Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods to Improve Gaussian Mixture Model for Language Identification

Multiple-language phone recognition and n-gram language modeling produce the best performance in formal language identification (LID) evaluations, but this method needs many kinds of data that were phonetically transcribed utterances. This phone–based system isn’t easily to apply for dialects or minority languages identification, because it is difficulty to obtain kinds of utterances which were orthographically or phonetically transcribed. Gaussian mixture model with the Universal Background Model (GMM-UBM), which has been successfully employed in speaker verification, is an effective approach to solve this problem. The GMM-UBM LID system, which didn’t obtain utterances that were orthographically or phonetically transcribed, reduces the time requirement for both training and testing. In this paper we proposed a new combination method utilizing Output Scores Fusion techniques for acoustic scores and language model scores to improve the performance of the GMM-UBM based LID system. Experiment results show that the combination method as described above is another efficient method for language identification problems.
MobSens: Making Smart Phones Smarter

In this article, we discuss experiences and lessons learned from deploying four mobile sensing applications on off-the-shelf mobile phones within a recreational framework called MobSens that contains elements of health, social, and environmental sensing at both individual and community levels. We describe the main components of our applications, which facilitate logging and external communications. We also outline the challenges faced when building and testing these applications and describe our strategies for overcoming them.
An agent-based application of personal health record in homecare

Homecare is an important component of the continuum of care as it provides the potential to improve quality of life and quality of healthcare delivery while containing costs. Personal Health Record (PHR) systems constitute a technological infrastructure that can support greater flexibility for healthcare professionals and patients, thus allowing for more effective homecare services. In particular, PHRs are intended to reach patients outside of care settings, influence their behaviors, and satisfy their demand for greater information and access. To this end, PHR technology needs to evolve well beyond providing a consolidated patient record, in ways that make it more widely applicable and valuable to health systems. The development of applications and tools on top of PHR systems can allow the PHR to function as a platform for patients to exchange information and interact with the health system (e.g., scheduling appointments electronically). This paper presents a prototype PHR-based system that aims at supporting chronic disease management. The system has been developed on the grounds of a service-oriented architecture where healthcare process automation is realized by means of dynamic, patient-related workflows.
The Relationship Between Reading and Intelligence in Primary School Aged Children: Implications for Definitional Models of Dyslexia

The validity of definitional models of development dyslexia (DD) derived from the difference between intelligence and reading quotients, is challenged by the failure of these models to consider expected developmental changes in the relationship between these constructs. The purpose of this study was to examine age-related changes in the correlations between verbal and nonverbal intelligence and reading ability. One hundred and twenty six children aged between 7 11 years of age were administered the Peabody Picture Vocabulary Test Third Edition (PPVT-III, a measure of verbal intelligence) the Raven Coloured Progressive Matrices (CPM, as a measure of non-verbal intelligence) and various measures of reading ability (the Neale Analysis of Reading Ability Revised, NARA, and selected subtests from the Woodcock-Johnson Tests of AchievementRevised, WJ-Ach). The PPVT-III had significantly stronger correlations than the CPM with reading rate, single word reading, and a measure of text comprehension. Both measures correlated poorly with pseudo-word reading. Despite differences between the two intelligence measures, both correlated more strongly with measures of reading comprehension than with traditionally defined measures of decoding ability. These results are discussed in the context of the validity of current definitions of DD. The need for greater consideration of the appropriateness of certain assessment measures at various ages both in testing and in the interpretation of previous studies,
The Yield Curve and Financial Risk Premia: Implications for Monetary Policy

Introduction. Theoretical Foundations for Policy Analysis: Financial Markets and Asset Pricing. - The Theory of the Term Structure of Interest Rates. - A Systematic View on Term Premia. The Term Structure of Interest Rates and Monetary Policy Rules: The Macro-Finance View of the Term Structure of Interest Rates. - Monetary Policy in the Presence of Term Structure Effects. Financial Stability and Monetary Policy: Financial Risk and Boom-Bust Cycles. - Conclusion and Outlook. - Dynamic Optimization. - State-Space Model and Maximum Likelihood Estimation. - Recursive Nature of the Expectations Hypothersis. - Derivation of Affine Coefficient Loadings. - Optimal Monetary Policy.
Biogeochemical Typing of Paddy Field by a Data-Driven Approach Revealing Sub-Systems within a Complex Environment - A Pipeline to Filtrate, Organize and Frame Massive Dataset from Multi-Omics Analyses

