Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Automatic Mapping Generation for the DBpedia Information Extraction Framework

DBpedia is one of the very well known live projects from the Semantic Web. It is like a mirror version of the Wikipedia site in Semantic Web. Initially it publishes the information collected from the Wikipedia, but only that part which is relevant to the Semantic Web. Collecting information for Semantic Web from the Wikipedia is demonstrated as the extraction of structured data. DBpedia normally do this by using a specially designed framework called DBpedia Information Extraction Framework. This extraction framework do its works thorough the evaluation of the similar properties from the DBpedia Ontology and the Wikipedia template. This step is known as DBpedia mapping. At present most of the mapping jobs are done complete manually. In this paper a new framework is introduced considering the issues related to the template to ontology mapping. A semiautomatic mapping tool for the DBpedia project is proposed with the capability of automatic suggestion generation for the end users so that users can identify the similar Ontology and template properties. Proposed framework is useful since after selection of similar properties, the necessary code to maintain the mapping between Ontology and template is generated automatically.
Know-Center at PAN 2015 Author Identification: Notebook for PAN at CLEF 2015

Our system for the PAN 2015 authorship verification challenge is based upon a two step pre-processing pipeline. In the first step we extract different fea- tures that observe stylometric properties, grammatical characteristics and pure statistical features. In the second step of our pre-processing we merge all those features into a single meta feature space. We train an SVM classifier on the gener- ated meta features to verify the authorship of an unseen text document. We report the results from the final evaluation as well as on the training datasets.
What is the Contribution of Actual Motor Skill, Fitness, and Physical Activity to Children’s Self-Perception of Motor Competence?

This study aimed to examine the contribution of objective measures of physical fitness (musculoskeletal and cardiorespiratory), physical activity, and motor skill to motor perception. A total of 122 children (63 boys) aged 8–11 years were assessed. Independent t-tests assessed sex differences in all variables. Two linear mixed models adjusted for sex and age were performed with perceived object control and locomotor skills (Pictorial Scale of the Perceived Movement Skill Competence for Young Children) as outcomes. Aerobic (multi-stage fitness test) and muscular fitness (long jump, grip strength), moderate-to-vigorous physical activity (ActiGraph accelerometry), movement skill (Test of Gross Motor Development-2), age, and sex were predictors. Boys had higher object control skills (actual and perceived) and fitness. Age (decreasing) and long jump distance (positive) explained 16% of locomotor skill perception variance. Sex (boys) explained 13% of object control skill perception variance. Children’s skill self-perception may be influenced by fitness attributes as these are more evident to them. The fact that girls have lower actual object control competence and fitness than boys suggests girls may be an intervention target.
Self organizing neural network approach for identification of patients with Congestive Heart Failure

A new technique for identification of patients with Congestive Heart Failure (CHF) from normal controls is investigated in this paper using spectral analysis and neural networks. The data used in this work is obtained from Massachusetts Institute of Technology (MIT) databases. A data set of 17 CHF and 53 normal subjects is used as original learning data set. A larger learning data set, which is obtained by simulating 1000 CHF and 1000 normal subjects according to the spectral features obtained from the original learning data, is used by the self organizing neural network.
Area-efficient high-throughput sorted QR decomposition-based MIMO detector on FPGA

This paper aimed to improve MIMO detector's performance in both throughput and cost. Thus, it presents a FPGA architecture implementation for the SQRD detection in a 4 × 4 16-QAM MIMO wireless communication systems. The exploitation of fine-grained parallelism and coarse-grained parallelism strategies are responsible for bettering the performance of the implementation. Besides, this paper proposes a method to ensure the correctness of the implementation of time-sharing modules, which is general and applicable to any MIMO detector implementation. The work results in a real-time FPGA-based implementation delivering 32 MSQRD/s with 5.2 us latency and lowering more than 50\% cost in hardware resources on a Xilinx Virtex6.
On the Interactions Between Layered Quality Adaptation and Congestion Control for Streaming Video

This paper uses analysis and experiments to study the impact of various congestion control algorithms and receiver buffering strategies on the performance of streaming media delivery. While traditional congestion avoidance schemes such as TCP’s additive-increase/multiplicativedecrease (AIMD) achieve high utilization, they also cause large oscillations in transmission rates that degrade the smoothness and perceptual quality of the video stream. We focus on understanding the interactions of a family of congestion control algorithms that generalize AIMD, with buffer-based quality adaptation algorithms for hierarchically-encoded and simulcast video. Our work builds on and extends the results of Rejaie et al. [19]; we find that the combination of a non-AIMD algorithm that has smaller oscillations than AIMD and a suitable receiver buffer allocation and management strategy provides a good combination of low playout delay and TCP-friendly congestion control. The paper describes these mechanisms and the results of experiments conducted using a prototype video server for MPEG-4 video, showing that our approach can improve the interactivity and adaptivity of Internet video.
Single Nucleotide Polymorphisms: A New Paradigm in Predicting the Risk of Prostate Cancer

