Miracle-dz/UniFAR-FTUs huggingface dataset:
Concentration in Southeast Asia Banking

This paper will contribute to the structure and performance literature that involved research on the performance of Southeast Asia (SEA) banks after experiencing the financial crisis in mid-1997.In line with recent development in market structure research and literature, it expanded the analysis of concentration by investigating the structure performance consequence during the post Asian financial crisis period, 1999 to 2005 focusing on six countries that most effected by Asian financial crisis, Indonesia, Korea, Malaysia, Philippines and Thailand. This study looks into the concentration ratio by employing two most common measures; the k-bank concentration ratios and Herfindahl Hirschman Index (HHI), extended the analysis to test the Efficient Structure(ES) hypothesis.Our findings reject the traditional Structure Conduct Performance(SCP)paradigm in Southeast Asia banking.
Demo Abstract : An Infrastructure for Automated BAN Experimentation

We demonstrate an infrastructure for experimenting with Body Area Network (BAN) communication protocols and applications without the involvement of human test-person. The core of our infrastructure consists of a mobile robot, on which a simplistic human structure equipped with BAN nodes is located. This set-up can emulate human movements within a controlled RF interference in-door environment. Both, the robot movements and the RF environment, can be remotely controlled via standard Internet access. As a representative usage example, we compare different frequency selection schemes for BAN communication.
Ontology-based indexing of annotated images using semantic DNA and vector space model

The study presented in this paper focuses on the preprocessing stage of image retrieval by proposing an ontology-based indexing approach which captures the meaning of image annotations by extracting the semantic importance of the words in them. The indexing algorithm is based on the classic vector-space model that is adapted by employing index weighting and a word sense disambiguation. It uses sets of Semantic DNA, extracted from a lexical ontology, to represent the images in a vector space. As discussed in the paper, the use of Semantic DNA in text-based image retrieval aims to overcome some of the major drawbacks of well known traditional approaches such as ‘bags of words’ and term frequency-(TF) based indexing. The proposed approach is evaluated by comparing the indexing achieved using the proposed semantic algorithm with results obtained using a traditional TF-based indexing in vector space model (VSM) with singular value decomposition (SVD) technique. The experimental results show that the proposed ontology-based approach generates a better-quality index which captures the conceptual meaning of the image annotations.
Blind channel estimation for CDMA systems: an induced cyclostationarity approach

The previously introduced transmitter induced cyclostationarity (TIC) techniques are applied to the CDMA uplink blind identification problem. In the proposed transmission model, the spreading code vectors are periodic, each user having a period of his own. As a consequence, a cyclic covariance function is associated to each user. This function generates a structured signal subspace which depends only on the transfer function of this user. The approach leads to clear and mild identifiability conditions. An identification algorithm is described and its theoretical asymptotic performance is presented. Simulations and comparisons with the existing stationary subspace techniques are provided. The algorithm is particularly adapted to systems where the number of users is close to the spreading factor.
Scaffolding low quality genomes using orthologous protein sequences

Motivation: The ready availability of next-generation sequencing has led to a situation where it is easy to produce very fragmentary genome assemblies. We present a pipeline, SWiPS (Scaffolding With Protein Sequences), that uses orthologous proteins to improve low quality genome assemblies. The protein sequences are used as guides to scaffold existing contigs, while simultaneously allowing the gene structure to be predicted by homology. Results: To perform, SWiPS does not depend on a high N50 or whole proteins being encoded on a single contig. We tested our algorithm on simulated next-generation data from Ciona intestinalis, real next-generation data from Drosophila melanogaster, a complex genome assembly of Homo sapiens and the low coverage Sanger sequence assembly of Callorhinchus milii. The improvements in N50 are of the order of ∼20% for the C.intestinalis and H.sapiens assemblies, which is significant, considering the large size of intergenic regions in these eukaryotes. Using the CEGMA pipeline to assess the gene space represented in the genome assemblies, the number of genes retrieved increased by >110% for C.milii and from 20 to 40% for C.intestinalis. The scaffold error rates are low: 85–90% of scaffolds are fully correct, and >95% of local contig joins are correct. Availability: SWiPS is available freely for download at http://www.well.ox.ac.uk/∼yli142/swips.html. Contact: yang.li@well.ox.ac.uk or copley@well.ox.ac.uk
Hairpin Formation in Tn5 Transposition*