We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical consortia inherent to the nature, many environmental studies employ the holistic approach of multi-omics analyses undermining as much information as possible. Due to the massive amount of data produced applying multi-omics analyses, the results are hard to visualize and to process. The BGC typing analysis is a pipeline built using integrative statistical analysis that can treat such huge datasets filtering, organizing and framing the information based on the strength of the various mutual trends of the organismal and chemical fluctuations occurring simultaneously in the environment. To test our technique of BGC typing, we choose a rich environment abounding in chemical nutrients and organismal diversity: the surficial freshwater from Japanese paddy fields and surrounding waters. To identify the community consortia profile we employed metagenomics as high throughput sequencing (HTS) for the fragments amplified from Archaea rRNA, universal 16S rRNA and 18S rRNA; to assess the elemental content we employed ionomics by inductively coupled plasma optical emission spectroscopy (ICP-OES); and for the organic chemical profile, metabolomics employing both Fourier transformed infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (1H-NMR) all these analyses comprised our multi-omics dataset. The similar trends between the community consortia against the chemical profiles were connected through correlation. The result was then filtered, organized and framed according to correlation strengths and peculiarities. The output gave us four BGC types displaying uniqueness in community and chemical distribution, diversity and richness. We conclude therefore that the BGC typing is a successful technique for elucidating the sub-systems of organismal communities with associated chemical profiles in complex ecosystems.
Direct and quadrature axis voltage and current control of a three phase grid connected PV system with Adaptive Fuzzy Logic MPPT Controller

For improving dynamic response of grid connected photovoltaic systems voltage source inverter are operating in inner voltage and current control loop. This paper presents, a control technique for three phase grid connected photovoltaic system. Two stage power conditioning units has been used for system interfacing. First stage comprises of DC/DC converter and DC/AC conversion is done by voltage source inverter in second stage. Adaptive Fuzzy Logic Controller (AFLC) maximum power point tracking (MPPT) is proposed to achieve maximum power point operation. Using direct and quadrature axis voltage and current control voltage source inverter provides ac voltage by DC/DC boost converter output and total harmonic distortion (THD) is controlled as per IEEE Standard 929-2000. Finally, MATLAB/Simulation is carried out to confirm system operation and results.
Localiced Learning, Innovation and Regional Clusters

Introduction The globalising world economy is characterised by two partly contradictory tendencies. On the one hand we can identify the neo-Fordist development path, originating as the new international division of labour in the 1970s, of worldwide sourcing based on the principle of comparative advantage of lowest possible input costs (i.e. the relative best access to, and most efficient use of, “natural” production factors). Its rise was enabled by developments in transportation and communication technologies, and further stimulated by liberalisation and de-regulation of international trade and financial markets. On the other hand, we have the post-Fordist development path of the learning economy, in which global competition is based on the far more dynamic principle of competitive advantage, resting on “making more productive use of inputs, which requires continual innovation” (Porter 1998, 78). The new, post-Fordist understandings of industrialisation as a territorial process, i.e. underlining the importance of location, agglomeration and noneconomic factors, such as history, culture and institutions (i.e. constituting the social capital of a society) for economic development, and of innovation as a socially embedded process, represented a substantial contribution from heterodox economics (i.e. evolutionary economics and economic geography) to the economic orthodoxy. This challenge of bringing territoriality (back) into economics focused on learning as a localised process, pointing at the importance of historical trajectories. It regards innovation as an interactive learning process, involving a critique of the linear model of innovation, and clusters as the most efficient material contexts for interactive learning. Following this train of thinking, the specific aim of the article is to discuss how and why regional clusters can promote and support innovation and competitiveness in post-Fordist learning economies.
A Construction for Periodically-Cyclic Gale 2m-Polytopes

For each v, k and m such that v k 2m + 2 8, we construct a periodically-cyclic Gale 2m-polytope with v vertices and the period k. For such a polytope, there is a complete description of each of its facets based upon a labelling (total ordering) of the vertices so that every subset of k successive vertices generates a cyclic 2m-polytope.
Fault Tolerance and Recovery in Grid Workflow Management Systems

