Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishing a crosswalk between the Common European Framework for Languages (CEFR) and writing domains scored by automated essay scoring

ABSTRACT This article employs the Common European Framework Reference for Language Acquisition (CEFR) as a basis for evaluating writing in the context of machine scoring. The CEFR was designed as a framework for evaluating proficiency levels of speaking for the 49 languages comprising the European Union. The intent was to impact language instruction so that “mastery” of one language has the same meaning as it does in another. A second objective is to provide a crosswalk for what one automated writing evaluation (AWE) system does in attending to the dimensions of the framework. The CEFR Framework is divided into five traits and different proficiency levels. The question then becomes: Does the AWE system attempt to measure these dimensions of writing? And, if so, how is this operationalized? Is it measuring aspects of communication that are not specified? The goal here is to create a common vocabulary between the writing community and those interested in AWE systems as to what is actually being measured by their software, and mapping that to a developmental scale of writing performance.
An exploration of understandings of major depressive disorder in a South African Indian Muslim female community

ABSTRACT Depression is a serious mental illness that has severe socio-economic costs. Studying the impact of various cultural beliefs on the understandings of depression is imperative as findings could assist with developing effective intervention strategies. This research aimed to explore the understandings South African Muslim females have of Major Depressive Disorder (MDD) with a focus on cultural and religious factors that may underlie these understandings. A mixed methods study was conducted using 12 semi-structured interviews and 62 questionnaires. Thematic analysis was used to analyse the qualitative data. Descriptive statistics were used to analyse the quantitative data. The results clustered around four main themes: participants’ understanding of MDD, perceptions around the etiology of MDD, treatment of MDD and the influence of culture on perceptions of MDD. It is evident that there is a better understanding of MDD in the sample in comparison to previous studies but there is still much misperception around MDD in the broader community.
Human Motion Behaviour Aware Planner (HMBAP) for path planning in dynamic human environments

For a robot navigating in a human inhabited dynamic environment, the knowledge of how the robot's movement can influence the trajectory of people around it can be very valuable. In this work we present a Human Motion Behaviour Aware Planner (HMBAP) which incorporates a Human Motion Behaviour Model (HMBM) in its planning stage to take advantage of this. HMBM is an obstacle avoidance model for people based on social forces which gives the robot an understanding of how people would react to its planned path. This information is useful for the robot to avoid imminent collisions with people in constricted spaces. The resulting robot behaviour is similar to how a human would move (in terms of avoidance behaviour) in a similar situation. We believe that this is a desirable feature for a robot navigating in a human inhabited environment. Our method shows good human-like navigation behaviour in situations where past methods fail to find a solution.
3D object recognition based on confidence LUT of SIFT feature distance

In this paper, a confidence-based matching method for three dimensional (3D) object recognition is proposed. We are developing a remote control system combined with a camera and image recognition system that can recognize specific objects that a user wants to control. Previously, Scale Invariant Feature Transform (SIFT) feature point-based recognition algorithms have been proposed by numerous researchers. However, it is difficult to apply the conventional recognition methods to remote control systems because home appliances tend to have simple shapes, and thus normally produce very few SIFT feature points. To improve the performance under such low feature count situations, a confidence-based 3D feature point matching method is proposed. This method is a modified Best Bin First (BBF) approach that uses a trained confidence look up table (LUT) for decision making. An evaluation of this method is demonstrated on a dataset of 2,432 images.
Implementation of Transient Signal Detection Algorithms on FPGA

transient signals are non-periodic and discrete obtained from high energy physical processes in space. Most challenging issues in transient signal detection are the speed and accuracy with which a signal can be detected. Cumulative Sum (CUSUM) algorithm has been employed in this paper for transient signal detection and proved to be capable of meeting the necessary requirements. However, as ordinary software- based programs are unable to handle large scale sample of signals, the current research focuses on implementing the CUSUM algorithm on Field Programmable Gate Array (FPGA) which is a specific integrated circuit within the field of semi-customized circuits that can greatly enhance the speed of detection and analysis. Therefore a FPGA-based system was devoted to implement the algorithms for an efficient transient signal detection. A detection speed of 64 ns per sample set was achieved via implementation the algorithm on an Altera Cyclone IV device with a clock speed of 50 MHZ. The analyzed result shows the power consumption of the FPGA based system can be reduced to 136.75 mW. KeywordsSignal Processing, CUSUM Algorithm, FPGA Implementation
The Influence of Subject Sophistication on the Ability to Feign Mild Head Injury Symptoms.

