Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Image Fusion

This paper introduces temporal image fusion. The proposed technique builds upon previous research in exposure fusion and expands it to deal with the limited Temporal Dynamic Range of existing sensors and camera technologies. In particular, temporal image fusion enables the rendering of long-exposure effects on full frame-rate video, as well as the generation of arbitrarily long exposures from a sequence of images of the same scene taken over time. We explore the problem of temporal under-exposure, and show how it can be addressed by selectively enhancing dynamic structure. Finally, we show that the use of temporal image fusion together with content-selective image filters can produce a range of striking visual effects on a given input sequence.
Time-bounded model checking of infinite-state continuous-time Markov chains

The design of complex concurrent systems often involves intricate performance and dependability considerations. Continuous-time Markov chains (CTMCs) are widely used models for concurrent system designs making it possible to model check such properties. In this paper, we focus on probabilistic timing properties of infinite-state CTMCs, expressible in continuous stochastic logic (CSL). Such properties comprise important dependability measures, such as timed probabilistic reachability, performability, survivability, and various availability measures like instantaneous availabilities, conditional instantaneous availabilities and interval availabilities. Conventional model checkers explore the given model exhaustively which is not always possible either due to state explosion or because the model is infinite. This paper presents a method that only explores the infinite (or prohibitively large) model up to a finite depth, with the depth bound being computed on-the-fly. We provide experimental evidence showing that our method is effective.
Perception of need for help and use of mental health services in children and adolescents. Do they share the same predictors

The study identifies the variables that predict the perception of need for psychiatric help and the use of mental health services in children and adolescents from 7 to 17 years old attending psychiatric and pediatric outpatient consultation. The perception of problems and consultation depend on the degree of functional impairment, parents' perception of the existence of problems, the age of the child, temperamental traits, parental rearing style and severe stressful life-events. The variables that permit realization of the existence of problems but do not lead to consultation are primarily developmental difficulties. Families consult with professionals, despite not perceiving the existence of psychiatric problems, when children have difficulties in their behavioral style. The presence of psychopa tholog y is not enough to explain the perception of the need for help by the subject or the fact of attending consultation. Resultant indicators may be useful for planning mental health services.
COLLABORATIVE SPAM FILTERING WITH THE HASHING TRICK

User feedback is vital to the quality of the collaborative spam fi lters frequently used in open membership email systems such as Yahoo Mail or Gmail. Users occasionally designate emails as spam or non-spam (often termed as ham), and these labels are subsequently used to train the spam fi lter. Although the majority of users provide very little data, as a collective the amount of training data is very large (many millions of emails per day). Unfortunately, there is substantial deviation in users’ notions of what constitutes spam and ham. Additionally, the open membership policy of these systems makes it vulnerable to users with malicious intent – spammers who wish to see their emails accepted by any spam fi ltration system can create accounts and use these to give malicious feedback to ‘train’ the spam fi lter in giving their emails a free pass. When combined, these realities make it extremely diffi cult to assemble a single, global spam classifi er.
Promoting Individual , Family , and Community Connectedness to Prevent Suicidal Behavior Strategic Direction for the Prevention of Suicidal Behavior

In the United States and around the world, self-directed violence is a serious public health problem. Such violence includes acts of suicidal behavior (fatal and nonfatal attempts), suicidal ideation (thinking about, considering, or planning for suicide), and nonsuicidal, intentional self-harm (behaviors with the intention not to kill oneself, as in self­ mutilation). 2, 3 Fatal and nonfatal suicidal behavior has a most pronounced morbidity and mortality and is thus the focus of CDC’s Division of Violence Prevention’s (DVP) strategic direction. In 2005, 32,637 suicides occurred—the 11 leading cause of death. Because of suicide’s greater effect on adolescents and young adults relative to other causes of death, suicide ranked as the fifth leading cause of years of potential life lost before age 65. In fact, in 2005, suicide was the third leading cause of death for 10to 24-year-olds, and the second leading cause of death for 25to 34-year-olds. Moreover, although suicide remains a problem among youth and young adults, overall rates of death due to suicide continue highest among persons aged 65 and older.
The impact of short-term adventure-based outdoor programs on college students’ stress reduction

ABSTRACT This study examines the effect of short-term adventure-based outdoor programs (AOP) on reducing college students’ stress levels. The effects of sex and different outdoor adventure activities in stress reduction are also evaluated. Thirty-three college students participated in selected short-term backpacking, canoeing, and kayaking programs. Student's salivary samples were collected to evaluate physiological stress response (as measured by levels of cortisol) and their psychological stress levels were collected through a modified 12-item questionnaire. Stress responses were measured at three different time points, including the second weekly meeting in the classroom, before the field trip, and at the conclusion of the field trip. Findings suggest that short-term AOP affords opportunities for college students to be away from daily challenges, and in turn reduce college students’ psychological stress levels and provide a temporary relief of physiological stress levels. No significant differences in college students’ stress levels were found between sex and different outdoor adventure activities.
Immediate and Long-Term Effectiveness of Disciplinary Tactics by Type of Toddler Noncompliance