Complex scientific workflows are now commonly executed on global grids. With the increasing scale complexity, heterogeneity and dynamism of grid environments the challenges of managing and scheduling these workflows are augmented by dependability issues due to the inherent unreliable nature of large-scale grid infrastructure. In addition to the traditional fault tolerance techniques, specific checkpoint-recovery schemes are needed in current grid workflow management systems to address these reliability challenges. Our research aims to design and develop mechanisms for building an autonomic workflow management system that will exhibit the ability to detect, diagnose, notify, react and recover automatically from failures of workflow execution. In this paper we present the development of a Fault Tolerance and Recovery component that extends the ActiveBPEL workflow engine. The detection mechanism relies on inspecting the messages exchanged between the workflow and the orchestrated Web Services in search of faults. The recovery of a process from a faulted state has been achieved by modifying the default behavior of ActiveBPEL and it basically represents a non-intrusive checkpointing mechanism. We present the results of several scenarios that demonstrate the functionality of the Fault Tolerance and Recovery component, outlining an increase in performance of about 50% in comparison to the traditional method of resubmitting the workflow.
Autonomous Agents and Collective Robotics

The aim of this paper is twofold. First to take stock of the state of the AMoC project after three years of research (funded by the Swiss National Foundation for Research) by relating to available notes that we have published. Second, to show how potential collaborations may come within the framework of our research interest (in AMoC and more generally in the PAI group). Therefore, this paper does not contain any new experimental result, yet it proposes a rich synthesis allowing the state of the art of the AMoC project to be understood. All results till now have already appeared in internal working papers ([10], [11], [13], [19], [14], [21], [20], [59]), in conference proceedings ([12], [22], [16], [9], [38], [61], [60]) or in journals ([18], [37]). Papers printed in conference proceedings and/or in journals are generally short versions of internal working papers.
Optimization Approaches for Semi-Supervised Multiclass Classification

The purpose of this paper is to propose a semi-supervised learning method for the problem of multiclass classification. We first introduce the Laplacian of a graph and the associated graph kernels which are exploited in many semi-supervised binary classification methods. Then, we will introduce a new multiclass semi-supervised learning method based on a multiclass formulation of SVM. The proposed optimization problems can fully exploit the sparse structure of the Laplacian matrix, which enables us to optimize the problems with a large number of data points by standard optimization algorithms. Some numerical results indicate that our approaches achieve fairly high performance on large scale problems
A CMOS MedRadio Receiver RF Front-End With a Complementary Current-Reuse LNA

An ultra-low-power 401-406-MHz Medical Device Radiocommunications Service receiver RF front-end for biomedical telemetry applications is implemented using 0.18-μm CMOS technology. A single-ended complementary current-reuse low-noise amplifier (CCRLNA) is proposed that achieves a power gain of 20 dB, noise figure (NF) of 2.8 dB, IIP3 of -8.1 dBm, and second-order intercept point of +34 dBm while consuming only 150 μW at 1-V supply voltage. The total receiver RF front-end, including the proposed CCRLNA, in-phase/quadrature folded mixers, and local oscillator buffers, achieves a conversion gain of 28.7 dB, NF of 5.5 dB, and third-order intercept point of -25 dBm while consuming less than 500 μW from a 1-V supply voltage and occupying 0.7 mm2 of core die area.
Under Consideration for Publication in J. Functional Programming an Extended Comparative Study of Language Support for Generic Programming

Many modern programming languages support basic generics, sufficient to implement type-safe polymorphic containers. Some languages have moved beyond this basic support, and in doing so have enabled a broader, more powerful form of generic programming. This paper reports on a comprehensive comparison of facilities for generic programming in eight (with its proposed generics extension), and Cecil. By implementing a substantial example in each of these languages, we illustrate how the basic roles of generic programming can be represented in each language. We also identify eight language properties that support this broader view of generic programming: support for multi-type concepts, multiple constraints on type parameters, convenient associated type access, constraints on associated types, retroactive modeling, type aliases, separate compilation of algorithms and data structures, and implicit argument type deduction for generic algorithms. We find that these features are necessary to avoid awkward designs, poor maintainability, and painfully verbose code. As languages increasingly support generics, it is important that language designers understand the features necessary to enable the effective use of generics and that their absence can cause difficulties for programmers.
An efficient Gateway Discovery and Selection Approach Based on Quality of Service Attributes

