Miracle-dz/UniFAR-FTUs huggingface dataset:
Bravemind: Advancing the Virtual Iraq/Afghanistan PTSD Exposure Therapy for MST

Abstract : PTSD can result from exposure to actual or threatened death, serious injury or sexual violation. Military Sexual Trauma (MST) has been recognized as a significant risk factor for the development of PTSD. This has become an issue of grave concern within the military, as reports of sexual violations and assaults have been on the rise over the last ten years, and have garnered significant popular media attention. The current project proposes to develop content for inclusion in the BRAVEMIND virtual reality exposure therapy (VRET) system that will provide new customizable options for persons who have experienced MST and to run a pilot RCT with a sample of 34 persons diagnosed with PTSD due to MST. The effort in the first year focused on two major tasks: 1) Complete VRET System Development for use in an upcoming pilot RCT that will test the efficacy of this form of treatment for PTSD due to MST (DONE). And, 2)Deliver the system to Emory University and support installation for conduct of the RCT that would commence in the first year and carry on to the second year. Thus far, the system was built and delivered and the RCT has commenced.
Speech enhancement for non-stationary noise environment by adaptive wavelet packet

We consider the non-stationary or colored noise estimation by wavelet thresholding method. First, we propose node dependent thresholding for adaptation in colored or non-stationary noise. Next, we suggest a noise estimation method based on spectral entropy using histogram of intensity instead of estimation method based on median absolute deviation (MAD). And we use a modified hard thresholding to alleviate time-frequency discontinuities. The proposed methods are evaluated on various noise conditions - white Gaussian noise, car interior noise, F-16 cockpit noise, pink noise, and speech babble noise. We compare our proposed methods with the conventional one with level dependent thresholding based on MAD.
Support Vector Machine Components - Based Face Recognition Technique using 3D Morphable Modeling Method

We present a novel solution towards the problem of pose and illumination variation of face detection (FD) and face recognition (FR). In this paper, two advanced method are used to provide pose and illumination invariant FR. The 3D morphable model is implemented to generate 3D face images from our very own training database. This process requires a set of three input face images with varying pose and illumination constraints. The resulting 3D model is then used to train the Support Vector Machine (SVM) component-based FR. SVM component-based 3D model has promising results yielding close to 92.6% accuracy when tested on three training face images of each subject under test.
Cultural Links to Adolescent Weapon Carrying and Weapon Use

Although existing cross-cultural studies of adolescence have focused on topics including fighting, bullying, and gangs, little cross-national research has centered on weapon carrying. However, weapon carrying among youth has been identified as a worldwide concern, with significant variation by nation and region. This variation is not well understood. This article investigates the nation-level cultural and contextual determinants of adolescent weapon carrying, specifically focusing on human development, governmental corruption, and a nation’s orientation toward violence. Data are drawn from 27 countries in the International Self-Report Delinquency Study, Wave Two. Analyses use multilevel logistic and ordered logistic regression models to assess associations with weapon carrying frequency, likelihood of carrying a weapon with friends, and age of weapon carrying onset. Results show that residing in a nation with less corruption is associated with a decrease in weapon carrying frequency, a later age of onset, and a lower likelihood of carrying a weapon with friends. Mixed results were found for interactions with a nation’s orientation toward violence. Possible explanations for these results, practical implications, and directions for future research are discussed.
Replanning in Belief Space for Dynamical Systems : Towards Handling Discrete Changes of Goal Location

This paper presents an online planning/replanning strategy for dynamical systems, in the presence of uncertainty. Online replanning capability allows us to handle discrete changes in the goal location. Multi-query graph-based methods, such as PRM in the deterministic setting, and FIRM in the stochastic setting, are suitable frameworks to serve this purpose. However, in these methods, local planners (along the edges) are responsible to drive the state/belief to the final node of the edge. Nevertheless, for dynamical systems, driving the system belief to a sampled belief is a challenge. In this paper, we provide an overview of the FIRM strategy that extends PRM-based planning to belief space, and sketch the application of it to dynamical/non-stoppable systems where the belief is stabilized to an orbit (periodic path) in belief space instead of a point. The method takes obstacles into account, while it provides a query-independent graph, as a means for online replanning.
Patterns of usage and context in interaction with communication support applications in mobile devices

Contact lists are one of the most frequently used applications on mobile devices. Users are reluctant to delete or remove contacts from their repositories and as modern smartphones provide an unlimited contact list storage space, these become increasingly large, sometimes measuring several hundred entries. In this paper we present our findings from two experiments with user-subjective and quantitative data concerning the use of mobile contact lists. We examine the role that frequency and recency of usage plays in the determination of a contact's importance, with a view to aid the speed and efficacy of the information seeking and retrieval process during the use of the contact list application.
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms

A measure of search difficulty, fitness distance correlation (FDC), is introduced and its power as a predictor of genetic algorithm (GA) performance is investigated. The sign and magnitude of this correlation can be used to predict the performance of a GA on many problems where the global maxima are already known. FDC can be used to correctly classify easy deceptive problems and easy and difficult non-deceptive problems as difficult, it can be used to indicate when Gray coding will prove better than binary coding, it produces the expected answers when applied to problems over a wide range of apparent difficulty, and it is also consistent with the surprises encountered when GAs were used on the Tanese and royal road functions. The FDC measure is a consequence of an investigation into the connection between GAs and heuristic search.
Role of Polymorphonuclear Leukocyte-Derived Serine Proteinases in Defense against Actinobacillus actinomycetemcomitans

ABSTRACT Periodontitis is a chronic destructive infection of the tooth-supportive tissues, which is caused by pathogenic bacteria such as Actinobacillus actinomycetemcomitans. A severe form of periodontitis is found in Papillon-Lefèvre syndrome (PLS), an inheritable disease caused by loss-of-function mutations in the cathepsin C gene. Recently, we demonstrated that these patients lack the activity of the polymorphonuclear leukocyte (PMN)-derived serine proteinases elastase, cathepsin G, and proteinase 3. In the present study we identified possible pathways along which serine proteinases may be involved in the defense against A. actinomycetemcomitans. Serine proteinases are capable to convert the PMN-derived hCAP-18 into LL-37, an antimicrobial peptide with activity against A. actinomycetemcomitans. We found that the PMNs of PLS patients released lower levels of LL-37. Furthermore, because of their deficiency in serine proteases, the PMNs of PLS patients were incapable of neutralizing the leukotoxin produced by this pathogen, which resulted in increased cell damage. Finally, the capacity of PMNs from PLS patients to kill A. actinomycetemcomitans in an anaerobic environment, such as that found in the periodontal pocket, seemed to be reduced. Our report demonstrates a mechanism that suggests a direct link between an inheritable defect in PMN functioning and difficulty in coping with a periodontitis-associated pathogen.
Unregulated Space: Text-Messaging Habits as a Predictor of Punctuation Errors in the Academic Writing of College Students

Most college-aged students use text messaging to communicate with others (Smith, 2011). Text messaging, though it requires a writer to be concise, makes allowances for “textese,” an informal register of English. At the same time, college students and college graduates are expected to be proficient in writing. Using a theoretical framework influenced by self-regulation (Bandura, 1986), memory (Bartholomae, 1980; Flower & Hayes, 1981; Hayes, 1996; Hayes & Chenoweth, 2006; Kellogg, 2006), and transfer (Perkins & Salomon, 1992; Salomon & Perkins, 1989), a correlational study was conducted to compare the participants’ text messaging habits to participants’ frequency of punctuation errors in their academic writing. The predictor variables were the frequency of text messaging (Grace, Kemp, Martin, & Parrila, 2014; Rosen, Chang, Erwin, Carrier, & Cheever, 2010) and the frequency textese (Thurlow & Brown, 2003); the criterion variable was the frequency of punctuation errors (Lunsford & Lunsford, 2008) a participant uses in his or her academic writing. The participants were 115 college students from a four-year college in the Southeast United States. Data were collected via a survey, writing samples, and content analyses. The data obtained from these instruments were analyzed by a multiple regression, a statistical method that showed the relationship between the participants’ text-messaging habits and literacy. The null hypothesis was not rejected because there was no significant relationship between the combined predictor variables and the criterion variable.
Interactive shadows

It is often difficult in computer graphics applications to understand spatial relationships between objects in a 3D scene or effect changes to those objects without specialized visualization and manipulation techniques. We present a set of three-dimensional tools (widgets) called “shadows” that not only provide valuable perceptual cues about the spatial relationships between objects, but also provide a direct manipulation interface to constrained transformation techniques. These shadow widgets provide two advances over previous techniques. First, they provide high correlation between their own geometric feedback and their effects on the objects they control. Second, unlike some other 3D widgets, they do not obscure the objects they control.
Model selection in markovian processes

When analyzing data that originated from a dynamical system, a common practice is to encompass the problem in the well known frameworks of Markov Decision Processes (MDPs) and Reinforcement Learning (RL). The state space in these solutions is usually chosen in some heuristic fashion and the formed MDP can then be used to simulate and predict data, as well as indicate the best possible action in each state. The model chosen to characterize the data affects the complexity and accuracy of any further action we may wish to apply, yet few methods that rely on the dynamic structure to select such a model were suggested. In this work we address the problem of how to use time series data to choose from a finite set of candidate discrete state spaces, where these spaces are constructed by a domain expert. We formalize the notion of model selection consistency in the proposed setup. We then discuss the difference between our proposed framework and the classical Maximum Likelihood (ML) framework, and give an example where ML fails. Afterwards, we suggest alternative selection criteria and show them to be weakly consistent. We then define weak consistency for a model construction algorithm and show a simple algorithm that is weakly consistent. Finally, we test the performance of the suggested criteria and algorithm on both simulated and real world data.
The potential power of experience in communications of expert consensus levels

