Miracle-dz/UniFAR-FTUs huggingface dataset:
Higher order waveform symmetry measure and its application to periodicity detectors for speech and singing with fine temporal resolution

Another simple and high-speed F0 extractor with high temporal resolution based on our previous proposal has been developed by adding a higher-order symmetry measure. This extension made the proposed method significantly more robust than the previous one. The proposed method is a detector of the lowest prominent sinusoidal component. It can use several F0 refinement procedures when the signal is the sum of harmonic sinusoidal components. The refinement procedure presented here is based on a stable representation of instantaneous frequency of periodic signals. The whole procedure implemented by Matlab runs faster than realtime on usual PCs for 44,100 Hz sampled sounds. Application of the proposed algorithm revealed that rapid temporal modulations in both F0 trajectory and spectral envelope exist typically in expressive voices such as those those used in lively singing performance.
Effects of Physical Activity Programs on the Improvement of Dementia Symptom: A Meta-Analysis

Objective. To confirm that physical activity program improves the symptoms of dementia and the most effective physical activity was selected to help establish exercise programs. Methods. Three databases, PubMed, Science Direct, and Willey online, were used to collect articles. The databases were published between January 2005 and December 2015. Keywords such as “dementia,” and “physical activity” were used in searching for papers. As a result, nine studies were selected in the second screening of the meta-analyses. Results. The improvement in the dementia symptom of physical capacity was 1.05 (high effect size, 95% CI: 0.03 to 0.73), ability of activity of daily living was 0.73 (slightly high effect size, 95% CI: 0.23 to 1.23), cognitive function was 0.46 (medium effect size, 95% CI: 0.26 to 0.66), and psychological state was 0.39 (lower than the medium effect size, 95% CI: 0.01 to 0.77). Conclusion. The physical activity for patients with dementia had an effect on the improvement of physical capacity and combined exercise was the most effective physical activity.
Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research

In recent years there has been an increase in the number of portable low-cost electroencephalographic (EEG) systems available to researchers. However, to date the validation of the use of low-cost EEG systems has focused on continuous recording of EEG data and/or the replication of large system EEG setups reliant on event-markers to afford examination of event-related brain potentials (ERP). Here, we demonstrate that it is possible to conduct ERP research without being reliant on event markers using a portable MUSE EEG system and a single computer. Specifically, we report the results of two experiments using data collected with the MUSE EEG system—one using the well-known visual oddball paradigm and the other using a standard reward-learning task. Our results demonstrate that we could observe and quantify the N200 and P300 ERP components in the visual oddball task and the reward positivity (the mirror opposite component to the feedback-related negativity) in the reward-learning task. Specifically, single sample t-tests of component existence (all p's < 0.05), computation of Bayesian credible intervals, and 95% confidence intervals all statistically verified the existence of the N200, P300, and reward positivity in all analyses. We provide with this research paper an open source website with all the instructions, methods, and software to replicate our findings and to provide researchers with an easy way to use the MUSE EEG system for ERP research. Importantly, our work highlights that with a single computer and a portable EEG system such as the MUSE one can conduct ERP research with ease thus greatly extending the possible use of the ERP methodology to a variety of novel contexts.
Mathematical Modelling of the Anaerobic Digestion Including the Syntrophic Acetate Oxidation

Abstract Anaerobic digestion is an effective biotechnological process for treatment of different agricultural, municipal and industrial wastes. However, it is a very unstable process in regard to the biogas reactors operation. This is due to the complicated interactions between different microbial species as well as of the complex transformations of the organic matter affected by a variety of environmental factors. Anaerobic digestion process can be divided in four main stages: hydrolysis, acidogenesis, acetogenesis and methanogenesis. The syntrophic acetate oxidation is a two-stage process mediating by two different phylogenetical microbial groups, living in syntrophic consortia: acetate oxidizing Eubacteria which convert acetate to carbon dioxide and hydrogen, and hydrogenotrophic methanogenic Archae which use carbon dioxide and hydrogen for production of methane. Use of mathematical models is a powerful tool for investigations and optimisation of the AD. In this paper a new mathematical model of the AD of activated sludge from municipal wastewater treatment plants including hydrolysis and the syntrophic acetate oxidation process has been developed and verified experimentally and by computer simulations using Simulink. Analytical computer studies of the model have been performed using Symbolic Toolbox of Matlab.
A new architecture for digital stochastic pulse-mode neurons based on the voting circuit

