Miracle-dz/UniFAR-FTUs huggingface dataset:
Manipulating humor styles: Engaging in self-enhancing humor reduces state anxiety

Abstract We conducted three experiments to determine if engaging in self-enhancing humor can alleviate state anxiety associated with an anticipated stressful event. In all three experiments, participants imagined they were about to take a stressful math test. In Experiment 1 participants who engaged in self-enhancing humor while anticipating taking the test experienced less state anxiety than those who engaged in self-defeating humor or no humor at all. Experiment 2 demonstrated that engaging in self-enhancing humor reduced state anxiety more than mere instructions to adopt a non-serious, humorous outlook in the stressful situation. Experiment 3 revealed that self-enhancing humor alone induced participants to adopt a humorous perspective on the stressful math test, but that participants responded with the least state anxiety when they were also given instructions about how to use the humor to reframe the math test. Collectively, our findings corroborate and extend previous correlational studies showing that self-enhancing humor can alleviate state anxiety associated with a stressful event.
Indoor location algorithm research based on neural network

Most of the traditional indoor location algorithms based on the distance loss model always filter the received signal strength, and then we can use the distance loss model to infer the distance between the nodes and achieve location eventually. The accuracy of the traditional indoor location algorithm is very unstable due to multipath propagation effects and complex signal attenuation law in the indoor environment. On the basis of researching wireless signal propagation model and traditional indoor location algorithm, in this paper, firstly we converted the RSSI value into signal dropout rate and calculated the dropout rate information respectively by using different transmit power. Then we predicted location of the mobile node by BP neural network. With this method, the location accuracy is improved.
A range rate based detection technique for tracking a maneuvering target

In this paper a novel approach for detecting unknown target maneuver using range rate information is proposed based on the generalized Page's test with the estimated target acceleration magnitude. Due to the high nonlinearity between the range rate measurement and the target state, a measurement conversion technique is used to treat range rate as a linear measurement in Cartesian coordinates so that a standard Kalman filter can be applied. The detection performance of the proposed algorithm is compared with that of existing maneuver detectors over various target maneuver motions. In addition, a model switching tracker based on the proposed maneuver detector is compared with the state-of-the-art IMM estimator. The results indicate the effectiveness of the maneuver detection scheme which simplifies the tracker design. The tracking performance is also evaluated using a steady state analysis.
Compact Group selection From Redshift Surveys

For the first time, we construct a catalog of compact groups selected from a complete, magnitude-limited redshift survey. We select groups with $N \geq 3$ members based on projected separation and association in redshift space alone. We evaluate the characteristics of the Redshift Survey Compact Groups (RSCG's). Their physical properties (membership frequency, velocity dispersion, density) are similar to those of the Hickson [ApJ, 255, 382 (1982)] Compact Groups. Hickson's isolation criterion is a strong function of the physical and angular group radii and is a poor predictor of the group environment. In fact, most RSCG's are embedded in dense environments. The luminosity function for RSCG's is mildly inconsistent with the survey luminosity function --- the characteristic luminosity is brighter and the faint end shallower for the RSCG galaxies. We construct a model of the selection function of compact groups. Using this selection function, we estimate the abundance of RSCG's; for groups with $N \geq 4$ members the abundance is $3.8 \times 10^{-5} h^3 Mpc^{-3}$. For all RSCG's ($N \geq 3$) the abundance is $1.4 \times 10^{-4} h^3 Mpc^{-3}$.
Digital Signal Processing Fundamentals

Recent advances in digital signal processing (DSP) technology make it easier for scientists to develop powerful personal computer-based data acquisition and analysis systems. This application note discusses DSP technology from a hardware, software, and application point of view. The Reduced Instruction Set Computer (RISC) architecture of the Texas Instruments (TI) TMS320C30 chip is used as an example of the power of a DSP processor. For software, programming methods–from low-level machine code to high-level DSP operating systems–are compared. The application note concludes with examples of how DSP is being integrated into test-andmeasurement systems ranging from audio and speech analysis to image processing and real-time control.
Learning Translation Templates From Bilingual Text

This paper proposes a two-phase example-based machine translation methodology which develops translation templates from examples and then translates using template matching. This method improves translation quality and facilitates customization of machine translation systems. This paper focuses on the automatic learning of translation templates. A translation template is a bilingual pair of sentences in which corresponding units (words and pharases) are coupled and replaced with variables. Correspondence between units is determined by suing a bilingual dictionary and by analyzing the syntactic structure of the sentences. Syntactic ambiguity and ambiguity in correspondence between units are simultaneously resolved. All of the translation templates generated from a bilingual corpus are grouped by their source language part, and then further refined to resolved conflicts among templates whose source language parts are the same but whose target language parts are different. By using the proposed method, not only transfer rules but also knowledge for lexical selection is effectively extracted from a bilingual corpus.
Credo : A Framework for Semi-supervised Credibility Assessment for Social Networks

