Miracle-dz/UniFAR-FTUs huggingface dataset:
Mel-frequency Cepstral Coefficients for Eye Movement Identification

Human identification is an important task for various activities in society. In this paper, we consider the problem of human identification using eye movement information. This problem, which is usually called the eye movement identification problem, can be solved by training a multiclass classification model to predict a person's identity from his or her eye movements. In this work, we propose using Mel-frequency cepstral coefficients (MFCCs) to encode various features for the classification model. Our experiments show that using MFCCs to represent useful features such as eye position, eye difference, and eye velocity would result in a much better accuracy than using Fourier transform, cepstrum, or raw representations. We also compare various classification models for the task. From our experiments, linear-kernel SVMs achieve the best accuracy with 93.56% and 91.08% accuracy on the small and large datasets respectively. Besides, we conduct experiments to study how the movements of each eye contribute to the final classification accuracy.
Modeling Investment‐Sector Efficiency Shocks: When Does Disaggregation Matter?

The most straightforward way to analyze investment‐sector productivity developments is to construct a two‐sector model with a sector‐specific productivity shock. An often used modeling shortcut accounts for such developments using a one‐sector model with shocks to the efficiency of investment in a capital accumulation equation. This shortcut is theoretically justified when some stringent conditions are satisfied. Using a two‐sector model, we consider the implications of relaxing several of the conditions that are at odds with the U.S. Input–Output Tables, including equal factor shares across sectors. The effects of productivity shocks to an investment‐producing sector of our two‐sector model differ from those of efficiency shocks to investment in a one‐sector model. Notably, expansionary productivity shocks boost consumption in every period, whereas expansionary efficiency shocks cause consumption to fall substantially for many periods.
Fixed Exchange Rate Regimes and Inflation Performance: Evidence from MENA Countries

In this paper we study the empirical evidence on inflation performance of fixed but adjustable exchange rate regimes in MENA over the period of 1980-2007, using exchange rate regimes as declared (de jure) and as observed (de facto). We identify credible regimes by matching what is announced and what is observed. Our results suggest that, even when accounting for possible endogeneity, credible pegs were associated with lower inflation, while declared fixed exchange rates were not significantly associated with lower inflation. Moreover, there is substantial evidence that suggests that countries, who have often announced more exchange rate flexibility in order to avoid speculative attacks while maintaining a stable exchange rate, were successful in achieving low inflation.
Bmc Complementary and Alternative Medicine Knowledge, Attitudes and Practices regarding Gemstone Therapeutics in a Selected Adult Population in Pakistan

Background: Gemstones have been in use as part of alternative and complementary medicine for years. However, our understanding of the perceived healing powers of gemstones is limited. An extensive literature search revealed that there is a dearth of validated information on this subject. This study was therefore undertaken to explore the various aspects of the knowledge, attitudes, and practices of the public towards gemstone therapeutics.
Automatic Classification of Children's Antisocial and Prosocial Lies Using Facial Expressions

Research on the nonverbal facial expressions of children during lie-telling is extremely limited. As such, it is unknown whether the nonverbal behaviours of children telling an antisocial lie are the same or different when they tell a prosocial lie. The current study is the first to concurrently examine the facial movements of children during antisocial and prosocial lying. Through the use of the Computer Recognition Toolbox (Littlewort et al., 2011), an automated computer vision program using the Facial Action Coding System (Ekman & Friesen, 1978), children’s nonverbal behaviours were shown to be significantly different in terms of 8 different facial actions. Furthermore, linear support vector machine (SVM) analysis was successful in classifying children’s lies with an average accuracy of 72.68%, significantly above chance levels. Implications and limitations are discussed.
Symbolic representation of smart meter data

Currently smart meter data analytics has received enormous attention because it allows utility companies to analyze customer consumption behavior in real time. However, the amount of data generated by these sensors is very large. As a result, analytics performed on top of it become very expensive. Furthermore, smart meter data contains very detailed energy consumption measurement which can lead to customer privacy breach and all risks associated with it. In this work, we address the problem on how to reduce smart meter data numerosity and its detailed measurement while maintaining its analytics accuracy. We convert the data into symbolic representation and allow various machine learning algorithms to be performed on top of it. In addition, our symbolic representation admit an additional advantage to allow also algorithms which usually work on nominal and string to be run on top of smart meter data. We provide an experiment for classification and forecasting tasks using real-world data. And finally, we illustrate several directions to extend our work further.
A handbook of protocols for standardised and easy measurement of plant functional traits worldwide