This paper presents a new kind of architecture for artificial digital neurons based on the voting circuit, which may be considered an improved version of those presented in literature. Stochastic pulse modulation has been used, where the values of the neuron's inputs are coded in terms of bit probabilities. The resulting activation function closely resembles the logistic sigmoid, with a transition slope that can be selected at the architectural level with no additional hardware requirements. The proposed neuron architecture has been simulated in software. Simulation results confirm that the neuron features a sigmoid transfer characteristic similar to that of conventional voting circuits. The resource occupation of the neuron, as obtained from implementation on reconfigurable platforms, has been estimated to be significantly lower than previous implementations. The theoretical analysis of the neuron's behavior is also presented.
Social Media Retrieval for Music Education

Advances in content-based multimedia analysis, recommender systems and Web-based social platforms for content and metadata sharing provide opportunities to create novel applications for music education. In this paper we describe a framework for intelligent music tutoring systems, through the combined use of content and context-based approaches. First, we investigate traditional computer-assisted music education applications, and review music information retrieval and Web technologies relevant to social media retrieval and music education. We discuss semantic aspects of these technologies and the use of ontologies as common grounds for structuring heterogeneous information available on the Web and from machine analyses. The importance of multimodality in music education tools is highlighted before we discuss how the reviewed technologies and information resources may be combined in interactive tools for music learning, for instance, a tool for searching the Web for guitar tablatures and YouTube video tutorials.
Mining Concept Drifts from Data Streams Based on Multi-Classifiers

Mining concept drifts is one of the most important fields in mining data streams. In this paper, a new ensemble algorithm called ICEA is proposed for mining concept drifts from data streams, which uses ensemble multi-classifiers to detect concept changes from the data streams in an incremental way. The experimental results show that ICEA algorithm performs higher accuracy and better adaptability than the popular methods such as SEA algorithm.
Getting traffic statistics from network devices in an SDN environment using OpenFlow

Software Defined Networking (SDN) provides a significant advantage because it is easier to tune up and introduce new functionalities. Although there are previous work focusing on the problem of network management, monitoring and control to improve the QoS seen by the customers, only some supply solutions for measuring network performance, e.g., latency, throughput, and packet loss. In this study we introduce how to get traffic measurement statistics from network devices in an SDN environment. In terms of network monitoring, OpenFlow allows to build a monitoring solution adjusted to the specific network needs. We demonstrate how to use OpenFlow features to get traffic statistics from network devices. In our test environment, collected traffic statistics are used to calculate the available bandwidth on each link in the network. We analyse the effect of statistics collection frequency on the network load and the accuracy of the results collected. Keywords— SDN/OpenFlow1.3, Floodlight controller, Mininet, CPqD Switch, and Network virtualization.
Assisted Self Reflection: Combining Lifetracking, Sensemaking & Personal Information Management

The web, personal computers and instant ubiquitous digital communications have brought unprecedented constant demands on our attention. As a result, people do not spend as much time thinking, reflecting upon situations, decisions and activities as they used to. As a consequence, people feel less organized, and often lack the situational clarity to make informed decisions. In this paper, we present an ongoing project designed to make self-reflection an integral part of daily personal information management activity, and to provide facilities for fostering greater self-understanding through exploration of captured personal activity logs. We describe an application that visualizes such personal activity logs using many available “life-tracking” tools, and uses visual and textual PIM metaphors to convey this information in a familiar way that is personally relevant and meaningful.
Probability hypothesis density filtering with sensor networks and irregular measurement sequences