The present study examined the effects that personal experience with head injury, task specific instruction and brain injury knowledge have on the ability to feign postconcussion symptoms and neuropsychological performance patterns seen in mild head injured patients. A sample of non-head injured and head injured college students served as the experimental subjects. These subjects were randomly assigned to conditions in which they were told to either do their best, feign deficits consistent with mild head injury without task specific instruction, or feign deficits while given task specific instruction. Subjects were also classified into one of two head injury knowledge groups based upon above average or below-to-average performance on a head injury misconception survey. Postconcussive symptom complaints were affected by head injury knowledge, instruction, and gender. Coached male malingerers with above average head injury knowledge endorsed relatively higher rates of postconcussive symptoms than any of the other groups. However, other malingering groups accurately simulated levels of postconcussive symptoms seen in the mild head injured patients. Performance on malingering tests was affected by instruction to simulate head injury, however coaching was not shown to produce a more realistic pattern of performance on the malingering tests relative to the head injured patients. The results of this study indicate that analog malingerers can accurately replicate self-reported postconcussive symptoms reported in mild head injured patients. However, malingering subjects are unable to simulate (abstract, con’d.) vii mild head injured patient’s performance on clinical malingering tests. These results suggest that self-report measures of postconcussive symptoms and clinical tests are differentially vulnerable to instructions to malinger. In summary, personal experience, accuracy of head injury beliefs, and test specific coaching did not contribute to a more realistic mild head injury profile.
On the Simultaneous Preservation of Privacy and Community Structure in Anonymized Networks

We consider the problem of performing community detection on a network, while maintaining privacy, assuming that the adversary has access to an auxiliary correlated network. We ask the question "Does there exist a regime where the network cannot be deanonymized perfectly, yet the community structure could be learned? " To answer this question, we derive information theoretic converses for the perfect deanonymization problem using the Stochastic Block Model and edge sub-sampling. We also provide an almost tight achievability result for perfect deanonymization. We also evaluate the performance of percolation based deanonymization algorithm on Stochastic Block Model data-sets that satisfy the conditions of our converse. Although our converse applies to exact deanonymization, the algorithm fails drastically when the conditions of the converse are met. Additionally, we study the effect of edge sub-sampling on the community structure of a real world dataset. Results show that the dataset falls under the purview of the idea of this paper. There results suggest that it may be possible to prove stronger partial deanonymizability converses, which would enable better privacy guarantees.
Identification of dynamic model parameters for lithium-ion batteries used in hybrid electric vehicles

This paper presents an electrical equivalent circuit model for lithium-ion batteries used for hybrid electric vehicles (HEV). The model has two RC networks characterizing battery activation and concentration polarization process. The parameters of the model are identified using combined experimental and Extended Kalman Filter (EKF) recursive methods. The open-circuit voltage and ohmic resistance of the battery are directly measured and calculated from experimental measurements, respectively. The rest of the coupled dynamic parameters, i.e. the RC network parameters, are estimated using the EKF method. Experimental and simulation results are presented to demonstrate the efficacy of the proposed circuit model and parameter identification techniques for simulating battery dynamics.
Automatic Machine Learning of Decision Rules for Classification Problems in Image Analysis

A new method for automatic machine learning of decision rules for classification problems in image analysis is presented. The method aims at simultaneous decision rule inference and selection of discriminative features which characterize the image entities to be classified. The method is based on the approximation of class conditional densities by a mixture of parametrized densities of a special type using the EM algorithm. Its performance is tested on a classification problem involving real image data.
Do economic integration and fiscal competition help to explain location patterns

Do trade integration and fiscal competition contribute to shape the economic landscape? The answer is yes. This paper uses a theoretical model and an econometric analysis of Brazilian regional manufacturing employment data over the period 1990-1998 to address this question. Brazil is a natural case study because this country liberalized trade during the 1990s and their sub-national governments engaged in a fiscal dispute. Econometric results show that trade liberalization has had an impact on spatial developments and this effect is increasing over time. Moreover, subsidies also exert an influence, but their relative importance is falling.
The multidimensional systemic representation of actor networks: modelling breast cancer treatment decision-making

This paper explores how the soft information systems and technologies methodology (SISTeM) and actor network theory may be combined to address real world problematisations that lead to integrated human/machine interventions in the real world. An important component of this is the need for a modeling language for the multidimensional systemic representation of actor networks. The dimensions include human/machine activities, the information systems and technologies, clinical competencies, psychosocial relationships and power/politics. A scenario focusing on breast cancer treatment decision-making is used to illustrate the need for such a multidimensional modeling language and integrated interventions.
Changing Patterns of Domestic and Cross-Border Fiscal Policy Multipliers in Europe and the US