The initial chemical steps in Tn5transposition result in blunt end cleavage of the transposon from the donor DNA. We demonstrate that this cleavage occurs via a hairpin intermediate. The first step is a 3′ hydrolytic nick by transposase. The free 3′OH then attacks the phosphodiester bond on the opposite strand, forming a hairpin at the transposon end. In addition to forming precise hairpins, Tn5 transposase can form imprecise hairpins. This is the first example of imprecise hairpin formation on transposon end DNA. To undergo strand transfer, the hairpin must to be resolved by a transposase-catalyzed hydrolytic cleavage. We show that both precise and imprecise hairpins are opened by transposase. A transposition mechanism utilizing a hairpin intermediate allows a single transposase active site to cleave both 3′ and 5′ strands without massive protein/DNA rearrangements.
SAPM: ANFIS based prediction of student academic performance metric

A methodology for evaluating the academic performance metric of students is proposed in this paper based on their performance in periodic assessment tests, attendance and complexity of the question set. These are the major features determining the students learning efficiency evaluation. The impact of these metrics plays a major role in predicting the final grade of a student. The application of adaptive neuro fuzzy inference system helps to model the frame work for evaluating Student Academic Performance Metric(SAPM). The outcome of this methodology can be used to classify the students based on their academic skill and helpful in predicting the probability of their success in the final examinations.
Abstract and Effector-Specific Representations of Motor Sequences Identified with PET

Positron emission tomography was used to identify neural systems involved in the acquisition and expression of sequential movements produced by different effectors. Subjects were tested on the serial reaction time task under implicit learning conditions. In the initial acquisition phase, subjects responded to the stimuli with keypresses using the four fingers of the right hand. During this phase, the stimuli followed a fixed sequence for one group of subjects (group A) and were randomly selected for another group (group B). In the transfer phase, arm movements were used to press keys on a substantially larger keyboard, and for both groups, the stimuli followed the sequence. Behavioral indices provided clear evidence of learning during the acquisition phase for group A and transfer when switched to the large keyboard. Sequence acquisition was associated with learning-related increases in regional cerebral blood flow (rCBF) in a network of areas in the contralateral left hemisphere, including sensorimotor cortex, supplementary motor area, and rostral inferior parietal cortex. After transfer, activity in inferior parietal cortex remained high, suggesting that this area had encoded the sequence at an abstract level independent of the particular effectors used to perform the task. In contrast, activity in sensorimotor cortex shifted to a more dorsal locus, consistent with motor cortex somatotopy. Thus, activity here was effector-specific. An increase in rCBF was also observed in the cingulate motor area at transfer, suggesting a role linking the abstract sequential representations with the task-relevant effector system. These results highlight a network of areas involved in sequence encoding and retrieval.
Maximum likelihood estimation based DINA model and Q-matrix learning

Cognitive diagnosis models (CDMs) are of growing interest in test development and in the measurement of human performance. The DINA (deterministic input, noisy, and gate) model has been widely used in cognitive diagnosis tests and in the process of test development. Central to many such models, the well-known Q-matrix [1], which specifies the item-attribute relationships and two noise parameters in DINA model related to item response functions are termed as slip and guessing. Slip parameter indicates a student with mastery of all of the attributes that an item requires fails to answer the item correctly. In contrast, guessing parameter indicates a student lacks the attributes that are required by an item but succeeds to answer the item correctly. In this paper, we developed a new method and presented an alternate recursive algorithm to learn Q-matrix and uncertainty variables slip and guessing based on Boolean Matrix Factorization (BMF) and Maximum Likelihood Estimation (MLE) respectively for DINA model of CDM. In particularly, we spontaneously transferred the deterministic Q-matrix learning problem into BMF problem. Because BMF is an NP-hard problem [2], we proposed an alternate recursive method to find approximate solution by adding one dimension in attribute latent space in each step. On the other hand, we analytically estimated the slip and guessing parameters through the maximum likelihood of uncertainty variables. The optimum process is alternate recursive between latent attribute space and uncertainty variable space. Simulation results show that the MLE Q-matrix learning algorithm has fast convergence to the optimal solution under suitable initial values of Qinit and Ainit. This is extremely important and applicable when the method is extended to big data.
Integrating camera calibration and hand-eye calibration into robot vision

In conventional techniques of robot vision system calibration, camera parameters and robot hand-eye parameters are computed separately, i.e., first performing camera calibration and then carrying out hand-eye calibration based on the calibrated parameters of the cameras. In this paper, we propose a joint algorithm that combines the camera calibration and the hand-eye calibration together. The proposed algorithm gives the solutions of the cameraspsila parameters and the hand-eye parameters simultaneously by using nonlinear optimization. Both simulations and real experiments show the superiority of our algorithm.
Clarifying and reframing the neoliberal critique of educational policy using policy process theories

