Miracle-dz/UniFAR-FTUs huggingface dataset:
Meetbaarheid van ‘openheid voor verandering’ door middel van de geautomatiseerde methoden : een literatuurreview

Introduction. One of the factors that play a role in therapy success is 'openness to change'. So far ‘openness to change’ has been measured through questionnaires. However, this method has a number of disadvantages, among which the most important is that it provide indirect measurement that can lead to socially desirable answers. Text mining is an automated method that allows more direct measurement by examining everyday language use. Due to the popularity of e-health, this method is increasingly being applied within psychology. This led to the following research question: "Which automated methods can be used to measure 'openness to change'? " To answer this question, a literature review was carried out. Method. The search strategy consisted of a combination of key-word driven database searches in data bases Scopus, Web of Science, PsychINFO and Google Scholar with 20 key words of which the most important were ‘openness to change’, ‘openness to experience’ and ‘text mining’, and snowballing based on the references of useful articles. Research strategy was broadened after the search combination of 'openness to change' and ‘text mining’ didn’t give any results. In all cases this resulted in articles in which personality trait 'openness to experience' was measured through text mining. Openness to experience is one of the aspects of 'openness to change'. Results. The definition of 'openness to experience' appeared to vary per article, from no definition or very poor to a very extensive definition. However, in all cases it was mentioned that it was about Big Five personality trait. Moreover, different questionnaires were used to measure ‘openness to experience’ although the IPPI questionnaire dominated. Furthermore, in every study the researches tried to achieve sufficient mass of (textual) information to enable better prediction value. Finally, linguistic features, extracted from the textual content, were found to outweigh the use of non-linguistic features for the personality trait 'openness to experience'. Conclusion. Text mining makes measuring concepts such as 'openness to experience' possible by applying non-linguistic features in addition to the linguistic features. This means that non-textual data can also be measured. The use of both types of features, can make the measurement of 'openness to change' much better by means of text mining than with the questionnaires. However, 'openness to change' must be well defined, because a good and complete definition promotes reliability by making clear what should be measured by analyzing (textual) data. Still, the question remains if the non-textual data and non-linguistic features are useful in the therapeutic setting. This gives the researches a new motive for further research. Furthermore, some limitations of the study and new ideas for further research are discussed.
A N EW A LGORITHM F OR S

Ensembles of neural networks have been used in the last years as regression machines, showing improved generalization capabilities that outperform those of single networks. However, it has been recognized that, for aggregation to be effective, the individual predictors must be as accurate and diverse as possible. An important problem is, then, how to choose the aggregate members in order to have an optimal compromise between these two conflicting conditions. In particular, for neural network ensembles some proposals in this direction have been already discussed in the literature. We present here a new method for selecting members of neural network ensembles that leads to small aggregates with few but very diverse individual networks. This algorithm is favorably tested against an optimal earlystopping method recently proposed, using the sunspot time series as benchmark. We also provide preliminary results on the actual prediction of the remaining of the current cycle 23 of solar activity. E-MAILS: navone@ifir.edu. ar verdes@ifir.edu. ar granitto@ifir.edu. ar ceccatto@ifir.edu. ar APPLICATION AREA: Neural Networks
WP:clubhouse?: an exploration of Wikipedia's gender imbalance

Wikipedia has rapidly become an invaluable destination for millions of information-seeking users. However, media reports suggest an important challenge: only a small fraction of Wikipedia's legion of volunteer editors are female. In the current work, we present a scientific exploration of the gender imbalance in the English Wikipedia's population of editors. We look at the nature of the imbalance itself, its effects on the quality of the encyclopedia, and several conflict-related factors that may be contributing to the gender gap. Our findings confirm the presence of a large gender gap among editors and a corresponding gender-oriented disparity in the content of Wikipedia's articles. Further, we find evidence hinting at a culture that may be resistant to female participation.
Software ranking based on mining market requirements

