Miracle-dz/UniFAR-FTUs huggingface dataset:
Risky Sexual Behavior in American White College Women: The Role of Sex Guilt and Sexual Abuse

Ninety-five sexually active White American female college students participated in a questionnaire study about their sexual behavior in the past 12 months. A path model was tested in order to assess specific hypothesized predictors of risky sexual behavior. As predicted, participants with greater sex guilt reported using condoms more and having had fewer sexual partners. The findings of this study suggest that White American female college students are at some degree of risk due to risky sexual behavior. Taking into account attitudes about sexuality and past sexual abuse along with the requisite training in condom use self-efficacy may enhance the success of interventions designed to reduce risky sexual behavior among White American female college students.
Vehicle Detection Method using Haar-like Feature on Real Time System

This paper presents a robust vehicle detection approach using Haar-like feature. It is possible to get a strong edge feature from this Haar-like feature. Therefore it is very effective to remove the shadow of a vehicle on the road. And we can detect the boundary of vehicles accurately. In the paper, the vehicle detection algorithm can be divided into two main steps. One is hypothesis generation, and the other is hypothesis verification. In the first step, it determines vehicle candidates using features such as a shadow, intensity, and vertical edge. And in the second step, it determines whether the candidate is a vehicle or not by using the symmetry of vehicle edge features. In this research, we can get the detection rate over 15 frames per second on our embedded system. Keywords—vehicle detection, haar-like feauture, single camera, real time
The Role of Visual Communication in Social Exchange

Twelve male and twelve female pairs of subjects, assuming management and union roles, engaged in a negotiation-type task either face-to-face or in separate rooms communicating via a headphone–microphone link. Transcripts of their discussions were taken and analysed using Conference Process Analysis, a new system for describing the content of negotiations. It was predicted that in comparison with face-to-face conversations, conversations by audio link would be ‘depersonalized’ and ‘task-oriented’. Results supported both hypotheses. In the absence of visual communication there was less praise for one's opponent and more ‘party’ references. The second hypothesis was supported by a significantly greater exchange of ‘offers of information’ in the absence of visual communication, while union representatives exchanged more ‘offers of information about their opponent's party’ by audio link than face-to-face. Results highlighted interesting and unpredicted differences in behaviour between the representatives of the two sides in relation to medium of communication. Taken together, the results suggest that visual communication has three main effects upon the content of exchanges: it encourages spontaneity and, hence, a more wide-ranging discussion, while at the same time promoting the adoption of conventional role relationships by the participants.
A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification

The identification of network applications through observation of associated packet traffic flows is vital to the areas of network management and surveillance. Currently popular methods such as port number and payload-based identification exhibit a number of shortfalls. An alternative is to use machine learning (ML) techniques and identify network applications based on per-flow statistics, derived from payload-independent features such as packet length and inter-arrival time distributions. The performance impact of feature set reduction, using Consistency-based and Correlation-based feature selection, is demonstrated on Naïve Bayes, C4.5, Bayesian Network and Naïve Bayes Tree algorithms. We then show that it is useful to differentiate algorithms based on computational performance rather than classification accuracy alone, as although classification accuracy between the algorithms is similar, computational performance can differ significantly.
Video captioning with recurrent networks based on frame- and video-level features and visual content classification

In this paper, we describe the system for generating textual descriptions of short video clips using recurrent neural networks (RNN), which we used while participating in the Large Scale Movie Description Challenge 2015 in ICCV 2015. Our work builds on static image captioning systems with RNN based language models and extends this framework to videos utilizing both static image features and video-specific features. In addition, we study the usefulness of visual content classifiers as a source of additional information for caption generation. With experimental results we show that utilizing keyframe based features, dense trajectory video features and content classifier outputs together gives better performance than any one of them individually.
Effects of electronic mail policies on invasiveness and fairness

Purpose – The widespread use of electronic mail (e‐mail) at work has prompted a growing number of companies to implement e‐mail policies to protect both business interests and the privacy of employees. However, very little is known about the effects of such policies on such outcomes for employees such as perceived invasiveness and fairness. Thus, the paper aims to consider these issues.Design/methodology/approach – This paper uses a 2×2 experimental design and a sample of 592 employed internet users to examine the effects of e‐mail policy characteristics (i.e. policy restrictiveness and policy justification) on perceptions of invasion of privacy (invasiveness) and fairness.Findings – Results indicate that policy restrictiveness has effects on both invasiveness and fairness, and that policy justification has an effect on fairness. In addition, privacy values are related to both invasiveness and fairness, and moderated the effect of policy restrictiveness on fairness.Practical implications – Implications fo.
Learning to Obtain Reward, but Not Avoid Punishment, Is Affected by Presence of PTSD Symptoms in Male Veterans: Empirical Data and Computational Model