There is growing recognition that classifying terrestrial plant species on the basis of their function (into 'functional types') rather than their higher taxonomic identity, is a promising way forward for tackling important ecological questions at the scale of ecosystems, landscapes or biomes. These questions include those on vegetation responses to and vegetation effects on, environmental changes (e.g. changes in climate, atmospheric chemistry, land use or other disturbances). There is also growing consensus about a shortlist of plant traits that should underlie such functional plant classifications, because they have strong predictive power of important ecosystem responses to environmental change and/or they themselves have strong impacts on ecosystem processes. The most favoured traits are those that are also relatively easy and inexpensive to measure for large numbers of plant species. Large international research efforts, promoted by the IGBP–GCTE Programme, are underway to screen predominant plant species in various ecosystems and biomes worldwide for such traits. This paper provides an international methodological protocol aimed at standardising this research effort, based on consensus among a broad group of scientists in this field. It features a practical handbook with step-by-step recipes, with relatively brief information about the ecological context, for 28 functional traits recognised as critical for tackling large-scale ecological questions.
High-Performance Research Organization

Here are ten attributes that help managements do the right things to turn their visions into reality. OVERVIEW: Performance measures based on generic frameworks, such as the balanced scorecard, are too general for reporting to senior RD not all research activity leads to expected results. Furthermore, the benefits from research sometimes take years to materialize. * The information available to Boards and CEOs for assessing the performance of research organizations is inadequate. The information tends to focus on past performance (e.g., published papers and patents) and on process and activities. However, high past performance does not guarantee the same in the future; today's executives need better and more current information. …
A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction

In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and effect of the NWP error on forecasting prediction, working days and weekends load forecasting models are selected and constructed, respectively. The ABC-SVR method is used to forecast weekends load with large fluctuation, in which the best parameters of SVR are determined by the ABC algorithm. The working days load forecasting model is constructed based on SARIMA modified by ABC-SVR (AS-SARIMA). In the AS-SARIMA model, the ability of SARIMA to respond to exogenous variables is improved and the effect of NWP error on prediction accuracy is reduced more than with ABC-SVR. Contrast experiments are constructed based on International Organization for Standardization (ISO) New England load data. The experimental results show that prediction accuracy of the proposed method is less affected by NWP error and has higher forecasting accuracy than contrasting approaches.
Cooperation cannot be sustained in a discounted repeated prisoners' dilemma with patient short and long run players

This study presents a modified version of the repeated discounted prisoners' dilemma with long and short-run players. In our setting a short-run player does not observe the history that has occurred before he was born, and survives into next phases of the game with a probability given by the current action profile in the stage game. Thus, even though it is improbable, a short-run player may live and interact with the long-run player for infinitely long amounts of time. In this model we prove that under a mild incentive condition on the stage game payoffs, the cooperative outcome path is not subgame perfect no matter how patient the players are. Moreover with an additional technical assumption aimed to provide a tractable analysis, we also show that payoffs arbitrarily close to that of the cooperative outcome path, cannot be obtained in equilibrium even with patient players.
A comparison of three methods of measuring dementia-specific quality of life: perspectives of residents, staff, and observers.

PURPOSE This exploratory study compared three methods of assessing dementia specific quality of life, corresponding to the perspectives of residents, staff members, and trained observers. DESIGN AND METHODS We collected data on 172 residents with dementia in four special care nursing facilities and three assisted living facilities. Analyses assessed the relationship of each quality-of-life method or perspective to the others and to resident characteristics such as cognitive and functional status. RESULTS The relationship of staff quality-of-life measures to resident characteristics varied by care setting while no significant relationships were found for resident quality-of-life measures. Staff and observational measures were moderately correlated in both settings. Moderate correlations of resident measures with staff and observational measures were found in the assisted living sample. IMPLICATIONS Each perspective is relatively independent and somewhat unique. Measures that focus on specific aspects of quality of life may be more appropriate to use with assisted living residents than with residents of special care facilities.
Common region of interest generation between mobile robots by cognitive ontology

This paper describes about common region of interest (CROI) generation between two mobile robots with the help of cognitive ontology for solving a cooperative task. In this research, both robots are observing different region of the scene and have different capabilities. In this method, robots use cognitive ontology and evaluate various semantic relations of concepts in a ROI. After evaluation, robots select the best region of interest (ROI) based on semantic relatedness and feature ambiguity value with the help of best feature selection algorithm. To make it useful in cooperation, robots generate CROI by sharing the best ROI. The experimental results showed that the method is very useful for distributed cognition which is very crucial problem in AI.
Robust velocity sliding mode control of mobile wheeled inverted pendulum systems