The problem of multi-object tracking with sensor networks is studied using the probability hypothesis density filter. The sensors are assumed to generate signals which are sent to an estimator via parallel channels which incur independent delays. These signals may arrive out-of-order (out-of-sequence), be corrupted or even lost due to, e.g., noise in the communication medium and protocol malfunctions. In addition, there may be periods when the estimator receives no information. A closed-form, recursive solution to the considered problem is detailed that generalizes the Gaussian-mixture probability hypothesis density (GM-PHD) filter previously detailed in the literature.
Approximate retrieval from multimedia databases using relevance feedback

We address the problem of retrieving stored multimedia presentations using relevance feedback. We model multimedia presentations using a crisp relational or object oriented database, augmented with a text attribute. We also introduce a language for retrieval by content from such databases. The language is based on fuzzy logic. We also introduce a method for query refinement that uses relevance feedback provided by the user.
The monadic quantifier alternation hierarchy over graphs is infinite

We show that in monadic second-order logic over finite directed graphs, a strict hierarchy of expressiveness is obtained by increasing the (second-order) quantifier alternation depth of formulas. thus, the "monadic analogue" of the polynomial hierarchy is found to be strict, which solves a problem of Fagin. The proof is based on automata theoretic concepts (rather than Ehrenfeucht-Fraisse games) and starts from a restricted class of graph-like structures, namely finite two-dimensional grids. We investigate monadic second-order definable sets of grids where the width of grids is a function of the height. In this context, the infiniteness of the quantifier alternation hierarchy is witnessed by n-fold exponential functions for increasing n. It is notable that these witness sets of the monadic hierarchy all belong to the complexity class NP, the first level of the polynomial hierarchy.
A STEPWISE TECHNIQUE FOR INVERSE PROBLEM IN OPTIMAL BOUNDARY CONTROL OF THERMAL SYSTEMS

In the paper a procedure for stepwise solution of an inverse heat conduction problem is proposed. Based on the procedure a control system for optimal boundary control of one class of thermal systems is submitted. The controlled thermal system is considered to be decomposed to two subsystems a subsystem which is easy to control by a feedback using measurable outputs of the system and to a subsystem which distributed state-usually a spatial temperature distribution inside a heated material-is inacessible to direct measurement. The dynamics of the state is supposed to be described by a suitable distributed parameter model with a boundary excitation performed via the measurable system output. The control task then consists of optimal varying the measurable system output, that governs the boundary excitation of the distributed parameter subsystem, until it is calculated that the required shape of the distributed state has been reached. In the paper an optimal reference values for the system output, which should be tracked by a controller , are generated using stepwise technique for inversion of the distributed parameter model. The models of both subsystems are considered in continuous time, nonparametric convolutional integral forms. Using a spline approximation of the convolutional integral describing the rst subsystem a predictive controller with receding horizon strategy is designed. The own inverse problem is solved by an iterative regularization method. The resulting stepwise procedure is illustrated on a one-dimensional problem of the boundary heating of a thin metal bar.
Improving the precision of simulated hydrologic fluxes in land surface models

The numerical method for estimating soil moisture transport coefficients in land surface models (LSMs) is evaluated through comparison to a weighted-average method based on successive steady states [Warrick, 1991] and a proposed new scheme [Dong and Wang, 1997]. The focus is on situations with large gradients of soil moisture. The reference cases used to evaluate the performance of the various methods are based on numerical integrations with fine spatial resolution and small time steps. A series of tests were performed to detect sources of discrepancies and to assess the impact of various numerical methods on the simulation of evaporation, transpiration, and drainage. Off-line LSM simulations driven by observed atmospheric forcings were analyzed to understand the propagation of errors between the various hydrologic processes. We suggest a simple modification to LSM that can improve the precision of simulated hydrologic fluxes without increasing vertical resolution.
AC-RDVT: Acyclic Resource Distance Vector Routing Tables for Dynamic Grid Resource Discovery

