Miracle-dz/UniFAR-FTUs huggingface dataset:
Zone-based color flow imaging

A zone-based technique for real-time color flow imaging is described. The technique utilizes a broad transmit beam which is equivalent to 20-50 focused transmit beams, such that the full field of view can be scanned using only 3-5 firings, times the flow sample count (FSC) required for color flow estimation. On receive, the channel domain RF data is pre-processed and accumulated in memory, and then transferred to a software-based image formation system, which performs dynamic receive focusing, clutter filtering, mean velocity estimation and scan conversion. Various methods of compensating for the lack of transmit focusing gain are discussed, including a larger FSC, over-sampling pulsed repetition frequencies, and coded excitation using FM chirp, Barker and Golay codes. Both 7.5 MHz linear array and 3 MHz curved array images obtained using a research platform show very good agreement with the predicted signal-to-noise-ratio (SNR) gain for a 5-chip Barker coded signal.
SPIDAR-if: A Joint User Interface Approach for Hapticised Web Content and Augment Reality Enhancement

The paper presents a prototyped jointed control interface approach for understanding hapticised web content and augmented reality (AR) on a work-in-progress instructive platform of haptics (IPOH) over the unreliable network. Our works, in this context, are comprised by two parts. Firstly, we propose a joint user interface SPIDAR-if empowering finger tracking and physical force sense. SPIDAR-if includes a recently notable pocket-sized 3D sensor named “Leap Motion” as an input device and incorporates with another passive 2-degree-of-freedom (DOF) tactile device “SPIDAR” with upturned manipulations in order to truly represent the sense of touch interactions on IPOH system, such as tactile feedbacks derived from in-progressive verbs, HTML components and moving 3D spheres. On the other side, we study the eligibility of SPIDAR-if and its aids with regard to AR world and web content intelligibility in IPOH system which offers the Internet users a bridge to embrace virtual reality by virtue of assisted user interface, wrapping up haptic and web graphical rendering techniques as well as provided virtual environments (VE). Examples of hapticised web content and a 3D AR scenario approaching SPIDAR-if on IPOH with the fingertip are also presented along with preliminary findings.
Efficient Learning of Sparse Representations with an Energy-Based Model

We describe a novel unsupervised method for learning sparse, overcomplete features. The model uses a linear encoder, and a linear decoder preceded by a sparsifying non-linearity that turns a code vector into a quasi-binary sparse code vector. Given an input, the optimal code minimizes the distance between the output of the decoder and the input patch while being as similar as possible to the encoder output. Learning proceeds in a two-phase EM-like fashion: (1) compute the minimum-energy code vector, (2) adjust the parameters of the encoder and decoder so as to decrease the energy. The model produces “stroke detectors” when trained on handwritten numerals, and Gabor-like filters when trained on natural image patches. Inference and learning are very fast, requiring no preprocessing, and no expensive sampling. Using the proposed unsupervised method to initialize the first layer of a convolutional network, we achieved an error rate slightly lower than the best reported result on the MNIST dataset. Finally, an extension of the method is described to learn topographical filter maps
OPTIMIZATION OF DOCUMENT CLUSTERING USING PHRASES

Most of the documents clustering techniques rely on single term analysis of the document data set, such as the Vector space model. More informative features including phrases and their weights are particularly important to achieve more accurate document clustering. Document clustering is particularly useful in many applications such as automatic categorization of documents, grouping search engine results, building taxonomy of documents and others. The motivation behind the work in this paper is that we believe that document clustering should be based not only on single word analysis, but on phrases as well. Phrase based analysis means that the similarity between documents should be based on matching phrases rather than on single words Document clustering is particularly useful in many applications such as automatic categorization of documents, grouping search engine results, building taxonomy of documents and others. The motivation behind the work in this paper is that we believe that document clustering should be based not only on single word analysis, but on phrases as well. Phrase based analysis means that the similarity between documents should be based on matching phrases rather than on single words only Owing to the widely application in the fields of information retrieval, document analysis and information extraction, document cluster analysis has been concerned broadly, and gotten a great deal of research Issues It is one of essential theme in document. Most of the existing techniques for document clustering rely on a “bag of words “document representation. Each word in the document is considered as a separate feature, ignoring the word order. We investigate the use of phrases rather than words as document features for the document clustering. We present a phrase grammar extraction technique, and use the extracted phrases as the features document clustering algorithm1.0
Discrete shuffled frog leaping algorithm for examination timetabling problem

