Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Auditory Distraction on Performance of the StroopWord Test: A Preliminary Study

Attention is finite, and auditory distractions can interfere with one’s ability to concentrate on high level thought processes. Previous researchers have found limitations on performance in the presence of distractors. Contemporary students find increasingly challenging conditions under which to do schoolwork and study. In the current study, investigators studied the effect of an auditory distraction on performance of the Stroop Color Word Test by a group of undergraduate university students. There were no statistically significant differences between the group performing the Stroop Color Word Test in silence versus the group that completed the test with an auditory distractor. It may be that students have learned to tune out distractors more effectively than in the past, and the investigators recommend replication of the study.
Leukoaraiosis Significantly Worsens Driving Performance of Ordinary Older Drivers

Background Leukoaraiosis is defined as extracellular space caused mainly by atherosclerotic or demyelinated changes in the brain tissue and is commonly found in the brains of healthy older people. A significant association between leukoaraiosis and traffic crashes was reported in our previous study; however, the reason for this is still unclear. Method This paper presents a comprehensive evaluation of driving performance in ordinary older drivers with leukoaraiosis. First, the degree of leukoaraiosis was examined in 33 participants, who underwent an actual-vehicle driving examination on a standard driving course, and a driver skill rating was also collected while the driver carried out a paced auditory serial addition test, which is a calculating task given verbally. At the same time, a steering entropy method was used to estimate steering operation performance. Results The experimental results indicated that a normal older driver with leukoaraiosis was readily affected by external disturbances and made more operation errors and steered less smoothly than one without leukoaraiosis during driving; at the same time, their steering skill significantly deteriorated. Conclusions Leukoaraiosis worsens the driving performance of older drivers because of their increased vulnerability to distraction.
High Genetic Diversity and Insignificant Interspecific Differentiation in Opisthopappus Shih, an Endangered Cliff Genus Endemic to the Taihang Mountains of China

Opisthopappus Shih is endemic to the Taihang Mountains, China. It grows in the crevice of cliffs and is in fragmented distribution. This genus consists of two species, namely, O. taihangensis (Ling) Shih and O. longilobus Shih, which are both endangered plants in China. This study adopted intersimple sequence repeat markers (ISSR) to analyze the genetic diversity and genetic structure from different levels (genus, species, and population) in this genus. A total of 253 loci were obtained from 27 primers, 230 of which were polymorphic loci with a proportion of polymorphic bands (PPB) of up to 90.91% at genus level. At species level, both O. taihangensis (PPB = 90.12%, H = 0.1842, and I = 0.289) and O. longilobus (PPB = 95.21%, H = 0.2226, and I = 0.3542) have high genetic diversity. Their respective genetic variation mostly existed within the population. And genetic variation in O. longilobus (84.95%) was higher than that in O. taihangensis (80.45%). A certain genetic differentiation among populations in O. taihangensis was found (G st = 0.2740, Φst = 0.196) and genetic differentiation in O. longilobus was very small (G st = 0.1034, Φst = 0.151). Gene flow in different degrees (N m = 1.325 and 4.336, resp.) and mating system can form the existing genetic structures of these two species. Furthermore, genetic differentiation coefficient (G st = 0.0453) between species and the clustering result based on the genetic distance showed that interspecific differentiation between O. taihangensis and O. longilobus was not significant and could occur lately.
Microwave Circuit Design by Means of Direct Decomposition in the Finite-Element Method

In this paper, a domain decomposition approach for design purposes is proposed. The analysis domain is divided into subdomains according to the arbitrarily shaped parts that should be modified in the synthesis process. A full-wave matrix-valued transfer function describes each decomposition subdomain, namely, an admittance-type matrix. Field continuity between subdomains is directly enforced by an admittance matrix connection. This methodology makes it possible to analyze only those parts of the analysis domain that are supposed to evolve in order to satisfy the design specifications. Furthermore, several modifications in the shape of the components are allowed as a consequence of the easy matrix connection process, where the consideration of different admittance-type matrices or absence of them gives rise to distinct geometric structures. A model order reduction technique is also considered for fast frequency sweeping. Finally, several numerical examples illustrate the capabilities of the proposed procedure, as well as its accuracy.
Analysis and Indentifying Variation in Human Emotion Through Data Mining