Because of the rapid growth of open source software, how to choose software from many alternatives becomes a great challenge. Traditional ranking approaches mainly focus on the characteristics of the software themselves. In this paper we investigate the market demands for software engineers, and propose a novel approach for ranking software by analyzing the market requirements for special software. As industries always need to balance several different factors for selecting software, the market demands can be a good indicator for ranking software. This paper provides quite a different perspective, and it can be a valuable supplement for traditional ranking methods.
Nutraceutical Improvement Increases the Protective Activity of Broccoli Sprout Juice in a Human Intestinal Cell Model of Gut Inflammation

Benefits to health from a high consumption of fruits and vegetables are well established and have been attributed to bioactive secondary metabolites present in edible plants. However, the effects of specific health-related phytochemicals within a complex food matrix are difficult to assess. In an attempt to address this problem, we have used elicitation to improve the nutraceutical content of seedlings of Brassica oleracea grown under controlled conditions. Analysis, by LC-MS, of the glucosinolate, isothiocyanate and phenolic compound content of juices obtained from sprouts indicated that elicitation induces an enrichment of several phenolics, particularly of the anthocyanin fraction. To test the biological activity of basal and enriched juices we took advantage of a recently developed in vitro model of inflamed human intestinal epithelium. Both sprouts’ juices protected intestinal barrier integrity in Caco-2 cells exposed to tumor necrosis factor α under marginal zinc deprivation, with the enriched juice showing higher protection. Multivariate regression analysis indicated that the extent of rescue from stress-induced epithelial dysfunction correlated with the composition in bioactive molecules of the juices and, in particular, with a group of phenolic compounds, including several anthocyanins, quercetin-3-Glc, cryptochlorogenic, neochlorogenic and cinnamic acids.
Two-Phased Event Relation Acquisition: Coupling the Relation-Oriented and Argument-Oriented Approaches

Addressing the task of acquiring semantic relations between events from a large corpus, we first argue the complementarity between the pattern-based relation-oriented approach and the anchor-based argument-oriented approach. We then propose a two-phased approach, which first uses lexico-syntactic patterns to acquire predicate pairs and then uses two types of anchors to identify shared arguments. The present results of our empirical evaluation on a large-scale Japanese Web corpus have shown that (a) the anchor-based filtering extensively improves the accuracy of predicate pair acquisition, (b) the two types of anchors are almost equally contributive and combining them improves recall without losing accuracy, and (c) the anchor-based method also achieves high accuracy in shared argument identification.
Co-activity manager: integrating activity-based collaboration into the desktop interface

Activity-Based Computing (ABC) has been proposed as an organisational structure for local desktop management and knowledge work. Knowledge work, however, typically occurs in partially overlapping subgroups and involves the use of multiple devices. We introduce co-Activity Manager, an ABC approach that (i) supports activity sharing for multiple collaborative contexts, (ii) includes collaborative tools into the activity abstraction and (iii) supports multiple devices by seamlessly integrated cloud support for documents and activity storage. Our 14 day field deployment in a multidisciplinary software development team showed that activity sharing is used as a starting point for long-term collaboration while integrated communication tools and cloud support are used extensively during the collaborative activities. The study also showed that activities are used in different ways ranging from project descriptions to to-do lists, thereby confirming that a document-driven activity roaming model seems to be a good match for collaborative knowledge work
Instruction-Level Execution Migration

Abstract NWe introduce the Execution Migration Machine (EM 2 ), a novel data -centric multicore memory system architectu re based on computation migration. Unlike traditional distributed memory multicores, which rely on complex cache coherence protocols to move the data to the core where the computation is taking place, our scheme always moves the computation to the core whe re the data resides. By doing away with the cache coherence protocol, we are able to boost the effectiveness of per-core caches while drastically reducing hardware complexity. To evaluate the potential of EM 2 architectures, we developed a series of PIN/Gra phite-based models of an EM 2 multicore with 64 x86 cores and, under some simplifying assumptions (a timing model restricted to data memory performance, no instruction cache modeling, high -bandwidth fixed -latency interconnect allowing concurrent migrations) , compared them against corresponding directory -based cache -coherent architecture models. We justify our assumptions and show that our conclusions are valid even if our assumptions are removed. Experimental results on a range of SPLASH -2 and PARSEC benchma rks indicate that EM
Conversational Strategies that Support Idea Generation Productivity in Groups

