Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting sexual sensation seeking : the third variable effect of time spent on the internet

ENGLISH ABSTRACT: The most commonly searched for topic on the internet revolve around sexuality. These searches, known as online sexual pursuits, may be influenced by one’s personality traits, such as sexual sensation seeking (Kalichman et al., 1994), which has been associated with various sexual risk behaviours and could increase one’s chances of contracting sexually transmitted diseases and infections. It is therefore vital for researchers to examine the association between sexual sensation seeking and the internet. This study collected data from 336 participants who responded to instruments on an online survey which consisted of a demographic questionnaire, the Sexual Sensation Seeking Scale (Kalichman et al., 1994), the Sexual Compulsivity Scale (Kalichman & Rompa, 1995), the Big Five Inventory (John, Naumann, & Soto, 2008), the Real Me Questionnaire (Amichai-Humburger, Wainapel, & Fox, 2002), Klein’s Sexual Orientation Grid (Klein, 1993), and items associated with sexual risk behaviour (Mashegoane, Moalusi, Ngoepe, & Peltzer, 2002), online deception (Capri & Gorski, 2006; Stieger , Eichinger, & Honeder, 2009), and internet use. Results from multiple regression analyses indicated that extraversion, agreeableness, conscientiousness, neuroticism, sexual compulsivity, sexual risk behaviour, and online self-disclosure were statistically significant (p < .05) predictors of sexual sensation seeking. The study used product-term regression analysis to examine the influence that time spent on the internet has on sexual sensation seeking and its relationship with the statistical predictors thereof. Three distinct third variables were used, namely, hours spent on the internet for work purpose (work hours), hours spent on the internet associated with online sexual pursuits (sexual hours), and hours spent on the internet for personal purposes (personal hours). Through the use of product-term regression analyses I was able to show that work hours as a third variable moderated the relationship between extraversion and sexual sensation seeking; work hours indirectly influenced sexual sensation seeking through sexual compulsivity; and that work hours as a third variable moderated the relationship between online self-disclosure and sexual sensation seeking. Furthermore, sexual hours as a third variable moderated the relationship between sexual compulsivity and sexual sensation seeking. Finally, I have shown that, within the sample, personal hours as a third variable moderated the relationship between sexual compulsivity and sexual sensation seeking; personal hours indirectly influenced sexual sensation seeking through sexual risk behaviour; and personal hours indirectly influenced sexual sensation seeking through online self-disclosure. It thus seems that time spent online influences sexual sensation seeking. It is advised that internet use policies be put in place to establish and maintain a professional culture within the workplace, and to ensure that job performance is consistently met. Internet software packages may be used…
Random logic programs: Linear model

Abstract This paper proposes a model, the linear model, for randomly generating logic programs with low density of rules and investigates statistical properties of such random logic programs. It is mathematically shown that the average number of answer sets for a random program converges to a constant when the number of atoms approaches infinity. Several experimental results are also reported, which justify the suitability of the linear model. It is also experimentally shown that, under this model, the size distribution of answer sets for random programs tends to a normal distribution when the number of atoms is sufficiently large.
Stability analysis of nonlinear power electronic systems utilizing periodicity and introducing auxiliary state vector

Variable-structure piecewise-linear nonlinear dynamic feedback systems emerge frequently in power electronics. This paper is concerned with the stability analysis of these systems. Although it applies the usual well-known and widely used approach, namely, the eigenvalues of the Jacobian matrix of the Poincare/spl acute/ map function belonging to a fixed point of the system to ascertain the stability, this paper offers two contributions for simplification as well that utilize the periodicity of the structure or configuration sequence and apply an alternative simpler and faster method for the determination of the Jacobian matrix. The new method works with differences of state variables rather than derivatives of the Poincare/spl acute/ map function (PMF) and offers geometric interpretations for each step. The determination of the derivates of PMF is not needed. A key element is the introduction of the so-called auxiliary state vector for preserving the switching instant belonging to the periodic steady-state unchanged even after the small deviations of the system orbit around the fixed point. In addition, the application of the method is illustrated on a resonant dc-dc buck converter.
A Mixed-Integer Programming Approach to Multi-Class Data Classification Problem by

This is to certify that I have examined this copy of a master's thesis by Fadime Yüksektepe Üney and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the final examining committee have been made. ABSTRACT Data classification is an important data mining problem that aims to determine the membership of different data points to a number of different sets. Traditional approaches that are based on partitioning the data sets into two groups perform poorly for multi-class data classification problems. A new data classification method based on mixed-integer programming is presented in this thesis. The proposed approach is based on the use of hyper-boxes for defining boundaries of the classes that include all or some of the points in that set. A mixed-integer programming model is developed for representing existence of hyper-boxes and their boundaries. In addition, the relationships among the discrete decisions in the model are represented using propositional logic and then converted to their equivalent integer constraints using Boolean algebra. The proposed approach for multi-class data classification is illustrated on an example problem. The efficiency of the proposed method is tested on two separate data sets; the well-known IRIS data set and the protein folding type data set. The computational results on the illustrative example and the benchmark problems show that the simplicity and accuracy of the proposed method provides scientific insight into the multi-class data classification problems.
Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning

In this paper, we propose a visualization method for driving behavior that helps people to recognize distinctive driving behavior patterns in continuous driving behavior data. Driving behavior can be measured using various types of sensors connected to a control area network. The measured multi-dimensional time series data are called driving behavior data. In many cases, each dimension of the time series data is not independent of each other in a statistical sense. For example, accelerator opening rate and longitudinal acceleration are mutually dependent. We hypothesize that only a small number of hidden features that are essential for driving behavior are generating the multivariate driving behavior data. Thus, extracting essential hidden features from measured redundant driving behavior data is a problem to be solved to develop an effective visualization method for driving behavior. In this paper, we propose using deep sparse autoencoder (DSAE) to extract hidden features for visualization of driving behavior. Based on the DSAE, we propose a visualization method called a driving color map by mapping the extracted 3-D hidden feature to the red green blue (RGB) color space. A driving color map is produced by placing the colors in the corresponding positions on the map. The subjective experiment shows that feature extraction method based on the DSAE is effective for visualization. In addition, its performance is also evaluated numerically by using pattern recognition method. We also provide examples of applications that use driving color maps in practical problems. In summary, it is shown the driving color map based on DSAE facilitates better visualization of driving behavior.
Oncogenic miR-181a/b affect the DNA damage response in aggressive breast cancer

Breast cancer is a heterogeneous tumor type characterized by a complex spectrum of molecular aberrations, resulting in a diverse array of malignant features and clinical outcomes. Deciphering the molecular mechanisms that fuel breast cancer development and act as determinants of aggressiveness is a primary need to improve patient management. Among other alterations, aberrant expression of microRNAs has been found in breast cancer and other human tumors, where they act as either oncogenes or tumor suppressors by virtue of their ability to finely modulate gene expression at the post-transcriptional level. In this study, we describe a new role for miR-181a/b as negative regulators of the DNA damage response in breast cancer, impacting on the expression and activity of the stress-sensor kinase ataxia telangiectasia mutated (ATM). We report that miR-181a and miR-181b were overexpressed in more aggressive breast cancers, and their expression correlates inversely with ATM levels. Moreover we demonstrate that deregulated expression of miR-181a/b determines the sensitivity of triple-negative breast cancer cells to the poly-ADP-ribose-polymerase1 (PARP1) inhibition. These evidences suggest that monitoring the expression of miR-181a/b could be helpful in tailoring more effective treatments based on inhibition of PARP1 in breast and other tumor types.
Feasibility of a Molecular Screening Method for Detection of Salmonella enterica and Campylobacter jejuni in a Routine Community-Based Clinical Microbiology Laboratory

ABSTRACT Conventional diagnostic methods for the detection of Salmonella enterica and Campylobacter jejuni are laborious and time-consuming procedures, resulting in final results, for the majority of specimens, only after 3 to 4 days. Molecular detection can improve the time to reporting of the final results from several days to the next day. However, molecular assays for the detection of gastrointestinal pathogens directly from stool specimens have not made it into the routine clinical microbiology laboratory. In this study we have assessed the feasibility of a real-time PCR-based molecular screening method (MSM), aimed at S. enterica and C. jejuni, in the daily practice of a routine clinical microbiology laboratory. We have prospectively analyzed 2,067 stool specimens submitted for routine detection of gastrointestinal bacterial pathogens over a 7-month period. The MSM showed 98 to 100% sensitivity but routine culture showed only 77.8 to 86.8% sensitivity when an extended “gold standard” that included all culture-positive and all MSM-positive specimens, as confirmed by an independent secondary PCR of a different target gene, was used. An overall improvement in the rate of detection of both pathogens of 15 to 18% was observed. Both approaches performed nearly identically with regard to the specificity, positive predictive value, and negative predictive value, with the values for MSM being 99.7%, 93.1 to 96.6%, and 99.8 to 100%, respectively, and those for routine culture being 100%, 100%, and 97.6 to 99.5%, respectively. Finally, the final results were reported between 3 and 4 days earlier for negative specimens compared to the time of reporting of the results of routine culture. Positive specimens, on the other hand, required an additional 2 days to obtain a final result compared to the time required for routine culture, although preliminary MSM PCR-positive results were reported, on average, 2.9 to 3.8 days before the final routine culture results were reported. In conclusion, MSM can be incorporated into the daily practice of a routine clinical microbiology laboratory with ease. Furthermore, it provides an improvement in the screening for S. enterica and C. jejuni and substantially improves the time to the reporting of negative results.
On the performance of energy detection spectrum sensing for interweave cognitive radio