This paper explored to detect emotion variation in adolescent age group through data mining techniques. In this age mood swings is maximum as compared to other age group. Children become more moody and irritable during this period of their lives . This moodiness is commonly attributes to the sudden and fluctuating hormonal level, brain still developing, peer pressure, Cognitive immaturity . Some people get more hyper but others feel under the weather and just want to be left alone, but everyone is different, and it isn't always the same emotion change. During this age period the people around us will experience drastic changes in our moods. If you're out-of-control happy one minute, totally depressed the next minute, and furious the next, you're completely normal! But mood swings can also be frightening and confusing. Proposed paper is based on collection of dataset from this large group of emotions in adolescent age by using data mining technique. By classifying emotions and using decision tree different emotional variations are analyzed in this paper. Outlier analysis is used to identify emotion variation in child having any kind of disability. GeneralTerms:-emotion classification, adolescent age, classification decision tree, outlier analysis, data mining.
The Early Expression of (Un)certainty in Typologically Different Languages: Evidence from Russian, Estonian and Hebrew

The article discusses the early means of expressing the epistemic notions of certainty and uncertainty (epistemic markers) in three typologically-different languages – Russian, Estonian and Hebrew. The results are based on an analysis of naturalistic speech samples of 9 typically-developing monolingual children, recorded from ages 1;3 to 6;2. The emergence of epistemic markers in child speech is compared to child-directed speech. Acquisition of means of expressing epistemic modality starts to develop at the end of the second year of life with the marking of uncertainty. The findings indicate an expansion of epistemic evaluation from objective situations in the physical world to the mental world. A comparison of child speech with the input reveals that both the frequency of a marker in the target system and the degree of epistemic semantics influence its emergence and development. Differences between languages mostly concern the frequency of usage of epistemic markers and the degree of epistemic evaluation children start with.
A Learning Algorithm for Agents in Electronic Marketplaces

In this paper, we propose a reputation oriented reinforcement learning algorithm for buying and selling agents in electronic market environments. We take into account the fact that multiple selling agents may offer the same good with dierent qualities. In our approach, buying agents learn to avoid the risk of purchasing low quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable sellers. Selling agents learn to maximize their expected profits by adjusting product prices and by optionally altering the quality of their goods. Modelling the reputation of sellers allows buying agents to focus on those sellers with whom a certain degree of trust has been established. We also include the ability for buying agents to optionally explore the marketplace in order to discover new reputable sellers. As detailed in the paper, we believe that our proposed strategy leads to improved satisfaction for buyers and sellers, reduced communication load, and robust systems. In addition, we outline some possible experimentation with an implementation of the algorithm, to determine its potential advantages.
Dimensions and issues of mobile agent technology

Mobile Agent is a type of software system which acts "intelligently" on one's behalf with the feature of autonomy, learning ability and most importantly mobility. Now mobile agents are gaining interest in the research community. In this article mobile agents will be addressed as tools for mobile computing. Mobile agents have been used in applications ranging from network management to information management. We present mobile agent concept, characteristics, classification, need, applications and technical constraints in the mobile technology. We also provide a brief case study about how mobile agent is used for information retrieval.
Web Data Mining and Social Media Analysis for better Communication in Food Safety Crises

Although much effort is made to prevent risks arising from food, food-borne diseases are an ever present-threat to the consumers’ health. The consumption of fresh food that is contaminated with pathogens like fungi, viruses or bacteria can cause food poisoning that leads to severe health damages or even death. The outbreak of Shiga Toxin-producing enterohemorrhagic E. coli (EHEC) in Germany and neighbouring countries in 2011 has shown this dramatically. Nearly 4.000 people were reported of being affected and more than 50 people died during the so called EHEC-crisis. As a result the consumers’ trust in the safety of fruits and vegetables decreased sharply. In situations like that food crisis managers from public authorities as well as from privately owned companies must react quickly: They have to identify and track back contaminated products and they have to withdraw them from the market. At the same time they have to inform the stakeholders’ about potential threats and recent developments. This is a particularly challenging task because when an outbreak is just detected information about the actual scope is sparse and the demand for information is high. Thus, ineffective communication among crisis managers and towards the public can result in inefficient crisis management, health damages and a major loss of trust in the food system. This is why crisis communication is a crucial part of successful crisis management, whereas the quality of crisis communication largely depends on the availability of and the access to relevant information. In order to improve the availability of information, we have explored how information from public accessible internet sources like Twitter or Wikipedia can be harnessed for food crisis communication. In this paper we are going to report on some initial insight from a web mining and social media analysis approach to monitor health and food related issues that can develop into a potential crises. We have chosen Twitter and Wikipedia as sources for our study since it’s publicly accessible and reveal what people state about certain topics and what people are looking for in order to answer their questions.
A proximal splitting method for separable convex programming and its application to compressive sensing

