Miracle-dz/UniFAR-FTUs huggingface dataset:
Early life adverse experiences and the effect on parenting stress and schizotypal symptoms

A robust amount of research indicates that childhood adverse experiences can have a detrimental impact on later relational experiences and mental health as an adult. Adverse childhood experiences, such as childhood sexual abuse (CSA), or other interpersonal traumas can affect the formation of secure attachments to caregivers. These insecure attachment styles persist into adulthood, affecting all subsequent relationships including that between parent and child. This thesis firstly examines the relationship between CSA and later parenting stress in a systematic literature review. The results indicate there is no strong, consistent evidence of a direct association between CSA and later parenting stress. However, it is suggested that contact-only CSA may produce a significant association with parenting stress and that studies including both contact and non-contact CSA may need larger sample sizes to detect smaller effects. Additionally, an indirect relationship between CSA and parenting stress through current level of depression is proposed. The review highlighted that clearer definitions of CSA and use of properly validated questionnaires are essential to progress this field of research and enable generalisability of results. The aim of the second paper was to investigate associations between attachment, parenting and schizotypy in a non-clinical sample. Participants (N = 134) completed self-report measures online and hypotheses were tested using correlation and mediation analysis. Results found that parenting stress mediated the association between attachment anxiety/avoidance and schizotypy, though parenting competence was not significant as a mediator in a parallel model. Childhood trauma was associated with schizotypy and attachment but was not associated with parenting variables, preventing inclusion in mediation analysis. The study adds to the understanding of what may exacerbate schizotypal symptoms in the first 12 months postpartum as parental attachment insecurity and parental stress together predicted elevated self-reported experiences of schizotypal symptoms in this period.
Training Protein Threading Models using Structural SVMs

Protein threading is the problem of inferring the structure of a protein from its sequence by matching the sequence against a set of known structures. Unlike conventional sequence to sequence alignment tasks, alignment models for threading can exploit a rich set of features derived from the geometry of the known structure. To make use of these complex and interdependent features, we explore the use of discriminative training with structural Support Vector Machines. We present empirical results for the CASP5 dataset and compare against conventional generative training.
An Investigation of Brand-Related User-Generated Content on Twitter

The big data of user-generated content (UGC) on social media are laden with potential value for brand managers. However, there are many obstacles to using big data to answer brand-management questions. This article presents a framework that automatically derives latent brand topics and classifies brand sentiments. It applies text mining with latent Dirichlet allocation (LDA) and sentiment analysis on 1.7 million unique tweets for 20 brands across five industries: fast food, department store, footwear, electronics, and telecommunications. The framework is used to explore four brand-related questions on Twitter. There are three main findings. First, product, service, and promotions are the dominant topics of interest when consumers interact with brands on Twitter. Second, consumer sentiments toward brands vary within and across industries. Third, separate company-specific analyses of positive and negative tweets generate a more accurate understanding of Twitter users' major brand topics and sentiments. Our findings provide brand managers with actionable insights in targeted advertising, social customer relationship management (CRM), and brand management.
Affective Assessment by Digital Processing of the Pupil Diameter

Previous research found that the pupil diameter (PD) can be an indication of affective state, but this approach to the detection of the affective state of a computer user has not been investigated fully. We propose a new affective sensing approach to evaluate the computer user's affective states as they transition from &#x0022;relaxation&amp;#x201D; to &#x0022;stress,&amp;#x201D; through processing the PD signal. Wavelet denoising and Kalman filtering were used to preprocess the PD signal. Then, three features were extracted from it and five classification algorithms were used to evaluate the overall performance of the identification of &#x0022;stress&amp;#x201D; states in the computer users, achieving an average accuracy of 83.16 percent, with the highest accuracy of 84.21 percent reached with a Multilayer Perceptron and a Naive Bayes classifier. The Galvanic Skin Response (GSR) signal was also analyzed to study the comparative efficiency of affective sensing through the PD signal. We compared the discriminating power of the three features derived from the preprocessed PD signal to three features derived from the preprocessed GSR signal in terms of their Receiver Operating Characteristic curves. The results confirm that the PD signal should be considered a powerful physiological factor to involve in future automated affective classification systems for human-computer interaction.
Structural Transfer of Cognitive Skills

