Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-group mixture weight HMM

This paper presents a new modeling method of the continuous density Hidden Markov Model. As we know, speech signal is characterized by a hidden state sequence and each state is described by the mixture of weighted Gaussian density functions. Usually if we want to describe speech signal more precisely, we need to use more Gaussian functions for each state. But it will increase the computation significantly. On the other hand, the weight of each Gaussian component is the statistical average of Gaussian component probabilities for the whole training data. So it just can depict the average characteristics of speech signal. For some speech signal these weights are not proper in fact. Therefore, we propose Multi-group Mixture Weight HMM to solve this problem. In this kind of HMM, each state has several groups of mixture weight for the Gaussian components and it only needs very little additional computation. In our experiments, it achieved 12% reduction for errors.
Incorporating Prior Information in Compressive Online Robust Principal Component Analysis

We consider an online version of the robust Principle Component Analysis (PCA), which arises naturally in time-varying source separations such as video foreground-background separation. This paper proposes a compressive online robust PCA with prior information for recursively separating a sequences of frames into sparse and low-rank components from a small set of measurements. In contrast to conventional batch-based PCA, which processes all the frames directly, the proposed method processes measurements taken from each frame. Moreover, this method can efficiently incorporate multiple prior information, namely previous reconstructed frames, to improve the separation and thereafter, update the prior information for the next frame. We utilize multiple prior information by solving $n\text{-}\ell_{1}$ minimization for incorporating the previous sparse components and using incremental singular value decomposition ($\mathrm{SVD}$) for exploiting the previous low-rank components. We also establish theoretical bounds on the number of measurements required to guarantee successful separation under assumptions of static or slowly-changing low-rank components. Using numerical experiments, we evaluate our bounds and the performance of the proposed algorithm. In addition, we apply the proposed algorithm to online video foreground and background separation from compressive measurements. Experimental results show that the proposed method outperforms the existing methods.
A Framework and Methods for Dynamic Scheduling of a Directed Acyclic Graph on Multi -

The data flow model is gaining popularity as a programming paradigm for multi-core processors. Efficient scheduling of an application modeled by Directed Acyclic Graph (DAG) is a key issue when performance is very important. DAG represents computational solutions, in which the nodes represent tasks to be executed and edges represent precedence constraints among the tasks. The task scheduling problem in general is a NP-complete problem[2]. Several static scheduling heuristics have been proposed. But the major problem in static list scheduling is the inherent difficulty in exact estimation of task cost and edge cost in a DAG and also its inability to consider and manage with runtime behavior of tasks. This underlines the need for dynamic scheduling of a DAG. This paper presents how in general, dynamic scheduling of a DAG can be done. Also proposes 4 simple methods to perform dynamic scheduling of a DAG. These methods have been simulated and experimented using a representative set of DAG structured computations from both synthetic and real problems. The proposed dynamic scheduler performance is found to be in comparable with that of static scheduling methods. The performance comparison of the proposed dynamic scheduling methods is also carried out.
Psychopathological profile and antipsychotic treatment may be linked to internalised stigma in schizophrenia – a cross-sectional study

Purpose: The aim of this study was to assess the relationship between the internalised stigma of mental illness associated with the use of antipsychotic treatment and psychopathological profile among patients diagnosed with schizophrenia. Method: The study group comprised 64 patients diagnosed with schizophrenia. The Internalised Stigma of Mental Illness Inventory was used to assess the degree of self-stigmatisation in five aspects: Alienation, Stereotype Endorsement, Discrimination experience, Social withdrawal and Stigma resistance. The symptoms were assessed by a psychiatrist using the Clinical Assessment of Schizophrenic Syndromes Scale. Other variables of interest included data on current pharmacotherapy, body-mass index (BMI) and general functioning. Multivariate linear regression models were created to assess the association between variables of interest and ISMI subscale scores. Results were considered statistically significant when p < 0.05. Results: Zuclopenthixol, a long-acting injectable (LAI), was linked to greater Alienation, Stereotype endorsement and Discrimination experience, while risperidone LAI was associated with higher indices of Stereotype endorsement. Oral risperidone was linked to lower severity of Alienation and a greater level of Stigma resistance. A rise in the Lack of insight score predicted falls in Alienation, Stereotype endorsement, Discrimination experience and Social withdrawal scores. A rise in the Disturbances of Sense of Self score predicted rises in Alienation, Stereotype endorsement and Discrimination experience. ISSN: 1230-2813 volume 26 / Issue 1 2017 ADVANCES IN PSYCHIATRY & NEUROLOGY
Software quality assurance for object-oriented systems using meta-heuristic search techniques

