Miracle-dz/UniFAR-FTUs huggingface dataset:
Online vocabulary games to promote vocabulary acquisition among primary school students

The purpose of this study wasto investigate howan online vocabulary game engages students and assists students to learn, acquire and increase vocabulary.A qualitative method was applied for this study. A total of seven Year 5primary school studentsincluding two Year 5 primary school students for pilot study were chosen to participate in this study. They were from the same school located in Johor Bahru. Students were asked to learn vocabulary using the online vocabulary game and were being observed. They were also being requested to list all new vocabulary that they have learned from the use of online vocabulary game andwere asked to participate in recorded interview sessions.Data from observations, vocabulary lists and recorded interviews were analysed using thematic analysis. It was discovered thatstudents engaged, learned, acquired and increased their vocabulary in a conducive learning environment with the help of the online vocabulary game. The principal conclusion was that online vocabulary game promotes vocabulary acquisition among primary school studentsin a creative, innovative and interesting way.Some suggestions have been put forward to improve theimplementation of the online vocabulary game for teaching and learning vocabulary.
Development of a data acquisition system for the MiCES small animal PET scanner

We have previously reported on the design of a firewire (1394a) based data acquisition system for small animal positron emission tomography (PET) scanners. Here we report on the evolution of that design for our micro-crystal element mouse PET scanner - MiCES. The scanner utilizes 72 position sensitive photomultiplier tubes (PMT), each coupled to a 22/spl times/22 array of 0.8/spl times/0.8/spl times/10 mm MLS crystals. Each electronics module supports two PMTs. The module consists of digital and analog boards. The digital board contains a field programmable gate array (FPGA). a microcontroller. , and a firewire transport device. We are building two versions of the analog board. One version uses a 32 channel amplifier and pulse height analyzer application specific integrated circuit (ASIC) from Integrated Detectors and Electronics. This version processes each signal from the PMT (12 per tube) independently. The second version uses an analog ASIC from Concorde Microsystems Inc. This version uses resistive summing to combine the PMT signals into the 4 analog channels processed by the ASIC. In both cases. the analog board is a daughter board on the digital board, and the two configurations are supported by different microcode loaded into the FPGA. The resulting 36 sets of detector boards are connected to a host Macintosh computer via the firewire bus. Another microcontroller in the gantry acts as a master controller and is connected to the Macintosh host via a serial cable. The master controller provides command signals (e.g., stop, start, reset, etc) to the detector boards as well as supporting all gantry motions. and sends data to the host via firewire at regular intervals to report the current table and gantry positions.
EVALUATION OF INTELLIGIBILITY AND SPEAKER SIMILARITY OF VOICE TRAN SFORMATION

OF THESIS EVALUATION OF INTELLIGIBILITY AND SPEAKER SIMILARITY OF VOICE TRANSFORMATION Voice transformation refers to a class of techniques that modify the voice characteristics either to conceal the identity or to mimic the voice characteristics of another speaker. Its applications include automatic dialogue replacement and voice generation for people with voice disorders. The diversity in applications makes evaluation of voice transformation a challenging task. The objective of this research is to propose a framework to evaluate intentional voice transformation techniques. Our proposed framework is based on two fundamental qualities: intelligibility and speaker similarity. Intelligibility refers to the clarity of the speech content after voice transformation and speaker similarity measures how well the modified output disguises the source speaker. We measure intelligibility with word error rates and speaker similarity with likelihood of identifying the correct speaker. The novelty of our approach is, we consider whether similarly transformed training data are available to the recognizer. We have demonstrated that this factor plays a significant role in intelligibility and speaker similarity for both human testers and automated recognizers. We thoroughly test two classes of voice transformation techniques: pitch distortion and voice conversion, using our proposed framework. We apply our results for patients with voice hypertension using video self-modeling and preliminary results are presented.
Noise Robust Automatic Speech Recognition Based on Spectro-Temporal Techniques

Speech technology today has a wide variety of existing and potential applications in so many areas of our life. From dictating systems to voice translation, from digital assistants like Siri, Google Now, and Cortana, to telephone dialogue systems. Many of these applications have to rely on an Automatic Speech Recognition (ASR) component. This component not only has to perform well, but it also has to perform well in adverse environments. After all, a dictating system which requires that we insulate our office, or a digital assistant that cannot work in traffic, or in a room full of chatting people is not so helpful. For this reason, noise robust ASR has been a topic of intensive research. Yet, human-equivalent performance has not been achieved. This motivated many to search for ways to improve the robustness of automatic speech recognition based on human speech perception. One popular method inspired by the examination of the receptive fields of auditory neurons is that of spectro-temporal processing. In spectro-temporal processing, the aim is to capture the spectral and temporal modulations of the signal simultaneously. One simple way to do so is to extract the features to be used from spectro-temporal patches, and then use the resulting features in the same manner one would use traditional features like MFCCs. There is more than one way to bake a cake, however. And in this case this is true twice over. For one, there are various ways to extract our features from the patches. But there are other, more sophisticated ways to incorporate the concept of spectro-temporal processing into a speech recognition system. In this study we examine many such methods -- some simpler, some more sophisticated, but all stemming from the same basic idea. By the end of this study we will demonstrate that these methods can indeed lead to more robust speech recognition. So much so, that they can provide results that are competitive with the state-of-the-art results.
Design and Study of Compact and Wideband Microstrip U-Slot Patch Antenna for Wi-Max Application

