Miracle-dz/UniFAR-FTUs huggingface dataset:
The absent-present researcher: data analysis of pre-recorded parent-driven campaign videos

ABSTRACT In recent years, there has been a proliferation of sophisticated, user-friendly and accessible instruments of video data collection (e.g. mobile/cell phones and tablets) which facilitate video-based research and analysis. This paper reports on the opportunities and challenges of undertaking video analysis by reporting on the qualitative video analysis of a subset of 30 purposively selected videos from #notanurse_but, a parent-driven video campaign initiated by WellChild, a UK charity. This paper provides insight into one way of conducting video analysis, appreciating that a variety of approaches exist and that methodological reflections on analytical work with video recordings are limited. The authors critically consider researcher subjectivity; the everydayness of video data; making assumptions; and the incomplete picture provided by video data. Despite notable limitations to the approach of video analysis as a standalone method, the authors conclude that video analysis is capable of eliciting data that may not otherwise be obtained.
Centre De Referència En Economia Analítica Barcelona Economics Working Paper Series Working Paper Nº 129 I Want You! an Experiment Studying Motivational Effects When Assigning Distributive Power I Want You! an Experiment Studying Motivational Effects When Assigning Distributive Power

Will the fact that a person is selected among others on the basis of personal information affect that person's behavior? In our experiment participants first answer a personality questionnaire. They then play a 3-person game: one of the three players decides, after inspecting the personality questionnaires, between an outside option and granting allocation power over a pie to one of the other two players. Treatments differ in the procedure by which distribution power is assigned: to a randomly determined or to a knowingly selected partner. Results show that knowingly selected allocators keep less for themselves than randomly selected ones. We also find evidence that knowingly selected allocators treat selectors more generously than the third player. However, these effects become weaker over time.
On-line learning with linear loss constraints

We consider a generalization of the mistake-bound model (for learning {0, 1}-valued functions) in which the learner must satisfy a general constraint on the number M"+ of incorrect 1 predictions and the number M"- of incorrect 0 predictions. We describe a general-purpose optimal algorithm for our formulation of this problem. We describe several applications of our general results, involving situations in which the learner wishes to satisfy linear inequalities in M"+ and M"-.
Neural Mechanisms Mediating Contingent Capture of Attention by Affective Stimuli

Attention is attracted exogenously by physically salient stimuli, but this effect can be dampened by endogenous attention settings, a phenomenon called “contingent capture. ” Emotionally salient stimuli are also thought to exert a strong exogenous influence on attention, especially in anxious individuals, but whether and how top–down attention can ameliorate bottom–up capture by affective stimuli is currently unknown. Here, we paired a novel spatial cueing task with fMRI to investigate contingent capture as a function of the affective salience of bottom–up cues (face stimuli) and individual differences in trait anxiety. In the absence of top–down cues, exogenous stimuli validly cueing targets facilitated attention in low-anxious participants, regardless of affective salience. However, although high-anxious participants exhibited similar facilitation following neutral exogenous cues, this facilitation was completely absent following affectively negative exogenous cues. Critically, these effects were contingent on endogenous attentional settings, such that explicit top–down cues presented before the appearance of exogenous stimuli removed anxious individuals' sensitivity to affectively salient stimuli. fMRI analyses revealed a network of brain regions underlying this variability in affective contingent capture across individuals, including the fusiform face area (FFA), posterior ventrolateral frontal cortex, and SMA. Importantly, activation in the posterior ventrolateral frontal cortex and the SMA fully mediated the effects observed in FFA, demonstrating a critical role for these frontal regions in mediating attentional orienting and interference resolution processes when engaged by affectively salient stimuli.
Predict Foreground Object in Still Image Stanford Univeresity CS 229 Fall 2013

In this paper, we are interested in the detection of foreground objects in a single still image. Using the collections of outdoor scene RGB images, we applied supervised and unsupervised machine learning techniques to predict the foreground objects. The main challenge of foreground object detection, is that single pixel from a given image is not sufficient to distinguish between foreground or background, and thus, combination of features needs to be considered.
Molecular Dynamics Simulation of Saposin C-Membrane Binding