Identifying fault prone modules at the very early stage of software development life cycle is very much necessary. This helps software developers to concentrate more on quality assurance, use the man power in proper perspective and mainly reduce the fault removal cost to be in-cured for the software system being developed. In literature, it is found that numerous authors have come up with cost based evaluation frameworks to find the effectiveness of the proposed fault prediction model based on the application of neural network models, but less emphasizes is provided on the manner in which networks are trained, where in a elementary approach of assigning random weights to the nodes is followed. This paper evaluates the capability of meta-heuristic search techniques in software fault classification for Apache Integration Framework. Genetic algorithm (GA) and Particle swarm optimization (PSO) is coupled with neural network for designing prediction models. It is observed that Modified Neuro PSO model is more effective and efficient in classifying faults accurately when compared to Neuro-GA, Adaptive Neuro-GA and Neuro-PSO.
Summarizing Situational and Topical Information During Crises

The use of microblogging platforms such as Twitter during crises has become widespread. More importantly, information disseminated by affected people contains useful information like reports of missing and found people, requests for urgent needs etc. For rapid crisis response, humanitarian organizations look for situational awareness information to understand and assess the severity of the crisis. In this paper, we present a novel framework (i) to generate abstractive summaries useful for situational awareness, and (ii) to capture sub-topics and present a short informative summary for each of these topics. A summary is generated using a two stage framework that first extracts a set of important tweets from the whole set of information through an Integer-linear programming (ILP) based optimization technique and then follows a word graph and concept event based abstractive summarization technique to produce the final summary. High accuracies obtained for all the tasks show the effectiveness of the proposed framework.
Efficient query recommendations in the long tail via center-piece subgraphs

We present a recommendation method based on the well-known concept of center-piece subgraph, that allows for the time/space efficient generation of suggestions also for rare, i.e., long-tail queries. Our method is scalable with respect to both the size of datasets from which the model is computed and the heavy workloads that current web search engines have to deal with. Basically, we relate terms contained into queries with highly correlated queries in a query-flow graph. This enables a novel recommendation generation method able to produce recommendations for approximately 99% of the workload of a real-world search engine. The method is based on a graph having term nodes, query nodes, and two kinds of connections: term-query and query-query. The first connects a term to the queries in which it is contained, the second connects two query nodes if the likelihood that a user submits the second query after having issued the first one is sufficiently high. On such large graph we need to compute the center-piece subgraph induced by terms contained into queries. In order to reduce the cost of the above computation, we introduce a novel and efficient method based on an inverted index representation of the model. We experiment our solution on two real-world query logs and we show that its effectiveness is comparable (and in some case better) than state-of-the-art methods for head-queries. More importantly, the quality of the recommendations generated remains very high also for long-tail queries, where other methods fail even to produce any suggestion. Finally, we extensively investigate scalability and efficiency issues and we show the viability of our method in real world search engines.
Adapted Utilitarian Judo: The Adaptation of a Traditional Martial Art as a Program for the Improvement of the Quality of Life in Older Adult Populations

This article reports on the Adapted Utilitarian Judo project. The use of the foundations and technical elements of traditional Judo, adapted and contextualised to the requirements of the older adult population, orienting the activity toward the field of health and the improvement of the quality of life of the older adult. Its mission is to facilitate maintaining and improving the functionality and autonomy of this population, with respect to the performance of the basic and instrumental activities of daily life. At the same it seeks to achieve this while maintaining the essence of the values that are inherent to the practice of Judo as a traditional martial art. It is argued that Judo, once adapted to focus on a utilitarian function, not only allows to actively influence aspects such as maintaining the physical qualities of the elderly but also fosters other key elements for active and healthy ageing by participating in an ongoing group activity, such as socialisation and self-esteem. More specifically, the paper presents how adapted Judo represents an innovation in the treatment of a risk factor associated with aging: the active prevention of falls. We conclude that Adapted Utilitarian Judo (JUA) is both a timely and relevant as a social and educational tool of great value, responds to propos called for by the international scientific community for programs aimed at improving the health and quality of life of the older adult population, especially in Europe, that is ageing at a fast pace.
Evaluating the Black-Scholes Model and the GARCH Option Pricing Model

ii Acknowledgments I am grateful to my supervisor, Wulin Suo, for giving me the opportunity to evaluate the empirical performance of the Black-Scholes Model and the GARCH option pricing model and for his exceptional comments on my manuscript. Also, I dedicate this paper to my family and my love, who always supported and encouraged me in the writing process.
The ITC-irst statistical machine translation system for IWSLT-

Focus of this paper is the system for statistical machine translation developed at ITC-irst. It has been employed in the evaluation campaign of the International Workshop on Spoken Language Translation 2004 in all the three data set conditions of the Chinese-English track. Both the statistical model underlying the system and the system architecture are presented. Moreover, details are given on how the submitted runs have been produced.
Principles of miRNA-Target Regulation in Metazoan Models