: Our recent work has shown that conversational tutors that use social interaction strategies can achieve significant learning improvement through better management of student attention. In continuation with this line of investigation, this paper presents an experiment that measures the separate and joint effects of the nature and style of automatically generated interventions. We investigate aspects of designing intervention prompts from two different angles: first in terms of the addition of socially oriented turns over and above the instructional turns required to keep the students on track, and second in terms of the style of presentation of the tutor’s instructional turns. We see positive effects of both manipulations, but on different outcome metrics. Thus, we conclude that the two directions can be productively combined into a single socially enhanced feedback approach, which contributes to the literature on dynamic forms of support for collaborative learning.
A Compile-Time Data Locality Optimization Framework for NUCA Chip Multiprocessors

With increasing numbers of cores, future CMPs (Chip Multi-Processors) are likely to have a tiled architecture with a portion of shared L2 cache on each tile and a bank-interleaved distribution of the address space. For data-parallel programming models, there is a mismatch between such a non-uniform cache organization and the canonical row-major or column-major lay-outs of multi-dimensional arrays – causing a signiﬁcant number of non-local L2 accesses for many commonly occurring data access patterns. In this paper we develop a compile-time framework for data locality optimization via data layout transformation. Using a polyhedral model for dependences, the program’s localizability is determined by analysis of intra-and inter-statement dependences, followed by non-canonical data layout transformation to reduce non-local accesses for local-izable computations. Simulation-based results on a 16-core 2D tiled CMP demonstrate the effectiveness of the approach.
Entropy-based nonlinear analysis for electrophysiological recordings of brain activity in Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disorder in which the death of brain cells causes memory loss and cognitive decline. As AD progresses, changes in the electrophysiological brain activity take place. Such changes can be recorded by the electroencephalography (EEG) and magnetoencephalography (MEG) techniques. These are the only two neurophysiologic approaches able to directly measure the activity of the brain cortex. Since EEGs and MEGs are considered as the outputs of a nonlinear system (i.e., brain), there has been an interest in nonlinear methods for the analysis of EEGs and MEGs. One of the most powerful nonlinear metrics used to assess the dynamical characteristics of signals is that of entropy. The aim of this thesis is to develop entropy-based approaches for characterization of EEGs and MEGs paying close attention to AD. Recent developments in the field of entropy for the characterization of physiological signals have tried: 1) to improve the stability and reliability of entropy-based results for short and long signals; and 2) to extend the univariate entropy methods to their multivariate cases to be able to reveal the patterns across channels. To enhance the stability of entropy-based values for short univariate signals, refined composite multiscale fuzzy entropy (MFE RCMFE) is developed. To decrease the running time and increase the stability of the existing multivariate MFE (mvMFE) while keeping its benefits, the refined composite mvMFE (RCmvMFE) with a new fuzzy membership function is developed here as well. In spite of the interesting results obtained by these improvements, fuzzy entropy (FuzEn), RCMFE, and RCmvMFE may still lead to unreliable results for short signals and are not fast enough for real-time applications. To address these shortcomings, dispersion entropy (DispEn) and frequency-based DispEn (FDispEn), which are based on our introduced dispersion patterns and the Shannon’s definition of entropy, are developed. The computational cost of DispEn and FDispEn is O(N) – where N is the signal length –, compared with the O(N2) for popular sample entropy (SampEn) and FuzEn. DispEn and FDispEn also overcome the problem of iii
Visualization Technique for Analyzing Non-dominated Set Comparison