An important requirement of interweave cognitive radio networks is spectrum sensing. There are several techniques to detect available spectrum holes, but energy detection is still the most common spectrum sensing scheme. In this work, we provide a performance study of energy detection spectrum sensing in cognitive radio networks operating in the interweave spectrum sharing context. The key performance parameters of energy detectors, i.e., detection and false alarm probabilities, are revisited. Numerical examples with various combinations of system parameters (spectrum sensing overhead, primary signal-to-noise ratio, and probability of the primary user activity) are provided to illustrate the accuracy of our analysis, and highlight the intrinsic tradeoff between primary and secondary users interests in the interweave context.
Designing Tabletop Groupware

Interactive tables are an emerging class of devices that are subject to different design constraints than other types of single display groupware. Through prototype-building and evaluation, we have identified three key design challenges for tabletop groupware: integrating access to public and private information, managing display elements, and mediating group dynamics. We discuss each of these issues and present approaches we have developed to address the challenges they present. ACM Classification: H5.3 [Information interfaces and presentation]: Group and Organization Interfaces computer-supported cooperative work. General terms: Design, Human Factors, Experimentation
Monitoring and analysis of lung sounds for the diagnosis of lung abnormalities

Lung infections and lung diseases are common and can be potentially deadly. Hence preventing lung diseases is more mandatory than curing them. Specialist doctors are less available in rural areas compared to urban areas. Thus it has become necessary to develop some method by which people in remote areas can be monitored by specialist doctors who might be far away. This paper proposes a method for preliminary detection of lung infections by analyzing lung sounds obtained by auscultation. To analyze the lung sounds, FFT was calculated and plotted. A number of pre-recorded lung sounds aided the analysis as well. Classification of lung sounds, as normal or adventitious, was done based on the variations observed in the above calculations. This data can be sent wirelessly via GSM/3G to the server in hospital where a specialist doctor can further analyze it for confirmation on lung infection.
Harmful alcohol habits were no more common in a sample of newly sick-listed Swedish women and men compared with a random population sample.

AIMS To estimate harmful alcohol habits in a sample of incident sick-listed individuals compared with a random sample from the general population taking social background, health and work-related factors into account. METHODS Data for this cross-sectional questionnaire study were collected in 2008 in the Västra Götaland region, Sweden. The study population (19-64 year olds) consisted of 2888 consecutive incident sick-leave sample (ISS) and 3567 individuals from a random population sample (RPS). The mailed questionnaire included Alcohol Use Disorder Identification Test and validated instruments on health and work-related factors. Socio-demographic data came from register data. Analyses were made with χ(2) tests and logistic regression analyses. RESULTS No differences in prevalence of harmful alcohol habits were found between men in the ISS (22%) and the RPS (21%). Compared with women in the ISS, a higher proportion of women in the RPS were likely to report harmful alcohol habits [14 versus 9% (P < 0.001)]. This difference was confirmed in the logistic regression analyses where women in the RPS had higher odds of having harmful alcohol habits compared with women in the ISS [odds ratio (OR) = 1.54 (95% confidence interval (CI): 1.23-1.89)]. Even after controlling for significant confounders (age, low income, high self-reported health and high level of perceived symptoms), we found that the differences in harmful alcohol habits remained [OR = 1.44 (95% CI: 1.16-1.81)]. CONCLUSIONS Harmful alcohol habits were no commoner in men and women who belonged to the sample of incident sick-leave cases. Future studies are needed to analyse the predictive value of harmful alcohol habits on sickness absence length and the time until return to work after sickness absence.
Parallelizing query optimization

Many commercial RDBMSs employ cost-based query optimization exploiting dynamic programming (DP) to efficiently generate the optimal query execution plan. However, optimization time increases rapidly for queries joining more than 10 tables. Randomized or heuristic search algorithms reduce query optimization time for large join queries by considering fewer plans, sacrificing plan optimality. Though commercial systems executing query plans in parallel have existed for over a decade, the optimization of such plans still occurs serially. While modern microprocessors employ multiple cores to accelerate computations, parallelizing query optimization to exploit multi-core parallelism is not as straightforward as it may seem. The DP used in join enumeration belongs to the challenging nonserial polyadic DP class because of its non-uniform data dependencies. In this paper, we propose a comprehensive and practical solution for parallelizing query optimization in the multi-core processor architecture, including a parallel join enumeration algorithm and several alternative ways to allocate work to threads to balance their load. We also introduce a novel data structure called skip vector array to significantly reduce the generation of join partitions that are infeasible. This solution has been prototyped in PostgreSQL. Extensive experiments using various query graph topologies confirm that our algorithms allocate the work evenly, thereby achieving almost linear speed-up. Our parallel join enumeration algorithm enhanced with our skip vector array outperforms the conventional generate-and-filter DP algorithm by up to two orders of magnitude for star queries-linear speedup due to parallelism and an order of magnitude performance improvement due to the skip vector array.
Binary object recognition system on FPGA with bSOM