This paper investigates a computational approach to transfer: the ability to use previously learned knowledge on related but distinct tasks. We study transfer in the context of an agent architecture, Icarus, and we claim that many forms of transfer follow automatically from its use of structured concepts and skills. We show that Icarus can acquire structured representations from domain experience, and subsequently transfer that knowledge into new tasks. We present results from multiple experiments in the Urban Combat Testbed, a simulated, real-time, three-dimensional environment with realistic dynamics.
Linked Data methodologies for managing information about television content: Applying Linked Data principles in the OntoTV system, in order to improve the collection processes and the way television information is accessed

OntoTV is a television information management system designed for improving the quality and quantity of the formation available in the current television platforms. In order to achieve this objective, OntoTV (1) collects the information offered by the broadcasters, (2) integrates it into a ontology-based data structure, (3) extracts extra data from alternative television sources, and (4) makes possible for the user to perform queries over the stored information. This document shows the way Linked Data methodologies have been applied in OntoTV system, and the improvements in the data consumption and publication processes that have been obtained as result. By the one hand, the possibility of accessing to information available in the Web of Data has made possible to offer more complete descriptions about the programs, as well as more detailed guides than those obtained by using classic collection methods. On the other hand, as the information of the television programs and channels are published according to the Linked Data philosophy, it becomes available not only for OntoTV clients, but also for other agents able to access Linked Data resources, who could offer the viewer more fresh and innovative features.
Automated story generation with multiple internal focalization

In this paper we describe our ongoing effort for automated story generation with a computational model of focalization using a planning-based approach. To generate narrative with focalization, we suggest the use of different plan libraries for story characters. Based on the narrative theories on focalization, we also introduce the concept of focalizing factors to generate stories with multiple internal focalization, with the illustrated example of Rashomon (film directed by Akira Kurosawa, 1950). We believe that our approach might be beneficial for interactive storytelling and story-based games for both entertainment and educational purposes.
Mapping Land Subsidence Related to Underground Coal Fires in the Wuda Coalfield (Northern China) Using a Small Stack of ALOS PALSAR Differential Interferograms

Coal fires have been found to be a serious problem worldwide in coal mining reserves. Coal fires burn valuable coal reserves and lead to severe environmental degradation of the region. Moreover, coal fires can result in massive surface displacements due to the reduction in volume of the burning coal and can cause thermal effects in the adjacent rock mass particularly cracks and fissures. The Wuda coalfield in Northern China is known for being an exclusive storehouse of prime coking coal as well as for being the site of occurrence of the maximum number of known coal fires among all the coalfields in China and worldwide, and is chosen as our study area. In this study, we have investigated the capabilities and limitations of ALOS PALSAR data for monitoring the land subsidence that accompanies coal fires by means of satellite differential interferometric synthetic aperture radar (DInSAR) observations. An approach to map the large and highly non-linear subsidence based on a small number of SAR images was applied to the Wuda coalfield to reveal the spatial and temporal signals of land subsidence in areas affected by coal fires. The DInSAR results agree well with coal fire data obtained from field investigations and thermal anomaly information, which demonstrates that the capability of ALOS PALSAR data and the proposed approach have remarkable potential to detect this land subsidence of interest. In addition, our results also provide a spatial extent and temporal evolution of the land subsidence behavior accompanying the coal fires, which indicated that several coal fire zones suffer accelerated ongoing land subsidence, whilst other coal fire zones are newly subsiding areas arising from coal fires in the period of development.
A method for automated document classification using Wikipedia-derived weighted keywords

The pace of knowledge creation such as in academic research has accelerated rapidly in recent years, resulting in ever more new research publications. This has made it difficult to keep abreast of new developments, or to know which new publications are relevant to a given research area. We have developed a method for analysing and automatically classifying publications. Our method makes use of the Wikipedia category hierarchy, and the content of Wikipedia articles associated to Wikipedia categories. Initially we perform pre-processing and simplification of the Wikipedia category hierarchy, resulting in a rooted directed graph. Wikipedia articles are then analysed, and a set of keywords per Wikipedia category are extracted using a modified tf-idf (term frequency-inverse document frequency) model proposed in this paper. To classify a given input document, tf-idf weights are used to extract relevant keywords from the document, which are then matched to the keywords previously extracted from Wikipedia. The closest matching top-level categories are identified from all categories containing the document's keywords. A cosine similarity metric is then applied to select the closest matching sub-category, recursing down the category hierarchy until the best matching categories are identified. The final result produced shows a set of categories matching the input document, together with a matching percentage. This result can be used to identify new documents that are relevant to a specific research area, or to classify a whole set of documents into different topic areas, with sub-topics, main keywords, and associated weights. We present an experimental study using data from English Wikipedia.
Supervised Kemeny Rank Aggregation for Influence Prediction in Networks