ABSTRACT This paper explores the vocabulary and frameworks offered by two theories of public policy process: the advocacy coalition framework (ACF) and the narrative policy framework (NPF) and what they offer to the study of global education reform. The foci of ACF are policy subsystems, formation of advocacy coalitions around policy issues, and their impact on policy change; the emphasis of NPF is the divergent narratives developed and used by these coalitions. This paper reviews each theoretical framework, summarizes existing research literature on their education policy applications, and then poses alternative questions and suggest alternatives to study global education policies, especially those presently critiqued using the term neoliberal which we position as nebulous in its current usage. The aim is to open channels of communication for scholars from different disciplines by introducing different theoretical approaches to analyse the role of the numerous elements that shape global education policy.
Output Feedback Regulation of a Chain of Integrators With an Unbounded Time-Varying Delay in the Input

There are several results on the stabilization or regulation of a chain of integrators in the presence of a constant or time-varying delay in the input. However, the existing results commonly assume that the delay is restricted to be bounded. In this paper, the regulation problem of a chain of integrators with an unbounded time-varying delay in the input is considered. We propose an output feedback controller with a time-varying gain-scaling factor which is determined based on the condition of time-varying rate of the delay. As a benefit over the existing results, the delay is now allowed to be unbounded under the proposed condition. Also, uncertain delays can be accommodated with the proposed controller. Moreover, our proposed control scheme exhibits robustness such that the considered system can be extended to include some uncertain feedforward nonlinearities.
Predicting Expressive Dynamics in Piano Performances using Neural Networks

This paper presents a model for predicting expressive accentuation in piano performances with neural networks. Using Restricted Boltzmann Machines (RBMs), features are learned from performance data, after which these features are used to predict performed loudness. During feature learning, data describing more than 6000 musical pieces is used; when training for prediction, two datasets are used, both recorded on a B¨ osendorfer piano (accurately measuring note on- and offset times and velocity values), but describing different compositions performed by different pianists. The resulting model is tested by predicting note velocity for unseen performances. Our approach differs from earlier work in a number of ways: (1) an additional input representation based on a local history of velocity values is used, (2) the RBMs are trained to result in a network with sparse activations, (3) network connectivity is increased by adding skip-connections, and (4) more data is used for training. These modifications result in a network performing better than the state-of-the-art on the same data and more descriptive features, which can be used for rendering performances, or for gaining insight into which aspects of a musical piece influence its performance.
Antioxidants as a preventive therapeutic option for age related neurodegenerative diseases

Advances in our understanding for neurodegenerative mechanisms have increased significantly in last few decades. But still no novel disease modifying therapy has been developed which could prevent the disease progression and drawn our attention towards the development of new alternative therapy. The major obstacle for the development of disease therapy is incomplete knowledge about neurodegenerative mechanisms. Due to the insufficient information about neurodegenerative mechanisms no standard diagnostic tests for the detection of neurodegenerative disease could be develop to date, causes delayed diagnosis and disease worsening. So far the exploratory reports and clinical data have suggested the involvement of oxidative stress, mitochondrial impairment and apoptosis as major neurodegenerative mechanisms. Investigations have elicited that once initiated the degeneration of neurons could not be arrested therefore in the scarcity of curative therapy we should approach for the preventive neurodegenerative therapy. Since oxidative stress has noteworthy involvement in neuronal death solely, and in connection to other death pathways therefore, antioxidants as preventive therapeutics might provide beneficial effects in age related neurodegenerative diseases. With this hypothesis in this review we are discussing about the use of antioxidants as a preventive treatment of neurodegenerative diseases. Such therapies may work in the prodromal phase, or when given prophylactically. However, in the symptomatic patient there may be too much damage to the neuronal networks to restore functionality by reducing or even eliminating the primary stressor. As functional neuronal demise and excessive neuronal death are almost certainly the key factors that mediate the functional impairment, it is ostensible that preventing neuronal death and dysfunction at early stage may provide a huge clinical benefit.
A Hybrid Image Retargeting Approach via Combining Seam Carving and Grid Warping

Image retargeting is a critical technique for browsing images in diversified terminals. In this paper, we propose a hybrid image resizing approach by jointly using seam carving and warping. Firstly, based on the importance partition with the saliency map, we apply a weighted seam carving approach to make the seams distributed dispersedly in the important regions. Then we propose Content Aware Image Distance (CAID) to assess the deformation caused by removing seams. The weighted seam carving will stop with a fixed threshold to assure little visual image quality degradation. Finally, the grid based warping is utilized to achieve the final size with a global optimization model, since warping tends to avoid discontinuity artifacts of important region and typically make the distortion distribution of unimportant region more coherently. Experiments and comparison in the public RetargetMe dataset [1] with Dong [2], Energy-based deformation [3], Multi-operator [4], SeamCarving [5], Simple scaling operator, Shift-maps [6], Scale and Stretch [7], Streaming Video [8], Non-homogeneous warping [9], show the superiority of the proposed approach
Performance monitoring and the medial prefrontal cortex: a review of individual differences and context effects as a window on self-regulation

