Negation in general represents the contradiction and denial of anything. In classical logic, negation is identified as the logical connective that takes truthfulness to falsehood. However, there can be situations in which we have to deal with the possibility of any truth value besides true or false. The probability theory has been effective in modelling such situations. Therefore exploring the concept of probability functions from the negation point of view should be helpful in investigating new horizons related with a random phenomena. The negation of a probability distribution results from a transformation which reallocates the probability of each event equally among the other alternatives in a finite sample space. Proposed by Yager, this transformation possesses the maximum entropy allocation. Since any type of mixing increases the disorder(uncertainty) in a system, intuitively the uncertainty associated with the obtained negation must be greater than the uncertainty associated with the original probability distribution. A closer look at Yager's negation reveals that negation of probabilities are convex combinations of the probabilities in the original distribution and we have utilized this for estimating the unpredictability associated with the negation and the implications have been discussed in detail. Also, we have shown that the concept of negation can be helpful for estimation of symmetry and biasness in probability distributions. The binomial distribution has been taken as an example. An extension in case of Atanassov's intuitionistic fuzzy sets has also been discussed.

The negation of a probability distribution developed by Yager and Liu deals with the uncertainty when the probability of a particular event is equally distributed among the other alternatives. In this case, intuitively the uncertainty associated with that particular event should be equally distributed among the uncertainty associated with the other alternatives. However, for verifying this, we need to develop uncertainty measures that will measure the uncertainty associated with the negation of a probability distribution. In the present study, we have developed measures analogous to the well known Shannon entropy function and the Kullback Leibler’s divergence for determining the uncertainty related with the negation and studied its properties. Finally, we have derived the Baye’s thoerem associated with the negation of probabilities of a sequence of events.

The properties of negation of a probability distribution recently defined by Yager1 are studied. Furthermore, the negation of joint and marginal probability distributions in the bivariate case has been defined and their properties are studied. Finally, we have defined a new entropy function for determination of uncertainty associated with the negation of a probability distribution and the events associated with it.

We describe a system for content-based retrieval from large surveillance video archives, using behavior, action and appearance of objects. Objects are detected, tracked, and classified into broad categories. Their behavior and appearance are characterized by action detectors and descriptors, which are indexed in an archive. Queries can be posed as video exemplars, and the results can be refined through relevance feedback. The contributions of our system include the fusion of behavior and action detectors with appearance for matching; the improvement of query results through interactive query refinement (IQR), which learns a discriminative classifier online based on user feedback; and reasonable performance on low resolution, poor quality video. The system operates on video from ground cameras and aerial platforms, both RGB and IR. Performance is evaluated on publicly-available surveillance datasets, showing that subtle actions can be detected under difficult conditions, with reasonable improvement from IQR.

We describe a pilot experiment building a capability to automatically read documents, develop a knowledge base, support analytics, and visualize the information found. The capability allows someone researching a topic of interest of focus on analysis and synthesis rather than on reading. We show how information from multiple modalities (speech, text, structured databases) and multiple approaches (ontology driven and open information extraction) can be fused to create a resource about both previously known and novel entities. We describe an extensible framework for language understanding tools that allows for scalability, plug-and-play of alternative components, and incorporation of additional input streams, including video, images, and foreign language text.

Psychological health disorders pose a growing threat to society. Disorders such as Depression, Post-Traumatic Stress Disorder (PTSD), and mild Traumatic Brain Injury (mTBI), are often under-diagnosed and under-treated. Crisis hotlines are often the last resort for people who, from the lack of proper treatment, are considering suicide or intend to harm themselves or others. This paper describes a system that automatically analyzes online crisis hotline chats to (1) extract fine-grained distress indicators that map to Diagnostic and Statistical Manual of Mental Disorders (DSM) IV codes, and to (2) perform triage classification based on the severity of distress. For distress detection, we present several approaches which leverage annotator rationales and dialogue structure to improve classification performance, demonstrating significant gains over a state-of-the-art approach from literature. For triage classification, we demonstrate early detection capability for the most severe triage code. We evaluate our work on a large corpus of chats from the U.S. Department of Veterans Affairs' online Crisis Hotline.

