Miracle-dz/UniFAR-FTUs huggingface dataset:
Morfessor FlatCat: An HMM-Based Method for Unsupervised and Semi-Supervised Learning of Morphology

Morfessor is a family of methods for learning morphological segmentations of words based on unannotated data. We introduce a new variant of Morfessor, FlatCat, that applies a hidden Markov model structure. It builds on previous work on Morfessor, sharing model components with the popular Morfessor Baseline and Categories-MAP variants. Our experiments show that while unsupervised FlatCat does not reach the accuracy of Categories-MAP, with semisupervised learning it provides state-of-the-art results in the Morpho Challenge 2010 tasks for English, Finnish, and Turkish.
Circularly Polarized Microstrip Leaky Wave Antenna using Composite Right / Left-Handed Transmission Line

Recently, researches for antennas with metamaterials have been studied actively [1-2]. A leaky wave antenna using a composite right/left handed (CRLH) concept has been investigated in particular. The CRLH leaky wave antennas with microstrip line [3-5] or rectangular waveguide [6] have been reported. The size of this type of antenna is smaller than that of the conventional antenna. In addition to this feature, this antenna can steer a beam direction from the backfire to the end-fire continuously by using peculiar characteristics in the CRLH region. The CRLH leaky wave antennas reported so far are mainly concerned with a linearly polarized operating; on the other hand, reports on circularly polarized antennas are very few. The circularly polarized antennas proposed so far employ mainly a phase shifter [7-8] to combine linearly polarized waves. The circularly polarized antenna is attractive in the deteriorated environment of the radio wave communications, and is useful for the sensor, radar, and mobile telecommunications as well. In this paper, we propose a circularly polarized microstrip leaky wave antenna using a composite right/left-handed transmission line without a phase shifter. The specific characteristics of the circularly polarized wave have been confirmed by the simulation and the experiment. First, we refer to the structure and the operation of the proposed antenna. Secondly, we show the simulated results on the generation of the circularly polarized waves and its possible wide steering. Finally, we exhibit the experimental results in detail.
Network user dial-up behavior analysis

With the development of the Internet and network services, broadband user behavior tends to diversify. It is a rising challenge for the network operators to understand network user behavior. In this paper, we use the Postgres to sort the data records which are caught and extracted from the network monitoring equipment. Then we analyze the characteristics of the data by SPSS, such as login time, logout time, on-line duration and log times and the relationships among them. We extract the main feature, and then use the K-MEAN algorithm to classify the users and carry on the corresponding description. Our results provide a basis for network operators to understand dial-up user behavior, optimize network planning and adjust customer.
Analytical kinematics and working-condition simulation for friction stir welding (FSW) robot

In this paper, combining with the engineering reality needs, we developed a new type of friction stir welding robot welding system. Through the configuration analysis, introduces the structural components and functional roles of each part of the FSW robot, as well as the commonly used five kinds of typical working-conditions. Using D-H parameters method, establishment the forward kinematics equation of FSW robot and verify its rightness. With Piper criterion method, solve the inverse solution of FSW robot's kinematics equation. Finally, take melon petals welding condition for example, carry out trajectory planning for a given size rockets head weld and conduct welding simulation analysis. We have got different parameters' change curves relative to time on FSW robot's joint space and verified the correctness of its mechanism design and kinematics model.
Machine Learning in Structural Genomics

Proteins are polymer chains composed of twenty simpler molecules, called amino acids, that carry out most of the molecular functions in living organisms. Although a protein can be first characterized by its amino acid sequence, or primary sequence, most proteins fold into three-dimensional (3D) structures that determine their function in living organisms. After the completion of several genome-sequencing projects, well over a million protein sequences are known and the sequence-structure gap has dramatically increased. Experimental methods for structure determination, X-ray crystallography and NMR spectroscopy, are costly and time consuming and do not keep pace with sequencing speed: at the beginning of 2004, the ratio of known structures to known sequences is approaching 1:50. Prediction of structure and function of novel proteins thus represents a strategic research frontier: bridging the gap between sequence and structure would increase our understanding of biological processes and our ability to enhance the quality and span of our lives. The ability to provide effective computational tools for protein structure prediction is a key to overcome experimental problems and to guide part of the future scientific effort in molecular biology. In spite of four decades of intensive research effort, reliable protein structure prediction from sequence is far from being achieved, mainly because no comprehensive theory of folding exists and a global search in the conformational space of proteins is inherently intractable. Only recently, this task has been aided by the development of intelligent tools borrowed from machine learning approaches. These methods extract relevant pieces of information from databases of known structures in order to focus the search on more tractable problems. So far, research has mainly focused on intermediate local structural descriptions (e.g. secondary structure, relative solvent accessibility etc.). These are commonly referred to as one-dimensional (1D) features, as they can be represented with linear strings
Using HMMs and ANNs for mapping acoustic to visual speech