Prostate cancer (PC) is a most important health care problem because of its high prevalence, health-related costs, and mortality. Even though most patients have clinically localized and indolent tumors at diagnosis, worldwide, this disease still holds second place in the leading causes of cancer deaths. Research on susceptibility genes is one of hot issues in risk factors of prostate cancer. Nevertheless, the confirmation of prostate cancer susceptibility genes has been challenging. Thus focusing on the increasing number of single nucleotide polymorphisms (SNPs) that have been suggested to be implicated in the development and progression of PC. While individual SNPs are only moderately associated with PC risk, in combination, they have a stronger association. Therefore, identification of numerous variations in genes and analysis of their effects may lead to a better understanding of their impact on gene function and health of an individual. This improved knowledge may provide a starting point for the development of new useful SNP markers for medical testing and a safer individualized medication to treat the most common devastating disorders. This will revolutionize the medical field in the future. To illustrate the effect of SNPs on gene function and phenotype, this review focuses on genetic susceptibility of prostate cancer and role of single nucleotide polymorphism and revealing the impact of SNPs on the development and progression of prostate cancer.
Post-Truth and Vices Opposed to Truth

Abstract:Philosopher Harry Frankfurt has famously coined "bullshit" as a technical term—it refers not to outright lying but rather to a casual indifference to truth. Disregard for truth is accepted and even expected in many contexts, yet it creates conditions for gross injustice and dehumanization. I offer an account of widespread cultural indifference to truth as structural sin, a condition I call "truth indifference. " Drawing on Thomas Aquinas's understanding of the virtue of truth (veracitas), I map out the conceptual framework that must be in place before Christian ethicists can provide an adequate moral analysis of structural truth indifference.
Improved Face Detection Algorithm Based on Adaboost

In view of the training time-consuming shortcoming of the conventional AdaBoost algorithm in face detection, in this paper, a new algorithm was presented combining effectively the optimizing rect-features and weak classifier learning algorithm, which can largely improve the hit-rate and decrease the train time. Optimized rect-feature means that when searching rect-feature we can establish a growth step length of the rect-feature and reduce its features.And the new weak classifier training method is seeking the weak classifier error rate directly, which can avoid the iterative training, the statistic probability distribution and any other time-consuming process. The experimental result showed that the algorithm mentioned in this paper, reduces training time cost greatly compared with conventional AdaBoost algorithm. In addition, it speeds up weak classifier and improves the detection speed on the premise of high detection accuracy.
Estimating Optical Flow in Segmented Images Using Variable-Order Parametric Models With Local Deformations

This paper presents a new model for estimating optical flow based on the motion of planar regions plus local deformations. The approach exploits brightness information to organize and constrain the interpretation of the motion by using segmented regions of piecewise smooth brightness to hypothesize planar regions in the scene. Parametric flow models are estimated in these regions in a two step process which first computes a coarse fit and then estimates the appropriate parametrization of the motion of the region. The initial fit is refined using a generalization of the standard area-based regression approaches. Since the assumption of planarity is likely to be violated, we allow local deformations from the planar assumption in the same spirit as physically-based approaches which model shape using coarse parametric models plus local deformations. This parametric plus deformation model exploits the strong constraints of parametric approaches while retaining the adaptive nature of regularization approaches. Experimental results on a variety of images model produces accurate flow estimates while the incorporation of brightness segmentation boundaries.
Histopathological findings in wild Nutrias (Myocastor coypus) with Capillaria hepatica infection

Capillaria hepatica is a zoonotic nematode that uses rodents and other mammals as hosts, especially rats and mice, and causes hepatic granuloma and eventually fibrosis/cirrhosis. However, C. hepatica infection in nutria, a large semiaquatic rodent, has rarely been reported, and histopathologic features of the infection have not been described in detail. We conducted necropsy on 36 wild nutrias. Some animals were found to have milky spots, parasitic eggs and worms within hepatic microgranuloma involving central calcification with cell debris, macrophages, eosinophils and multinucleated giant cells (MGCs). Interestingly, the eggs were closely surrounded by MGCs and appeared to be destroyed without inducing further chronic changes. Based on microscopical examination, C. hepatica infection was diagnosed, and we describe its histopathological characteristics in wild nutrias.
Employing a Binaural Auditory Model to Classify Everyday Sound Events