Post-traumatic stress disorder (PTSD) symptoms include behavioral avoidance which is acquired and tends to increase with time. This avoidance may represent a general learning bias; indeed, individuals with PTSD are often faster than controls on acquiring conditioned responses based on physiologically-aversive feedback. However, it is not clear whether this learning bias extends to cognitive feedback, or to learning from both reward and punishment. Here, male veterans with self-reported current, severe PTSD symptoms (PTSS group) or with few or no PTSD symptoms (control group) completed a probabilistic classification task that included both reward-based and punishment-based trials, where feedback could take the form of reward, punishment, or an ambiguous “no-feedback” outcome that could signal either successful avoidance of punishment or failure to obtain reward. The PTSS group outperformed the control group in total points obtained; the PTSS group specifically performed better than the control group on reward-based trials, with no difference on punishment-based trials. To better understand possible mechanisms underlying observed performance, we used a reinforcement learning model of the task, and applied maximum likelihood estimation techniques to derive estimated parameters describing individual participants’ behavior. Estimations of the reinforcement value of the no-feedback outcome were significantly greater in the control group than the PTSS group, suggesting that the control group was more likely to value this outcome as positively reinforcing (i.e., signaling successful avoidance of punishment). This is consistent with the control group’s generally poorer performance on reward trials, where reward feedback was to be obtained in preference to the no-feedback outcome. Differences in the interpretation of ambiguous feedback may contribute to the facilitated reinforcement learning often observed in PTSD patients, and may in turn provide new insight into how pathological behaviors are acquired and maintained in PTSD.
Inter-Agency Collaboration in Child Welfare and Child Mental Health Systems

Abstract While most children living in foster care experience emotional disturbance as a result of their maltreatment or out-of-home placement, most do not receive needed mental health assistance that can prevent a variety of negative outcomes. Child welfare services related to placement prevention, treatment foster care, and group care provide unique opportunities for promoting inter-agency collaboration with mental health services. In order to address more effectively the needs of families utilizing both service systems, this analysis focuses on the factors that enhance or impede child welfare and child mental health collaboration and strategies for enriching the relationship.
Recommending branded products from social media

E-commerce companies are increasingly encouraging their users to connect to social media venues such as Facebook and Pinterest. The main strategic goal of such social connections is to boost user interaction and adoption on social media. However only a few efforts have been focused so far on leveraging users' social profiles to personalize the e-commerce experience and to recommend products of interest. In this paper, we start exploring this topic by investigating if a user's social media profile can be used to predict and recommend what type of products and what brands the social user is more likely to buy. More specifically, we study the correlation between the brands liked by the user on social media sites and those purchased on an e-commerce site. We then leverage these correlations in a brand prediction system, showing that social media can be effectively used to recommend branded products when user-user collaborative filtering techniques are used.
Educating Earth-literate Leaders

The World Summit for Sustainable Development (WSSD) in Johannesburg made it clear that political leadership the world over is incapable of rising to the challenges of sustainability. Yet, most of the hundred or so world leaders who attended have a higher education degree from some of the world's most prestigious universities. This paper tries to address some of the key challenges this poses for universities. It looks at the changing professional landscape within the EU, parameters of a higher education (HE) that would produce Earth-literate leaders, at the concept of a sustainable university and ecological footprinting as a tool to measure this, and at the implications all this has for the HE and geography curriculum.
Pathogen-Induced Arabidopsis WRKY7 Is a Transcriptional Repressor and Enhances Plant Susceptibility to Pseudomonas syringae1[W]

The Arabidopsis (Arabidopsis thaliana) WRKY7 gene is induced by pathogen infection and salicylic acid (SA) treatment and may therefore play a role in plant defense responses. Here, we show that WRKY7 is localized in the nucleus, recognizes DNA molecules with the W-box (TTGAC) elements, and functions as a transcriptional repressor in plant cells. To study its biological functions directly, we have characterized both loss-of-function T-DNA insertion and RNAi mutants and gain-of-function transgenic overexpression plants for WRKY7 in Arabidopsis. The T-DNA insertion and RNAi mutant plants displayed enhanced resistance to a virulent strain of the bacterial pathogen Pseudomonas syringae as measured by significant decrease in both bacterial growth and symptom development as compared to those in wild-type plants. The enhanced resistance in the loss-of-function mutants was associated with increased induction of SA-regulated Pathogenesis-Related 1 (PR1) by the bacterial pathogen. Transgenic plants that constitutively overexpress WRKY7 have altered leaf growth and morphology strikingly similar to those observed in the previously isolated eds8 mutant plants. Like eds8 mutant plants, WRKY7-overexpressing plants supported more growth of P. syringae and developed more severe disease symptoms than wild-type plants. The enhanced susceptibility of both the WRKY7-overexpressing plants and the eds8 mutant correlated with reduced expression of defense-related genes, including PR1, but significantly increased accumulation of SA after pathogen infection, probably due to reduced negative feedback of SA synthesis. Thus, pathogen-induced WRKY7 transcription factor play a negative role in defense responses to P. syringae.
Race and Relationship advice: Effects of marriage market differences on cultural expectations of behavior