Recently, by taking full exploitation to the special structure of the separable convex programming, some splitting methods have been developed. However, in some practical applications, these methods need to compute the inverse of a matrix, which maybe slow down their convergence rate, especially when the dimension of the matrix is large. To solve this issue, in this paper we shall study the Peaceman-Rachford splitting method (PRSM) by adding a proximal term to its first subproblem and get a new method named proximal Peaceman-Rachford splitting method (PPRSM). Under mild conditions, the global convergence of the PPRSM is established. Finally, the efficiency of the PPRSM is illustrated by testing some applications arising in compressive sensing. c ©2016 All rights reserved.
Enhanced apoptosis and electrostatic acetylcholi-nesterase activity of abnormally hydrophobic envi-ronment in alzheimer’s plaques

Alzheimer’s disease (AD) is considered a slow neuronal dysfunction process through hypoxia, ischemia and leads to apoptosis mediated senile plaques and neurofibrillary tangles (NFTs). Due to non-invasive approach of plaque characterization, computational techniques based on Brownian dynamics simulation are unique to speculate the electrostatic and kinetic properties of Acetylcho-linesterase (AChE). Typically the MRI spectros-copy high choline peak and enzyme specific to Alzheimer’s Disease (specificity constant (kcat/Km) of AChE) appeared associated with apoptosis and hypoxia. A simple display between synergy of cytokines, apoptosis, elevated AChE and choline is postulated as initial events. The events may be distributed heterogeneously within the senile plaques and neurofibrillary tangles (NFTs) of Alzheimer’s Disease (AD). The role of decreased brain AChE and synergy was associated with specific Magnetic Resonance Spectroscopic (MRS) pattern profiles in AD. These findings suggest that that the altered AChE and early apoptosis events in AD may be associated with specific MR spectral peak patterns. This study opens the possibility of reduced AChE levels causing high choline and reduced N-acetyl ace-tate (NAA) neurotransmitter by MRS after initial apoptosis and/or inflammation to make amyloid plaques in the cerebral tissue of Alzheimer’s disease (AD) patients. These results can be useful in clinical trials on AD lesions.
XCraft : A Dynamic Optimizer for the Materialization of Active XML Documents

An active XML (AXML) document contains special tags that represent calls to Web services. Retrieving its contents consists inmaterializingits data elements by invoking all its embedded service calls in a P2P network. In this process, the results of some service calls are often used as inputs to other calls. Also, usually several peers provide each requested Web service, and peers can collaborate to invoke these services. This implies many equivalent materialization alternatives, with different performance. Optimizing the AXML materialization process is a hard problem, which often involves searching a huge space of solutions. Current techniques for workflow scheduling and distributed query processing are insufficient for this problem, since in AXML materialization: ( i) the set of participating peers is not known in advance; ( ii) service calls in the result of other calls forbid a simple “optimize-thenexecute” strategy; and ( iii) due to the peer volatility in the network, a plan computed by the optimizer may become invalid at the moment of its execution. Moreover, most of the current optimizers are based on centralized coordination. We propose adynamic, cost-based optimization strategy to efficiently materialize AXML documents considering the volatility of a P2P scenario. We formalize the problem from a performance-oriented perspective, and present an optimization strategy that incrementally generates and executes materialization plans. This enables the optimizer to reduce the size of the search space, get more up-to-date information on the status of the peers, and deliver partial results earlier. Our strategy can handle arbitrarily complex AXML documents, and exploits decentralization in many levels. We also present a service-oriented optimization architecture calledXCraft. We evaluated our approach in an XCraft prototype for the ActiveXML system, an open-source P2P platform. Our results show promising performance gains compared to centralized, static materialization strategies.
Temporal bipartite projection and link prediction for online social networks

In user-item networks, the link prediction problem has received considerable attentions and has many applications (e.g., recommender systems, ranking item popularity) in recent years. Many previous works commonly fail to utilize the dynamic nature of the networks. This paper focuses on dealing with the temporal information and proposes an algorithm to cope with the link prediction problem on bipartite networks. We describe a temporal bipartite projection method that yields a projected item graph, called the temporal projection graph (TPG). Based on the TPG, we propose a scoring function called STEP (Score for TEmporal Prediction) for each user-item pair. STEP leverages the historical behaviors of individual users and the social aggregated behaviors learned from the TPG for the link prediction problem. Furthermore, we use TPG and PageRank to rank the popularity of items. To validate our algorithms, we perform various experiments by using the DBLP author-conference dataset, the Flickr dataset and the Delicious dataset. We show that our results of the link prediction problem for new links are substantially better than other temporal link prediction algorithms. We also find the item rankings generated by our approach match very well with that existed in the real world.
The Origin and Control of Pandemic Influenza