SYNOPSIS Objective. To clarify when the disciplinary recommendations of positive parenting and behavioral parent training apply, this study investigated how the effectiveness of seven disciplinary tactics varies by type of toddler noncompliance, using methods to improve the validity of causal inferences. Design. Multilevel modeling and hierarchical longitudinal regression are used to test immediate and long-term effectiveness of disciplinary tactics in a convenience sample of 102 mother–toddler pairs, using coded information from detailed descriptions of a sample of five discipline episodes along with survey measures. Results. Offering alternatives is the most effective disciplinary tactic for reducing noncompliance severity immediately regardless of the type of noncompliance. Reasoning is the second best tactic for immediately reducing noncompliance severity when responding to parent-oriented noncompliance (negotiating and whining), whereas power assertive and punishment tactics are least effective, but the immediate effectiveness of those tactics reverses when responding to parent-opposing noncompliance (defiance and hitting). Long-term outcomes also differ for toddlers whose predominant noncompliance is parent-oriented or parent-opposing. For parent-oriented toddlers, frequently offering alternatives leads to reduced externalizing problems, whereas punishments increase their behavior problems. For oppositional toddlers, offering alternatives too frequently increases externalizing problems, whereas moderate use of punishments (<16% of the time) decreases total behavior problems. Frequent reasoning also reduces subsequent externalizing problems for oppositional toddlers, despite being the least effective disciplinary response for de-escalating parent-opposing noncompliance immediately. Conclusions. Parenting advice should move beyond universal disciplinary recommendations to help parents match their disciplinary tactics to their child’s type of noncompliance.
Optimal Sites for Supplementary Weather Observations: Simulation with a Small Model

Abstract Anticipating the opportunity to make supplementary observations at locations that can depend upon the current weather situation, the question is posed as to what strategy should be adopted to select the locations, if the greatest improvement in analyses and forecasts is to be realized. To seek a preliminary answer, the authors introduce a model consisting of 40 ordinary differential equations, with the dependent variables representing values of some atmospheric quantity at 40 sites spaced equally about a latitude circle. The equations contain quadratic, linear, and constant terms representing advection, dissipation, and external forcing. Numerical integration indicates that small errors (differences between solutions) tend to double in about 2 days. Localized errors tend to spread eastward as they grow, encircling the globe after about 14 days. In the experiments presented, 20 consecutive sites lie over the ocean and 20 over land. A particular solution is chosen as the true weather. Every 6 h obs.
The Model of Oil Spills Due to Ships Collisions in Southern Baltic Area

The paper presents implementation of probabilistic ships collision model to evaluation of possible oil spills in the Southern Baltic Sea area. The results of the model is time, place and size of the oil spill due to ships collision. The results could be used for oil spill response action plans. The paper will open the discussion about validation of achieved results and will try to answer the question about verification of Baltic Sea oil spills data in comparison to worlds statistics.
Gamification and Smart, Competence-Centered Feedback: Promising Experiences in the Classroom

Gamification appears being a promising approach to utilize the strong motivational potential of “gaming” in classroom without suffering from shortcomings such as low efficiency, weak pedagogy, or maybe most importantly the high costs. In the context of a European project we developed a rather light weight tool for learning and practicing multiplications. The target age group of the tool is 6 to 8 years. To benefit from the motivational potential of games we used a “gamification” approach. Accordingly we designed and developed a game-like, attractive user interface and integrated aspects of competition. The system is capable of providing students formative, competence-oriented feedback in real-time. Tailored to the age group this feedback is presented in form of a ninja character. For an experimental comparison of the effects of different feedback modes, we realized the conditions (i) no feedback, (ii) written only right/wrong feedback, (iii) audio right/wrong feedback, and (iv) competence-based, smart formative feedback. We applied and evaluated the tool in Austrian classrooms and found some evidence for the motivational aspect of the gamification elements, in particular the scoring. We also found strong positive effects of an individualized and meaningful feedback about achievements and progress.
A Privacy-Preserving Scheme for Online Social Networks with Efficient Revocation

Online social networks (OSNs) are attractive applications which enable a group of users to share data and stay connected. Facebook, Myspace, and Twitter are among the most popular applications of OSNs where personal information is shared among group contacts. Due to the private nature of the shared information, data privacy is an indispensable security requirement in OSN applications. In this paper, we propose a privacy-preserving scheme for data sharing in OSNs, with efficient revocation for deterring a contact's access right to the private data once the contact is removed from the social group. In addition, the proposed scheme offers advanced features such as efficient search over encrypted data files and dynamic changes to group membership. With slight modification, we extend the application of the proposed scheme to anonymous online social networks of different security and functional requirements. The proposed scheme is demonstrated to be secure, effective, and efficient.
Opinion Mining and Analysis for Arabic Language