This paper documents time variation in domestic fiscal policy multipliers in Germany, the UK and the US, and in cross-border fiscal spillovers from Germany to the seven largest European Union economies. We propose two VAR models which incorporate three “global factors” representing developments in the world economy, and we combine them with identification of fiscal shocks a la Blanchard and Perotti (2002) and Perotti (2005), to study the effects of net tax and government spending shocks on GDP, inflation and interest rates. By recursively estimating these models on different samples of data, we find that the domestic impact of tax shocks has been positive but vanishing for Germany and the US, stably not significant for the UK. Financial markets deregulations may play an important role in that since they allow households to be less dependent on disposable income and to smooth more easily consumption. Domestic government spending multipliers are found to be positive but feeble in the short-run and close to zero or slightly negative in the medium-run, implying that private consumption and investments might be crowded out. These results suggest that, in the European Monetary Union, discretionary fiscal policy “surprises” (i.e. unexpected tax cuts and government spending expansions) cannot be used by governments as substitutes for lost national monetary instruments, since they have shown to be progressively ineffective over time. Finally, we find that fiscal expansions in Germany have had beneficial (though declining) effects for neighboring countries, especially the smaller ones. This may indicate that the trade channel of transmission of fiscal policy dominates the interest rate one.
Optimal Array Geometries for Wideband DOA Estimation

Abstract : We consider the role of array geometry on the Direction of Arrival (DOA) estimating performance of the array where the impinging signal is wideband. We concentrate on arrays that have isotropic performance. An isotropic array is one whose Cramer Rao bound (CRB) on the DOA of a single source is uniform for all angles. We derive the necessary and sufficient conditions on the location of array elements so that the array is isotropic. Both planar arrays and three dimensional arrays are considered. We also present several designs of isotropic planar and volume arrays and give example geometries.
Space odyssey: efficient exploration of scientific data

Advances in data acquisition---through more powerful supercomputers for simulation or sensors with better resolution---help scientists tremendously to understand natural phenomena. At the same time, however, it leaves them with a plethora of data and the challenge of analysing it. Ingesting all the data in a database or indexing it for an efficient analysis is unlikely to pay off because scientists rarely need to analyse all data. Not knowing a priori what parts of the datasets need to be analysed makes the problem challenging. Tools and methods to analyse only subsets of this data are rather rare. In this paper we therefore present Space Odyssey, a novel approach enabling scientists to efficiently explore multiple spatial datasets of massive size. Without any prior information, Space Odyssey incrementally indexes the datasets and optimizes the access to datasets frequently queried together. As our experiments show, through incrementally indexing and changing the data layout on disk, Space Odyssey accelerates exploratory analysis of spatial data by substantially reducing query-to-insight time compared to the state of the art.
Development and Field Test of Voltage VAR Optimization in the Korean Smart Distribution Management System

This paper is a summary of the development and demonstration of an optimization program, voltage VAR optimization (VVO), in the Korean Smart Distribution Management System (KSDMS). KSDMS was developed to address the lack of receptivity of distributed generators (DGs), standardization and compatibility, and manual failure recovery in the existing Korean automated distribution system. Focusing on the lack of receptivity of DGs, we developed a real-time system analysis and control program. The KSDMS VVO enhances manual system operation of the existing distribution system and provides a solution with all control equipment operated at a system level. The developed VVO is an optimal power flow (OPF) method that resolves violations, minimizes switching costs, and minimizes loss, and its function can vary depending on the operator’s command. The sequential mixed integer linear programming (SMILP) method was adopted to find the solution of the OPF. We tested the precision of the proposed VVO on selected simulated systems and its applicability to actual systems at two substations on the Jeju Island. Running the KSDMS VVO on a regular basis improved system stability, and it also raised no issues regarding its applicability to actual systems.
Visual Impairment does not Limit Training Effects in Development of Aerobic and Anaerobic Capacity in Tandem Cyclists

