Abstract

We present a system employing domain specific dictionaries and features to recognize chemical entities. The system utilizes sentence segmentation, tokenization, feature generation, Conditional Random Field (CRF) training and one post-processing step. The dictio-naries were compiled from PubChem, Wikipedia, ChEMBL, DrugBank,word2vec clusters from US patents belonging to A61K class. We report the evaluation results of the run where development set was not included as part of the training set. The best performing model for CEMP taskh as the micro average precision, recall and F-score values of87.26%,79.98%and83.46%, respectively.

Abstract

Emotions in human speech are short lived. In an emotive utterance, the emotive gestures produced due to the emotive state of the speaker persists only to a shorter duration. In this study, the regions of an utterance that are highly influenced by the emotive state of the speaker are detected. These regions are labeled as emotionally significant regions. Data from the detected emotionally significant regions is used for developing an emotion recognition system. Physiological constraints of human speech production system are explored for detecting the emotionally significant regions of an utterance. Spectral features from the detected emotionally significant regions are used to develop an emotion recognition system. A significant improvement in the performance of the emotion recognition system is observed inthe present approach. An average improvement of 11% is in noted owing to the use of data from emotionally significant regions while developing an emotion recognition system. Gaussian mixture modelling (GMM) technique is employed to develop an emotion recognition system. During the present study, speech samples from Berlin emotion speech database (EMO-DB) are used. Four basic emotions such as anger, happy, neutral and fear are considered for study

Abstract

In recent past a lot of scientific attention is paid on recognizing the emotional state of the speaker from his speech. Emotion recognition is a challenging task as human emotions are complex, subtle and emotive state in human speech does not persist long. So it is important to study the presence of emotion identifiable information in smaller segments of speech. This study is aimed at studying the presence of emotional specific information with relevance to the position of the word in the utterance. During the present study, spectral features are employed to represent emotion specific information in speech. Spectral features from smaller speech segments of speech based on their position in the utterance are employed to study the presence of emotion in speech. Due to the lack of adequate data in small speech segments to support conventional GMM during the course of present study Gaussian mixture modeling with a universal background model (GMM-UBM) is used for developing a emotion recognition system. Speech data from IITKGP-SESC is used during the course of the present study. During the present study 4 (Anger, Fear, Happy and Neutral) emotions are considered.

Abstract

We present an automated disease term classification model using machine learning techniques that classifies a medical term to a specific disease class. We work on five particular diseases: Cancer, AIDS, Arthritis, Diabetes and heart related ailments. We identify and classify medical terms like drug names, symptoms, abbreviations, disease names, tests, etc., into their specific diseases classes. The results illustrate that our model for disease term classification finds all disease term classes with an average F-score of 0.966.

Abstract

We propose a model of coverage patterns (CPs) and approaches for extracting CPs from transactional databases. The model is motivated by the problem of banner advertisement placement in e-commerce web sites. Normally, an advertiser expects that the banner advertisement should be displayed to a certain percentage of web site visitors. On the other hand, to generate more revenue for a given web site, the publisher makes efforts to meet the coverage demands of multiple advertisers. Informally, a CP is a set of non overlapping items covered by certain percentage of transactions in a transactional database.The CPs do not satisfy the downward closure property. Efforts are being made in the literature to extract CPs using level-wise pruning approach. In this paper, we propose CP extraction approaches based on pattern growth techniques. Experimental results show that the proposed pattern growth approaches improve the performance over the level-wise pruning approach. The results also show that CPs could be used in meeting the demands of multiple advertisers.

Abstract

Image based reconstruction of urban environments is a challenging problem that deals with optimization of large number of variables, and has several sources of errors like the presence of dynamic objects. Since most large scale approaches make the assumption of observing static scenes, dynamic objects are relegated to the noise modelling section of such systems. This is an approach of convenience since the RANSAC based framework used to compute most multiview geometric quantities for static scenes naturally confine dynamic objects to the class of outlier measurements. However, reconstructing dynamic objects along with the static environment helps us get a complete picture of an urban environment. Such understanding can then be used for important robotic tasks like path planning for autonomous navigation, obstacle tracking and avoidance, and other areas. In this paper, we propose a system for robust SLAM that works in both static and dynamic environments. To overcome the challenge of dynamic objects in the scene, we propose a new model to incorporate semantic constraints into the reconstruction algorithm. While some of these constraints are based on multi-layered dense CRFs trained over appearance as well as motion cues, other proposed constraints can be expressed as additional terms in the bundle adjustment optimization process that does iterative refinement of 3D structure and camera / object motion trajectories. We show results on the challenging KITTI urban dataset for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth. We are able to show average relative error reduction by 41 % for moving object trajectory reconstruction relative to state-of-the-art methods like TriTrack[16], as well as on standard bundle adjustment algorithms with motion segmentation.