Microstrip patch antennas are strong candidates for use in many wireless communication applications. This paper proposes the use of a patch antenna with U-shaped slot to achieve wideband application with very low return loss. The objective of this paper is to design, construct and fabricate microstrip antennas suitable for Wi-Max application that centred at frequency 5.25GHz. The antenna must operate within the band of 5.25GHz band. This band is currently being used for industrial, medical and scientific applications. The antenna is proposed to be used as a transmitting as well as receiving antenna in wireless network and the mentioned applications. A thick substrate with finite ground dimensions broadens the bandwidth of the antenna. The proposed antenna reduced the return loss as well as increases the bandwidth of the antenna.
An Improved PI-MultiStart Control Algorithm for Standalone PV Inverter System

This paper proposes an improved voltage controller using MultiStart optimization (MS) algorithm based PI (MS-PI) to control the modulation index for stand-alone PV inverter under different load conditions. In this proposed control algorithm (MS-PI), and parameters are automatically tuned to avoid the trial and error procedure in the classical PI controller in order to minimize the output voltage error. To do so, the mean square error (MSE) is used as an objective function with the MultiStart algorithm code. MATLAB models (Simulink and Code) for a PV inverter and the proposed control algorithm are developed. In order to get the desired output voltage of the inverter, a statistical evaluation for the proposed controller (MS-PI) is investigated and compared with that one’s obtained by particle swarm optimization (PSO) algorithm based PI approach (PSO-PI). Based on the results, the proposed controller has proven that its performance is robust and efficient in terms of total harmonic distortion (THD), regulated voltage amplitude in term of oscillation, and the minimum value of mean square error (MSE) as compared to the PSO-PI algorithm.
Design and Inference in Finite Population Sampling

A Unified Setup for Probability Sampling. Inference in Finite Population Sampling. The Horvitz--Thompson Estimator. Simple Random and Allied Sampling Designs. Uses of Auxiliary Size Measures in Survey Sampling: Strategies Based on Probability Proportional to Size Schemes of Sampling. Uses of Auxiliary Size Measures in Survey Sampling: Ratio and Regression Methods of Estimation. Cluster Sampling Designs. Systematic Sampling Designs. Stratified Sampling Designs. Superpopulation Approach to Inference in Finite Population Sampling. Randomized Response: A Data--Gathering Tool for Sensitive Characteristics. Special Topics: Small Area Estimation, Nonresponse Problems, and Resampling Techniques. Author Index. Subject Index.
Implementasi Penerjemah Bahasa Isyarat Pada Bahasa Isyarat Indonesia (BISINDO) Dengan Metode Principal Component Analysis (PCA)

Deaf people can communicate with other normal people by utilizing hearing impairment or by using sign language. Bahasa Isyarat Indonesia (BISINDO) is a sign language promoted by Gerakan Kesejahteraan Tunarungu Indonesia (GERKATIN). An application is required to make people easier to communicate and recognize sign language especially Bahasa Isyarat Indonesia (BISINDO). This research aimed to develop a translator application that can translate a movement of sign language into a text form that can be understood by the normal person. The method used in this research is PCA (Principal Component Analysis) to identify patterns in the data and then express the data to other forms to show differences and similarities between patterns. To recognize the object, this research used a viola-jones method that gives a specific indication of a picture or image. This research will produce an application that can translate 26 letters sign language to the form of letters in general.
Enhanced real-time performance using a secondary bus for cache write-backs

Abstract In spite of advances to improve cache efficiency, memory access bottlenecks still prevent processors from executing at full speed. This paper introduces the concept of using a secondary bus, connecting the level-2 cache to memory, for retiring cache write-backs. Simulations, using the sim-alpha version of the SimpleScalar tool set, demonstrate the feasibility and possible advantages of such a secondary bus. Based on simulation results, the added secondary bus can decrease queuing delays on the system bus by 75–90% when sufficient write-backs are present. Such reduction in queuing delays offers superior temporal determinacy in real-time environments. Real-time and near real-time embedded applications that depend on intense graphics processing and movement of large blocks of data, such as printer controllers and medical imaging, are prime candidates for applications of the secondary bus. The feasibility of implementing such a secondary bus using a serial line or wireless technology is also discussed.
The Role of Positional Cues in Non- Adjacent Dependency-Learning An Artificial Grammar Study