Abstract The study aimed to investigate the differences in the effects of 7-month training on aerobic and anaerobic capacity in tandem cycling athletes with and without visual impairment. In this study, Polish elite (n=13) and sub-elite (n=13) visually impaired (VI) (n=13; 40.8 ±12.8 years) and properly sighted (PS) (n=13; 36.7 ±12.2 years) tandem-cycling athletes participated voluntarily in 7-month routine training. The following pre-/post-training measurements were conducted on separate days: maximal oxygen uptake (VO2max) was estimated with age correction using the Physical Working Capacity test on a bicycle ergometer according to the Astrand-Ryhming method. Maximal power output (Pmax) was evaluated using the Quebec test on a bicycle ergometer. At baseline, VO2max (47.8 ±14.1 vs 42.0 ±8.3 ml/kg/min, respectively) and Pmax (11.5 ±1.5 vs 11.5 ±1.0 W/kg) did not differ significantly between PS and VI cyclists. However, differences in aerobic capacity were considered as clinically significant. Two-way ANOVA revealed that after 7 month training, there were statistically significant increases in VO2max (p=0.003) and Pmax (p=0.009) among VI (VO2max, +9.1%; Pmax, +6.3%) and PS (VO2max, +9.1%; Pmax, +11.7%) cyclists, however, no time x visual impairment interaction effect was found (VO2max, p=0.467; Pmax, p=0.364). After training, VO2max (p=0.03), but not Pmax (p=0.13), was significantly greater in elite compared to sub-elite tandem cyclists. VI and PS tandem cyclists showed similar rates of improvement in VO2max and Pmax after 7-month training. VO2max was a significant determinant of success in tandem cycling. This is one of the first studies providing reference values for aerobic and anaerobic capacity in visually impaired cyclists.
PICEA-g using an enhanced fitness assignment method

The preference-inspired co-evolutionary algorithm using goal vectors (PICEA-g) has been demonstrated to perform well on multi-objective problems. The superiority of PICEA-g originates from the smart fitness assignment, that is, candidate solutions are co-evolved with goal vectors along the search. In this study, we identify a limitation of this fitness assignment method, and propose an enhanced fitness assignment method which considers both the performance of goal vectors and the Pareto dominance rank on the fitness calculation of candidate solutions. Experimental results show that PICEA-g with the enhanced approach is effective, especially for bi-objective problems.
Rising Prevalence of HIV-1 Non-B Subtypes in Belgium: 1983–2001

This study documented the HIV-1 subtype distribution in 2 Belgian hospitals and determined predictive demographics for non-B subtypes. Overall, subtype B was the most prevalent subtype in this population, followed by subtypes A and C. Several recombinants were detected, circulating recombinants as well as new ones. We found a rise in non-B subtypes from 0% in 1983 to 57% in 2001. The Cochran-Armitage trend test (P < 0.001) as well as the correlation analysis (R = 0.71, P = 0.0006) was highly significant. Recombinants were also increasing in this patient population from 0% in 1983 to 10% in 2001, with good support from the statistical analyses (trend test P < 0.001; correlation analysis R = 0.67, P = 0.0016). Heterosexual route of infection, black African race, African origin of the virus, and year of diagnosis were predictors for infection with non-B subtypes in multivariate analysis. This analysis indicates that the prevalence of non-B subtypes and recombinants in this patient population is high and increasing. Gathering demographic and sequence information from newly diagnosed patients could be useful to further follow the spread of non-B subtypes in Belgium and Europe, but subtyping based on sequence information still remains the most reliable method.
El realismo social y metáforas del Socavón en la novela minera peruana

espanolEl articulo analiza dos vertientes importantes del proceso de la novela minera peruana. Por un lado, los discursos social-realistas como: las injusticias sociales, reivindicaciones, pesimas condiciones de trabajo, inseguridad y conflicto ideologico; por otro, las construcciones metaforicas del socavon que expresan imagenes literarias del mundo minero. Para ello usamos algunas categorias de la teoria literaria y metodologicamente agrupamos algunas novelas modelicas donde examinamos sus similitudes y diferencias. Con ello demostramos que la primera vertiente literaria es mas verosimil con la cotidianeidad; mientras que el segundo es una construccion literaria de revelacion de un universo propio e inconfundible. EnglishThe article analyzes two important aspects of the process of the Peruvian mining novel. On the one hand, the social-realist discourses such as: social injustices, wage demands, working conditions, insecurity and ideological conflict; on the other, the metaphoric constructions of the tunnel that express literary images of the mining world. For this we use some categories of literary theory and methodologically we group some model novels where we examine their similarities and differences. With this we demonstrate that the first literary aspect is more plausible with everyday life; while the second is a literary construction of revelation of an own and unmistakable universe.
Metrics for Assessing The Design of Software Interfaces

Recent studies have largely investigated the detection of class design anomalies. They proposed a large set of metrics that help in detecting those anomalies and in predicting the quality of class design. While those studies and the proposed metrics are valuable, they do not address the particularities of software interfaces. Interfaces define the contracts that spell out how software modules and logic units interact with each other. This paper proposes a list of design defects related to interfaces: shared similarity between interfaces, interface clones and redundancy in interface hierarchy. We identify and describe those design defects through real examples, taken from well-known Java applications. Then we define three metrics that help in automatically estimating the interface design quality, regarding the proposed design anomalies, and identify refactoring candidates. We investigate our metrics and show their usefulness through an empirical study conducted on three large Java applications.