Humans benefit considerably from exploiting two ears in everyday listening tasks. It therefore seems to be a promising concept for machine listening approaches to emulate the biological mechanisms of binaural signal processing before applying methods of artificial intelligence. In this work we employ a cross-correlation-based auditory model to automatically perform classification tasks on elementary everyday sound events. We present a heuristic scheme to extract the relevant features from the model’s output data. Given a set of training data, a classifier is then constructed using support vector machine (SVM) learning. The proposed method is validated in classification experiments performed on a database of natural sounds. We further discuss its robustness against variation of room acoustics.
Applying and adapting the IEC 61346 standard to industrial automation applications

The IEC 61346 standard describes a reference designation system for identifying the objects and structuring the information of virtually any kind of technical system. The generality of the standard leads in some cases to a slightly loose definition of its concepts, which can hamper its applicability to a concrete domain. Our aim is to make the IEC 61346 standard suitable for the construction of reference designations of industrial plants. Therefore, we impose some constraints to the standard and define the process to follow in order to apply it to industrial automation applications. The removal of ambiguities should also facilitate the interoperability of computer implementations of the standard coming from different vendors. Finally, we show as well how to combine IEC 61346 to existing standards on enterprise-control system integration
Conflict Handling Strategies in an Integrated Information System

Integrated information systems provide users and applications with a unified view of heterogeneous data sources. To provide a single consistent result for every object represented in these data sources, data fusion is concerned with resolving data inconsistencies within and among the sources. We present a classification of conflict resolution strategies and show how these are implemented within an integrated information system, the Humboldt-Merger.
Enhanced stochastic resonance induced by mean field feedback in synaptic coupled networks

Recent studies have suggested that endogenous electric fields can not only be considered as an observation window of neural activities but also form a feedback on membranes of adjacent cells which implies it may play an important role in network dynamic behaviors. However, the role of endogenous field feedback in the detectability of external weak signal is still unknown. In this study, we introduced a simplified mean field model into a chemical synaptic coupled network, and found the endogenous field, as a common non-synaptic coupling form, indeed can enhance the detectability of external weak signal by enhancing stochastic resonance in neural population.
Intravenous non-opioid analgesia for peri- and postoperative pain management: a scientific review of intravenous acetaminophen and ibuprofen

Pain is a predictable consequence following operations, but the management of postoperative pain is another challenge for anesthesiologists and inappropriately controlled pain may lead to unwanted outcomes in the postoperative period. Opioids are indeed still at the mainstream of postoperative pain control, but solely using only opioids for postoperative pain management may be connected with risks of complications and adverse effects. As a consequence, the concept of multimodal analgesia has been proposed and is recommended whenever possible. Acetaminophen is one of the most commonly used analgesic and antipyretic drug for its good tolerance and high safety profiles. The introduction of intravenous form of acetaminophen has led to a wider flexibility of its use during peri- and postoperative periods, allowing the early initiation of multimodal analgesia. Many studies have revealed the efficacy, safety and opioid sparing effects of intravenous acetaminophen. Intravenous ibuprofen has also shown to be well tolerated and demonstrated to have significant opioid sparing effects during the postoperative period. However, the number of randomized controlled trials confirming the efficacy and safety is small and should be used in caution in certain group of patients. Intravenous acetaminophen and ibuprofen are important options for multimodal postoperative analgesia, improving pain and patient satisfaction.
Multi-view Exploratory Learning for AKBC Problems

In this paper, we argue that many Automatic Knowledge Base Construction (AKBC) tasks which have previously been addressed separately can be viewed as instances of single abstract problem: multiview semi-supervised learning with an incomplete class hierarchy. We also present a general EM framework for solving this abstract task, and summarize past work on various special cases of multiview semi-supervised exploratory learning.
Variable rate channel coding and enhanced interleaving for image transmission using an outage criterion

We consider an image transmission system consisting of set partitioning in hierarchical trees (SPIHT) wavelet-based source coding, and list Viterbi algorithm (LVA) channel coding using rate compatible punctured convolutional (RCPC) codes. Our goal is to minimize bandwidth usage while maintaining received image quality. We introduce an outage criterion for image transmission. A received image is considered to be in outage if its pixel values are not identical to the image transmitted. In order to reduce correlation in fading, an enhanced block interleaving scheme employing a subblock structure is proposed. Using RCPC codes, we devise a variable rate channel coding system based on the current channel state information. Combining enhanced interleaving and variable rate channel coding, we construct a method to select the highest rate RCPC code that satisfies the image outage criterion, thus balancing the requirements of high bandwidth efficiency and high image quality.
Who Are the Workers Who Never Joined a Union? :Empirical Evidence from Western and Eastern Germany

