Miracle-dz/UniFAR-FTUs huggingface dataset:
First impressions from faces : ideal partner preferences dominated by attractiveness-related concerns

When people first encounter a potential partner, they derive a wealth of objective and subjective impressions simply from their faces (e.g., age, gender, attractiveness, trustworthiness). Facial first impressions are consequential, for instance, impacting on decisions to approach a potential partner. Hence, it is relevant to have a solid theoretical understanding of how first impressions relate to ideal partner preferences, particularly as romantic relationship researchers primarily use verbal measures. The current research revealed that individuals can perceive traits and factors related to their ideal partner preferences in highly variable everyday face images, and these factors overlapped largely (although not completely) with those identified by face perception researchers. Partner preferences for face images were dominated by attractiveness-related concerns in both sexes. Further, a minimum-exposure paradigm revealed that, even in some non-romantic contexts, attractiveness is particularly salient in face images. Yet, these findings could not be attributed to an attractiveness halo effect, given that attractiveness did not dominate all non-romantic first impressions of face images (e.g., evaluations of faces in terms of occupations). There are multiple potential reasons why individuals might prioritise facial attractiveness (e.g., from an evolutionary perspective, attractiveness is a cue to fertility and resistance to environmental and genetic stressors). Of note, though, a verbal measure of partner preferences revealed that individuals prioritised warmth-trustworthiness, suggesting that face images and verbal measures may capture different elements of preferences. Therefore, these findings attest the relevance of using face images to complement verbal measures of partner preferences.
The Role of Subjective Perceptions in Health Decisions: A Field Experiment among Disadvantages Youth

Disadvantaged youth are particularly at risk of under-investing in their health. Costs of healthcare and bias in health needs perceptions are likely to be key factors of underinvestment. Relying on a randomized experiment, we find that providing them with personalized information both on public health insurance and on their health status based on a medical diagnosis raises their curative and preventive investments. More specifically, they are more likely to consult a psychologist and to use contraception, while depression and risky sexual behaviors are key issues in this population. In order to distinguish between the two barriers, financial constraints and underestimation of health needs, we also test a program providing information on public health insurance only. This limited program improves their medical coverage in the same way as the combined program, but it does not translate into higher health investments. These findings highlight the importance of taking into account the role of subjective perceptions of health needs when considering health decisions among disadvantaged youth.
A BAYES COMPARISON OF TWO DIFFERENT CANCER THERAPIES UNDER THE ASSUMPTION OF WEIBULL SURVIVAL MODEL OR ITS SUBFAMILY

The paper considers a group of patients suffering from leukemia B non-small lung cancer. Such patients are generally suggested to undergo for either radiotherapy or chemotherapy followed by radiotherapy. The objective of the paper is to compare the two therapies based on survival functions of the patients assuming Weibull survival model for each therapy. The paper further examines the feasibility of a subfamily of Weibull model, namely the exponential distribution, for a date set available from a clinical trial experiment. This feasibility is judged based on Bayes information criterion by comparing the Weibull model with its subfamily. The model compatibility study with the data based on posterior p-values has also been given to ensure the suitability of the two models. Finally, the recommendations are made accordingly.
The Influence of the Input Parameters Selection on the RANSAC Results

The RANSAC (RANdom SAmpling and Consensus) enables us to search within a given group of points for subgroups of points that belong to a mathematically describable object or a part of an object. The number of iterations within a single repetition depends on the data, selection and settings of the input parameters (percentage of inliers, probability and minimum number of points that uniquely define a geometrical shape). In our research we applied simulation modelling to analyse the influence of the selection of input parameters on the approximation of the sphere and plane models. (Received in June 2013, accepted in January 2014. This paper was with the authors 2 months for 1 revision.)
Analysis of Market Dynamics by Self-Organizing Methods

The self-organizing methods were used for investigation of financial market. As an example following R. Mantegna 1 we consider data time-series of Dow Jones index during 2002 year. In order to reveal new structures in stock market behaviour of the companies drawing up Dow Jones index we apply SOM (Self-Organizing Map) and GMDH (Group Method of Data Handling) algorithms. New properties and structures in data time-series have been revealed by applying GMDH and SOM techniques to processing these data. Using SOM techniques we obtain the following SOM-map from the input data.The analysis of the clusters was made by GMDH. As a result we get analytical formulas for description of data time series behaviour. GMDH methods use polynomial referenced functions and our results are presented as analogue of Kolmogorov-Gabor polynomials. The comparison of our results with obtained by standard statistical methods is also presented in the paper.
Privacy Preserving Probabilistic Possibilistic Fuzzy C Means Clustering