1. ABSTRACT Social networks enable users to share information about events and themselves. We present a framework that assesses the credibility of messages of a social network. We offer a semi-supervised framework to judge the credibility of a message, Credo. The framework is based on PageRank. We test our model on data collected from Twitter. We find that our model outperforms the baseline, Bayesian networks. We designed several models that consider the postermessage, poster-reporter-message and poster-reporter-topK-messages. Credo considers the trustworthiness of the poster, reporters and the credibility of the top K semantically similar messages. We were able to improve the perforamance of the proposed model by including features such as the semantic relatedness of messages and posting characteristics. Our model performs 18% better at ranking the credibility of a piece of information than baselines used.
A UTOMATIC U PDATION OF U SER B EHAVIOR P ROFILES FOR S EARCH E NGINE P ERSONALIZATION

Knowledge about computer users is useful for predicting their future behaviours. An user profile is erratic, a method is needed to update the evolving user profiles. An approach used for creating and updating automatically the profile of a computer user is used called Evolving Agent behavior Classification based on Distributions of relevant events (EVABCD). This approach depends upon the observed behavior of the user . It changes search interest specified in the profile of the user automatically when the user’s search interest changes and to retrieve search results based on that interest.The behavior of the user is characterized by the commands that the user types. The EVABCD approach along with proposed features is highly efficient to adapt to the evolving user profiles and return better search results according to evolving user’s interest .
Lane detection algorithm for an onboard camera

After analysing the major causes of injuries and death on roads, it is understandable that one of the main goals in the automotive industry is to increase vehicle safety. The European project SPARC (Secure Propulsion using Advanced Redundant Control) is developing the next generation of trucks that will fulfil these aims. The main technologies that will be used in the SPARC project to achieve the desiderated level of safety will be presented. In order to avoid accidents in critical situations, it is necessary to have a representation of the environment of the vehicle. Thus, several solutions using different sensors will be described and analysed. Particularly, a division of this project aims to integrate cameras in automotive vehicles to increase security and prevent driver's mistakes. Indeed, with this vision platform it would be possible to extract the position of the lane with respect to the vehicle, and thus, help the driver to follow the optimal trajectory. A definition of lane is proposed, and a lane detection algorithm is presented. In order to improve the detection, several criteria are explained and detailed. Regrettably, such an embedded camera is subject to the vibration of the truck, and the resulting sequence of images is difficult to analyse. Thus, we present different solutions to stabilize the images and particularly a new approach developed by the "Laboratoire de Production Microtechnique". Indeed, it was demonstrated in previous works that the presence of noise can be used, through a phenomenon called Stochastic Resonance. Thus, instead of decreasing the influence of noise in industrial applications, which has non negligible costs, it is perhaps interesting to use this phenomenon to reveal some useful information, such as for example the contour of the objects and lanes.
DO CUSTOMER AND COWORKER INCIVILITY MODERATE THE EFFECT OF POLYCHRONICITY ON JOB PERFORMANCE? EVIDENCE FROM HOTEL FRONTLINE EMPLOYEES IN NORTH CYPRUS

The present paper tests a model that investigates the effect of polychronicity on job performance among hotel frontline employees; concurrently, customer incivility and coworker incivility -two important social stressors- are considered as moderators in the relationship between employees' polychronicity and job performance. Data collection is done among frontline employees in the four and five-star hotels in Northern Cyprus. The results indicate that polychronicity encourages higher employees’ job performance. However, the effect of customer incivility on job performance is negative and significant and the moderating effect of customer incivility in the relationship between polychronicity and job performance is supported. That is to say high customer incivility lessens the positive relationship between polychronicity and job performance. Coworker incivility did not affect job performance and failed to moderate the relationship between polychronicity and job performance. Further, managerial implications and guidance for future research are specified.
Minimum backward fréchet distance

We propose a new measure to capture similarity between polygonal curves, called the minimum backward Fréchet distance. It is a natural optimization on the weak Fréchet distance, a variant of the well-known Fréchet distance. More specifically, for a given threshold ε, we are searching for a pair of walks for two entities on the two input curves, T1 and T2, such that the union of the portions of backward movements is minimized and the distance between the two entities, at any time during the walk, is less than or equal to ε. Our algorithm detects if no such pair of walks exists. This natural optimization problem appears in many applications in Geographical Information Systems, mobile networks and robotics. We provide an exact algorithm with time complexity of O(n2 log n) and space complexity of O(n2), where n is the maximum number of segments in the input polygonal curves.
The toxic effects and possible mechanisms of Bisphenol A on oocyte maturation of porcine in vitro