This thesis collects studies on machine learning methods applied to speech technology and speech research problems. The six research papers included in this thesis are organised in three main areas. The first group of studies were carried out within the European project Synface. The aim was to develop a low latency phonetic recogniser to drive the articulatory movements of a computer generated virtual face from the acoustic speech signal. The visual information provided by the face is used as hearing aid for persons using the telephone. Paper A compares two solutions to the problem of mapping acoustic to visual information that are based on regression and classification techniques. Recurrent Neural Networks are used to perform regression while Hidden Markov Models are used for the classification task. In the second case the visual information needed to drive the synthetic face is obtained by interpolation between target values for each acoustic class. The evaluation is based on listening tests with hearing impaired subjects were the intelligibility of sentence material is compared in different conditions: audio alone, audio and natural face, audio and synthetic face driven by the different methods. Paper B analyses the behaviour, in low latency conditions, of a phonetic recogniser based on a hybrid of Recurrent Neural Networks (RNNs) and Hidden Markov Models (HMMs). The focus is on the interaction between the time evolution model learnt by the RNNs and the one imposed by the HMMs. Paper C investigates the possibility of using the entropy of the posterior probabilities estimated by a phoneme classification neural network, as a feature for phonetic boundary detection. The entropy and its time evolution are analysed with respect to the identity of the phonetic segment and the distance from a reference phonetic boundary. In the second group of studies, the aim was to provide tools for analysing large amount of speech data in order to study geographical variations in pronunciation (accent analysis). Paper D and Paper E use Hidden Markov Models and Agglomerative Hierarchical Clustering to analyse a data set of about 100 millions data points (5000 speakers, 270 hours of speech recordings). In Paper E, Linear Discriminant Analysis was used to determine the features that most concisely describe the groupings obtained with the clustering procedure. The third group belongs to studies carried out during the international project MILLE (Modelling Language Learning) that aims at investigating and modelling the language acquisition process in infants. Paper F proposes the use of an incremental form of Model Based Clustering to describe the unsupervised emergence of phonetic classes in the first stages of language acquisition. The experiments were carried out on child-directed speech expressly collected for the purposes of the project
China's Great Economic Transformation

This landmark study provides an integrated analysis of China's unexpected economic boom of the past three decades. The authors combine deep China expertise with broad disciplinary knowledge to explain China's remarkable combination of high-speed growth and deeply flawed institutions. Their work exposes the mechanisms underpinning the origin and expansion of China's great boom. Penetrating studies track the rise of Chinese capabilities in manufacturing and in research and development. The editors probe both achievements and weaknesses across many sectors, including China's fiscal, legal, and financial institutions. The book shows how an intricate minuet combining China's political system with sectorial development, globalization, resource transfers across geographic and economic space, and partial system reform delivered an astonishing and unprecedented growth spurt.
A Toolkit for Orthogonal and in vivo Optical Manipulation of Ionotropic Glutamate Receptors

The ability to optically manipulate specific neuronal signaling proteins with genetic precision paves the way for the dissection of their roles in brain function, behavior, and disease. Chemical optogenetic control with photoswitchable tethered ligands (PTLs) enables rapid, reversible and reproducible activation or block of specific neurotransmitter-gated receptors and ion channels in specific cells. In this study, we further engineered and characterized the light-activated GluK2 kainate receptor, LiGluR, to develop a toolbox of LiGluR variants. Low-affinity LiGluRs allow for efficient optical control of GluK2 while removing activation by native glutamate, whereas variant RNA edited versions enable the synaptic role of receptors with high and low Ca2+ permeability to be assessed and spectral variant photoswitches provide flexibility in illumination. Importantly, we establish that LiGluR works efficiently in the cortex of awake, adult mice using standard optogenetic techniques, thus opening the door to probing the role of specific synaptic receptors and cellular signals in the neural circuit operations of the mammalian brain in normal conditions and in disease. The principals developed in this study are widely relevant to the engineering and in vivo use of optically controllable proteins, including other neurotransmitter receptors.
Keeping in touch with the family: home and away with the ASTRA awareness system