Due to this uncontrollable growth of data, clustering played major role to partition into a small sets to do relevant processes within the small sets. Recently, the privacy and security are extra vital essentials when data is large and the data is distributed to other sources for various purposes. According to that, the privacy preservation should be done before distributing the data. In this study, our proposed algorithm meets the both requirements of achieving the clustering accuracy and privacy preserving of the data. Initially, the whole dataset is divided to small segments. The next step is to find the best sets of attributes combinations, which are attained through, attribute weighing process, which leads to attain the privacy preservation through vertical partitioning. The next is to apply the proposed Probabilistic Possibilistic Clustering Algorithm (PPFCM) for each segment, which produces the number of clusters for each segment. The next step is applying the PPFCM on the centroids of the clusters. The corresponding data tuples of the grouped centroids join to attain the final clustered result. The implementation is done using JAVA and the performance of the proposed PPFCM algorithm is compared with possibilistic FCM and probability-clustering algorithm for the benchmark datasets.
Finding Patterns that Correspond to Episodes

We present two algorithms for elucidating structures in time series. These are unsupervised algorithms; they discover patterns without any knowledge about the episodic structures in the time series data. Yet, these patterns correspond with episodes, at least in an experiment with data from robot episodes. We offer a preliminary explanation for this result based on the idea that episodes persist. If this explanation is correct, then the algorithms are apt to be more generally applicable.
Universalising Childcare, Changing Mothers’ Attitudes: Policy Feedback in Norway

Abstract The way that welfare policies influence the interpretative processes of social actors is attracting increasing attention. In this article, we investigate policy change impacts on ideas mothers have about the best form of care for their children. The data are taken from representative surveys among mothers of preschool-age children in 2002 and 2010 in Norway. The surveys cover a decade of marked reforms in childcare services with regard to the supply of places, parents’ fees and the right to be given a place in care. Policy change gave rise to major shifts in attitude. From conditional attitudes to the suitability of institutional care, the majority view shifted towards ‘childcare services only’ being considered the best form of care for preschool-age children. This occurred among mothers in all socio-economic groups and in all parts of the country. Based on policy feedback theories, mechanisms likely to have caused this shift – policy visibility, proximity and timing – are considered.
Automatic Profiling of Twitter Users Based on Their Tweets: Notebook for PAN at CLEF 2015

In this paper we go through our approach at solving the PAN Author Profiling task. We introduce a novel way of computing the type/token ratio of an author and show that, although strong correlations have been observed between high extroversion and low type/token ratios in the past, this ratio is not necessarily a strong indicator of extroversion. Since the text of a person is influenced by all 7 features (gender, age, and big five personality traits) that are required to be automatically identified in this task, we used this ratio, along with Term frequency-Inverse document frequency (tf-idf ) matrices, in all 7 subtasks and all 4 corpora and obtained good results.
Contribution of Langerhans Cell-Derived IL-18 to Contact Hypersensitivity

The epidermal Langerhans cells (LC), a member of the dendritic cell family, and the LC-derived cytokine IL-12 play a pivotal role in the initiation of contact hypersensitivity (CHS), a Th1 immune response in the skin. Because IL-18, another LC-derived cytokine, shares functional and biological properties with IL-12, we examined a potential role for IL-18 in CHS initiation. Our studies demonstrated that during the induction phase of murine CHS, IL-18 mRNA was significantly up-regulated in the skin-draining lymph nodes (LN). Migratory hapten-modified LC in LN expressed high levels of IL-18 mRNA and secreted functional IL-18 protein. LN cells produced significant amounts of IFN-γ following in vitro IL-12 stimulation, which could be partially blocked by anti-IL-18 Ab, suggesting a synergistic role for endogenous IL-18 in IFN-γ production by LN cells. Because mature IL-18 requires cleavage of immature precursors by caspase-1, we further examined IL-12-induced IFN-γ production in caspase-1−/− LN cells. An impaired IFN-γ production was seen in caspase-1−/− LN cells, which could be restored by addition of exogenous IL-18, supporting a role for caspase-1-cleaved, mature IL-18 in IFN-γ production. Finally, in vivo studies showed that CHS responses were significantly inhibited in mice treated with neutralizing IL-18 Ab as well as in caspase-1−/− mice deficient in mature IL-18, indicating functional relevance for IL-18 in CHS. Taken together, our studies demonstrate that LC-derived IL-18 significantly contributes to CHS initiation.
A reverse order interview does not aid deception detection regarding intentions