A Discrete Shuffled Frog Leaping Algorithm(DSFLA) is proposed to solve Timetabling Problem(ETP),and employs the simple neighborhood search to present two improvement strategies.In light of the optimization mechanism of general SFLA,the algorithm adopts an encoding scheme based on time permutation and a new method of individual production to extend the traditional model of SFLA.The experimental results show that the proposed algorithm and its improvements are effective and efficient for different scale benchmarks of ETP.
Big Data Analytics for Development: Events, Knowledge Graphs and Predictive Models

Volatility in critical socio-economic indices can have a significant negative impact on global development. This thesis presents a suite of novel big data analytics algorithms that operate on unstructured Web data streams to automatically infer events, knowledge graphs and predictive models to understand, characterize and predict the volatility of socioeconomic indices. This thesis makes four important research contributions. First, given a large volume of diverse unstructured news streams, we present new models for capturing events and learning spatio-temporal characteristics of events from news streams. We specifically explore two types of event models in this thesis: one centered around the concept of event triggers and a probabilistic meta-event model that explicitly delineates named entities from text streams to learn a generic class of meta-events. The second contribution focuses on learning several different types of knowledge graphs from news streams and events: a) Spatiotemporal article graphs capture intrinsic relationships between different news articles; b) Event graphs characterize relationships between events and given a news query, provide a succinct summary of a timeline of events relating to a query; c) Event-phenomenon graphs that provide a condensed representation of classes of events that relate to a given phenomena at a given location and time; d) Causality testing on word-word graphs which can capture strong spatio-temporal relationships between word occurrences in news streams; e) Concept graphs that capture relationships between different word concepts that occur in a given text stream. The third contribution focuses on connecting the different knowledge graph representations and structured time series data corresponding to a socio-economic index to automatically learn event-driven predictive models for the given socio-economic index to predict future volatility. We propose several types of predictive models centered around our two event models: event triggers and probabilistic meta-events. The final contribution focuses on a broad spectrum of inference case studies for different
Mathematical Morphology and Main Applications of Face Recognition Preprocessing

Four basic operations of mathematical morphology as follows were introduced in binary,gray- scale and colored morphology: Erosion,Dilation,Opening and Closing. The functions of these basic operations were verified by experiments. The principles and methods of extracting structure elements were analyzed and the main application of morphology operations in face recognition preprocessing was discussed.
Keterampilan Guru PAK Untuk Meningkatkan Minat Belajar Murid Dalam Proses Pembelajaran Di Kelas

AbstrakSalah satu tantangan dalam proses belajar mengajar di kelas adalah murid tidak antusias. Setiap guru mengharapkan selama mengajar di kelas semua murid antusias dan penuh minat. Kenyataannya tidak seperti itu, karena tidak semua guru memiliki kemampuan untuk mengatasi masalah murid yang kurang antusias. Oleh karena itu kajian tentang keterampilan guru dalam meningkatkan minat murid untuk belajar sangat diperlukan. Tujuan dari kajian ini untuk mendorong guru menambah keterampilan dalam melaksanakan tugasnya sehingga murid belajar dengan penuh perhatian. Penulis menggunakan metode deskriptif kualitatif yaitu menjelaskan istilah-istilah yang digunakan pada judul tulisan berdasarkan sumber yang berkaitan, dihubungkan dengan pengalaman penulis secara subyektif selama mengajar di kelas. Poin penting hasil kajian ini ialah guru harus memiliki ketrampilan untuk mengelola kelas dengan baik. Guru harus menguasai keterampilan untuk mulai pembukaan sampai pada penutupan kelas. Guru harus menguasai keterampilan dalam bertanya, menjelaskan, memberi penguatan, dan juga dalam membuat variasi. Keterampilan dalam pengelolaan kelas ini berpengaruh besar terhadap perhatian murid untuk belajar.Kata kunciKeterampilan, guru, minat, belajar, murid
A Neurocomputational Model of Smooth Pursuit Control to Interact with the Real World

Whether we want to drive a car, play a ball game, or even enjoy watching a flying bird, we need to track moving objects. This is possible via smooth pursuit eye movements (SPEMs), which maintain the image of the moving object on the fovea (i.e., a very small portion of the retina with high visual resolution). At first glance, performing an accurate SPEM by the brain may seem trivial. However, imperfect visual coding, processing and transmission delays, wide variety of object sizes, and background textures make the task challenging. Furthermore, the existence of distractors in the environment makes it even more complicated and it is no wonder why understanding SPEM has been a classic question of human motor control. To understand physiological systems of which SPEM is an example, creation of models has played an influential role. Models make quantitative predictions that can be tested in experiments. Therefore, modelling SPEM is not only valuable to learn neurobiological mechanisms of smooth pursuit or more generally gaze control but also beneficial to give insight into other sensory-motor functions. In this thesis, I present a neurocomputational SPEM model based on Neural Engineering Framework (NEF) to drive an eye-like robot. The model interacts with the real world in real time. It uses naturalistic images as input and by the use of spiking model neurons controls the robot. This work can be the first step towards more thorough validation of abstract SPEM control models. Besides, it is a small step toward neural models that drive robots to accomplish more intricate sensory-motor tasks such as reaching and grasping.
EXECUTIVE RETENTION IN A MALAYSIAN RESEARCH AND DEVELOPMENT ORGANIZATION