To understand the mechanism of Saposin C-membrane binding at low pH, several molecular dynamics (MD) simulations of saposin C with 1,2-dioleoyl-sn-glycero-3-phosphatidylserine (DOPS 18; 1) or modified DOPS were per- formed in the default charge states at neutral pH or, based on the pKa calculations, with some protonated acidic residues to approximate low pH conditions. It was found that the electrostatic attraction between the protein-COO - and lipid-NH3 + groups played an important role in the binding process in addition to the electrostatic attraction between the lipid-COO - and protein-NH3 + groups. The two pairs of attractive interactions acted basically on the opposite sites of saposin C. Neu- tralization of the amino groups of the lipids enabled saposin C-binding with the membrane at neutral pH. The simulation results provide insight into the nature and mechanism of the Saposin C-membrane binding process at low pH.
Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization

histogram which can be modeled as a mixture of two Gaussian density functions, estimating these densities inpractice is not simply feasible. The objective of this paper is to use adaptive particle swarm optimization (APSO) forthe suboptimal estimation of the means and variances of these two Gaussian density functions; then, the computationof the optimal threshold value is straightforward. The comparisons of experimental results in a wide range of complexbimodal images show that this proposed thresholding algorithm presents higher correct detection rate of object andbackground in comparison to the other methods including Otsu’s method and estimating the parameters of Gaussiandensity functions using genetic algorithm (GA). Meanwhile, the proposed thresholding method needs lower executiontime than the PSO-based method, while it shows a little higher correct detection rate of object and background, withlower false acceptance rate and false rejection rate.
Radiometric measurements of the microwave emissivity of foam

Radiometric measurements of the microwave emissivity of foam were conducted in May 2000 at the Naval Research Laboratory's Chesapeake Bay Detachment using radiometers operating at 10.8 and 36.5 GHz. Horizontal and vertical polarization measurements were made at 36.5 GHz; horizontal, vertical, +45, -45, left circular, and right circular polarization measurements were obtained at 10.8 GHz. Surface foam was generated by blowing compressed air through gas permeable tubes supported by an aluminum frame and floats. Video micrographs of the foam were used to measure bubble size distribution and foam layer thickness. A video camera was boresighted with the radiometers to determine beam-fill fraction of the foam generator.
Plasma vitamin C level, fruit and vegetable consumption, and the risk of new-onset type 2 diabetes mellitus: the European prospective investigation of cancer--Norfolk prospective study.

BACKGROUND Epidemiologic studies suggest that greater consumption of fruit and vegetables may decrease the risk of diabetes mellitus, but the evidence is limited and inconclusive. Plasma vitamin C level is a good biomarker of fruit and vegetable intake, but, to our knowledge, no prospective studies have examined its association with diabetes risk. This study aims to examine whether fruit and vegetable intake and plasma vitamin C level are associated with the risk of incident type 2 diabetes. METHODS We administered a semiquantitative food frequency questionnaire to men and women from a population-based prospective cohort (European Prospective Investigation of Cancer-Norfolk) study who were aged 40 to 75 years at baseline (1993-1997) when plasma vitamin C level was determined and habitual intake of fruit and vegetables was assessed. During 12 years of follow-up between February 1993 and the end of December 2005, 735 clinically incident cases of diabetes were identified among 21 831 healthy individuals. We report the odds ratios of diabetes associated with sex-specific quintiles of fruit and vegetable intake and of plasma vitamin C levels. RESULTS A strong inverse association was found between plasma vitamin C level and diabetes risk. The odds ratio of diabetes in the top quintile of plasma vitamin C was 0.38 (95% confidence interval, 0.28-0.52) in a model adjusted for demographic, lifestyle, and anthropometric variables. In a similarly adjusted model, the odds ratio of diabetes in the top quintile of fruit and vegetable consumption was 0.78 (95% confidence interval, 0.60-1.00). CONCLUSIONS Higher plasma vitamin C level and, to a lesser degree, fruit and vegetable intake were associated with a substantially decreased risk of diabetes. Our findings highlight a potentially important public health message on the benefits of a diet rich in fruit and vegetables for the prevention of diabetes.
Eye-S: a full-screen input modality for pure eye-based communication

To date, several eye input methods have been developed, which, however, are usually designed for specific purposes (e.g. typing) and require dedicated graphical interfaces. In this paper we present Eye-S, a system that allows general input to be provided to the computer through a pure eye-based approach. Thanks to the "eye graffiti" communication style adopted, the technique can be used both for writing and for generating other kinds of commands. In Eye-S, letters and general eye gestures are created through sequences of fixations on nine areas of the screen, which we call hotspots. Being usually not visible, such sensitive regions do not interfere with other applications, that can therefore exploit all the available display space.
A generalized approach to parameterizing convection combining ensemble and data assimilation techniques

A new convective parameterization is introduced that can make use of a large variety of assumptions previously introduced in earlier formulations. The assumptions are chosen so that they will generate a large spread in the solution. We then show two methods in which ensemble and data assimilation techniques may be used to find the best value to feed back to the larger scale model. First, we can use simple statistical methods to find the most probable solution. Second, the ensemble probability density function can be considered as an appropriate “prior” (a'priori density) for Bayesian data assimilation. Using this “prior”, and information about observation likelihood, measured meteorological or climatological data can be directly assimilated into model fields. Given proper observations, the application of this technique is not restricted to convective parameterizations, but may be applied to other parameterizations as well.
A methodology for diagnostic evaluation of spoken human -- machine dialogue