Promising recent research suggests that more cognitively demanding interviews improve deception detection accuracy. Would these cognitively demanding techniques work in the same way when discriminating between true and false future intentions? In Experiment 1 participants planned to complete a task, but instead were intercepted and interviewed about their intentions. Participants lied or told the truth, and were subjected to high (reverse order) or low (sequential order) cognitive load interviews. Third-party observers watched these interviews and indicated whether they thought the person was lying or telling the truth. Subjecting participants to a reverse compared to sequential interview increased the misidentification rate and the appearance of cognitive load in truth tellers. People lying about false intentions were not better identified. In Experiment 2, a second set of third-party observers rated behavioral cues. Consistent with Experiment 1, truth tellers, but not liars, exhibited more behaviors associated with lying and fewer behaviors associated with truth telling in the reverse than sequential interview. Together these results suggest that certain cognitively demanding interviews may be less useful when interviewing to detect false intentions. Explaining a true intention while under higher cognitive demand places truth tellers at risk of being misclassified. There may be such a thing as too much cognitive load induced by certain techniques
How do you make software components argue? An exploration of argumentative reasoning within autonomous software agents

Autonomous software components, more commonly referred to as software agents, are often cited as a key enabling technology for the next-generation of online services. However, in these services, consisting of large-scale open multi-agent systems, classical forms of reasoning (such as provided by traditional monotonic logics) will be unsuitable. In this work we discuss our vision for a non-monotonic reasoning component for software agents, and present our current work on a prototype light-weight Java-based reasoning engine. The logical machinery underlying our implementation is based on defeasible argumentation, a relatively new paradigm in logical reasoning based upon sound theoretical concepts from the study of argument. The use of argumentation allows an agent to reason about and provide support for opinions, claims, proposals and ultimately decisions and conclusions in a flexible manner. The primary motivations behind this report are to illustrate that a practical agent-based implementation of argumentation is now viable. However, much work remains to be done, particularly in regard to the inherent computational complexity of the process of argumentation. A fundamental message of our work is that we take seriously the need for open source tools and benchmarking applications, which will allow us to gather sound empirical evidence for the applicability of argumentation within this domain.
Low Quality Fingerprint Image Using Spatial and Frequency Domain Filter

The Biometrics indicators, Fingerprints are one of the highest levels of reliability and have been extensively used by forensic experts in criminal investigations. Anyway the performance of these techniques relies heavily on the quality of input fingerprint. But already existing STFT (short-time Fourier transform) analysis is not much perfect and best suit, to recover these unrecoverable regions of the fingerprint. In proposed a two-stage scheme to enhance the low-quality fingerprint image in both the spatial domain and the frequency domain based on the learning from the images.we use FFT (Fast Fourier Transform) algorithm to overcome the problem. Keyword: Fingerprint enhancement, learning, privacy in biometrics systems, two-Stage filtering.
Impact of BRCA1 BRCT Domain Missense Substitutions on Phosphopeptide Recognition

The BRCA1 BRCT domain binds pSer-x-x-Phe motifs in partner proteins to regulate the cellular response to DNA damage. Approximately 120 distinct missense variants have been identified in the BRCA1 BRCT through breast cancer screening, and several of these have been linked to an increased cancer risk. Here we probe the structures and peptide-binding activities of variants that affect the BRCA1 BRCT phosphopeptide-binding groove. The results obtained from the G1656D and T1700A variants illustrate the role of Ser1655 in pSer recognition. Mutations at Arg1699 (R1699W and R1699Q) significantly reduce peptide binding through loss of contacts to the main chain of the Phe(+3) residue and, in the case of R1699W, to a destabilization of the BRCT fold. The R1835P and E1836K variants do not dramatically reduce peptide binding, in spite of the fact that these mutations significantly alter the structure of the walls of the Phe(+3) pocket.
Learning to Rank User Queries to Detect Search Tasks

We present a framework for discovering sets of web queries having similar latent needs, called search tasks, from user queries stored in a search engine log. The framework is made of two main modules: Query Similarity Learning (QSL) and Graph-based Query Clustering (GQC). The former is devoted to learning a query similarity function from a ground truth of manually-labeled search tasks. The latter represents each user search log as a graph whose nodes are queries, and uses the learned similarity function to weight edges between query pairs. Finally, search tasks are detected by clustering those queries in the graph which are connected by the strongest links, in fact by detecting the strongest connected components of the graph. To discriminate between "strong" and "weak" links also the GQC module entails a learning phase whose goal is to estimate the best threshold for pruning the edges of the graph. We discuss how the QSL module can be effectively implemented using Learning to Rank (L2R) techniques. Experiments on a real-world search engine log show that query similarity functions learned using L2R lead to better performing GQC implementations when compared to similarity functions induced by other state-of-the-art machine learning solutions, such as logistic regression and decision trees.
External PolymorphismAn Object Structural Pattern for Transparently Extending C + + Concrete Data