This paper describes research in supporting close family members living apart to keep in touch with each other. We introduce an awareness system for supporting lightweight social communication between mobile individuals and people at home. Communication is based on pictures, short messages and reachability information. A field test has provided empirical evidence that affective benefits, to this point only hypothesized by researchers in awareness systems, are indeed experienced by users of our system.
Leaf Shapes using L-system and Genetic Algorithms

In this paper presents a method that combines with techniques L-system and Genetic Algorithms (GA) to search for a rewriting expression describing leaf shapes. L-system is used to construct a shape of leaf of a given rewriting expression, and GA is used to search an unknown rewriting expression's fitting parameters. Replacement of real value parameters to tag-function is introduced. The result shows both L-system and GA work together and produce an acceptable output.
VidaMine : User-Centred Development of a Visual Mining Environment

Tremendous technological breakthroughs have virtually revolutionized the world. One major consequence is that humans are confronted with ever-increasing already massive amounts of data at virtually every turn. Visual data mining (VDM) intends to exploit effective visual strategies in the entire process of mining knowledge. Though there have been many efforts in this area, most of them have paid little attention, if any, to usability studies. While ensuring that a VDM system involves the user in the entire mining process is indeed significant, ensuring that the user is involved in the system development process needs prior consideration. This work describes how a user-centered approach was adopted in the development of a VDM system named VidaMine.
Experience and generalization in a connectionist model of Mandarin Chinese relative clause processing

Sentences containing relative clauses are well known to be difficult to comprehend, and they have long been an arena in which to investigate the role of working memory in language comprehension. However, recent work has suggested that relative clause processing is better described by ambiguity resolution processes than by limits on extrinsic working memory. We investigated these alternative views with a Simple Recurrent Network (SRN) model of relative clause processing in Mandarin Chinese, which has a unique pattern of word order across main and relative clauses and which has yielded mixed results in human comprehension studies. To assess the model's ability to generalize from similar sentence structures, and to observe effects of ambiguity through the sentence, we trained the model on several different sentence types, based on a detailed corpus analysis of Mandarin relative clauses and simple sentences, coded to include patterns of noun animacy in the various structures. The model was evaluated on 16 different relative clause subtypes. Its performance corresponded well to human reading times, including effects previously attributed to working memory overflow. The model's performance across a wide variety of sentence types suggested that the seemingly inconsistent results in some prior empirical studies stemmed from failures to consider the full range of sentence types in empirical studies. Crucially, sentence difficulty for the model was not simply a reflection of sentence frequency in the training set; the model generalized from similar sentences and showed high error rates at points of ambiguity. The results suggest that SRNs are a powerful tool to examine the complicated constraint-satisfaction process of sentence comprehension, and that understanding comprehension of specific structures must include consideration of experiences with other similar structures in the language.
Adaptive model learning for continual verification of non-functional properties

A growing number of business and safety-critical services are delivered by computer systems designed to reconfigure in response to changes in workloads, requirements and internal state. In recent work, we showed how a formal technique called continual verification can be used to ensure that such systems continue to satisfy their reliability and performance requirements as they evolve, and we presented the challenges associated with the new technique. In this paper, we address important instances of two of these challenges, namely the maintenance of up-to-date reliability models and the adoption of continual verification in engineering practice. To address the first challenge, we introduce a new method for learning the parameters of the reliability models from observations of the system behaviour. This method is capable of adapting to variations in the frequency of the available system observations, yielding faster and more accurate learning than existing solutions. To tackle the second challenge, we present a new software engineering tool that enables developers to use our adaptive learning and continual verification in the area of service-based systems, without a formal verification background and with minimal effort.
Recognition of Building Roof Facets by Merging Aerial Images and 3D Lidar Data in a Hierarchical Segmentation Framework

We investigate in this paper an original methodology for detecting roof facets through the fusion of aerial images and lidar data (3D point cloud). Based on a hierarchical segmentation of the image, we define a cost function that manages the merging order of regions. It depends on both radio-metric similarities of two neighbouring regions as well as on extracted information from lidar data. Considering that lidar data have been filtered into points belonging either to ground or non-ground classes, we define semantic and geometric rules in the binary merging process. Building roof facets are finally detected by selecting a level of generality for representing roof building components. Some remarks are given concerning the reliability of the integration of lidar and image data. Reconstructed roof facets are finally shown onto complex buildings
An Empirical Study of Tax Evasion and Tax Avoidance: A Critical Issue in Nigeria Economic Development