MicroRNAs (miRs) are key post-transcriptional regulators that silence gene expression by direct base pairing to target sites of RNAs. They have a wide variety of tissue expression patterns and are differentially expressed during development and disease. Their activity and abundance is subject to various levels of control ranging from transcription and biogenesis to miR response elements on RNAs, target cellular levels and miR turnover. This review summarizes and discusses current knowledge on the regulation of miR activity and concludes with novel non-canonical functions that have recently emerged.
Teacher Reflection in the Context of an Online Professional Development Course: Applying Principles of Cognitive Science to Promote Teacher Learning

ABSTRACT Teacher reflection has been at the core of teacher development efforts for almost 100 years, yet inconsistency in the literature results from a lack of clarity on how reflection is conceptualized—from its purpose to processes, and how they relate to refining teacher practice. This article draws from research in cognitive science to explain how engaging in an ongoing reflection approach, focused on observations of real classrooms, can help teachers refine their responses to classroom situations over time. The authors also provide examples of this reflective approach from an online course for early childhood teachers focused on improving teacher–child interactions in the classroom.
On the formalization of Invariant Mappings for Metaphor Interpretation

In this paper we provide a formalization of a set of default rules that we claim are required for the transfer of information such as causation, event rate and duration in the interpretation of metaphor. Such rules are domain-independent and are identified as invariant adjuncts to any conceptual metaphor. We also show a way of embedding the invariant mappings in a semantic framework.
More realistic land‐use and vegetation parameters in a regional climate model reduce model biases over China

Accurate vegetation cover data are important for realistic simulation of regional climate. The default vegetation parameters from Global Land Cover 2000, currently incorporated into global climate models and used in regional climate model RegCM, are not realistic for China, which may have contributed to serious bias in surface climate simulation. In this study, a new set of vegetation parameters considering the Plant Functional Type (PFT) fractions and the corresponding monthly leaf area index (PFT_LAI), were developed based on the land cover and MODIS LAI data sets. The regional climate model RegCM4.5 coupled with the land surface model CLM4.5 were utilized to test the performance of the new vegetation parameters by comparing simulations with observations using different surface parameters. The surface energy balance was analysed to examine the effects of changed vegetation parameters on regional climate. The results showed that the new parameters were more accurate than the GLC2000 parameters when describing the distribution of crops, grassland, and forests over China. The improved vegetation parameters reduced model biases for winter air temperature and precipitation over southern China by 0.9°C and 8%, respectively, and reduced the winter temperature and summer precipitation biases over northeastern China by approximately 0.7°C and 8%, respectively. More accurate surface albedo are the main reasons for reductions in model bias. However, certain biases, such as the cold and dry bias over the Tibetan Plateau, still remained in the simulation results using our new vegetation data.
Sensing and analyzing physiological data with a smart phone to secure an e-health system

A smart phone and a Body Area Network (BAN) can be used to monitor the elderly or patients with chronic diseases. Securing data and commands between smart phones and BANs is essential for preserving the privacy of health data and for ensuring the safety of the patient. We propose a new type of key establishment protocols for a body area network. The protocols takes the error margin that can be found in sensed data into account. We provide an example of this new type of key establishment protocol. The protocol can establish a key between two devices without the need for any form of initialization or a priori knowledge. Previous protocols that used sensed data to establish keys required wired electrode patches to gather ECG data. We show how photoplethysmographic data obtained from the video camera on a smart phone can be used as a secure environmental value. Our initial experiments show that we can successfully create secure communication after discovering a single inter-pulse interval. We also show preliminary results of photoplethysmographic measurements from an Arduino device and demonstrate that they do not overlap with existing measurements, which are obtained from a smart phone.
Disc-halo interaction — I. Three-dimensional evolution of the Galactic disc

The results of a three-dimensional model for disc–halo interaction are presented here. The model considers explicitly the input of energy and mass by isolated and correlated supernovae in the disc. Once disrupted by the explosions, the disc never returns to its initial state. Instead it approaches a state where a thin H i disc is formed in the Galactic plane, overlaid by thick H i and H ii gas discs with scaleheights of 500 pc and 1–1.5 kpc, respectively. The upper parts of the thick H ii disc (the diffuse ionized medium) act as a disc–halo interface, and its formation and stability are directly correlated to the supernova rate per unit area in the simulated disc.
A Position Encoding Convolutional Neural Network Based on Dependency Tree for Relation Classification