Diagnostic evaluation is an important instrument for the development of high-quality spoken language dialogue systems. Yet no rigorous methodology exists for the systematic and exhaustive diagnostic evaluation of all aspects of spoken language interaction: recognition, synthesis, grammar, vocabulary, dialogue, etc. The paper addresses part of this problem by presenting a methodology for the diagnostic evaluation of spoken human? machine dialogue. The first part of the methodology is general and supports the detection of any kind of user? system interaction problem. The second part concerns the classification, diagnosis and repair of problems of dialogue interaction. These problems are of two broad kinds: dialogue design errors and user errors. Use of the methodology is demonstrated through analysis of a corpus from the user test of the Danish Dialogue System.
From requirements elicitation to variability analysis using repertory grid: A cognitive approach

The growing complexity and dynamics of the execution environment have been major motivation for designing self-adaptive systems. Although significant work can be found in the field of formalizing or modeling the requirements of adaptive system, not enough attention has been paid towards the requirements elicitation techniques for the same. It is still an open challenge to elicit the users' requirements in the light of various contexts and introduce the required flexibility in the system's behavior at an early phase of requirements engineering. We explore the idea of using a cognitive technique, repertory grid, to acquire the knowledge of various stakeholders along multiple dimensions of problem space and design space. We aim at discovering the scope of variations in the features of the system by capturing the intentional and technical variability in the problem space and design space respectively. A stepwise methodology for finding the right set of features in the changing context has also been provided in this work. We evaluate the proposed idea by a preliminary case study using smart home system domain.
Predictive multicast polling for wireless networks with multipacket reception and queuing

In this paper, we propose the predictive multicast polling scheme for medium access control in wireless networks with multipacket reception capability. We concentrate on the case in which the packet arrival process is general and the maximum queue size is finite but larger than one. We derive both analytical results and simulation results. We use the theory of discrete-time Markov chain to analyze the evolution of the system state. In addition, we propose to use Markov reward processes to calculate the exact value of the network throughput. Furthermore, we obtain the average system size, the packet blocking probability, and the average packet delay. We show that our numerical results are consistent with simulation results. We also use simulation results to justify the usage of the proposed approach. Our study shows that the system performance can be significantly improved with a few additional buffers in the queues.
A split and merge EM algorithm for color image segmentation

As an extremely powerful probability model, Gaussian mixture model (GMM) has been widely used in the fields of pattern recognition, information processing and data mining. However, in many practical applications, the number of the components is unknown. In the case, model selection of GMM, i.e., the selection of the number of the components in the mixture, has been a rather difficult problem. Recently, the minimum message length(MML) criterion has been proposed and used to make model selection. In this paper, we propose a split and merge algorithm to decide the number of the components, which is applied to the color image segmentation. Based on MML criterion, the proposed algorithm can determine the number of components in the Gaussian mixture model automatically during the parameter learning. By splitting and merging the uncorrect components, the algorithm can converge to the maximization of the MML criterion function and get a better parameter estimation of the Gaussian mixture. It has been demonstrated well by the experiments that the proposed split and merge algorithm can make both parameter learning and model selection efficiently for color image segmentation.
Dynamic power management for multidomain system-on-chip platforms

Reducing energy consumption in multiprocessor systems-on-chip (MPSoCs) where communication happens via the network-on-chip (NoC) approach calls for multiple voltage/frequency island (VFI)-based designs. In turn, such multi-VFI architectures need efficient, robust, and accurate runtime control mechanisms that can exploit the workload characteristics in order to save power. Despite being tractable, the linear control models for power management cannot capture some important workload characteristics (e.g., fractality, nonstationarity) observed in heterogeneous NoCs; if ignored, such characteristics lead to inefficient communication and resources allocation, as well as high power dissipation in MPSoCs. To mitigate such limitations, we propose a new paradigm shift from power optimization based on linear models to control approaches based on fractal-state equations. As such, our approach is the first to propose a controller for fractal workloads with precise constraints on state and control variables and specific time bounds. Our results show that significant power savings can be achieved at runtime while running a variety of benchmark applications.
SVM and ANN: A comparative evaluation

Support vector machines (SVMs) are among the most robust classifiers for the purpose of speech recognition. This paper compares one of the more contemporary methods of classification, artificial neural network (ANN) with support vector machines and draws conclusions based on a comparison of accuracy. The neural network is a pattern network for variable hidden neurons and transfer functions. C- Support vector classifier is used with three different kernels and kernel parameters. MFCC has been used as the feature extraction technique for a noiseless database of 50 independent speakers. The results were found to be best for SVM with RBF kernel in comparison to bi-quadratic polynomial and sigmoid kernels and pattern network.
Centralized provision of public goods : static vs dynamic performance in the presence of political pressure , lobbying and heterogeneity of tastes

