Miracle-dz/UniFAR-FTUs huggingface dataset:
Multilingual speech recognition and its application in a multilingual voice browser

Hong Kong is a two literature three language (Cantonese, Mandarin and English) city. The people speak in a mixed-code mannermany English terms are used in the Cantonese or Mandarin utterance. Therefore, a speech system used in Hong Kong should be able to handle these three languages and mixed-language manner. Because of this reason, the focus in the research undertaken for this thesis was to upgrade the speech system, SALSA, form English only to become a trilingual system which can support Cantonese, Mandarin and English, and also support links with Chinese and English words together. Since we did not have access to mult lingual resources, the first idea of builc ing a multilingual system was to use the monolingual resources of these three languages combined together. However, this approach did not work. Therefore, we applied cross-lingual recognition in the SALSA system; we used an English model to recognize all three languages. Through our research we showed that cross-lingual recognition can be applied on a small domain recognition tasks. In the trilingual SALSA system, by using this method, the recognition rate of English and Cantonese is over 90% and Mandarin is over 80%. In addition we also did some work on cross-lingual adaptation. We used an English model as a base model, and used the MAP adaptation technique to adapt a small amount of Mandarin data. The use of this technique showed that without enough training data, using the cross-lingual adaptation method ensures a better performance. Using cross-lingual adaptation, a speech system with small amount of training data.
Artificial Intelligence: Neural Networks Simplified

Now standing as a major paradigm for data mining applications, Neural Networks have been widely used in many fields due to its ability to capture complex patterns present in the data. Under its feature extractor capability, Neural Networks extrapolate past pattern into future ones and thereby relieves the burden of having recourse to complex detection algorithm in case of pattern recognition such as face detection or fingerprint detection. The development of Neural Networks has been so rapid that they are now referred as the sixth generation of computing. While the main strength of Neural Networks is embedded in its non-linearity and data-driven aspects, its main shortcoming relates to the lack of explanation power in the trained networks due to the complex structure of the networks. This paper explains the distinct mechanisms embodied in Neural Networks, its strengths, weaknesses and applications.
Radial basis function network estimation of neural activity fields

Estimating the neural activity fields of biological neurons is an important aspect of computational neuroscience research. Unfortunately, the experimental data is usually characterized by very high noise levels and follows a sparse and uneven spatial distribution, complicating the task of obtaining a reliable estimate. A technique is introduced article that integrates a computational geometry method with radial basis function networks to obtain reliable estimates of activity fields of individual neurons. The specific problem of extrapolating the spatio-temporal movement fields of neurons in the superior colliculus during saccadic eye movements is then addressed.
Three-phase power flow calculations using direct Z BUS method for large-scale unbalanced distribution networks

In this study, a three-phase power flow solution method using graph theory, injection current, and sparse matrix techniques for large-scale unbalanced distribution networks is proposed. In the bus frame of reference, a direct iterative method is adopted. To integrate the electric characteristics of transformers and step voltage regulators into the proposed method, the existing component models are modified by equivalent injected currents. To validate the performance and effectiveness of the proposed method, four three-phase IEEE test systems and random test systems are used for comparison purposes. As the size of the network increases, the proposed direct Z BUS method drastically shows its superiority over the other methods. The results reveal that the proposed method has good potential for improving the computational efficiency of optimal planning and design as well as real-time power dispatch applications in large-scale distribution systems.
Real Time Medical Image Consultation System Through Internet

Teleconsultation among doctors using a telemedicine system typically involves dealing with and sharing medical images of the patients. This paper describes a software tool written in Java which enables the participating doctors to view medical images such as blood slides, X-Ray, USG, ECG etc. online and even allows them to mark and/or zoom specific areas. It is a multi-party secure image communication system tool that can be used by doctors and medical consultants over the Internet.
Most Possible Partition: Utilizing Semantic Links for Duplicate Detection

Duplicate detection is a hotspot in the study of heterogeneous data integration and information retrieval. The efficiency and precision of detection are the goals of this study. In this paper, we introduce a duplicate detecting method based on semantic links among data, and propose a novel approach, named Most Possible Partition (MPP) to help detect duplicates efficiently. The main principle of MPP is to partition those data into most-possible-duplicate parts, in which there is a higher probability of duplicates. MPP does not sort data into certain order as classical Sorted Neighborhood Method (SNM) did. We give an effective partition method using semantic links among entities. Experiments on publication datasets show that the proposed method is efficient, and performance and accuracy of MPP are better than those of SNM.
Evaluation: How Much Evaluation is Enough? IEEE VIS 2013 Panel

Most of us agree that evaluation is a critical aspect of any visualization research paper. There are many different aspects to the topic of evaluation including: performance-based such as evaluating computational speed and memory requirements. Other angles are human-centered like user-studies, and domain expert reviews to name a few examples. In order to demonstrate that a piece of visualization work yields a contribution, it must undergo some type of evaluation.
Development of a fast method for the psychophysical estimation of nonlinear cochlear function using schroeder-phase masking.