With the renaissance of neural network in recent years, relation classiﬁcation has again become a research hotspot in natural language processing, and leveraging parse trees is a common and effective method of tack-ling this problem. In this work, we offer a new perspective on utilizing syntactic information of dependency parse tree and present a position encoding convolutional neural network (PECNN) based on dependency parse tree for relation classiﬁcation. First, tree-based position features are proposed to encode the relative positions of words in dependency trees and help enhance the word representations. Then, based on a redeﬁnition of “context”, we design two kinds of tree-based convolution kernels for capturing the semantic and structural information provided by dependency trees. Finally, the features extracted by convolution module are fed to a classiﬁer for labelling the semantic relations. Experiments on the benchmark dataset show that PECNN outperforms state-of-the-art approaches. We also compare the effect of different position features and visualize the inﬂuence of tree-based position feature by tracing back the convolution process.
Abnormalities in cortical and peripheral excitability in flail arm variant amyotrophic lateral sclerosis

Background: While some regard the flail arm syndrome as a variant of amyotrophic lateral sclerosis (ALS), others have argued that it is a distinct clinical entity. Consequently, the present study applied novel central and peripheral nerve excitability techniques to further explore disease pathophysiology in flail arm syndrome. Methods: Cortical and peripheral nerve excitability studies were undertaken in 11 flail arm patients, defined by muscle weakness limited to the proximal aspects of the upper limbs for at least 24 months. Results: Mean age at disease onset (60.3 years) was similar to other ALS phenotypes (58.3 years), with strong male predominance (male:female distribution: flail arm 10:1; ALS 1.5:1; p<0.05) and prolonged disease duration (flail arm 62.5 months; ALS 15.8 months; p<0.05). There was evidence of cortical hyperexcitability in flail arm patients, similar to findings in ALS, with reduction in short interval intracortical inhibition (flail arm 0.8 (0.6)%; ALS 4.1 (1.1)%; controls 8.5 (1.0)%; p<0.0001) and resting motor threshold (flail arm 53.4 (2.8)%; ALS 56.6 (1.8)%; controls 60.7 (1.5)%; p<0.05), along with an increase in motor evoked potential amplitude (flail arm 49.5 (9.0)%; ALS 44.4 (4.9)%; controls 25.8 (2.8)%; p<0.05). Peripheral nerve excitability studies demonstrated changes consistent with upregulation in persistent Na+ currents and reduction of slow K+ conductances, similar to findings in ALS. Conclusion: This study has demonstrated the presence of cortical hyperexcitability in flail arm syndrome, along with abnormalities in peripheral nerve excitability, findings consistent with previous studies in other ALS phenotypes. By demonstrating the presence of upper motor neuron dysfunction, the present study suggests that the flail arm syndrome is an unusual variant of ALS.
Sociometric Status as Mediator in the Relation Between Effortful Control and Achievement

Abstract:The aim of this study was to analyze the relationship between effortful control and language performance, and the mediation effect of sociometric status on this relationship. The sample comprised 472 Spanish children 6–8 years old. To measure temperament, the parents were given the Temperament in Middle Childhood Questionnaire (TMCQ). The nomination method was used in the classroom context to measure social acceptance and rejection, whereas language performance was reported by the teachers at the end of the academic year. The results confirmed the positive relationship between effortful control and language performance. By using a causal mediation analysis, we found a mediation effect for both variables, which confirms the significant effect of self-regulatory skills on the integration of children in their group, and this social adjustment / maladjustment being what determines language performance.
Perceiving Rhythmic Repetition and Change Across Development: Effects of Concurrent Pitch

The ability to perceive repetition and change in rhythm is fundamental to music understanding. How is this ability affected by other musical dimensions, such as pitch? We compared the perception of rhythmic repetition and change in rhythm-only stimuli versus rhythm-and-pitch stimuli. A sample of 357 participants, aged from 6 to 22 years, performed Same (repetition) versus Different (change) judgments on rhythmic stimuli with and without concurrent pitch variation. Rhythm-and-pitch stimuli impaired the perception of rhythmic repetition but not the perception of change, and this was independent from participants’ age. Our findings are consistent with two concurrent effects of pitch on rhythmic perception: a change-highlighting effect, acting only in rhythmic change, and a working-memory-overload effect that acts in both repetition and change. We discuss the implications regarding composer–listener communication across development.
PHACT : Parallel HOG and Correlation Tracking Histogram of Oriented

Histogram of Oriented Gradients (HOG) based methods for the detection of humans have become one of the most reliable methods of detecting pedestrians with a single passive imaging camera. However, they are not 100 percent reliable. This paper presents an improved tracker for the monitoring of pedestrians within images. The Parallel HOG and Correlation Tracking (PHACT) algorithm utilises self learning to overcome the drifting problem. A detection algorithm that utilises HOG features runs in parallel to an adaptive and stateful correlator. The combination of both acting in a cascade provides a much more robust tracker than the two components separately could produce.