Since the objective of grid is sharing the numerous and heterogeneous resources, resource discovery is a challenging issue. Recently appeared, Ontosum, is a resource discovery method based on semantically linked organizations and a routing algorithm Resource Distance Vector (RDV), has been presented to forward resource discovery queries into the clusters. Although this framework is efficient for large-scale grids and nodes are clustered automatically based on semantic attributes to constitute a semantically linked overlay network, but the dynamic behavior of grid isn’t considered. In this method, deceptive information is stored in RDV tables (RDVT) which cause some problems in routing process. In this paper, a method is proposed to improve the dynamism of RDV routing algorithm, so the consistency with grid environments is increased. The developed algorithm is assessed by investigating the success probability, number of hops and routing time of resource discovery. DOI: http://dx.doi. org/10.11591/ijece.v3i1. 1837 Full Text: PDF
Effects of Attention on Neuroelectric Correlates of Auditory Stream Segregation

A general assumption underlying auditory scene analysis is that the initial grouping of acoustic elements is independent of attention. The effects of attention on auditory stream segregation were investigated by recording event-related potentials (ERPs) while participants either attended to sound stimuli and indicated whether they heard one or two streams or watched a muted movie. The stimuli were pure-tone ABA-patterns that repeated for 10.8 sec with a stimulus onset asynchrony between A and B tones of 100 msec in which the A tone was fixed at 500 Hz, the B tone could be 500, 625, 750, or 1000 Hz, and was a silence. In both listening conditions, an enhancement of the auditory-evoked response (P1-N1-P2 and N1c) to the B tone varied with f and correlated with perception of streaming. The ERP from 150 to 250 msec after the beginning of the repeating ABA-patterns became more positive during the course of the trial and was diminished when participants ignored the tones, consistent with behavioral studies indicating that streaming takes several seconds to build up. The N1c enhancement and the buildup over time were larger at right than left temporal electrodes, suggesting a right-hemisphere dominance for stream segregation. Sources in Heschl's gyrus accounted for the ERP modulations related to f-based segregation and buildup. These findings provide evidence for two cortical mechanisms of streaming: automatic segregation of sounds and attention-dependent buildup process that integrates successive tones within streams over several seconds.
Acoustical Pre-Processing for Robust Speech Recognition

In this paper we describe our initial efforts to make SPHINX, the CMU continuous speech recognition system, environmentally robust. Our work has two major goals: to enable SPHINX to adapt to changes in microphone and acoustical environment, and to improve the performance of SPHINX when it is trained and tested using a desk-top microphone. This talk will describe some of our work in acoustical pre-processing techniques, specifically spectral normalization and spectral subtraction performed using an efficient pair of algorithms that operate primarily in the cepstral domain. The effects of these signal processing algorithms on the recognition accuracy of the Sphinx speech recognition system was compared using speech simultaneously recorded from two types of microphones: the standard close-talking Sennheiser HMD224 microphone and the desk-top Crown PZM6fs microphone. A naturally-elicited alphanumeric speech database was used. In initial results using the stereo alphanumeric database, we found that both the spectral subtraction and spectral normalization algorithms were able to provide very substantial improvements in recognition accuracy when the system was trained on the close-talking microphone and tested on the desk-top microphone, or vice versa. Improving the recognition accuracy of the system when trained and tested on the desk-top microphone remains a difficult problem requiring more sophisticated noise suppression techniques.
The protein tyrosine kinase p56lck inhibits CD4 endocytosis by preventing entry of CD4 into coated pits