There has been an increasing interest in a kind of underactuated mechanical systems, mobile wheeled inverted pendulum (MWIP) models, which are widely used in the field of autonomous robotics and intelligent vehicles. Robust velocity tracking problem of MWIP systems is investigated in this study. In the velocity control problem, model uncertainties accompany uncertain equilibriums, which make the controller design become more difficult. A sliding mode control (SMC) method based on a novel sliding surface is proposed for the systems, which are capable of handling both parameter uncertainties and external disturbances. By assuming the specially designed sliding surface, the proposed SMC controller is capable of eliminating the steady velocity tracking error. The asymptotical stability of the closed-loop system is achieved through selecting sliding surface parameters in terms of some rules. The effectiveness of the proposed methods is finally confirmed by numerical simulations.
A Comment on Nonsupersymmetric Fixed Points and Duality at large N

We review some of the problems associated with deriving field theoretic results from nonsupersymmetric AdS, focusing on how to control the behavior of the field theory along the flat directions. We discuss an example in which the origin of the moduli space remains a stable vacuum at finite N, and argue that it corresponds to an interacting CFT in three dimensions. Associated to this fixed point is a statement of nonsupersymmetric duality. Because 1/N corrections may change the global picture of the RG flow, the statement of duality is much weaker than in the supersymmetric case.
Bridging the semantic gap in OLAP models: platform-independent queries

The development of data warehouses is based on a three-stage process that starts specifying both the static and dynamic properties of on-line analytical processing (OLAP) applications by means of an intuitive, semantically rich abstraction, namely the conceptual model. Then, developers design its logical counterpart where platform-specific details such as performance or storage are also considered. Nevertheless, it is well known the existence of a semantic gap between the conceptual and logical levels that decreases the feasibility of their mapping. In order to bridge this gap, we propose the use of conceptual OLAP queries, i.e., platform-independent, that can be automatically traced to their logical implementation in a coherent and integrated way. For this aim, in this paper, we focus on describing the specification of an OLAP algebra at the conceptual level by using the object-constraint language (OCL). Its operations are then translated into a particular OLAP system by using a model-driven architecture (MDA). The great advantage of our approach is that we allow analysts to query data warehouses without being aware of logical details.
Bounds on the separation of two parallel disk models

The single-disk, D-head model of parallel I/0 was introduced by Agarwal and Vitter to analyze algorithms for problem instances that are too large to fit in primary memory. Subsequently Vitter and Shriver proposed a more realistic model in which the disk space is partitioned into D disks, with a single head per disk. To date, each problem for which there is a known optimal algorithm for both models has the same asymptotic bounds on both models. Therefore, it has been unknown whether the models are equivalent or whether the singledisk model is strictly more powerful. In this pape:r we provide evidence that the single-disk model is strictly more powerful. We prove a lower bound on any general simulation of the single-disk model on the multi-disk model and establish randomized and deterministic upper bounds. Let N be the problem size and let T be the number of parallel I/Os required by a program on the single-disk model. Then any simulation of this pro€:ram on the multi-disk model will require Q ( T 10~/:,C:Ct~b:~) parallel I/Os. This lower bound holds even if replication is allowed in the multi-disk model. *Department of Computer Science, University of Hartford, 200 Bloomfield Avenue, W. Hartford, CT 06117-1599. Email: armenGhartford.edu. This work was done while the author was a graduate student at Dartmouth College. Permission 10 make digital/hard copies of all or part of this material for personal or classroom use its granted without fee provided that the copies a.re not made or distributed for profit or commercial advantage, the copynght notice, the title of the publication and its date appear, and notice is given that copyright is by permission of the ACM, Inc. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires specific permission and/or fee. 10PADS'96, Philadelphia PA, USA o 1996 ACM 0-89791-813-4/96/05 . $3.50 122 We also show an 0 Co~0f0~ D) randomized upper bound and an 0 (log D(log log D) ) deterministic upper bound. These results exploit an interesting analogy between the disk models and the PRAM and DCM models of parallel computation.
The Phase Transition Behaviour of Non-binary Forward Checking Algorithms

In this paper the phase transition, that is to say the transition from a region in which almost all problems have many solutions to a region in which almost all problems have no solution as the constraints become tighter, is investigated by examining n-ary Constraint Satisfaction Problems (CSPs). The paper reports a series of experiments with samples of randomly-generated problems, using a family of non-binary Forward Checking algorithms. We observe the behaviour of these algorithms with respect to the phase transition properties of n-ary Constraint Satisfaction Problems (CSPs). An n-ary random problem has an arity for the constraints, a number of variables, a uniform domain size, connectivity, and tightness of constraints. It was observed that for a problem with a given arity, number of variables, domain size, and connectivity there is a critical level of constraint tightness at which a phase transition occurs, such that the problems change from being soluble to insoluble. At the phase transition the search effort becomes orders of magnitude harder than elsewhere. It is shown that the location of the really hard problems can be predicted with a reasonable degree of accuracy. The accuracy of prediction based on the expected number of solutions is discussed. MOTS-CLÉS : Transition de phase, satisfaction de contraintes
Photochemical Aryl Radical Cyclizations to Give (E)-3-Ylideneoxindoles