Executives are the backbone in a research and development organization in carrying out research activities. In depth interviews are carried out in a Malaysian research and development organization to explore the executives experience on their decision to stay with the organization. The human resource development practices are identified and studied from their point of views. Their experience is explored in order to find out how the human resource development practices contribute to their decision to stay in the organization. Result shows how training and development practices, career development practices and organizational development practices, the three human resource development areas have contributed to their retention in the organization. The training and development practices are individual training (analytical skill, soft skill and management skill) and organizational training (soft skill, leadership skill and academic programme), career development practices are individual effort and organizational effort and organizational development practices are working environment, mentoring and coaching, interpersonal relationship. These human resource development practices are found to have contributed to the executive’s decision to stay. However, some remain in the organization because they are just happy working in the organization and passion of being researchers.
Neighborhood sustainability in urban renewal: An assessment framework

Urban renewal provides valuable opportunities for sustainable development. Sustainability assessment is considered a useful tool in ensuring sustainable development in practice. Although a number of studies have been conducted to evaluate the potential of urban renewal, studies on sustainability assessment in urban renewal at a neighborhood scale are often ignored. However, urban renewal is normally accompanied by many social, economic, and environmental conflicts among various stakeholders. The present paper proposes a framework for assessing neighborhood sustainability to support urban renewal decision making in high-density cities such as Hong Kong. This framework includes two components: (1) sustainability and building condition and (2) a decision-making matrix for urban renewal strategies. A case study was conducted to illustrate how this framework can be applied in the decision-making process of urban renewal projects. The results are expected to provide references for urban renewal decision making in high-density cities.
Forecasting and evaluating water quality of Changjiang River based on composite least square SVM with intelligent genetic algorithms

Forecasting and evaluation water quality is a complicated problem due to its nonlinearity and uncertainty.Least square support vector machine(LSSVM) has been successfully employed to solve regression and time series problem.This paper proposed a novel IGALSSVM model.The model based on a new genetic algorithm,intelligent genetic algorithm to optimize the parameters of LSSVM.In addition,applied the model to classify and forecast water quality of Changjiang River.Experimental results show that IGALSSVM model performs better than neural networks,implying that IGALSSVM is very practical.
An Approach of Chinese Text Representation Based on Semantic and Statistic Feature

The approach of Chinese text representation based on set cannot represent the semantic information of text,and then results in low quality of text clustering.To settle this problem,this paper introduces Ontology and the idea of Term Co-occurrence into Chinese text representation and presents an approach of Chinese text representation based on semantic and statistic feature.This approach adds semantic feature based on Hownet and feature co-occurrence.Experimental results show that this approach can represent the semantic information of text more precisely and improve the quality of text clustering greatly.
TECHNOLOGY-MEDIATED L2 LITERACY PRACTICES AND IDENTITY DEVELOPMENT

This case study explores the second language literacy practices of two Chinese graduate students studying at a Southeastern U.S. university. The findings of this study reveal that technology (technology and computer interchangeable in this paper) and multimedia can foster these two Chinese students" English learning and literacies development and further facilitate their identity negotiation. In addition, the marriage of funds of knowledge with technologies not only boosts these students" English learning confidence, but also helps them accumulate linguistic and cultural capital (Bourdieu, 1986). This empirical research used interviews and semi-structured interviews, classroom observations, and documents to collect data. The first section of this paper discusses L2 literacy and the influences of computer-assisted language learning (CALL) and multiple modes mediated communication on L2 learning and literacy. The second section of this paper analyzes the data derived from the interviews, field notes, and documents. The last section of this paper addresses the implications for second language teaching and learning, which emphasizes the intertwined relationships among multimodality, language, and identity. The implications of this research might provide new insights into second language literacy development and support English instructors to meet the needs of second language learners in the context of the increased population of Chinese students.
Reproducing Concurrency Bugs Using Local Clocks