The power spectrum model of masking (Patterson and Moore, 1986) assumes that the masking ability of a sound is affected only by the frequency and intensity of the masker, but not the phase. The well-documented difference in masking produced by different phases of Schroeder- phase harmonic complexes the ("phase effect") is thought to be contributed by the phase curvature of the auditory filter and nonlinear cochlear processing (Kohlrausch & Sander, 1995). We have recently developed a fast method for recording Schroeder-phase masking functions in humans which takes 8 minutes of testing time rather than the 45 minutes taken by the conventional three alternative forced choice (3AFC) method. We demonstrate here the reliability of this new method and its potential use in measuring cochlear non-linearity. Schroeder-phase masking functions were measured in 38 normal hearing and 15 hearing impaired participants using the conventional and fast methods. Results from our fast method agreed well with those from the conventional 3AFC method. We also used the fast method to measure functions at 0.25, 0.5, 1, 2, and 4 kHz at 45 and 75 dB A masker levels in normal hearing and hearing impaired participants, and found significantly reduced phase effects at low presentation levels and in participants with SNHL compared to normal hearing participants (p<0.05), consistent with reductions in cochlear nonlinearity in those conditions. With an 80% reduction of testing time as compared to the conventional method, the fast method has great potential to aid future studies estimating nonlinear cochlear function.
Classification via Clustering for Anonymization Data

Due to the exponential growth of hardware technology particularly in the field of electronic data storage media and processing such data, has raised serious issues related in order to protect the individual privacy like ethical, philosophical and legal. Data mining techniques are employed to ensure the privacy. Privacy Preserving Data Mining (PPDM) techniques aim at protecting the sensitive data and mining results. In this study, the different Clustering techniques via classification with and without anonymization data using mining tool WEKA is presented. The aim of this study is to investigate the performance of different clustering methods for the diabetic data set and to compare the efficiency of privacy preserving mining. The accuracy of classification via clustering is evaluated using K-means, Expectation-Maximization (EM) and Density based clustering methods.
Hybrid learning fuzzy neural models in stock price forecasting

Prediction of stock markets is one of the most important topics in business. However, the stock markets are very complicated and thus difficult to predict. The ellipsoidal fuzzy system (EFS) learning with and without supervision has been successfully applied in solving control systems and pattern recognition problems. This study modified the ellipsoidal fuzzy system to accept time series data in forecasting stock prices. Furthermore, a scaled conjugate gradient learning method was applied to accelerate supervised learning process. Finally, back-propagation neural networks (BPNN) are used to compare the forecasting accuracy. The empirical results including 10 companies show that the presented mode has higher prediction accuracy than the BPNN model.
Discourse Functions without Peripheral Syntax 1

The present paper explores the extent to which narr ow syntax is responsible for the computation of discourse functions such as focus/to pic. More specifically, it challenges the claim that language approximates ‘pe rfection’ with respect to economy and conceptual necessity by reconsidering the roles and interactions of the different modules of the grammar. Empirical considerations st rongly indicate that narrow syntax is ‘blind’ to properties and operations invo lving the interpretive components, i.e., PF and LF. As a result, syntax-phonology inte rface rules do not ‘see’ everything in the levels they connect. In essence, the archite cture of grammar proposed here from the perspective of focus marking necessitates the a utonomy of the different levels of grammar, presupposing that NS is minimally structured only when liberated from any discourse implementations, i.e., movement operation s to satisfy both interface needs.
Investigating Tradeoffs between Practice and Observation in Automated Instruction

This study examined the performance to observation ratio in training a complex computer-based flight simulator task. Subjects were divided into five different groups. One group performed 100% of the time during training, the second group performed 75% of the time and observed 25% of the time, the third group performed 50% of the time and observed 50% of the time, the fourth group performed 25% of the time and observed 75%, and the fifth group observed 100% of the time. Based on previous observational learning literature, we predicted a linear relationship between performance and percent of time performing. Results however, suggest a non-linear relationship between performance and percent of time performing. Performance was slightly better when a combination of performance and observation was used rather than performance alone. These results indicate that observational learning plays an important, yet often neglected role in learning complex computer-based tasks and suggests that small group oriented computer-based training systems may be more instructionally and cost effective than individually oriented computer-based training systems.
A user friendly environment for the Spectral Analysis of Geomagnetic Data