To see if and how marriage market differences between black and white women in the US are reflected in relationship advice, relationship articles in two popular women's magazines, one targeting black women and one targeting white women, are systematically coded and analyzed. The results of this content analysis of 213 relationship articles indicate that marriage market differences are reflected in such advice. Magazine articles aimed at black women focus on men's financial problems, infidelity, and prior relationships significantly more frequently than articles targeting white women, mirroring the issues that black women face in their particular marriage market. Furthermore, the results demonstrate a greater focus on the male perspective in advice targeting black women.
Find your best match: predicting performance of consolidated workloads

Modern multicore platforms allow system administrators to reduce the costs of the IT infrastructure by consolidating heterogeneous workloads on the same physical machine. To this end, it is important to develop efficient profiling techniques and accurate performance predictions to avoid violating service level objectives. In this work we present Tresa, a novel tool to automatically characterize workloads and accurately estimate the execution time of different consolidations. These results can be used to optimize consolidations depending on service-level objectives.
Employing data flow testing on object-oriented classes

At the class testing level, state-based testing and data flow testing techniques have been employed. However, while the former only involves the variables that have an effect on the behaviour of the object under test, it is possible for errors to occur in variables, which do not define an object’s state. Data flow testing has been applied to generate test cases for testing classes using data flow criteria, but this is a difficult task. Moreover, some of data flow test cases thus generated may be unworkable. Selecting data flow test cases based on sequences of specification messages is a way to reduce the effort of generating feasible intra-class data flow test cases. However, some test cases cannot be selected, if data flow anomalies exist within the sequences. The data flow testing technique in this research is divided into two stages; first detecting data flow anomalies and then computing data flow test cases.
Emotional Suppression as a Moderator for the Impact of a Transgression on Consumers’ Satisfaction

Despite the increasing amount of research about the effects of a seller’s transgression on consumers’ relationship quality evaluation, existing theory still demands more insight into consumer’s capacity to suppress the negative emotions that a transgression might generate. This research proposes that consumers’ are not always equally influenced by a transgression because some individuals demonstrate a higher capacity to suppress the negative emotions that arise from a seller’s transgression. An experimental study in a controlled virtual bookstore was developed, simulating a real website. Participants were randomly allocated into one of the two conditions: transgression vs. non-transgression scenario. We demonstrate that consumers who are better able to suppress the negative emotions experienced a lower decrease in their satisfaction evaluation of the relationship with the seller after a transgression than those who had a lower negative emotion suppression capacity following the behavior. These results shed light into the boundary conditions of the transgressions in the business to consumer marketing relationship. This research is therefore intended to make contributions to the literature of marketing relationship in a transgression context.
The AP-1 Transcription Factor c-Jun Promotes Arthritis by Regulating Cyclooxygenase-2 and Arginase-1 Expression in Macrophages

Activation of proinflammatory macrophages is associated with the inflammatory state of rheumatoid arthritis. Their polarization and activation are controlled by transcription factors such as NF-κB and the AP-1 transcription factor member c-Fos. Surprisingly, little is known about the role of the AP-1 transcription factor c-Jun in macrophage activation. In this study, we show that mRNA and protein levels of c-Jun are increased in macrophages following pro- or anti-inflammatory stimulations. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment cluster analyses of microarray data using wild-type and c-Jun–deleted macrophages highlight the central function of c-Jun in macrophages, in particular for immune responses, IL production, and hypoxia pathways. Mice deficient for c-Jun in macrophages show an amelioration of inflammation and bone destruction in the serum-induced arthritis model. In vivo and in vitro gene profiling, together with chromatin immunoprecipitation analysis of macrophages, revealed direct activation of the proinflammatory factor cyclooxygenase-2 and indirect inhibition of the anti-inflammatory factor arginase-1 by c-Jun. Thus, c-Jun regulates the activation state of macrophages and promotes arthritis via differentially regulating cyclooxygenase-2 and arginase-1 levels.
SPECTRAL GEOMETRY OF EIGENVALUES AND RESONANCES : A RETROSPECTIVE ON THE WORK OF ROBERT BROOKS

Spectral geometry has its roots in the study of the geometric content of eigenvalues on a compact surface or the Dirichlet eigenvalues of a bounded planar domain. On the one hand, explicit constructions of discrete and continuous families of manifolds with the same spectrum show that the spectrum does not completely determine the geometry of a manifold; on the other, compactness theorems show that the spectrum does place strong constraints on the geometries allowed by a given spectrum. Here we will explore the techniques used in each of these two approaches and discuss their recent application to the spectral geometry of resonances.
Images of electricity: how do novices and experts model electric current?