Identifying influential individuals in a network is commonly addressed through various socio-metrics like PageRank, Hub and Authority scores [1], etc. These metrics are primarily based on the actor's location in the network [2] and often captures only a subset of the critical factors that are usually at play while predicting influence in networks like, relationship of the network (type of edge), degree of relationship (weight of the edge), etc. As each measure captures some aspect of a user's influence in the network, it may be beneficial to combine them in order to more accurately identify influencers. In this paper, we focus on methods for combining such influence measures. One straightforward way to combine socio-metrics is to aggregate the scores given by each metric to produce an aggregate score for an individual, using methods like Logistic Regression. However, given that the individual influence measures produce an ordering of elements and not just a point-wise score, we can instead leverage approaches of rank aggregation. Methods for rank aggregation have been extensively used in Social Choice Theory, where there is no ground-truth ranking, and as such are unsupervised. In this paper we introduce several supervised approaches to rank aggregation that can be trained on the ground-truth ordering of a subset of elements. We empirically illustrate various advantages of supervised rank aggregation methods through two case studies: (1) social network data from Twitter and (2) citation network data from arXiv.org. Supervised Rank Aggregation: We consider two key rank aggregation techniques, Borda [3] and Kemeny [4]. Borda aggregation is easy to compute but does not satisfy an important goodness property called Extended Condorcet Criteria [4]. Kemeny satisfies this criterion but is NP-hard to compute [4]. So, we use Local Kemenization (LK) [4], which is a relaxation of Kemeny aggregation that still satisfies the Extended Condorcet Criterion. Borda and Kemeny aggregations, being motivated from social choice theory, strive for fairness and hence treat all rankers as equally important. However, fairness is not a desirable property in our setting, since we know that some individual rankers (measures) are likely to perform better than others in our target tasks. In fact, given the ordering of a (small) set of candidates, we can estimate the performance of individual rankers and use this to produce a better ranking on a new set of candidates. In order to accommodate such supervision, we extend Borda and LK aggregation to incorporate weights associated …
Bigprovision: a provisioning framework for big data analytics

In the past few years, big data has attracted significant attention, and many analytics platforms, such as Hadoop, have been developed to enable the analysis of massive data. Nevertheless, it is still very challenging to provision, let alone optimize, a comprehensive system that includes various aspects, from the computing infrastructure to the analytics programs. To tackle this challenge, in this article, we propose a novel provisioning framework, BigProvision, to provision big data analytics systems. The main idea of the framework is to first evaluate and model the performance of different big data analytics approaches, given a set of sample data and various analytics requirements, such as the expected results, budget, response time, and so on. Based on the evaluation and modeling results, BigProvision can generate a provisioning configuration that can be used to configure the whole system for big data analytics. To evaluate the performance of the proposed framework, we develop an experimental prototype that supports three analytics platforms, Hadoop, Spark, and GraphLab. Our experiments show that for the classic PageRank analysis, both GraphLab and Spark can outperform Hadoop under different requirements. Moreover, by modeling the results, our prototype can determine the expected settings, such as the number of machines and network capacity, for the system that shall handle the complete data set. The prototype and experiments demonstrate that the proposed framework has great potential to facilitate the provision and optimization of future big data analytics systems.
The relationship between bipolar disorder and financial difficulties: a qualitative examination of patient’s views

Objectives: Impulsive spending is listed as a possible symptom in the diagnostic criteria for a manic episode. However no research has examined whether those with bipolar disorder believe that their mental health and finances are related. Methods: Qualitative data was obtained from two sources: qualitative questionnaire responses (n=44) and a focus group (n=6). These were thematically analysed by 2 independent reviewers. Results: Seven key themes and 3 sub-themes were identified. Clients spoke of over-spending, impulse shopping and being excessively generous with money when elated in mood and this having a detrimental effect on finances. Participants reported experiencing severe anxiety, depression, suicidal thoughts, regret and guilt when recovering from a hypomanic/manic episode and realising the extent of their financial difficulties. During episodes of depression, participants reported comfort spending and avoiding dealing with their financial situation as a means to cope. Participants reported that having bipolar made maintaining employment difficult, resulting in taking on a less stressful role or being reliant on benefits. Finally, participants reported a vicious cycle with financial difficulties driving mental health and vice versa. Conclusions: The present findings provide evidence in support of the impulsive spending diagnostic criteria for bipolar disorder. The research has identified a number of possible psychological mechanisms such as impulsivity, self-critical thoughts and avoidance behaviours. It may be that psychological interventions can help tackle these variables and reduce the impact of poor mental health on finances and vice versa in those with bipolar disorder.
Examining the feasibility of the integrated hope scale in American college students