Abstract Understanding the scientific consensus on anthropogenic climate change has been dubbed a ‘gateway belief’ to engaging people in sustainable behaviour. We consider the question of how the impact of a consensus communication can be maximised. Firstly, the credibility of the communicator should be maximised. One way of achieving this is to present the opinions of a sample of scientists directly to individuals. The decision-making literature suggests that such a technique will confer an additional advantage over standard descriptions of consensus (e.g. ‘97% of scientists agree’). In decision-making research, low probabilities tend to be overweighted when probabilities are described, but underweighted when probability information is experienced. Consequently, statements of high consensus may lead to an overweighting of the dissensus, a phenomenon that may be reversed were the consensus to be ‘experienced. ’ We obtain some positive support for our proposal that consensus is best ‘experienced’ in one of two experiments. We suggest that the lack of stronger positive support could relate to ceiling effects for the topics studied and propose that investigation of effective methods for ‘experiencing’ the consensus is a fruitful area for future research.
Utility of electrodiagnostic testing in evaluating patients with lumbosacral radiculopathy: An evidence‐based review

This is an evidence‐based review of electrodiagnostic (EDX) testing of patients with suspected lumbosacral radiculopathy to determine its utility in diagnosis and prognosis. Literature searches were performed to identify articles applying EDX techniques to patients with suspected lumbosacral radiculopathy. From the 355 articles initially discovered, 119 articles describing nerve conduction studies, electromyography (EMG), or evoked potentials in adequate detail were reviewed further. Fifty‐three studies met inclusion criteria and were graded using predetermined criteria for classification of evidence for diagnostic studies. Two class II, 7 class III, and 34 class IV studies described the diagnostic use of EDX. One class II and three class III articles described H‐reflexes with acceptable statistical significance for use in the diagnosis and confirmation of suspected S1 lumbosacral radiculopathy. Two class II and two class III studies demonstrated a range of sensitivities for use of muscle paraspinal mapping. Two class II studies demonstrated the utility of peripheral myotomal limb electromyography in radiculopathies. Muscle Nerve 42:276–282, 2010
A Probabilistic Framework for Region-Specific Remodeling of Dendrites in Three-Dimensional Neuronal Reconstructions

Dendritic arborization is an important determinant of single-neuron function as well as the circuitry among neurons. Dendritic trees undergo remodeling during development, aging, and many pathological conditions, with many of the morphological changes being confined to certain regions of the dendritic tree. In order to analyze the functional consequences of such region-specific dendritic remodeling, it is essential to develop techniques that can systematically manipulate three-dimensional reconstructions of neurons. Hence, in this study, we develop an algorithm that uses statistics from precise morphometric analyses to systematically remodel neuronal reconstructions. We use the distribution function of the ratio of two normal distributed random variables to specify the probabilities of remodeling along various regions of the dendritic arborization. We then use these probabilities to drive an iterative algorithm for manipulating the dendritic tree in a region-specific manner. As a test, we apply this framework to a well-characterized example of dendritic remodeling: stress-induced dendritic atrophy in hippocampal CA3 pyramidal cells. We show that our pruning algorithm is capable of eliciting atrophy that matches biological data from rodent models of chronic stress.
The parallel algorithm of Clique and it's application on Data Mining Grid system

Computing of some parameters using great amount of data is a great challenge in data mining fields so far. DMG (Data Mining Grid) is designed to solve the computing problem. The design of the workflow service in DMG and the distributed data mining algorithm, are investigated. A sample application in telecom field Customer Value Analysis is and illustrated. In the sample application, Clique, which is computed by parallel algorithm on data mining grid system, is an important measure in Telecom Social Network, which benefits to enhance the precise of context-based prediction of the customer value in telecom social network and work out information in expected duration. Telecom operators can take measures to gain more profit.
Access control: principles and practice

The purpose of access control is to limit the actions or operations that a legitimate user of a computer system can perform. Access control constrains what a user can do directly, as well what programs executing on behalf of the users are allowed to do. In this way access control seeks to prevent activity which could lead to breach of security. This article begins with an explanation of access control and its relationship to other security services such as authentication, auditing and administration. It then reviews the access matrix model and describes di erent approaches to implementing the access matrix in practical systems. This is followed by a discussion of access control policies which are commonly found in current systems. Finally, we brie y consider the administration of access control.
Wavelet-Based Multi-Scale Variation Image Noise Removal Model and the Image Geometry-Adapted Method for Multi-Scale Parameters Determining