: Previous research has shown that both adults and infants are capable of detecting and implicitly learning dependencies between non-adjacent elements in streams of spoken input. The learning mechanism underlying this ability, arguably a form of statistical learning, may be essential to infants’ acquisition of morpho-syntactic dependencies in their native language. In this study we tested the limitations of this learning mechanism, using an artificial grammar that replicates some of the properties of morpho-syntactic dependencies in natural languages. Endress & Mehler (2009) proposed that non-adjacent dependencies are just as successfully tracked when they are instantiated at string edges, as when they occur string-internally (i.e. the dependency between a and b was shown to be learned just as easily in strings of the type XaYbZ as in strings of the type aXYZb). We predicted a replication of their results with our own materials, designed to emulate dependencies in natural languages. Subjects exposed to an artificial grammar with the structure YaXb (with a, b, monosyllabic, dependent elements, and Y, X, bisyllabic ‘fillers’) remained sensitive to the dependency despite the fact that the left edge of the dependency did not coincide with the left edge of the string, confirming our expectations. On the other hand, subjects exposed to a minimally different grammar, with the structure aYXb were no longer able to track the dependencies, suggesting that the length and nature of the intervening material plays an important part in the ability to keep track of non-adjacent dependencies.
The Prediction of Back Titration Based on Kernel Principal Component Analysis and Radial Basis Function Neural Network

A sampling method on the basis of imitation orthogonalization is proposed to ensure the typicality and ergodicity of samples. Considering the characteristics of back titration (BT) during cobalt removal with arsenic salt, such as many influencing factors and strong coupling, kernel principal component analysis (KPCA) is applied at first. Through KPCA, the effective characteristics of data can be extracted to reduce the dimensions of variables and to eliminate the coupling between variables. Then the extracted characteristic components are utilized as the input of radial basis function (RBF) neural network. Owing to there are many parameters in RBF neural network, which means that it is difficult to obtain the global optimal parameters, rival penalized competitive learning (RPCL) algorithm is adopted first to determine the original values of hidden nodes. On this basis, the improved particle swarm optimization (IPSO) is employed to select the parameters of RBF neural network. It is proved by the simulation results of industrial data that the BT prediction model is effective. KeywordsSampling method; KPCA ; RBF; IPSO; BT
Ericsson/Mannesmann GSM field-trials with adaptive antennas

This paper describes Ericsson's GSM testbed for evaluation of adaptive antenna technology and joint field-trials made by Ericsson and Mannesmann Mobilfunk. The implementation principles and algorithms are described together with the field-trial setup. The field-trials was carried out in Dusseldorf, Germany, by Mannesmann Mobilfunk and Ericsson. The paper also presents results from the field-tests.
Calibration of a multi-sensor system laser rangefinder/camera

This paper proposes a new calibration method for a perception system designed for a mobile robot. The perception system we use is made of a gray-level CCD camera and a laser rangefinder. The problem is to match data provided by those two sensors and therefore to identify the laser rangefinder/camera transformation. A calibration process based upon a specific calibration pattern is used to identify such a transformation. Adapted procedures define a geometric relationship between these two sensors. We have tested in simulation several models in order to identify the transformation. The most efficient one has been chosen and implemented on the perception system of an experimental mobile robot. Experimental results are presented.
A System For Impact Insertion Of A 100 Electrode Array Into Cortical Tissue

ABSTRACI Complex silicon microelectrode arrays capable of having up to 100 electrodes per device are now a reality. By directly interfacing to the central nervous system, these devices could allow for restoration of sensory or motor functions at a level previously unobtainable. However, the structural complexity of these devices has mandated the development of a new insertion technology not necessary for simpler electrode designs. To meet this demand, a pneumatic momentum-transfer system has been developed. This portable system uses a sliding mass mechanism to achieve an impact insertion of a silicon structure consisting of 100 sharpened needles into cortical tissue. The system has successfully implanted these silicon electrode arrays in over 30 feline preparations. Preliminary histological evidence shows an expected inflammatory response with a small amount of bleeding, yet no significant long-term neural tissue damage due to the insertion procedure. These results suggest that impact insertion is a viable way to introduce complicated electrode structures into the central nervous system.
Multinomial Probit Estimation of Spatially Interdependent Choices: An Empirical Comparison of Two New Techniques