Using representative data from the German social survey ALLBUS 2002 and the European Social Survey 2002/03, this paper provides the first empirical analysis of trade union 'never-membership' in Germany. We show that between 54 and 59 percent of all employees in Germany have never been members of a trade union. In western Germany, individuals' prob ability of 'never-membership' is significantly affected by their personal characteristics, their political orientation and (to a lesser degree) their family background. In addition, the presence of a union at the workplace plays a significant role. While the latter factor is also important in eastern Germany, many of the variables which are relevant for 'never-membership' in the west prove to be irrelevant in the east. This difference probably reflects the fact that most employ ees in eastern Germany did not really have a choice not to become union members during the communist regime. Welche Arbeitnehmer waren nie Mitglied einer Gewerkschaft? Empirische Evidenz aus Westund Ostdeutschland Zusammenfassung — Mit Daten des ALLBUS 2002 und des European Social Survey 2002/03 analysiert diese Arbeit erstmalig die ,Nie-Mitgliedschaft' in deutschen Gewerkschaf ten. Wir zeigen, dass 54 bis 59 Prozent aller Beschaftigten in Deutschland niemals Mitglied einer Gewerkschaft waren. Die individuelle Wahrscheinlichkeit einer ,Nie-Mitgliedschaft' hangt in Westdeutschland signifikant mit personlichen Merkmalen, der politischen Ausrichtung und (in geringerem Mase) dem familiaren Hintergrund zusammen. Daruber hinaus spielt das Vor handensein einer Gewerkschaft am Arbeitsplatz eine signifikante Rolle. Wahrend letzteres auch in Ostdeutschland von Bedeutung ist, erweisen sich viele der im Westen relevanten Erkla rungsvariablen im Osten als irrelevant. Dieser Unterschied spiegelt wahrscheinlich wider, dass die meisten Arbeitnehmer in Ostdeutschland wahrend des SED-Regimes kaum die Wahl hat ten, auf eine Gewerkschaftsmitgliedschaft zu verzichten.
Why Has Artificial Intelligence Failed? and How Can it Succeed?

In the 1960s, pioneers in artificial intelligence made grand claims that AI systems would surpass human intelligence before the end of the 20th century. Except for beating the world chess champion in 1997, none of the other predictions have come true. But AI research has contributed a huge amount of valuable technology, which has proved to be successful on narrow, specialized problems. Unfortunately, the field of AI has fragmented into those narrow specialties. Many researchers claim that their specialty is the key to solving all the problems. But the true key to AI is the knowledge that there is no key. Human intelligence comprises every specialty that anyone in any culture or civilization has ever dreamed of. Each one is adequate for a narrow range of applications. The power of human intelligence comes from the ability to relate, combine, and build on an open-ended variety of methods for different applications. Successful AI systems require a framework that can support any and all such combinations.
Effect of defensive pressure on international women's rugby sevens attacking skills frequency and execution

The aim of the present study was to determine the effect of defensive pressure on the execution of attacking skills in international women's rugby sevens. Performance analyses were conducted across 42 training camp drills during the 2012–2013 season. Training camp sessions were recorded using a video camera positioned from an aerial perspective, with the attacking team's performance analysed during game-based drills. Technical skills assessed included pass type, pass accuracy, catching, evasive moves and line breaks. Results indicated that defensive pressure had a significant effect on a player's pass type, evasive moves and line breaks (p < 0.05), but no significant effect on passing accuracy and catching skill (p > 0.05). A cut-out pass (standard residual = 3.3) was more likely to happen than expected by chance during no defence. An offload was more likely to occur than expected by chance during contested defence (standard residual = 9.6). A line break (standard residual = 4.7) and a swerve (standard residual = 2.5) were more likely to happen than expected by chance during contested defence. Coaches should progressively overload the degree of defensive pressure during training. This may encourage the performance of non-conventional passes under contested defensive pressure, more closely reflecting the requisites of competition game-play.
Videogames in therapy: a therapist's perspective

This paper describes a user and task analysis that was conducted in order to examine the role of therapists in the use of video games in therapy. The results show that video games were used often, but improvements could be made to make them more effective for the therapist. From these results recommendations for video game design were derived. Recommendations include that a therapeutic video game should be easy to startup and configure, should allow the therapist to support a patient during play, and should support the therapist in tracking a patient's performance.
Towards Contextual SSVEP-based BCI controller : smart activation of stimuli and controls weighting

Brain-Computer Interfaces became more available for general public and they have been already used to control applications such as computer games. One disadvantage is that they are not completely reliable. In order to increase BCI performances, some adjustments can be made on low levels, such as signal processing and on high levels by modifying the controller paradigm. In this study, we explore a novel, context-dependant, approach for SSVEP-based BCI controller. This controller uses two kinds of behaviour alternation, commands can be added and removed if their use is irrelevant to the context or the actions resulting from their activation can be weighted depending on the likeliness of the actual intention of the user. This controller has been integrated within a BCI computer game and its influence in performance and mental workload has been addressed through a pilot experiment. Preliminary results have shown a workload reduction and performance improvement with the context-dependent controller while keeping the engagement levels untouched.