The Integrated Hope Scale (IHS) provides a multi-dimensional evaluation of factors common across measures of hope, examining social relationships, trust and confidence, perspectives of the future, and a lack of hopefulness. Despite promise for the instrument’s utility, research on the IHS is sparse has been limited to non-English-speaking populations, requiring translated items, and the cross-cultural transportability of hope constructs has previously been raised. Predominant theories of hope used in American populations have been criticized for not measuring important aspects that are included within the IHS. Using confirmatory factor analysis and latent class analysis, this study explored the IHS’s structural integrity and interpretive potential using a regionally and racially diverse sample of American college students. Results suggest that the IHS offers a valid global assessment of hope as well as for targeted measurement of discrete areas identified by instrument factor scores. The IHS also shows promise in differentiating individuals according to their level of hopefulness.
At the mercy of a prisoner three dictator experiments

ABSTRACT We test male juvenile prisoners on a dictator game with another anonymous co-prisoner as recipient. Prisoners give more than students, but less than nonstudents of their age. They give more to a charity than to another prisoner. In one of two experiments, those convicted for violent crime give more than those convicted for property crime.
Comparison of Adaptive Control Methods in pH Process Control

In number of research projects various adaptive control methods have been applied to the control of pH with varying success. Recently, intelligent control methods based on fuzzy systems and neural networks have been studied intensively (for example, see Ylén and Jutila, 1996). In some of these studies (see Loh et al, 1995 and Aoyama et al, 1995) nonlinear models are created explicitly for modeling the titration curve of the process liquids; however, this kind of approach is not perhaps most suited for a pH process where continuous and significant changes in pH level and buffering effects in the process liquids occur.
Caustic Triangles on the GPU

This paper proposes a robust algorithm to compute caustics caused by multiple reflections and refractions on the GPU. The proposed algorithm solves two problems of previous methods, caustic light leaks and caustic undersampling. In order to eliminate caustic light leaks, terminal photon hits are stored and caustic patterns are reconstructed on the faces of a layered distance map attached to the caustic generator. To avoid undersampling, we propose caustic triangles to be drawn instead of splatting photon hits. Unlike splatting, caustic triangles adapt to the local density of the uneven photon distribution, always form continuous patterns, do not cause excessive blurring, and do not require manual user intervention to set the size of these splats.
Retrieving broken web links using an approach based on contextual information

In this short note we present a recommendation system for automatic retrieval of broken Web links using an approach based on contextual information. We extract information from the context of a link such as the anchor text, the content of the page containing the link, and a combination of the cache page in some search engine and web archive, if it exists. Then the selected information is processed and submitted to a search engine. We propose an algorithm based on information retrieval techniques to select the most relevant information and to rank the candidate pages provided for the search engine, in order to help the user to find the best replacement. To test the different methods, we have also defined a methodology which does not require the user judgements, what increases the objectivity of the results.
Grid-based cloaking area creation scheme supporting continuous location-based services

Recent development in wireless communication technology and mobile equipment like PDA, cellular phone and GPS is making location-based services (LBSs) more popular day by day. However, because, in the LBSs, users continuously send queries to LBS servers by using their exact locations, private information could be in danger. Therefore, a mechanism for users' privacy protection is required for the safe and comfortable use of LBSs. For this, in this paper, we propose a grid-based cloaking area creation scheme in order to support continuous queries in LBSs. Our scheme creates a cloaking area rapidly by using grid-based cell expansion to efficiently support the continuous LBSs. In addition, to generate a cloaking area which lowers the exposure probability of a mobile user to a minimum level, our scheme computes a privacy protection degree by granting weights to the mobile users. Finally, we show from a performance analysis that our cloaking scheme outperforms the existing cloaking schemes in terms of response time and privacy protection degree.
A grid clustering algorithm using cluster boundaries

Grid-based clustering methods have been extensively applied on large data sets because of low computational cost. In this paper, we propose a new algorithm for grid-based clustering by finding the optimal grid-size using the boundaries of the clusters. The algorithm has linear time complexity. The problem of outliers is resolved with the help of local outlier factor (LOF). We apply the proposed method on various synthetic as well as biological data sets. The results are compared with K-means and few existing grid-based techniques. The comparison results show the effectiveness of the proposed method.