This paper describes a user-friendly Graphical User Interface also called Windows (You click and get something) environment or platform and computational paradigms developed using Java programming language for the spectral analysis of Geomagnetic Data. For the spectral analysis Digital Signal Processing methods such as Fast Fourier Transform (FFT), Cosine and Sine Transforms are used. The FFT transforms a function from the time domain to the frequency domain and is an extremely important and widely used method of extracting useful information from sampled signals. In the design of the system, the methodology used is Unified Modeling Language (UML), an advanced flowchart that uses Use Case Diagrams (UCD). The background and motivation is the automation of the entire process of obtaining spectra analysis.
Improving KNN Arabic Text Classification with N-Grams Based Document Indexing

Text classification is the task of assigning a document to one or more of pre-defined categories based on its contents. This paper presents the results of classifying Arabic language documents by applying the KNN classifier, one time by using N-Gram namely unigrams and bigrams in documents indexing, and another time by using traditional single terms indexing method (bag of words) which supposes that the terms in the text are mutually independent which is not the case. Results show that using N-Grams produces better accuracy than using Single Terms for indexing; the average accuracy of using N-grams is . 7357, while with Single terms indexing the average accuracy is . 6688.
Performance comparison of smart antenna techniques for spatial multiplexing in wireless ad hoc networks

Current wireless ad hoc networks use omnidirectional antennas at each node that allow it to transmit or receive only a single packet of data at given time. We investigate the use of smart antennas at each node which would facilitate simultaneous transmission (or simultaneous reception) of packets, thus improving the network throughput by making use of the many spatial channels created at the node. We show via simulations that it is preferable to use the beamforming technique over the switched beam technique at each of the nodes to obtain higher throughput.
Comparative Analysis on Performance of Pixel-Based Face Recognition Methods by Considering Various Facial Poses

Recently, according to expansion of face recognition application fields, study on face recognition is actively conducted. However, high performance algorithm sometimes cannot be applied because of environmental reasons such as pose variation or lighting condition at outdoor CCTV. Also, low-resolution face image captured by CCTV camera cannot be used for appearance based high-performance method. Therefore, we consider pixel based face recognition methods as CCTV applications. To apply the methods, various facial poses and three kinds of face recognition methods are comparatively analyzed in this paper. At experimental results, linear discriminant analysis showed the best performance at the matching method considering various facial poses.
Predictive Feedback Routing Control Strategy for Freeway Network Traffic

Available routing strategies for freeway networks may be classified as feedback and iterative strategies. Feedback strategies base their routing decisions on real-time measurable or estimable information only, via employment of simple regulators, while iterative strategies run a freeway network model repeatedly to achieve exact user equilibrium conditions over a future time horizon. A predictive feedback routing control strategy was developed with the aim of incorporating the advantages of both classes of strategies on the one hand and attenuating their disadvantages on the other hand. The new strategy runs a mathematical model only once at each time step and bases its routing decisions on the predicted instead of the currently prevailing traffic conditions. The investigations indicate that satisfactory routing results are achieved by use of this strategy. The corresponding performance evaluation was conducted in detail by comparison with the feedback and iterative strategies.
Modelling Adopter Behaviour Based on the Navier Stokes Equation

Aiming to improve the current models describing the diffusion of innovation, a method based on an analogy with a physical system is presented. In particular, we model the movement of adopters of a particular innovation as a flow and the diffusion of the innovation as heat diffusion, thus making use of mathematical tools such as the Navier-Stokes equation. Building on our previous work, in this study we focus on the movement of adopters in the simulation domain, which we refer to as convection. As an application to modeling the convection phenomenon, we simulate the flow of people in a real building floor, the Tokyo Aquarium. Besides being a necessary part when simulating diffusion of innovations, such a modeling approach may also prove useful in different fields, such as deciding layout of public spaces or evacuation policies.
Integer N Frequency Synthesizer using Phase Lock Loop

— A new architecture and simulation of an integer n frequency synthesizer using PLL for RF application has been illustrated in this paper. This design consists of low power phase frequency detector, low jitter charge pump, ring oscillator based VCO, passive loop filter and 8 bit frequency divider using 250nm technology. This presents the simplest way to design and simulate integer n frequency synthesizer and lock the PLL. The design and analysis of PLL is done on simulation EDA TANNER TOOL 13.0. The main benefit of using PLL technique in Frequency Synthesizer is that it can generate frequencies of 100-200MHz comparable to the accuracy of a crystal oscillator. This paper gives a brief introduction to the basics of Phase Locked loops.
Experiment Research of Dynamic Load Balancing Algorithm Based on Task Classification for Data Retrieval