In 1918 an epidemic of influenza killed 20 million people worldwide. Spanish flu, as it was called, was a horrific disease. The flu would start with headaches, muscular pain, and fever. These would be rapidly followed by vomiting, dizziness, labored breathing, and profuse sweating. Sometimes purple blisters would appear on the skin, and often blood would spurt out of the nose from hemorrhages in the lungs. Some of the victims of this dreadful, sudden, and unexpected illness went into violent fits of coughing. Death often followed, sometimes only hours after the first symptoms appeared. Influenza viruses infect a number of different animals, and some of these viruses can cause very serious disease indeed, particularly in domesticated chickens and turkeys. Avian influenza viruses sometimes rapidly kill these birds, with 100 percent mortality, and the symptoms resemble, at least to some extent, those of the Spanish flu in 1918. You can imagine, therefore, the concern felt when, in late 1997, a virulent bird flu virus, which had never before been seen in man, started infecting and killing people in Hong Kong [1]. This virus, designated H5N1, killed six of the 18 people it infected. The virus seems to have been transmitted to people from infected chickens in the live bird markets, but so far there has been no evidence that the virus had learned how to spread from person to person. But there is also no reason to suppose that this might not happen, some time in the future. Killing off all the chickens in Hong Kong seems to have stopped the epidemic, at least for the time being. What could be done to control such a virulent influenza virus, which, if it took off, would spread through
Viewpoint Invariant Object Detector

Object Detection is the task of identifying the existence of an object class instance and locating it within an image. Difficulties in handling high intra-class variations constitute major obstacles to achieving high performance on standard benchmark datasets (scale, viewpoint, lighting conditions and orientation variations provide good examples). Suggested model aims at providing more robustness to detecting objects suffering severe distortion due to < 60° viewpoint changes. In addition, several model computational bottlenecks have been resolved leading to a significant increase in the model performance (speed and space) without compromising the resulting accuracy. Finally, we produced two illustrative applications showing the potential of the object detection technology being deployed in real life applications; namely content-based image search and content-based video search.
Undoing fatherhood: postdivorce fathering practices in Lithuania

ABSTRACT Drawing on empirical data from biographical interviews with divorced Lithuanian men, this article examines how they undo fatherhood based on the breadwinner ideology. In a Lithuanian culture of strong economic obligations of men for child support and alimony and weak paternal rights to custody and care, the divorced fathers’ role consists primarily of their financial responsibility. By using the theoretical framework of undoing gender (Butler, J. (2004). Undoing gender. New York: Routledge; Deutsch, F. (2007). Undoing gender. Gender & Society, 21(1), 106–127), the authors argue that the economic aspects of fathering remain most important in the successful doing of fatherhood. However, in undoing the traditional fatherhood and refuting the widespread assumptions about men's secondary role as caregivers, the divorced men not only defied gendered perceptions of individual competencies but also clashed with gender normativity. Moreover, our research demonstrates that besides socio-legal constraints the fathers’ cooperative relationships with their former spouses were also a decisive factor in undoing fatherhood.
Antenna beamwidth control for improving Signal-to-Noise ratio in wireless sensor networks

In the article it demonstrated that the neighbor-finding procedure in many nowadays wireless sensor networks bears a potential of generating an excessive radio interference. It is shown, based on Stojmenovič Minimum Spanning Tree algorithm, that the situation can be improved even in the worst case scenario of simultaneous transmission from all network nodes, by utilizing the directional antennas, taking the Signal-to-Noise and Interference ratio as a performance measure.
Linear Size Distance Preservers

A distance preserver of $p$ node pairs in an $n$-node graph $G$ is a subgraph that agrees with $G$ on all $p$ of these distances. Trivial lower bounds show that the worst-case size of distance preservers is at least linear in $n$ and $p$; that is, $\Omega(n + p)$ edges are sometimes needed in the subgraph. In this work, we try to categorize in what situations this is tight, i.e. a distance preserver on $O(n+p)$ edges is guaranteed to exist. We give three fairly simple arguments that make new progress on this basic question: 1. Any $p = O(n^{1/3})$ node pairs have a distance preserver on $O(n)$ edges (even if $G$ is directed and weighted). 2. Any $p = \Omega\left(\frac{n^2}{rs(n)}\right)$ node pairs have a distance preserver on $O(p)$ edges when $G$ is undirected and unweighted. Here, $rs(n)$ is the Ruzsa-Szemeredi function from combinatorial graph theory. 3. We sometimes need $\omega(s^2)$ edges to preserve all pairwise distances within a subset of $s = o(n^{2/3})$ nodes, even if $G$ is undirected. If we additionally require that $G$ is unweighted, then the range falls slightly to $s \le n^{2/3 - o(1)}$.