Multi-objective evolutionary algorithms (MOEAs) have been proved successfully in many different applications. Over 900 publications [4] have since proposed various MOEA implementations and applications. However, there is still a significant lack of studies on metrics for MOEA comparison. In this paper, we propose a new visualization technique that will provide better analysis of the non-dominated solutions for any number of objectives.
Analyzing Memory Access on CPU-GPGPU Shared LLC Architecture

The data exchange between GPGPUs and CPUs are becoming more and more important nowadays. One trend in industry to alleviate the long latency is to integrate CPUs and GPGPUs on a single chip. In this paper, we analyze the reference interactions between CPU and GPGPU applications with a CPU-GPGPU co-simulator that integrates the gem5 and gpgpu-sim together. Since the memory controllers are shared among all cores, we observe severe memory contention between them. The CPU applications suffer a 1.26x slowdown and 64.79% blocked time in main memory when they run parallels with GPGPU applications. To alleviate the contention and provide more memory band-width, shared last level caches (LLCs) are commonly employed in such systems. We test a banked shared LLC structure that implanted into the co-simulator. We show that a simple shared LLC contributes mostly to the GPGPU (2.13x to running alone and 1.7x to running in parallel), rather than CPU. With the help of LLC, the memory requests issued to main memory is reduced to 30.74%, the blocked time is reduced to 49.64%, which provides more memory bandwidth. The latency-sensitive CPU applications are suffered as the LLC buffer occupation is very high when they run with GPGPU in parallel. Besides, as the number of LLC cache bank grows, we reveal that CPU achieves higher speedup than GPGPUs by increasing LLC parallelism. Finally, we also discuss the impact of GPGPU L2 cache. And we find that fewer GPGPU L2 cache banks will lower the performance as they limits the parallelism of GPGPU. The observations and inferences in this paper may serve as a reference guide to future CPU-GPGPU shared LLC design.
A DNA vector-based RNAi technology to suppress gene expression in mammalian cells

Double-stranded RNA-mediated interference (RNAi) has recently emerged as a powerful reverse genetic tool to silence gene expression in multiple organisms including plants, Caenorhabditis elegans, and Drosophila. The discovery that synthetic double-stranded, 21-nt small interfering RNA triggers gene-specific silencing in mammalian cells has further expanded the utility of RNAi into mammalian systems. Here we report a technology that allows synthesis of small interfering RNAs from DNA templates in vivo to efficiently inhibit endogenous gene expression. Significantly, we were able to use this approach to demonstrate, in multiple cell lines, robust inhibition of several endogenous genes of diverse functions. These findings highlight the general utility of this DNA vector-based RNAi technology in suppressing gene expression in mammalian cells.
Decreasing the ratio of matriptase/HAI-1 by downregulation of matriptase as a potential adjuvant therapy in ovarian cancer

Tumor invasion and metastasis are complex biological processes. Matriptase and its endogenous inhibitor, hepatocyte growth factor activator inhibitor-1 (HAI-1) are involved in invasion and metastasis. To evaluate the ratio of matriptase/HAI-1 and their potential therapeutic value in ovarian cancer, HO-8910 human ovarian cancer cells and the homologous high-metastatic HO-8910PM cells were used as in vitro cellular models ovarian cancer. The invasive and metastatic abilities, and the expression of matriptase and HAI-1 in these cells were detected using scratch assays, Transwell chamber assays, reverse transcription-quantitative polymerase chain reaction, western blotting and fluorescent immunocytochemistry. Following infection with lentivirus-mediated matriptase-targeting small interfering RNA (siRNA), cell cycle progression and apoptosis were also analyzed. The migration distance and number of invading HO-8910PM cells were significantly increased compared with HO-8910 cells. HO-8910PM cells exhibited a significantly higher ratio of matriptase/HAI-1 mRNA levels compared with HO-8910 cells (0.51 vs. 0.24, ~2.2 fold increase). Compared with HO-8910 cells, the matriptase mRNA level was increased by ~3.6 fold in HO-8910PM cells, whereas the HAI-1 mRNA level was increased by ~1.7 fold. Similar increases in protein expression levels were also observed in HO-8910PM cells compared with HO-8910 cells. Migration and invasiveness were positively correlated with matriptase expression level (r=0.994, P<0.01) and the ratio of matriptase/HAI-1 (r=0.929, P<0.01). Downregulation of matriptase using siRNA resulted in inhibition of the invasive and metastatic abilities of HO-8910PM cells, cell cycle arrest in the G0/G1 phase and increased apoptosis. The present study demonstrated that ovarian cancer cell metastasis and invasion were more dependent on upregulation of matriptase levels than downregulation of HAI-1. Matriptase may be a potential adjuvant therapeutic target for inhibiting ovarian cancer invasion and metastasis.
Approximate Note Transcription for the Improved Identification of Difficult Chords