Social media constitutes a major component of Web 2.0 and includes social networks, blogs, forum discussions, micro-blogs, etc. Users of social media generate a huge volume of reviews and comments on a daily basis. These reviews and comments reflect the opinions of users about different issues, such as: products, news, entertainments, or sports. Therefore different establishments may need to analyze these reviews and comments. For examples: It is essential for companies to know the pros and cons of their products or services in the eyes of customers. Governments may want to know the attitude of people towards certain decisions, services, etc. Although the manual analysis of textual reviews and comments can be more accurate than the automatic methods, nonetheless, it is time consuming, expensive, and can be subjective. Furthermore, the huge amount of data contained in social networks can make it impractical to perform analysis manually. This paper focuses on evaluating Arabic social content. Currently, Middle East is an area rich of major political and social reforms. The social media can be a rich source of information to evaluate such contexts. In this research we developed an opinion mining and analysis tool to collect different forms of Arabic language (i.e. Standard or MSA, and colloquial). The tool accepts comments and opinions as input and generates polarity based outputs related to the comments. Additionally the tool can determine the comment or review is: (subjective or objective), (positive or negative), and (strong or weak). The evaluation of the performance of the developed tool showed that it yields more accurate results when it is applied on domain-based Arabic reviews relative to general-based Arabic reviews.
An improved quantization scheme for lattice-reduction aided MIMO detection

Lattice reduction aided (LRA) has been introduced recently by Yao and Wornell as a new type of MIMO detector. However, the quantization step of LRA detector is only suboptimal. In this paper, we propose a list quantization scheme to reduce the effect of quantization error. The method is to generate a list of candidate symbols from the original LRA estimated symbol. The simulation shows that improved performance can be obtained while additional complexity is reasonable small. The performance of LRA-MMSE with list quantization is shown to be very close to ML performance in both case, 4-QAM and 16-QAM modulation.
Power Normalized Cepstral Coefficients based supervectors and i-vectors for small vocabulary speech recognition

Template-matching and discriminative techniques, like support vector machines (SVMs), have been widely used for automatic speech recognition. Both methods require that varying length sequences are mapped to vectors of fixed lengths: in template-matching, the problem is solved by means of dynamic time warping (DTW), while in SVM with dynamic kernels. The supervector and i-vector paradigms seem to represent a valid solution to such a problem when SVM are employed for classification. In this work, Gaussian mean supervectors (GMS), Gaussian posterior probability supervectors (GPPS) and i-vectors are evaluated as features both for template-matching and for SVM-based speech recognition in a comparative fashion. All these features are based on Power Normalized Cepstral Coefficients (PNCCs) directly extracted from speech utterances. The different methods are assessed in small vocabulary speech recognition tasks using two distinct corpora, and they have been compared to DTW, dynamic time alignment kernel (DTAK), outerproduct of trajectory matrix, and PocketSphinx as further recognition techniques to be evaluated. Experimental results showed the appropriateness of the supervector and i-vector based solutions with respect to the other state-of-the art techniques here addressed.
Face Recognition Using Independent Component Analysis of Gabor Responses

This paper addresses new face recognition method based on Independent Component Analysis(1CA) and Gabor filter. Our method consists of three parts. The first part is Gabor filtering on predefined fiducial points that could represent robust facial features from original face image. The second part is transforming the facial features into the basis space of ICA, which is able to represent individual facial features optimally. Thus, trained face images are represented as points in the space. In order to identify, test images are also projected into the basis space of ICA from image space and compared to the training images using fisher linear discriminant(FLD) in the space. The basic idea of combining ICA and Gabor filter is to overcome the short-comings of ICA. When raw images were used as input of ICA, the basis space of ICA cannot reflect the correlation of facial feature well, because original face images have deformation due to in-plane, in-depth rotation and brightness and contrast variation. So, we have overcome these problems using Gabor filter responses as input data. Gabor filter has the robust characteristics in illumination and rotation. Four face recognition method - PCA, Gabor filter response, PCA of Gabor filter response and ICA are used in the recognition experiments. We confirmed the improvement of discrimination ability when the Gabor responses had transferred to the space constructed by the independent components. And, our method has excessive advantage in gallery DB size than recognition method only using Gabor filter responses.
Low-Rank Matrix Recovery via Efficient Schatten p-Norm Minimization