The connection of ad-hoc networks to the Internet is typically established via gateways. To start an Internet connection, in a first step gateways have to be discovered by the mobile nodes within the ad-hoc cluster. Several gateway selection schemes have been proposed that select gateway nodes based on a single Quality of Service parameter, link capacity, link quality, path availability period, or end-to-end delay. Each scheme just focuses on the meant of improve only a single network performance, i.e., network throughput, packet delivery ratio, end-to-end delay, or packet drop ratio. However, none of these schemes improves the overall network performance because they focus on a single Quality of Service path parameter or on set of nonQuality of Service parameters. To improve the overall network performance, it is necessary to select a gateway with stable path , packet loss rate between two neighbor node should be minimum, a path with the maximum residual load capacity and the minimum latency. In this paper, we propose a gateway selection scheme that considers multiple Quality of Service path parameters such as path availability period, available capacity latency and link quality to select a potential gateway node . Computer simulations show that our gateway selection scheme improves throughput and packet delivery ratio with less per node energy consumption. It also improves the end-to-end delay compared to single Quality of Service path parameter gateway selection schemes. KeywordsManet , Quality of Service , Gateway Discovery .
Collective Media Annotation using Undirected Random Field Models

We present methods for semantic annotation of multimedia data. The goal is to detect semantic attributes (also referred to as concepts) in clips of video via analysis of a single keyframe or set of frames. The proposed methods integrate high performance discriminative single concept detectors in a random field model for collective multiple concept detection. Furthermore, we describe a generic framework for semantic media classification capable of capturing arbitrary complex dependencies between the semantic concepts. Finally, we present initial experimental results comparing the proposed approach to existing methods.
On the Scattering of Electromagnetic Waves by a Charged Sphere

Scattering of electromagnetic radiation by a charged homogeneous spherical particle/body is treated. Theoretical solution represents a generalization of the Mie's scattering theory for electrically neutral sphere. It is shown that classical and quantum physics approaches may lead to difierent conclusions, as documented by numerical computations assuming various permeabilities, refractive indices, surface charges, temperatures, and other physical parameters of the spherical particles. Two discrete wavelengths (5"m and 1mm) of the incident radiation are considered. Optical properties of charged particles composed of absorbing and slightly absorbing materials can essentially difier. Especially, the resonance peaks typically occur when imaginary part of particle refractive index is low. The relative permeability of a material may difier from unity at large wavelengths, e.g., in microwave region. Basically, the relative permeability appears to be less important factor than the surface charge. However, the permeability can in∞uence the scattering and extinction e-ciencies, as well as the backscattering features of small particles, under some conditions.
Encouraging Commitment in Online Communities

Online communities depend upon a core of committed members who participate, contribute, and stick with the group. To encourage commitment, community design has to accommodate people’s motivations for being in the community and unleash the social forces that cause people to feel attached to the community, to feel responsibility for the community, and to experience net benefits for staying in the community. By designing with these social forces in mind, we can increase members’ attachment and loyalty to the community and build a committed core.
Performance Comparison of Load Balancing Algorithms through Code Migration in Distributed Desktop Computing Grids

This paper describes performance comparison of load balancing control through code migration in distributed desktop computing grids. A prototype of the middleware which migrates object code for computing is implemented, and the trade-off of code migration was observed. Based on the prototype experiment, several algorithms for location policy in desktop computing grids were examined on simulations, and the methodology to control code migration to have suitable response time in the system is derived. The result will be applied to design the grid computing systems and the peer to peer computing systems to maintain the proper performance.
Recognition of visually induced emotions based on electroencephalography

The aim of this paper is to recognize human emotions using electroencephalography (EEG). Three categories of emotions have been examined: negative, positive and neutral. The emotions were evoked by stimuli in form of slideshows displayed on a computer screen. Electroencephalographic signals were recorded using g.USBAmp with 16 active electrodes. Preprocessing of the signal was done using a common average reference filter. Spectral lines of the signal were used as features. The obtained classification results, for nearest neighbour classifier, proved that distinguishing emotions was possible with good accuracy.
Trajectory tracking control of the nonholonomic mobile robot using torque method and neural network

This paper deals with the problem of tracking control of the mobile robot with non-holonomic constraint. A controller with two control loops is designed. The inner loop generates control laws for the tangent and angular velocities to control the robot to follow the target trajectory. It is derived based on the robot kinematics and the Lyapunov theory. The outer loop employs the torque method to control the robot dynamics. A neural network is implemented to compensate the uncertainties caused by the dynamics model. The asymptotic stabilization of the whole system is proven by direct Lyapunov stabilization theory. Simulations in MATLAB confirmed the validity of the proposed method.