The automatic detection and transcription of musical chords from audio is an established music computing task. The choice of chord profiles and higher-level time-series modelling have received a lot of attention, resulting in methods with an overall performance of more than 70% in the MIREX Chord Detection task 2009. Research on the front end of chord transcription algorithms has often concentrated on finding good chord templates to fit the chroma features. In this paper we reverse this approach and seek to find chroma features that are more suitable for usage in a musically-motivated model. We do so by performing a prior approximate transcription using an existing technique to solve non-negative least squares problems (NNLS). The resulting NNLS chroma features are tested by using them as an input to an existing state-of-the-art high-level model for chord transcription. We achieve very good results of 80% accuracy using the song collection and metric of the 2009 MIREX Chord Detection tasks. This is a significant increase over the top result (74%) in MIREX 2009. The nature of some chords makes their identification particularly susceptible to confusion between fundamental frequency and partials. We show that the recognition of these diffcult
Efavirenz plasma levels can predict treatment failure and central nervous system side effects in HIV-1-infected patients

ObjectiveLimited information exists on the clinical usefulness of drug level monitoring for efavirenz, a once-daily non-nucleoside reverse transcriptase inhibitor (NNRTI). The aim of this study was to determine whether efavirenz plasma concentration monitoring could predict treatment failure and central nervous system (CNS) tolerability. MethodsBlood samples were obtained from 130 HIV-infected patients receiving efavirenz in combination with other antiretroviral agents for more than 3 months. Efavirenz plasma concentrations were measured by high-performance liquid chromatography. An evaluation of CNS side-effects was performed and the viral load, CD4 cell count and other clinical and laboratory data were assessed. In 85 patients, these measures were repeated at 3 month intervals. ResultsEfavirenz plasma levels (n = 226) were measured at an average of 14 h after drug intake. Drug concentrations ranged from 125 to 15 230 μg/l (median 2188). Large inter-patient (CV 118%) and limited intra-patient (CV 30%) variabilities were observed in efavirenz levels. Virological failure was observed in 50% of patients with low efavirenz levels (< 1000 μg/l) versus 22 and 18% in patients with 1000–4000 μg/l or more than 4000 μg/l, respectively. CNS toxicity was approximately three times more frequent in patients with high efavirenz levels (> 4000 μg/l) compared with patients with 1000–4000 μg/l. ConclusionTreatment failure and CNS side-effects are associated with low and high efavirenz plasma levels, respectively. The important inter-individual variability in efavirenz levels strongly argues for dose adjustment on the basis of therapeutic drug monitoring to optimize treatment.
An Efficient Technique for De-Noising Sentences using Monolingual Corpus and Synonym Dictionary

We describe a method of correcting noisy output of a machine translation system. Our idea is to consider di erent phrases of a given sentence, and nd appropriate replacements of some of these from the frequently occurring similar phrases in the monolingual corpus. The frequent phrases in the monolingual corpus are indexed by a search engine. When looking for similar phrases we consider phrases containing words that are spelling variations of or are similar in meaning to the words in the input phrase. We use a framework where we can consider di erent ways of splitting a sentence into short phrases and combining them so as to get the best replacement sentence that tries to preserve the meaning meant to be conveyed by the original sentence.