Multi-threaded programs play an increasingly important role in current multi-core environments. Exposing concurrency bugs and debugging such multi-threaded programs have become quite challenging due to their inherent non-determinism. In order to eliminate such non-determinism, many approaches such as record-and-replay and other similar bug reproducing systems have been proposed. However, those approaches often suffer significant performance degradation because they require a large amount of recorded information and/or long analysis and replay time. In this paper, we propose an effective approach that takes advantage of the hardware clocks available on modern commercial processors. The key idea is to reduce the overhead in recording and analyzing events’ global order by using time stamps recorded in each thread. Those time stamps are used to determine the order of shared accesses among threads. To avoid the large overhead incurred in accessing system-wide global clock, we opt to use local per-core clocks that incur much less access overhead. We then propose techniques to resolve differences among local clocks and to get accurate global event order. By using per-core clocks, state-of-the-art bug reproducing systems such as PRES and CLAP can reduce the recording overheads by 1% ∼ 85%, and the analysis time by 84.66% ∼ 99.99%, respectively.
A New Method of Unknown Radar Signals Sorting Based on Clustering

The k-means clustering algorithm is considered that it is not applicable to unknown radar signal sorting.A sorting method based on improved grid clustering is proposed.Signal grid and noise grid are distinguished using the double-density threshold,after data points are mappinged to grid space.Finally,the signal sorting is achieved through the high density connectivity of grids.In addition,the signal sorting without any prior knowledge could be accomplished,and this method is unsensitive to the noise.The simulation results validate the effectiveness of the method.
Open-switch Fault-tolerant Control of Power Converters in a Grid-connected Photovoltaic System

This paper presents the study of an open switch fault tolerant control of a grid-connected photovoltaic system. The studied system is based on the classical DC–DC boost converter and a bidirectional 6-pulse DC–AC converter. The objective is to provide an open-switch fault detection method and fault-tolerant control for both of boost converter and grid-side converter (GSC) in a grid-connected photovoltaic system. A fast fault detection method and a reliable fault-tolerant topology are required to ensure continuity of service, and achieve a faster corrective maintenance. In this work, the mean value of the error voltages is used as fault indicator for the GSC, while, for the boost converter the inductor current form is used as fault indicator. The fault-tolerant topology was achieved by adding one redundant switch to the boost converter, and by adding one redundant leg to the GSC. The results of the fault tolerant control are presented and discussed to validate the proposed approach under different scenarios and different solar irradiances.
Explorations Using Extensions and Modifications to the Oppenheim et al. Model for Cumulative Semantic Interference

This thesis discusses extensions and modifications to a model of semantic interference originally introduced by Oppenheim et al. The first of the two networks presented extends the original toy model to be able to operate over realistic feature-norm datasets. The second of the two networks presented modifies the operation of this extended network in order to artificially activate non-shared features of competitor words during the selection process. Both networks were extensively tested over a wide range of possible simulation configurations. Metrics were developed to aid in predicting the behavior of these networks given the structure of the data used in the simulations. The networks were also tested for noise tolerance and duration of interference retention over time. The results of these experiments show resultant semantic interference behavior consistent with predictions over the parameter space tested, as well as high noise tolerance and the expected reductions in semantic interference effects as the networks were artificially aged. The new network models could be used as simulation platforms for experiments that wish to examine the emergence of semantic interference over complex or large datasets.
User Perception Differences in Enterprise Resource Planning Implementations

Enterprise Resource Planning (ERP) Systems have been developed to help coordinate the information flow that parallels the physical flow of goods, from raw materials to finished goods. The purpose of this study is to examine user perception differences in ERP phases — preparation and training, transition, performance and usefulness, and maintenance phases — for two ERP implementations. Our study uses both qualitative and quantitative research to triangulate our results and provide better insights and findings. Both quantitative and qualitative analysis points to the importance of all the four phases in ERP implementation. However, the study's results point only to differences in the transition and maintenance phases. The preparation and training, and performance and usefulness phases do not have significant difference between the two implementations.
Control architecture for docking UAVs with a 7-DOF manipulator

The SHERPA-project (www.sherpa-project.eu) focuses on developing a mixed ground/aerial robotic platform to improve human rescuers activities. In this collaboration, the human should be able to use the robots with ease when necessary, while the robot should be autonomous when control is not demanded by the operator. The robotic platform consists of a robotic arm integrated with a ground rover, the task of the system will be to autonomosly localize, pick up and dock a UAV1 in its neighbourhood to achieve a battery swapping. The MSc research focuses on designing and implementing a control architecture that integrates motion planning and control techniques to execute all the necessary steps of grasping and docking a UAV. During execution the architecture supervises the feasability of the given tasks to ensure correct execution. 1Unmanned Aerial Vehicle Robotics and Mechatronics Giuseppe Barbieri iv Control architecture for docking UAVs with a 7-DOF manipulator Giuseppe Barbieri University of Twente