The paper compares the empirical performance of two recently suggested techniques for estimating Multinomial Probit (MNP) models. The application concerns the choice of the first practice location of general practitioners in Quebec (Canada). Regional similarities are accounted for by modeling interdependent choice decisions. One technique is a simulated maximum likelihood based approach that relies on a Geweke, Hajivassiliou, and Keane (GHK) choice probability simulator, and the other one exploits the Gibbs sampler with data augmentation. The results indicate that both estimation techniques give similar results. Compared to its competitor, the Gibbs approach is much simpler to implement both conceptually and computationally.
Hand-gesture recognition using computer-vision techniques

We use our hands constantly to interact with things: pick them up, move them, transform their shape, or activate them in some way. In the same unconscious way, we gesticulate in communicating fundamental ideas: ‘stop’, ‘come closer’, ‘over there’, ‘no’, ‘agreed’, and so on. Gestures are thus a natural and intuitive form of both interaction and communication. Gestures and gesture recognition are terms increasingly encounteredin discussions of human-computer interaction. We present a tool created for human-computer interaction based on hand gestures. The underlying algorithm utilizes only computer-vision techniques. The tool is able to recognize in real time six different hand gestures, captured using a webcam. Experiments conducted to evaluate the system performance are reported.
Model Checking in General Game Playing: Automated Translation from GDL-II to MCK

General Game Playing (GGP) is the field of Artificial Intelligence (AI) that investigates generalized techniques for finding winning strategies in games. GGP agents are expected to be able to play a game with no prior knowledge by receiving the game shortly before starting to play. Games with perfect information, such as Tic-Tac-Toe, Chess and Go, can be described in the Game Description Language (GDL), and games with chance and hidden information such as Backgammon and Poker, can be describe in Game Description Language for Incomplete Information (GDL-II), an extension of GDL. This thesis is mainly concerned with the verification of games in GDL and GDL-II. Games described in these languages may contain bugs, just like the bugs in a program written in C or Java language. E.g., a game may never terminate or a player has a legal move but does not know it. In order to allow GGP agents play games properly, the games need to satisfy a set of well-formedness properties. In GDL the properties are Termination, Playability and Winnability and in GDL-II there is also the requirement that agents have sufficient knowledge to derive these properties. Our approach is to utilise model checking, which is a well-known method to check if a program is free from design errors, i.e., satisfying a set of formal properties. We build on an earlier work in verifying games in GDL-II by systematically translating the game descriptions into a system description model and the well-formedness properties of games into a logical language, then feeding them to the model checking program named Model Checking Knowledge (MCK). We refine the existing translation techniques to automatically
Eradicate Inconsistencies and Gaffe from Data Sets Using Data Sanitization

The problem of data cleaning, which consists of removing inconsistencies and errors from original data sets, is well known in the area of decision support systems and data warehouses. This holds regardless of the application - relational database joining, web-related, or scientific. In all cases, existing ETL (Extraction Transformation Loading) and data cleaning tools for writing data cleaning programs are insufficient. The main reason for using the computers is to organize the data in an efficient and effective manner . In early days for valuable data can be organization sake we have to use the tools like Queries. In these some problems are arises. That is why these languages are called as Data Management systems. There were so many limitations in the management system like data inconsistency, inconvenience in retrieval of data etc. Because of all these limitations we have to face the problems like memory inefficiency and heavy in consumption of time and also lack of quality. To overcome all these problems we have designed software (mean ETL tool) which organizes the data in a very efficient manner with respect to redundant data. Our project deals with the data organization by giving all data oriented features and by solving the problems like data inconsistency and data redundancy.
Investigation of financial distress with a dynamic logit based on the linkage between liquidity and profitability status of listed firms

Abstract The scope of this paper is to investigate the predictability of financial distress, adopting a survival model based on dynamic logit for a sample of NYSE listed firms. The main assumption of this study is that liquidity and profitability constitute the key criteria for the configuration of financial distress status of a firm. Specifically, two independent models are applied for the period after the financial crisis of 2007–2008. The first model is constructed on the pillar of liquidity, and the classification into the subgroup of distressed firms is based on specific criteria such as current ratio, current liabilities / total liabilities, Equity / Liabilities and Total Debt / Total Asset. The second model is based on the pillar of profitability where the specific criteria for the classification from the primary group into the subgroup of distressed firms are ROE < ROA and Net Profit Margin ≤ 0. Finally, a third model is established as a result of the combination of the two previous models. A further purpose of this work is to ascertain whether during the period of crisis there has been a differentiation in the policy of listed companies, namely whether their efforts have been shifted to addressing liquidity problems at the expense of profitability.