Post-traumatic stress disorder (PTSD) is an anxiety disorder that affects a large population and that is currently diagnosed mostly through subject interviews and manual analysis of self-reported symptoms and of subject behavior. However, most PTSD cases are believed to go underdiagnosed and un-dertreated. We present a multi-modal system for computer-aided diagnosis of PTSD and stress that requires no clinician interview and relies principally in the elicitation of multimodal neurophysiological responses to audio-visual stimuli. We conduct a thorough evaluation of the discriminative power of the modalities involved (electro encephalography, galvanic skin-response, electrocardiography, head motion and speech), type of stimuli presented (audio, images, audio-and-images and video), and emotions evoked (positive, negative, and trauma-specific) between PTSD subjects and high and low-stress control groups. Our analysis indicates that the multi-modal prediction from the elicitation of trauma-specific emotions from images and audio is a promising approach to computer-aided diagnosis.

The ability to quickly search for large volumes of videos for specific actions or events can provide a dramatic new capability to intelligence agencies. Example-based queries from video are a form of content-based information retrieval (CBIR) where the objective is to retrieve clips from a video corpus, or stream, using a representative query sample to find more like this. Often, the accuracy of video retrieval is largely limited by the gap between the available video descriptors and the underlying query concept, and such exemplar queries return many irrelevant results with relevant ones. In this paper, we present an Interactive Query Refinement (IQR) system which acts as a powerful tool to leverage human feedback and allow intelligence analyst to iteratively refine search queries for improved precision in the retrieved results. In our approach to IQR, we leverage discriminative models that operate on high dimensional features derived from low-level video descriptors in an iterative framework. Our IQR model solicits relevance feedback on examples selected from the region of uncertainty and updates the discriminating boundary to produce a relevance ranked results list. We achieved 358% relative improvement in Mean Average Precision (MAP) over initial retrieval list at a rank cutoff of 100 over 4 iterations. We compare our discriminative IQR model approach to a naïve IQR and show our model-based approach yields 49% relative improvement over the no model naïve system.

In this paper, we examine the problem of text-independent open-set speaker identification (OS-SI) in broadcast news. Particularly, the impact of the population of registered speakers to OS-SI performance is investigated, which is the central issue for designing practical OS-SI system. We amend the maximum mutual information (MMI)-based discriminative training scheme to facilitate its incorporation in OS-SI systems. We also improve the implementation to allow the application of MMI based approach with 2048-component Gaussian mixture models. All systems are evaluated using NIST RT-03, RT-04 and FBIS corpora, with a maximum of 82 registered speakers. Our study shows that notable performance improvement can be obtained with MMI-based discriminative training, which reduces the equal error rate (EER) by 15.9% relatively, in comparison to the GMM-MAP scheme.

This paper describes the development of the BBN Audio Indexing System for broadcast news in Arabic. Key issues addressed in this work revolve around the three major components of the audio indexing system: automatic speech recognition, speaker identification, and named entity identification. The system deals with several challenges introduced by the Arabic language, including the absence of short vowels in written text and the presence of compound words that are formed by the concatenation of certain conjunctions, prepositions, articles, and pronouns, as prefixes and suffixes to the word stem. The lack of short vowels in the transcripts prompted a novel solution that further demonstrated the power of hidden Markov models to deal with ambiguity. Another challenge was the acquisition of appropriate language modeling data, given the absence of broadcast news data for that purpose. We present performance results for all three components of the Audio Indexing System, which we believe represent the state of the art for Arabic broadcast news.

With the advent of essentially unlimited data storage capabilities and with the proliferation of the use of the Internet, it becomes reasonable to imagine a world in which it would be possible to access any of the stored information at will with a few keystrokes or voice commands. Since much of this data will be in the form of speech from various sources, it becomes important to develop the technologies necessary for indexing and browsing such audio data. This paper describes some of the requisite speech and language technologies that would be required and introduces an effort aimed at integrating these technologies into a system, called Rough 'n' Ready, which indexes speech data, creates a structural summarization, and provides tools for browsing the stored data. The technologies highlighted in the paper include speaker-independent continuous speech recognition, speaker segmentation and identification, name spotting, topic classification, story segmentation, and information retrieval. The system automatically segments the continuous audio input stream by speaker, clusters audio segments from the same speaker, identifies speakers known to the system, and transcribes the spoken words. It also segments the input stream into stories, based on their topic content, and locates the names of persons, places, and organizations. These structural features are stored in a database and are used to construct highly selective search queries for retrieving specific content from large audio archives.