Research findings indicate that students’ understanding of electric current in simple circuits is confused by many alternative conceptions. The model of current which is universally accepted is one of moving electrons in a wire, responding to a difference in potential across the ends of the wire. Remedial teaching strategies have all sought to clarify this model, to enable students to predict circuit behaviour and solve circuit problems. It is possible, however, that this is not the most useful model. An examination of expert images has shown that many experts hold a field concept rather than a particle one. This paper addresses the relevance of these findings for classroom practice.
Dimensional model reduction in non‐linear finite element dynamics of solids and structures

A general approach to the dimensional reduction of non‐linear finite element models of solid dynamics is presented. For the Newmark implicit time‐discretization, the computationally most expensive phase is the repeated solution of the system of linear equations for displacement increments. To deal with this, it is shown how the problem can be formulated in an approximation (Ritz) basis of much smaller dimension. Similarly, the explicit Newmark algorithm can be also written in a reduced‐dimension basis, and the computation time savings in that case follow from an increase in the stable time step length.
Potential and Problems of Existing Creativity and Innovation Indices

Creativity and innovation are important inputs in the global knowledge economy. However, while the theoretical concepts and the measurement of creativity on the individual level have made considerable progress during the last decades, so-called sectoral approaches to measuring creativity and innovation on the level of aggregate units are less well developed. The article is the first to review and compare the different approaches of the most popular international creativity rankings. Analyzed sectors are countries, companies, and universities. It becomes apparent that company rankings, in particular, are virtually arbitrary, yet different country and university rankings show much higher correlations. Weaknesses in the rankings’ operationalization and modeling of creativity and innovation measures, as well as potential improvements in methodology, are discussed in the context of the current debate in the literature.
Efficiently Handling Skew in Outer Joins on Distributed Systems

Outer joins are ubiquitous in databases and big data systems. The question of how best to execute outer joins in large parallel systems is particularly challenging as real world datasets are characterized by data skew leading to performance issues. Although skew handling techniques have been extensively studied for inner joins, there is little published work solving the corresponding problem for parallel outer joins. Conventional approaches to this problem such as ones based on hash redistribution often lead to load balancing problems while duplication-based approaches incurs significant overhead in terms of network communication. In this paper, we propose a new algorithm, query with counters (QC), for directly handling skew in outer joins on distributed architectures. We present an efficient implementation of our approach based on the asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate the performance of our approach on a cluster of 192 cores (16 nodes) and datasets of 1 billion tuples with different skew. Experimental results show that our method is scalable and, in cases of high skew, faster than the state-of-the-art.
A Domain-Based Frequency Count Approach for Protein-Protein Interaction Prediction using Support Vector Machine

Proteins are involved in many essential processes within cell. Uncovering the diverse function of proteins and their interactions within the cell may improve our understanding of protein functions. Several high-throughput techniques employed to decipher PPI are erroneous and are limited by the lack of coverage. Computational techniques are therefore sought to predict genome-wide PPI. In this paper, domain structure is used as a feature for computational prediction of PPI and support vector machines (SVM) as a learning system. We have used both, existing method and frequency count (FC) method for feature representation of protein domains and carried our experiment using SVM with different kernels. Both the methods achieved accuracy of about 78% for RBF kernel. But frequency count method reduced the storage requirement by half. These results indicate that PPI can be predicted from domain structure using frequency count method with reliable accuracy and reduced storage requirement.
Demonstration of 3D vertical RRAM with ultra low-leakage, high-selectivity and self-compliance memory cells

Developing high performance self-selective cell (SSC) is one of the most critical issues of the integration of 3D vertical RRAM (V-RRAM). In this work, a four-layer V-RRAM array, with high performance HfO2/mixed ionic and electronic conductor (MIEC) bilayer SSC, was demonstrated for the first time. Several salient features were achieved, including ultra-low half-select leakage (<;0.1 pA), very high nonlinearity (>103), low operation current (nA level), self-compliance, high endurance (>107), and robust read/write disturbance immunity.