The revenue generated by the government from taxation forms a major source of finance to the federal government capital expenditure which is crucial to sustainable economic development. A major challenge to the government in generating this revenue has been the increasing rate of tax evasion and tax avoidance. Therefore tax evasion and tax avoidance are important factors to be considered as they affect both the volume and nature of government finances which is the key to economic development. This paper considers the effects of tax avoidance and tax evasion on Nigeria economic development. This paper considers the effects of tax avoidance and tax evasion on Nigeria economic development. A survey research design was adopted and responses were obtained through the use of a well structured questionnaire administered to 150 Nigerians, out of which are tax payer and tax evader. Findings from the analysis using statistics techniques reveal that tax evasion and avoidance have adversely affected economic growth and development in Nigeria, and also, that lack of good governance and unpatriotic act of tax payer, is the basis for which tax evasion and tax avoidance activities is perpetrated. The study therefore recommends that the government should embrace and promote good governance so as to encourage voluntary compliance of tax liability by the tax payers. Key words: Tax evasion, tax avoidance, economic development, tax hostility.
What are the Effects of the EU Budget: Driving Force or Drop in the Ocean?

The study presents an overview of the impact of the main investment tools of the EU budget. The focus is on the increasing role of the financial instruments, which are fundamentally changing the budget’s nature and reach. Through these instruments, the EU can invest more efficiently in more areas and mobilise a multiple of funds. The EU budget has the potential to influence the European economy much more than its modest size in terms of GDP may suggest.
Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games

We investigate the problem of achieving global optimization for distributed channel selections in cognitive radio networks (CRNs), using game theoretic solutions. To cope with the lack of centralized control and local influences, we propose two special cases of local interaction game to study this problem. The first is local altruistic game, in which each user considers the payoffs of itself as well as its neighbors rather than considering itself only. The second is local congestion game, in which each user minimizes the number of competing neighbors. It is shown that with the proposed games, global optimization is achieved with local information. Specifically, the local altruistic game maximizes the network throughput and the local congestion game minimizes the network collision level. Also, the concurrent spatial adaptive play (C-SAP), which is an extension of the existing spatial adaptive play (SAP), is proposed to achieve the global optimum both autonomously as well as rapidly.
Understanding the Help Seeking Behavior of Accra Polytechnic Students: A qualitative approach

The aim of this study was to explore the barriers to seeking mental health care and to find out if help seeking behavior could be improved. Six focus group discussions were held using three groups of students from Accra Polytechnic. Themes emerging from the study indicated that facilitators and barriers to care included factors related to trusted and established relationships, the characteristics of the service provider/psychotherapist, accessibility issues, the characteristics of the condition for which help is being sought for, the characteristics of the person seeking help and other social and economic issues. The study also indicated that strategies like psycho-educational campaigns, outreach programs, the distribution of fliers, making working hours flexible and the use of hot line services could be utilized to promote help seeking behavior. The implications, limitations and recommendations of the study and areas for future research are also discussed in the write up. Keywords: Help seeking behavior, Accra Polytechnic, students, qualitative approach, help seeking model, help seeking pathways, focus group discussion, triangulation, interviews, intervention.
A systematic review of empirical software engineering studies that analyze individual changes

Understanding, managing and reducing costs and risks inherent in change are key challenges of software maintenance and evolution, addressed in empirical studies with many different research approaches. Change-based studies analyze data that describes the individual changes that are made to software systems. This approach can be effective in order to discover cost and risk factors that are hidden at more aggregated levels. However, it is not trivial to derive appropriate measures of individual changes for specific measurement goals. The purpose of this review is to improve change- based studies by 1) summarizing how attributes of changes have been measured to reach specific measurement goals, and 2) describing validity issues, and hence improvement areas, for change-based studies. Thirty-four papers conformed to the inclusion criteria. Forty-three attributes of changes were identified, and classified according to a conceptual model that we developed for the purpose of this classification. The goal of each study was to either characterize the evolution process, to assess causal factors of cost and risk, or to predict costs and risks. Effective accumulation of knowledge across change-based studies requires precise definitions of attributes and measures of change. We recommend that new change-based studies base such definitions on the proposed conceptual model.
A short course on non linear geometry of Banach spaces

In this course we show how some linear properties of Banach spaces, in particular properties related to the asymptotic structure of Banach spaces, are stable under coarse-Lipschitz embedddings or uniform homeomorphisms. We will focus on the recent use of some fundamental metric graphs or trees in the subject.