Abstract

A code is said to be data-local maximally recoverable if (i) all the information symbols have locality and (ii) any erasure pattern which can be potentially recovered (i.e., the number of equations is equal to the number of unknowns) is recovered by the code. A code is said to be local maximally recoverable if(i) all the symbols of the code have locality and (ii) from the above holds. In this paper, we establish the matroid structures corresponding to data-local and local maximally recoverable codes (MRC). The matroid structures of these codes can be used to determine the associated Tutte polynomial. Greene proved that the weight enumerators of any code can be determined from its associated Tutte polynomial. We will use this result to derive explicit expressions for the weight enumerators of data-local MRC. Also, Britz proved that the higher support weights of any code can be determined from its associated Tutte polynomial.We will use this result to derive expressions for the higher support weights of data-local and local MRC with two local codes.

Abstract

Matroidal networks were introduced by Doughert yet al.and have been well studied in the recent past. It was shown that a network has a scalar linear network coding solution if and only if it is matroidal associated with a representable matroid. A particularly interesting feature of this development is the ability to construct (scalar and vector)linearly solvable networks using certain classes of matroids.Furthermore, it was shown through the connection between network coding and matroid theory that linear network coding is not always sufficient for general network coding scenarios.The current work attempts to establish a connection between matroid theory and network-error correcting and detecting codes. In a similar vein to the theory connecting matroids and network coding, we abstract the essential aspects of linear network-error detecting codes to arrive at the definition of a matroidal error detecting network(and similarly, a matroidal error correcting networkabstracting from network-error correcting codes). An acyclic network (with arbitrary sink demands) is then shown to possess a scalar linear error detecting (correcting)network code if and only if it is a matroidal error detecting(correcting) network associated with a representable matroid.Therefore, constructing such network-error correcting and detecting codes implies the construction of certain representable matroids that satisfy some special conditions, and vice versa.We then present algorithms that enable the construction of matroidal error detecting and correcting networks with a specified capability of network-error correction. Using these construction algorithms, a large class of hitherto unknown scalar linearly solvable networks with multi source, multicast,and multiple- unicast network-error correcting codes is made available for theoretical use and practical implementation,with parameters, such as number of information symbols,number of sinks, number of coding nodes, error correcting capability, and so on, being arbitrary but for computing power(for the execution of the algorithms). The complexity of the construction of these networks is shown to be comparable with the complexity of existing algorithms that design multi cast scalar linear network-error correcting codes. Finally, we also show that linear network coding is not sufficient for the general network-error correction (detection) problem with arbitrary demands. In particular, for the same number of network errors,we show a network for which there is a nonlinear network-error detecting code satisfying the demands at the sinks, where as there are no linear network-error detecting codes that do the same.

Abstract

Cepstral analysis has been widely used in audio and speech processing applications because of its ability to reveal periodicities in a signal. The presence of cyclic prefix (CP) in orthogonal frequency division multiplexing (OFDM) signals induces periodicities. Motivated by this, the paper focuses on cepstral analysis of OFDM signal. The distributions of cepstral coefficients are de-rived for two scenarios of noise only and OFDM signal in noise. Itis shown that the OFDM cepstrum is significantly different from the additive white Gaussian noise (AWGN) cepstrum and can be used to detect OFDM waveforms. It is also shown that the cepstrum of OFDM is rich in features and can be used to estimate OFDM parameters such as number of sub carriers and length of the CPin an OFDM symbol. These OFDM waveform parameters can be used to automatically recognize or classify different OFDM waveforms, which are important for cognitive radios, coexistence of heterogeneous networks and signal intelligence. Two schemes are proposed to detect OFDM based primary user (PU) signalsin cognitive radio systems. The distributions of the test statistics under the two hypotheses are established.Neyman–Pearson detec-tions trategy is employed.Algorithms for estimating the number of subcarrier and the length of the CP are proposed and their performances studied through simulations. Later the proposed schemes are extended to cooperative sensing scenario with multiple secondary users (SUs) and it is shown that the collaboration between them significantly improve the performance of the proposed cepstrum based detection and estimation schemes.

Abstract

Quality of work life is the extent of relationships between individuals and organizational factors that exist in the working environment. Quality of work life is the extent to which workers can satisfy important personal needs through their experiences in the organization. It focuses strongly on providing a work environment conducive to satisfying individual needs. It is assumed that if employees have more positive attitudes about the organization and their productivity increases, everything else being equal, the organization should be more effective. The present study was conducted to examine whether the work related factors and demographic factors have any relationship with the perception of quality of work life and to explore the relationship between quality of work and quality of life in textile industries located at East Godavari district. The work related factors are combined in six categories: working environment, welfare measures, safety measures, supervision, participation in decision making and intercommunication. The results hold that demographic factors and work related factors have significant relationship with perception of quality of work life.