This paper analyses the efficiency of centralized public goods provision in a model of incomplete contracts. Politicians determine the level of provision through wasteful yet publicly unobservable lobbying efforts. Thereby a threat of dismissal from office caused by regional expectations in terms of reservation utility induces politicians to push for influence of their region’s taste. In a static setting the performance of central governance is solely hampered by larger diversity of regional preferences. On the contrary, in a repeated game setting the ability to cooperate depends crucially on voter expectations. Via endogenous discounting it can be shown that rather too much national pressure hinders efficiencysustaining cooperation. Contrasting static results furthermore high levels of heterogeneity within regional preferences support the implementation of efficient outcomes. JEL classification: H11, H73
Web workbench i Web workbench

V. Richard Benjamins, Erik Brieva, José Manuel López Cobo, Eduard Gimenez Intelligent Software Components, S.A. Arthur Duineveld, Bob Wielinga University of Amsterdam Pim Borst Unilever (NL) Abstract Abstract: The web workbench is an intranet/internet-enabled workbench where users can browse, select, and combine components. The work described in this deliverable is tightly coupled to one of the library deliverables: the knowledge management library, which describes IBROWcompatible knowledge management components. Those components form the subject matter of the web workbench. The workbench is however not limited to this domain. In principle it is applicable to any library of components that is IBROW-compatible. The application domain for the workbench is based on knowledge management at Unilever.
Optimal CEO Incentive Contracts: A Prospect Theory Explanation

This study examines the relationship between CEO incentive-based compensationand firm performance, based on the role of prospect theory in executivecompensation. Our results indicate that moderate levels of performance-based CEOcompensation are generally optimal. When an executive’s total compensation packageis based more on firm performance there is often a level of higher returns, but toa point of diminishing return. Our findings suggest that boards must clearly communicatewith CEOs to determine the most appropriate levels of incentive-based compensation.Additional managerial and theoretical implications are offered, as well as avenues for future research.
Multiscale principal component analysis to separate respiratory influences from the tachogram: Application to stress monitoring

The effects of mental stress on heart rate variability (HRV) have been studied widely. However, the influence of respiration on short-term HRV is often ignored. Therefore, this study uses multiscale principal component analysis to separate the tachogram in 2 components: a component which is directly related to respiration, and a residual component which contains only changes in the heart rate that are unrelated to respiration. This approach is applied on data of 40 subjects during a baseline condition, a mental stress task and an attention task. The application of power spectral HRV analysis on the 2 components of the tachogram, reveals that stress influences the tachogram both via the respiration as well as directly via the functioning of the ANS. These results show that separation of the respiratory component of the tachogram can be a valuable tool to interpret HRV measures. Moreover, this approach might unveil changes in the functioning of the ANS that are otherwise masked by differing respiratory patterns.
Object Identification and Colour Recognition for Human Blind

Navigation Assistance for Visually Impaired (NAVI) is a vision substitute system designed to assist blind people for autonomous navigation. NAVI working concept is based on ‘image to sound’ conversion. The vision sensor, which is mounted on a sunglass, captures the image infront of blind user. NAVI processing unit processes the captured image and enhances the significant vision data by employing a set of image processing procedures. The processed information is then presented as a structured form of acoustic signal and it is conveyed to the blind user using a set of earphones. This paper proposes a new object identification and colour recognition module for NAVI system. Image processing techniques are proposed to identify objects in the captured image and then the processed image is transformed into stereo sound patterns. Colour information from the interested object is evaluated to determine the colour of the object. The colour output is informed to the blind user through ‘verbal voice’.
Evidence for Anisotropic Motion of the Clouds in Broad-Line Regions of BL Lacertae Objects

The masses of central massive black holes in BL Lac objects are estimated from their host galaxy absolute magnitude ℳR at R-band by using the empirical relation between ℳR and black hole mass Mbh. Only a small fraction of BL Lac objects exhibit weak broad-line emission, and we derive the sizes of the broad-line regions (BLRs) in these BL Lac objects from the widths of their broad emission lines on the assumption of the clouds being virilized in BLRs. It is found that the sizes of the BLRs in these sources are usually 2-3 orders of magnitude larger than those expected by the empirical correlation RBLR-λLλ(3000 Å) defined by a sample of Seyfert galaxies and quasars. We discuss a variety of possibilities and suggest that this difference can probably be attributed to anisotropic motion of the BLR clouds in these BL Lac objects. If the BLR geometry of these sources is disklike, the viewing angles between the axis and the line of sight are in the range ~2°-12°, which is consistent with the unification schemes.