Allow C++ classes unrelated by inheritance and/or having no virtual methods to be treated polymorphically. These unrelated classes can be treated in a common manner by software that uses them. 2 Motivation Working with C++ classes from diierent sources can be diicult. Often an application may wish to \project" common behavior on such classes, but is restricted by the classes' existing design. If only the class interface requires adaptation an obvious solution is to apply an object structural pattern such as Adapter or Decorator 1]. Occasionally there are more complex requirements, such as the need to change both underlying interface and implementation. In such cases, classes may need to behave as if they had a common ancestor. For instance, consider the case where we are debugging an application constructed using classes from various C++ libraries. It would be convenient to be able to ask any instance to \dump" its state in a human-readable format to a le or console display. It would be even more convenient to gather all live class instances into a collection and iterate over that collection asking each instance to dump itself.
A NEW HYBRID FEATURE SELECTION METHOD BASED ON ASSOCIATION RULES AND PCA FOR DETECTION OF BREAST CANCER

In this study, a new hybrid feature selection method named as AP has been formed to detect breast cancer, using association rules (Apriori algorithm) and Principal Component Analysis (PCA) together with artificial neural network classifier. Thanks to this hybrid system, both the decrease in the size of data and the successful and fast training of classifiers have been achieved. In order to detect the accuracy of the suggested system, Wisconsin breast cancer data have been used. 10-fold cross-validation has been used on the classification phase. The average classification accuracy of the developed AP + NN system is 98.29%. Among the studies performed through cross-validation method for breast cancer, our study result appears to be very promising. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster and more accurate diagnosis of diseases.
A Rapid Method to Characterize Mouse IgG Antibodies and Isolate Native Antigen Binding IgG B Cell Hybridomas

B cell hybridomas are an important source of monoclonal antibodies. In this paper, we developed a high-throughput method to characterize mouse IgG antibodies using surface plasmon resonance technology. This assay rapidly determines their sub-isotypes, whether they bind native antigen and their approximate affinities for the antigen using only 50 μl of hybridoma cell culture supernatant. Moreover, we found that mouse hybridomas secreting IgG antibodies also have membrane form IgG expression without Igα. Based on this surface IgG, we used flow cytometry to isolate rare γ2a isotype switched variants from a γ2b antibody secreting hybridoma cell line. Also, we used fluorescent antigen to single cell sort antigen binding hybridoma cells from bulk mixture of fused hybridoma cells instead of the traditional multi-microwell plate screening and limiting dilution sub-cloning thus saving time and labor. The IgG monoclonal antibodies specific for the native antigen identified with these methods are suitable for in vivo therapeutic uses, but also for sandwich ELISA assays, histology, flow cytometry, immune precipitation and x-ray crystallography.
A pattern-based selective recrawling approach for object-level vertical search

Traditional recrawling methods learn navigation patterns in order to crawl related web pages. However, they cannot remove the redundancy found on the web, especially at the object level. To deal with this problem, we propose a new hypertext resource discovery method, called ``selective recrawling'' for object-level vertical search applications. The goal of selective recrawling is to automatically generate URL patterns, then select those pages that have the widest coverage, and least irrelevance and redundancy relative to a pre-defined vertical domain. This method only requires a few seed objects and can select the set of URL patterns that covers the greatest number of objects. The selected set can continue to be used for some time to recrawl web pages and can be renewed periodically. This leads to significant savings in hardware and network resources. In this paper we present a detailed framework of selective recrawling for object-level vertical search. The selective recrawling method automatically extends the set of candidate websites from initial seed objects. Based on the objects extracted from these websites it learns a set of URL patterns which covers the greatest number of target objects with little redundancy. Finally, the navigation patterns generated from the selected URL pattern set are used to guide future crawling. Experiments on local event data show that our method can greatly reduce downloading of web pages while maintaining comparative object coverage.
A New Method for the Characterization of the Degree of Fire Damage to Gypsum Wallboard for Use in Fire Investigations

A new method to characterize the degree of fire damage to gypsum wallboard is introduced, implemented, and tested to determine the efficacy of its application among novices. The method was evaluated by comparing degree of fire damage assessments of novices with and without the method. Thirty‐nine “novice” raters assessed damage to a gypsum wallboard surface, completing 66 ratings, first without the method, and then again using the method. The inter‐rater reliability was evaluated for ratings of damage without and with the method. For novice fire investigators rating degree of damage without the aid of the method, ICC(1,2) = 0.277 with 95% CI (0.211, 0.365), and with the method, ICC(2,1) = 0.593 with 95% CI (0.509, 0.684). Results indicate that the raters were more reliable in their analysis of the degree of fire damage when using the method, which support the use of standardized processes to decrease the variability in data collection and interpretation.