Bisphenol A (BPA) and Di-(2-ethylhexyl) phthalate (DEHP) are widely used in the plastic industry such as water bottles, containers, packaging and toys. BPA and DEHP are shown to be the endocrine disruptors which disturb the endocrine system and are linked to several diseases including infertility. In this study, we investigated the effects of BPA exposure on porcine oocyte maturation and its possible reasons. Our results showed that: (i) the rates of oocyte maturation significantly decreased with 250 μM BPA treatment in vitro, but not DEHP. This might be due to the delayed cell cycle progression of oocyte maturation. (ii) BPA treatment resulted in abnormal cytoskeletons on porcine oocytes, showing with aberrant actin distribution, spindle morphology and chromosome alignment, which was further confirmed by the reduced p-MAPK level. (iii) The fluorescence intensity of histone methylation (H3K4me2) and DNA methylation (5 mC) levels were altered after BPA treatment, indicating that epigenetic modification was disturbed. (iv) BPA-exposed oocytes had higher rates of early stage apoptosis/autophagy, and this may be resulted from the increased level of oxidative stress. Collectively, our results indicated that porcine oocytes maturation was disrupted after BPA treatment through disrupting cytoskeletal dynamics, epigenetic modifications and inducing apoptosis/autophagy.
Collective sensing: a fixed-point approach in the metric space

Conventional wisdom in signal processing heavily relies on the concept of inner product defined in the Hilbert space. Despite the popularity of Hilbert-space formulation, we argue it is overly-structured to account for the complexity of signals arising from the real-world. Inspired by the works on fractal image decoding and nonlocal image processing, we propose to view an image as the fixed-point of some nonexpansive mapping in the metric space in this paper. Recently proposed BM3D-based denoising and nonlocal TV filtering can be viewed as the special cases of nonexpansive mappings while differ on the choice of clustering techniques. The physical interpretation of clustering-based nonexpansive mappings is that they convey organizational principles of the dynamical system underlying the signals of interest. There is an interesting analogy between phases of matters in statistical physics and types of structures in image processing. From this perspective, image reconstruction can be solved by a deterministic-annealing based global optimization approach which collectively exploits the a priori information about unknown image. The potential of this new paradigm, which we call "ollective sensing" is demonstrated on the lossy compression application where significant gain over current state-of-the-art (SPIHT) coding scheme has been achieved.
BDI Goal Recognition for Agent Program Learning

Agent applications are often viewed as unduly expensive to develop and maintain in commercial contexts. Organizations often settle for less sophisticated and more traditional software in place of agent technology because of (often misplaced) fears about the development and maintenance costs of agent technology, and the often mistaken perception that traditional software offers better returns on investment. This paper aims to redress this by developing a plan recognition framework for agent program learning, where behavior logs of legacy applications (or even manually executed processes) are mined to extract a 'draft' version of agent code that could eventually replace these applications or processes. We develop and implement techniques for inferring agent plans, specifically inferring agent goals. We propose two ways to infer goals for plans without and with a goal library respectively. Besides, a preferred goal is considered when a goal library is provided, using the notions of consistency, maximal entailment and minimality. The complexity of the plan recognition framework is analyzed and the experimental results show that the average runtime for generating Belief-Desire-Intention (BDI) plans relying on the number of expansion nodes, choice branching factor and parallel branching factor in workflow nets (WF-nets), and that the plan recognition framework is feasible and computable.
Tracked Speech-To-Text Display: Enhancing Accessibility and Readability of Real-Time Speech-To-Text

Deaf and Hard of Hearing (DHH) students are under-served and under-represented in education in part because they miss spoken classroom information, even with aural-to-visual accommodations, such as a real-time speech to text Display (SD). Most SD systems utilize a trained typist to transcribe the speech into text (speech-to-text) onto a display. Still, these students encounter significant but subtle barriers in following speech-to-text displays, especially when detailed visuals are used or when the speaker is fast or uses uncommon words. Hearing students can simultaneously watch the visuals and listen to the spoken explanation, while DHH students constantly look away from the SD to search and observe details in the classroom visuals. As a result, they spend less time watching the visuals and gain less information than their hearing peers. They can also fall behind in reading the speech-text. We discuss the implementation and evaluation of a real-time Tracked Speech-To-Text Display (TSD), that addresses these subtle barriers presented by SD systems. The TSD system minimizes the student's viewing distance between the Speech-to-Text Display and the speaker. This is done through tracking the presenter and displaying the speech-text at a fixed distance above the presenter. Our evaluation showed that students significantly preferred TSD over SD and reported that it was easier to follow the lecture. They liked being able to see both the teacher and speech-to-text, and being able to set the number of displayed lines.
High Computational Complexity of the Decision Tree Induction with many Missing Attribute Values