As an emerging machine learning and information retrieval technique, the matrix completion has been successfully applied to solve many scientific applications, such as collaborative prediction in information retrieval, video completion in computer vision, \emph{etc}. The matrix completion is to recover a low-rank matrix with a fraction of its entries arbitrarily corrupted. Instead of solving the popularly used trace norm or nuclear norm based objective, we directly minimize the original formulations of trace norm and rank norm. We propose a novel Schatten $p$-Norm optimization framework that unifies different norm formulations. An efficient algorithm is derived to solve the new objective and followed by the rigorous theoretical proof on the convergence. The previous main solution strategy for this problem requires computing singular value decompositions - a task that requires increasingly cost as matrix sizes and rank increase. Our algorithm has closed form solution in each iteration, hence it converges fast. As a consequence, our algorithm has the capacity of solving large-scale matrix completion problems. Empirical studies on the recommendation system data sets demonstrate the promising performance of our new optimization framework and efficient algorithm.
OPTIMAL RANK-BASED TESTING FOR PRINCIPAL COMPONENTS

This paper provides parametric and rank-based optimal tests for eigenvectors and eigenvalues of covariance or scatter matrices in elliptical families. The parametric tests extend the Gaussian likelihood ratio tests of Anderson (1963) and their pseudo-Gaussian robustifications by Tyler (1981, 1983) and Davis (1977), with which their Gaussian versions are shown to coincide,symptotically, under Gaussian or finite fourth-order moment assumptions, respectively. Such assumptions however restrict the scope to covariance-based principal component analysis. The rank-based tests we are proposing remain valid without such assumptions. Hence, they address a much broader class of problems, where covariance matrices need not exist and principal components are associated with more general scatter matrices. Asymptotic relative efficiencies moreover show that those rank-based tests are quite powerful; when based on van der Waerden or normal scores, they even uniformly dominate the pseudo-Gaussian versions of Anderson’s procedures. The tests we are proposing thus outperform daily practice both from the point of view of validity as from the point of view of efficiency. The main methodological tool throughout is Le Cam’s theory of locally asymptotically normal experiments, in the nonstandard context, however, of a curved parametrization. The results we derive for curved experiments are of independent interest,and likely to apply in other setups.
Local analysis of hybrid systems on polyhedral sets with state-dependent switching

Abstract This paper deals with stability analysis of hybrid systems. Various stability concepts related to hybrid systems are introduced. The paper advocates a local analysis. It involves the equivalence relation generated by reset maps of a hybrid system. To establish a tangible method for stability analysis, we introduce the notion of a chart, which locally reduces the complexity of the hybrid system. In a chart, a hybrid system is particularly simple and can be analyzed with the use of methods borrowed from the theory of differential inclusions. Thus, the main contribution of this paper is to show how stability of a hybrid system can be reduced to a specialization of the well established stability theory of differential inclusions. A number of examples illustrate the concepts introduced in the paper.
Varying input segmentation for story boundary detection in English, Arabic and Mandarin broadcast news

Story segmentation of news broadcasts has been shown to improve the accuracy of the subsequent processes such as question answering and information retrieval. In previous work, a decision tree trained on automatically extracted lexical and acoustic features was trained to predict story boundaries, using hypothesized sentence boundaries to define potential story boundaries. In this paper, we empirically evaluate several alternatives to this choice of input segmentation on three languages: English, Mandarin and Arabic. Our results suggest that the best performance can be achieved by using 250ms pause-based segmentation or sentence boundaries determined using a very low confidence score threshold. Index Terms: story boundary detection, segmentation
Early pathological gambling in co-occurrence with semantic variant primary progressive aphasia: a case report

Abstract We have comprehensively documented a case of semantic variant of primary progressive aphasia (sv-PPA) presenting with early-onset pathological gambling (PG). While a growing number of studies have shown the presence of behavioral alterations in patients with sv-PPA, PG has been observed only in the behavioral variant of frontotemporal dementia (bv-FTD). To date, no case of PG with the co-occurrence of prominent semantic deficits at the onset of the disease has been reported in the literature. Impulse disorders at onset may wrongly lead to a misdiagnosis (ie, psychiatric disorders). Therefore, a wider characterization of cognitive/aphasia symptoms in patients presenting impulse disorders and predominant language dysfunctions is recommended.
Convergence analysis of the sign algorithm for adaptive filtering

We consider the convergence analysis of the sign algorithm for adaptive filtering when the input processes are uncorrelated and Gaussian and a fixed step size /spl mu/>0 is used. Exact recursive equations for the covariance matrix of the deviation error are established for any step size /spl mu/>0. Asymptotic time-averaged convergence for the mean-absolute deviation error, mean-square deviation error, and for the signal mean-square estimation error are established. These results are shown to hold for arbitrary step size /spl mu/>0. >