The lymphocyte glycoprotein CD4 is constitutively internalized and recycled in nonlymphoid cells, but is excluded from the endocytic pathway in lymphocytic cells (Pelchen-Matthews, A., J. E. Armes, G. Griffiths, and M. Marsh. 1991. J. Exp. Med. 173: 575-587). Inhibition of CD4 endocytosis is dependent on CD4 expressing an intact cytoplasmic domain and is only observed in cells where CD4 can interact with the protein tyrosine kinase p56lck, a member of the src gene family. We have expressed p56lck, p60c-src, or chimeras of the two proteins in CD4- transfected NIH-3T3 or HeLa cells. Immunoprecipitation of CD4 and in vitro kinase assays showed that p56lck and the lck/src chimera, which contains the NH2 terminus of p56lck, can associate with CD4. In contrast, p60c-src and the src/lck chimera, which has the NH2 terminus of p60c-src, do not associate with CD4. Endocytosis assays using radioiodinated anti-CD4 monoclonal antibodies demonstrated that coexpression of CD4 with p56lck, but not with p60c-src, inhibited CD4 endocytosis, and that the extent of the inhibition depended directly on the relative levels of CD4 and p56lck expressed. The uptake of mutant CD4 molecules which cannot interact with p56lck was not affected. Measurement of the fluid-phase endocytosis of HRP or the internalization of transferrin indicated that the effect of p56lck was specific for CD4, and did not extend to other receptor-mediated or fluid-phase endocytic processes. Immunogold labeling of CD4 at the cell surface and observation by electron microscopy demonstrated directly that p56lck inhibits CD4 endocytosis by preventing its entry into coated pits.
Study on Recommender Systems for Business-To-Business Electronic Commerce

Recommender systems have become a popular technique and strategy for helping users select desirable products or services. Most research in this area focused on applying the method to help the customers in Business-to-Customer (B2C) electronic commerce (e-commerce), however, the participants in Businessto-Business (B2B) market can also get useful assistance from the recommender system. In this article we discuss the application of recommender system to B2B e-commerce. First, we examine how recommender system help B2B participants do transactions easier, then we design an effective system framework for the B2B e-commerce's recommender system based on B2B business practices and business intelligence; and then, we define the model components and processes; in the end, the ongoing challenges of the application will be discussed.
Determining Bug severity using machine learning techniques

Software Bug reporting is an integral part of software development process. Once the Bug is reported on Bug Tracking System, their attributes are analyzed and subsequently assigned to various fixers for their resolution. During the last two decades Machine-Learning Techniques (MLT) has been used to create self-improving software. Supervised machine learning technique is widely used for prediction of patterns in various applications but, we have found very few for software repositories. Bug severity, an attribute of a software bug report is the degree of impact that a defect has on the development or operation of a component or system. Bug severity can be classified into different levels based on their impact on the system. In this paper, an attempt has been made to demonstrate the applicability of machine learning algorithms namely Naïve Bayes, k-Nearest Neighbor, Naïve Bayes Multinomial, Support Vector Machine, J48 and RIPPER in determining the class of bug severity of bug report data of NASA from PROMISE repository. The applicability of algorithm in determining the various levels of bug severity for bug repositories has been validated using various performance measures by applying 5-fold cross validation.
Attributions of Fluency in Fame Judgments by Younger and Older Adults

Judgments about stimulus characteristics are affected by enhanced processing fluency that results from an earlier presentation of the stimulus. By monitoring for an episodic source of processing fluency, younger adults can more easily avoid this influence than can older adults. In Experiment 1, older adults discounted the effects of fluency when task demands encouraged the use of analytic judgments based on general knowledge, rather than an appeal to episodic source monitoring. Younger subjects were not reliably affected by these same task demands and their judgments continued to be affected by processing fluency. In Experiment 2, introduction of more stringent demands led younger adults also to discount the effects of fluency. We conclude that the influence of processing fluency on younger and older adults varies, depending on whether memory for source or general knowledge is put forward in place of fluency as a basis for judgments.