The decision tree induction is a widely applied technique in machine learning. Algorithms based on such technique with careful implementation can reach the computational complexity (cid:10)( n log n ). However, a special treatment of decision trees is needed in case of missing values. The most popular and widely used approach to this problem is to divide such cases among all subsets (sub-nodes). Such an approach is implemented, e
Theoretical Status of Muon (g−2)

The theoretical status of the muon anomaly is reviewed including the recent change in the light by light hadronic correction. Specific attention is given to the implications of the shift in the difference between the BNL experiment result and the standard model prediction for sparticle mass limits. The implication of the BNL data for Yukawa unification is discussed and the role of gaugino mass nonuniversalities in the satisfaction of Yukawa unification is explored. An analysis of the BNL constraint for the satisfaction of the relic density constraint and for the search for dark matter is also given.
Exploring the Potential of Hypermedia Annotations for Second Language Reading

This study explores whether different types of hypermedia annotations facilitate reading comprehension for intermediate and advanced ESL learners. Data was collected from 84 intermediate and advanced level adult ESL learners using a tracking tool that recorded the amount of time readers spent on a given annotation. An expository text was annotated using different forms of media to provide information both at the word level and topic level. The participants were asked to read the text for general comprehension and to use the annotations to facilitate reading comprehension. Results indicate that annotation use did not facilitate reading comprehension for either group, while a negative relationship was found between the time spent on video and graphics annotations and reading comprehension for the intermediate group. Moreover, prior knowledge was found to be an important variable related to reading comprehension. Finally, the qualitative data collected through a questionnaire and interviews revealed that hypermedia reading had a positive impact on the participants' attitudes towards reading on the computer.
FEM1A and FEM1B: novel candidate genes for polycystic ovary syndrome.

BACKGROUND Human homologs (FEM1A, FEM1B, FEM1C) of nematode sex determination genes are candidate genes for polycystic ovary syndrome (PCOS). We previously identified a FEM1A mutation (H500Y) in a woman with PCOS; FEM1B has been implicated in insulin secretion. METHODS Women with and without PCOS (287 cases, 187 controls) were genotyped for H500Y and six FEM1A single nucleotide polymorphisms (SNPs), five FEM1B SNPs and five FEM1C SNPs. SNPs and haplotypes were determined and tested for association with PCOS and component phenotypes. RESULTS No subject carried the FEM1A H500Y mutation. FEM1A SNPs rs8111933 (P = 0.001) and rs12460989 (P = 0.046) were associated with an increased likelihood of PCOS whereas FEM1A SNP rs1044386 was associated with a reduced probability of PCOS (P = 0.013). FEM1B SNP rs10152450 and a linked SNP were associated with a reduced likelihood of PCOS (P = 0.005), and lower homeostasis model assessment (HOMA) for beta-cell function (HOMA-%B, P = 0.011) and lower HOMA for insulin resistance (HOMA-IR, P = 0.018). FEM1B SNP rs12909277 was associated with lower HOMA-%B (P = 0.008) and lower HOMA-IR (P = 0.037). Haplotype associations were consistent with SNP results, and also revealed association of FEM1B haplotype TGAGG with increased HOMA-%B (P = 0.007) and HOMA-IR (P = 0.024). FEM1C variants were not associated with PCOS. CONCLUSIONS This study presents evidence suggesting a role for FEM1A and FEM1B in the pathogenesis of PCOS. Only FEM1B variants were associated with insulin-related traits in PCOS women, consistent with prior evidence linking this gene to insulin secretion. Replication of these associations and mechanistic studies will be necessary to establish the role of these genes in PCOS.
The impact of new forms of work organisation on working conditions and health : background paper to the European Union Presidency Conference ‘For a better quality of work' : Brussels, 20-21 September 2001

This leaflet summarises the results of a literature review and two analyses of the findings of the Third European Survey on Working Conditions concerning the relationship between organisations, working conditions and health outcomes. According to these studies, different forms of work organisation ('old' and 'new' forms) are structured along two dimensions. This approach makes it possible to distinguish between four types of work organisation (Taylorism, Human Relations, Lean Production, Sociotechnology). For each of these forms of work organisation, the potential impact on working conditions and workers' health is assessed. Finally, moderators which have a positive impact on working conditions and health will be identified. There are only a few studies which examine the relationship between different forms of work organisation and health and they use different conceptualisations for the same topic. This leaflet presents the relationship between forms of work organisation and health in two steps: 1. What are the working conditions arising from the different forms of work organisation? 2. What are the effects on health and working conditions?