The medial prefrontal cortex (MPFC) is central to self-regulation and has been implicated in generating a cluster of event-related potential components, collectively referred to as medial frontal negativities (MFNs). These MFNs are elicited while individuals monitor behavioral and environmental consequences, and include the error-related negativity, Nogo N2, and the feedback-related negativity. A growing cognitive and affective neuroscience literature indicates that the activation of the anterior cingulate cortex (ACC) and surrounding medial prefrontal regions during performance monitoring is not only influenced by task context, but that these patterns of activity also vary as a function of individual differences (e.g., personality, temperament, clinical and non-clinical symptomatology, socio-political orientation, and genetic polymorphisms), as well as interactions between individual differences and task context. In this review we survey the neuroscience literature on the relations between performance monitoring, personality, task context, and brain functioning with a focus on the MPFC. We relate these issues to the role of affect in the paradigms used to elicit performance-monitoring neural responses and highlight some of the theoretical and clinical implications of this research. We conclude with a discussion of the complexity of these issues and how some of the basic assumptions required for their interpretation may be clarified with future research.
ILU Preconditioners for Nonsymmetric Saddle-Point Matrices with Application to the Incompressible Navier-Stokes Equations

Motivated by the numerical solution of the linearized incompressible Navier--Stokes equations, we study threshold incomplete LU factorizations for nonsymmetric saddle-point matrices. The resulting preconditioners are used to accelerate the convergence of a Krylov subspace method applied to finite element discretizations of fluid dynamics problems in three space dimensions. The paper presents and examines an extension for nonsymmetric matrices of the Tismenetsky--Kaporin incomplete factorization. It is shown that in numerically challenging cases of higher Reynolds number flows one benefits from using this two-parameter modification of a standard threshold ILU preconditioner. The performance of the ILU preconditioners is studied numerically for a wide range of flow and discretization parameters, and the efficiency of the approach is shown if threshold parameters are chosen suitably. The practical utility of the method is further demonstrated for the haemodynamic problem of simulating blood flow in a right c.
INDEPENDENT LEARNINGWITHIN THE CONTEXT OF HIGHER EDUCATION

Constant changes in external environment of higher education (massification of higher education, diversification, high requirements from society and state, students’ expectations, changes in financing system, external and internal competition, internationalisation of higher science and etc.) impose higher and higher study quality related requirements. When planning and organising the study process, higher education institutions have to guarantee and provide conditions facilitating achievement of the study outcomes foreseen in the study programmes. One of the most complicated and important part of the learning process is independent learning. The interrelation between teaching and independent learning within the context of higher education as well as the experiences of the researchers who have analyzed the phenomenon of independent learning are discussed is this article. The core elements needed for independent learning (abilities, skills, motives and etc.) are highlighted and the situations in which they appear are discussed. The research aimed at identifying factors fostering independent learning among the students of Social Sciences has been the basis for establishing the level of independence as well as providing recommendations for selection of more effective learning strategies and other supportive measures.
A community psychology approach to preventing violent extremism. Gaining the views of young people to inform primary prevention in secondary schools

The previous government developed guidelines for schools detailing primary prevention approaches that could be used to reduce risk and promote resilience in young people against extremist groups (DCSF, 2009). A community psychology approach is utilized in this research to gain the views of young people in one Local Authority (LA) about the most effective ways to implement the DSCF (2009) guidelines and build resilience locally. The guidelines will be adapted on the basis of the results so that implementation within the LA is relevant to local needs. Focus groups were designed using the structure of the Supply and Demand Model (Meah and Mellis, 2006) of radicalisation and were held with Year 9 students (n=22) from three secondary schools within the LA. A thematic analysis approach was taken to analyse the data gathered. The students developed their own thoughts about effective strategies to prevent violent extremism, which included: developing an environment that facilitates a sense of belonging in school; and providing opportunities for positive multi-cultural experiences. Students had concerns about approaches that encourage debates on current affairs (DCSF, 2009) because they felt this would create hostility in school. It was felt by participants that preventative approaches should focus on primary schools because secondary aged students already have established, fixed ideas. The utility of the Supply and Demand model (Meah and Mellis, 2006) as a risk and resilience framework for violent extremism is discussed.