. random). In this paper, we describe a fast and accurate multi-modal...
The recognition of contact names in mobile-device voice commands is a challenging problem. Some of the difficulties include potentially infinite vocabularies, low probability of contact tokens in the language model (LM), increased false triggering of contact voice commands when none are spoken, and very large and noisy contact name lists. In this paper we suggest solutions for each of these difficulties.
Current technology facilitates and increases connections through social media, allowing individuals everywhere to spread their ideas to the world. One social media platform is Twitter. One characteristic of a tweet is the requirement of conveying a message in a limited number of words. Proverbs are a feature of language that convey messages effectively in the least number of words. Therefore, we selected...
The increasing number of elderly persons, in addition to the lack of infrastructures designed to manage them brings an awareness of the importance of maintaining them at home by developing assistive technologies. Recent research on the latter focused on Human Activity Recognition (HAR). HAR aims to recognize the sequence of actions by a specific resident at home using sensor readings. In eldercare...
Hospitals are increasingly utilizing business intelligence and analytics tools to mine electronic health data to uncover inefficiencies in care delivery (e.g., slow turnaround times, high readmission rates). Given that the expertise and experience of healthcare providers may vary significantly, an area of potential improvement is optimizing the way patient cases are recommended to clinical experts...
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