The core technology of load balancing is the task scheduling. Different tasks have different resource occupation rate on the server node and influence the current server load also different. This paper presents a dynamic load balancing algorithm and we design some experimental environment. We do some simulation experiments based on some reasonable assumptions and actual state of network, to evaluate our dynamic load balancing algorithm. The experimental data shows that compared with the least weighted connection scheduling algorithm, the algorithm of this paper can make full use of machine resources. The system data retrieval efficiency is also improved.
Microwave monitoring of rainfall: Intercomparisons of data from the Chilbolton radar and the DMSP‐SSM/I

Ground‐based multi‐parameter radar is used to validate and evaluate satellite passive microwave parameters related to rainfall over land. Light precipitation over the southern part of England is considered. The issues of diverse resolutions of radar and satellite data in space and time are investigated, as well as the satellite beam‐filling problem, in relation to present passive microwave satellite algorithms related to rainfall over land, and their perceived behaviour. Several statistical analyses are applied and their sensitivities to data integration problems are quantified. Passive microwave parameters are evaluated and compared with each other. It is concluded that for radar passive microwave satellite data intercomparisons, calibrations and validations the time resolution seems to be more important than the spatial resolution, and high‐resolution radar can be used operationally to tackle the satellite beam‐filling problem. It is found that several passive microwave algorithms have performances very similar to each other. Copyright © 1997 Royal Meteorological Society
Learning from Home: Implementing Technical Infrastructure for Distributed Learning via Home-PC within Telenor

This article presents research findings from a study conducted at Telenor, a Norwegian telecom operator. The company distributed home Personal Computers (PCs) to their employees in an effort to encourage professional development outside working hours and mainly on an individual basis. The report includes results from a systematic survey of 2500 employees of the approximately 11,000 home-PC users concerning their use of home-PCs for learning. Findings suggest that home-PCs can contribute positively to increasing employees' knowledge about information and communications technology (ICT), and improve specific user skills. While the use of home PCs can also enable employees to learn while using information and communications technologies, the number of employees who actually do so is modest.
Research on Analysis of Sports Video based on the Statistics

We turn to statistical-based methods and propose a statistical inference approach by using Dynamic Bayesian Network, which is able to learn automatically from data set. By soccer video analysis is as an example, the proposed method is verified by experiment. We extract the color, shape and other low-level features from the video, to detect and identify 5 kinds of high-level semantic events using dynamic Bayesian network model. The experimental results show that our method is effective.
Study of Traffic flow using CETD Matrix

This paper presents a method to analyze the traffic flow pattern in Velachery Vijaya Nagar Signal, Chennai. Study of traffic congestion is significant these days so that traffic regulation can be made accordingly to avoid congestion and waiting time. The peak time and non-peak time are identified using the row sum of the Combined Effective Time Dependent Data (CETD) matrix in this duration.
Unsupervised Spelling Correction for Slovak

This paper introduces a method to automatically propose and choose a correction for an incorrectly written word in a large text corpus written in Slovak. This task can be described as a process of finding the best matching sequence of correct words to a list of incorrectly spelled words, found in the input. Knowledge base of the classification system - statistics about sequences of correctly typed words and possible corrections for incorrectly typed words can be mathematically described as a hidden Markov model. The best matching sequence of correct words is found using Viterbi algorithm. The system will be evaluated on a manually corrected testing set.
The KCRM knowledge audit: model and case illustration

Purpose – The purpose of this paper is to propose a methodology for conducting a knowledge‐enabled customer relationship management (KCRM) knowledge audit, which involves applying knowledge auditing principles to assess the existence and implementation level of KCRM processes within an organization. This type of audit enables an organization to thoroughly review the extent to which knowledge is generated, codified, distributed and utilized while the firm is identifying, differentiating between, interacting with, and customizing products and services for its customers.Design/methodology/approach – This paper draws upon the KM, CRM, and auditing literatures to develop a KCRM knowledge audit methodology. As an illustration, the proposed methodology for KCRM knowledge auditing was applied within the service arm of a paint manufacturing firm in the United Arab Emirates (UAE). Findings – The proposed methodology allows supplier organizations to systematically evaluate the extent to which their CRM approaches are.