(E)-3-Ylideneoxindoles are prepared in methanol in reasonable to good yields, as adducts of photochemical 5-exo-trig of aryl radicals, in contrast to previously reported analogous radical cyclizations initiated by tris(trimethylsilyl)silane and azo-initiators that gave reduced oxindole adducts.
Susceptibility Effects and Phase Contrast

Introduction Magnetic susceptibility effects resulting from the heterogeneous magnetic properties of living systems are of obvious importance for high field magnetic resonance imaging and spectroscopy. The relatively large differences in the magnetic susceptibility of water, bone and air, gives rise to the large length-scale magnetic field inhomogeneity, which causes distortion and signal attenuation in MRI [1], as well as a variety of other off-resonance effects. Smaller differences in the susceptibility of other MR-signal generating tissues (e.g. grey matter, white matter and cerebrospinal fluid) give rise to local variation in the phase of gradient echo images, which forms the basis of susceptibility weighted imaging (SWI) and phase contrast [2-12] both of which offer great promise for high field MRI. This presentation will: (i) describe the electromagnetic theory that links susceptibility and magnetic field; (ii) show how the spatial variation of magnetic susceptibility translates into a spatially varying NMR frequency; (iii) briefly discuss the artefacts caused by such variation before focussing on the exploitation of the small, local frequency offsets in phase contrast and SWI.
Simultaneous fractal image denoising and interpolation

In this paper, a simple and effective fractal-based simultaneous image denoising and interpolation scheme is proposed and implemented. The denoising is performed during the fractal encoding process while the interpolation is performed during the decoding process. The fractal-based image denoising involves predicting the fractal code of the original noiseless image from the statistics of the noisy observation. This fractal code can then be used to generate a fractally denoised estimate of the original image. The fractal interpolation can be easily achieved during the decoding process by iterating the predicted fractal code on a suitably sized blank initial image seed. The cycle spinning algorithm can also be incorporated in the proposed fractal joint denoising and resizing scheme in order to reduce some of the artifacts and enhance the visual quality of the fractally denoised and resized estimates.
Peer support groups for parent-carers of children with attention deficit hyperactivity disorder: the importance of solidarity as care

Abstract Parent-carers of children with complex needs often lead lives impacted by challenging constellations of disadvantage and can become enmeshed in complex and contradictory tapestries of care structures and relations. Against a backdrop of financial austerity and under-resourced or exclusionary service practices, peer support groups may become increasingly important and confer valuable benefits not available elsewhere. This article focuses on one such group developed for parent-carers of children diagnosed with attention deficit hyperactivity disorder, and reports on semi-structured interviews with 13 parent-carers. Drawing upon Pols’ empirical ethics of care, we nuance and detail the care that emerged in these settings, highlighting the material and relational practices that developed. We conclude that peer support groups can offer new and vital possibilities, re-situating participants from individualised positions of burden, isolation and social exclusion to confident, positive, active connectedness, by means of an affective and effective relational process we call ‘solidarity-as-care’.
Peg Precipitation Coupled with Chromatography is a New and Sufficient Method for the Purification of Botulinum Neurotoxin Type B

Clostridium botulinum neurotoxins are used to treat a variety of neuro-muscular disorders, as well as in cosmetology. The increased demand requires efficient methods for the production and purification of these toxins. In this study, a new purification process was developed for purifying type B neurotoxin. The kinetics of C.botulinum strain growth and neurotoxin production were determined for maximum yield of toxin. The neurotoxin was purified by polyethylene glycol (PEG) precipitation and chromatography. Based on design of full factorial experiment, 20% (w/v) PEG-6000, 4°C, pH 5.0 and 0.3 M NaCl were optimal conditions to obtain a high recovery rate of 87% for the type B neurotoxin complex, as indicated by a purification factor of 61.5 fold. Furthermore, residual bacterial cells, impurity proteins and some nucleic acids were removed by PEG precipitation. The following purification of neurotoxin was accomplished by two chromatography techniques using Sephacryl™ S-100 and phenyl HP columns. The neurotoxin was recovered with an overall yield of 21.5% and the purification factor increased to 216.7 fold. In addition, a mouse bioassay determined the purified neurotoxin complex possessed a specific toxicity (LD50) of 4.095 ng/kg.