This paper introduces the implementation of an FPGA-based tri-state rule binary Self Organizing Map (bSOM), which takes binary inputs and maintains tri-state weights, with a node labelling algorithm which makes it capable of object classification. The bSOM is used for appearance-based object identification during tracking in video sequences. It is designed to provide part of an end-to-end surveillance system implemented wholly on FPGA. It is trained off-line using a labelled training data set for nine objects, using binary signatures extracted from the colour histogram, and successfully used for appearance-based identification of objects in approximately 85% of cases in a fairly challenging data set. The paper identifies how this preliminary work can be extended to provide full on-line appearance-based identification and tracking.
Auction-Based Resource Access Protocols in IoT Service Systems

Internet of Things (IoT) enables many physical devices to connect to Internet, including wearable devices, home appliances, ambient devices, etc. IoT application developers have created new services and applications to control actuators to make our lives easier. Since many applications may co-exist in a given environment, some of them may want to use an actuator at the same time, causing resource access conflicts. How to resolve resource conflicts is critical in IoT systems. We propose an auction-based mechanism to coordinate applications and resolve their resource access conflicts. The mechanism is supported in the WuKong IoT middleware. We also conduct simulations to show that our method is efficient and can achieve good performances.
Measuring emotional labor in the classroom: The darker emotions

Research on emotional labor amongst teachers has focused on surface acting and deep acting of positive emotions. However, there is evidence that negative emotions play a vital role too. This study developed and examined two new scales, surface acting of negative emotions and deep acting of negative emotions, as an extension of the Dutch Questionnaire on Emotional Labor (D-QEL). Participants were two subsamples of secondary school teachers (total N = 399). A confirmatory factor analysis showed that both deep as well as surface acting of negative emotions have to be distinguished from acting positive emotions. The difference between the scores of experienced and inexperienced teachers on the new scales can be taken as proof of their validity
Fast Generation of Translation Forest for Large-Scale SMT Discriminative Training

Although discriminative training guarantees to improve statistical machine translation by incorporating a large amount of overlapping features, it is hard to scale up to large data due to decoding complexity. We propose a new algorithm to generate translation forest of training data in linear time with the help of word alignment. Our algorithm also alleviates the oracle selection problem by ensuring that a forest always contains derivations that exactly yield the reference translation. With millions of features trained on 519K sentences in 0.03 second per sentence, our system achieves significant improvement by 0.84 Bleu over the baseline system on the NIST Chinese-English test sets.
Bayesian Guided Pattern Search for Robust Local Optimization

Optimization for complex systems in engineering often involves the use of expensive computer simulation. By combining statistical emulation using treed Gaussian processes with pattern search optimization, we are able to perform robust local optimization more efficiently and effectively than when using either method alone. Our approach is based on the augmentation of local search patterns with location sets generated through improvement prediction over the input space. We further develop a computational framework for asynchronous parallel implementation of the optimization algorithm. We demonstrate our methods on two standard test problems and our motivating example of calibrating a circuit device simulator.
Institutional embeddedness and the strategic leeway of actors: the case of the German therapeutical biotech industry

This article aims at examining the strategic leeway of firms pursuing business strategies incompatible with the dominant institutional environment in a given market economy. In order to evaluate this question, we focus on the therapeutic biotech industry and draw a German-British comparison. Proponents of the varieties-of-capitalism (VoC) approach assume that German firms underperform in this industrial sector in comparison to British firms due to the institutional framework in which German firms operate; this framework is assumed to provide them with hardly any strategic latitude. The VoC approach is challenged by two alternative perspectives, in both of which it is believed that firms can have a high level of strategic leeway; in the first approach this is possible due to institutional heterogeneity within national market economies; and in the second approach, the above can be seen as the result of economic internationalization. Our empirical findings show that British firms are indeed more competitive in the therapeutical biotech industry, but only to a limited extent. German firms perform better than projected by the VoC approach because they operate in an institutionally heterogeneous environment and due to the impact of internationalization. Thus, we argue for the integration of these three perspectives in one explanatory approach.