In this paper, combining the two existing wavelet- based multi-scale variation (WBMV) models, wavelet-based multi-scale fourth-order partial differential equation (WBMF) and wavelet-based multi-scale total variation (WBMTV), we propose the wavelet-based multi-scale combination (WBMC) model, which overcome the defects of them. Furthermore, an image geometry-adapted (IGA) method for multi-scale parame- ters determining is proposed for WBMV model. The multi-scale parameters determined by IGA method (named IGA parameters) can radically promote the qualities of the restored image. In addition, we propose a multi-step time discretization scheme for WBMV model, which not only can accelerate the speed of convergence, but also can improve the qualities of the restored image. Numerical experiments show better performances of the proposed model, IGA method and multi-step time discretization scheme in preserving the shape of edges and important details, and removing the noise in image in comparison with the existing models and methods.
Variation Based Online Travel Time Prediction Using Clustered Neural Networks

This paper proposes a variation-based online travel time prediction approach using clustered Neural Networks with traffic vectors extracted from raw detector data as the input variables. Different from previous studies, the proposed approach decomposes the corridor travel time into two parts: 1) the base term, which is predicted by a fuzzy membership-value-weighted average of the clustered historical data to reflect the primary traffic pattern in the corridor; and 2) the variation term, which is predicted through the calibrated cluster-based artificial neural network model to capture the actual traffic fluctuation. To evaluate the effectiveness of the proposed approach, this paper has conducted intensive numerical experiments with simulated data from the microscopic simulator CORSIM. Experimental results under various traffic volume levels have revealed the potentials for the proposed method to be applied in online corridor travel time prediction.
COMPARATIVE APPROACH FOR RANKING BY SCRUTINIZING THE WEB OPINION USING SDCA

Web opinions are available for the social sites due to the advancement in the technology. The web opinion acts as an interface between the internet users and web. It allows internet users to communicate and express their opinions. But analyzing and clustering of the web opinion is a challenging task. The clustering of the typical document differs from clustering of the web opinion. The web opinions are taken from the social networks. Analyzing the social network helps in preventing the crimes, terrorists activities etc. The scalable density based algorithm enables the identification of themes within discussions in web social networks and their development. The predefined set for clustering is useful in web opinion clustering. The ranking methods that are being used for clustering are the XMLTF*IDF and Bin Ranking.
In sickness and in health: the longitudinal associations between marital dissatisfaction, depression and spousal health

The present study explored how spouses’ reports of marital dissatisfaction (independent variable) are associated with depression symptoms (mediator) and physical health (dependent variable) over time. Data were from the Marriage Matters Panel Survey (Nock et al., 2012). We used autoregressive cross-lagged models to test temporal connections between variables for newlywed husbands, wives and couples (N5707 couples) at Waves 1, 2 and 3, spanning five years. Results indicated physical health is an important predictor, as are wives’ depression symptoms and husbands’ marital dissatisfaction (all three demonstrate partner effects). However, the effects of health are no longer observed at Time 2. For wives there is a reciprocal relationship between marital dissatisfaction and depression symptoms; for husbands, marital dissatisfaction leads to increased depression. This study provides additional support that marital dissatisfaction, depression and physical health are interrelated across time.
A point-source norovirus outbreak caused by exposure to fomites.

We investigated a norovirus outbreak (genotype GII.2) affecting 9 members of a soccer team. Illness was associated with touching a reusable grocery bag or consuming its packaged food contents (risk difference, 0.636; P < .01). By polymerase chain reaction, GII norovirus was recovered from the bag, which had been stored in a bathroom used before the outbreak by a person with norovirus-like illness. Airborne contamination of fomites can lead to subsequent point-source outbreaks. When feasible, we recommend dedicated bathrooms for sick persons and informing cleaning staff (professional or otherwise) about the need for adequate environmental sanitation of surfaces and fomites to prevent spread.
A system for automatically annotating traditional Irish music field recordings

This paper presents MATT2 (machine annotation of traditional tunes). MATT2 is a novel system which can automatically annotate field recordings of traditional Irish music with useful metadata such as tune name, key signature, time signature, composer and discography. MATT2 works by using a number of algorithms to automatically transcribe digital audio to be annotated to the ABC music notation language. It then compares these transcriptions against a corpus of 860 human made transcriptions in ABC using a variation of the edit distance algorithm. Results using MATT2 to annotate fifty recordings of flute and fiddle tunes demonstrate a high success rate at annotating recordings made by different musicians. Additionally, several of the recordings successfully annotated in testing MATT2 were recorded in imperfect conditions, with badly degraded audio.