Miracle-dz/UniFAR-FTUs huggingface dataset:
THE CASE OF COMMUNITY-OF-PRACTICE DEMISE : WHY GOOD WILL AND REAL NEED ARE NECESSARY FOR PEER-LEARNING BUT ARE NOT ENOUGH

in communities-of-practice (COP) much effort is dedicated to prevent "knowledge hoarding", as if knowledge contribution is the chief success factor. Little attention is paid to knowledge consumers' motivation. Two case studies are presented in which a COP had been much appropriate but, despite organizational support, has dissolved. The prime impediment was reluctant demand for knowledge as community members preferred independency and self-reliance. Two causes are hypothesized: (1) a sense of over-responsibility, and (2) implicitness aversion, or “soft” knowledge mistrust; both require further research. Practically, the study demonstrates the negative effect of over-motivation in a COP context.
Perfect hash functions for large dictionaries

We describe a new practical algorithm for finding perfect hash functions with <i>no specification space at all</i>, suitable for key sets ranging in size from small to very large. The method is able to find perfect hash functions for various sizes of key sets in linear time. The perfect hash functions produced are optimal in terms of time (perfect) and require at most computation of <i>h</i><sub>1</sub>(<i>k</i>) and <i>h</i><sub>2</sub>(<i>k</i>); two simple auxiliary pseudorandom functions.
ICRS Recommendation Document

Objective: The purpose of this article is to describe and recommend patient-reported outcome instruments for use in patients with articular cartilage lesions undergoing cartilage repair interventions. Methods: Nonsystematic literature search identifying measures addressing pain and function evaluated for validity and psychometric properties in patients with articular cartilage lesions. Results: The knee-specific instruments, titled the International Knee Documentation Committee Subjective Knee Form and the Knee injury and Osteoarthritis and Outcome Score, both fulfill the basic requirements for reliability, validity, and responsiveness in cartilage repair patients. A major difference between them is that the former results in a single score and the latter results in 5 subscores. A single score is preferred for simplicity’s sake, whereas subscores allow for evaluation of separate constructs at all levels according to the International Classification of Functioning. Conclusions: Because there is no obvious superiority of either instrument at this time, both outcome measures are recommended for use in cartilage repair. Rescaling of the Lysholm Scoring Scale has been suggested, and confirmatory longitudinal studies are needed prior to recommending this scale for use in cartilage repair. Inclusion of a generic measure is feasible in cartilage repair studies and allows analysis of health-related quality of life and health economic outcomes. The Marx or Tegner Activity Rating Scales are feasible and have been evaluated in patients with knee injuries. However, activity measures require age and sex adjustment, and data are lacking in people with cartilage repair.
Pengaruh Pelatihan Shalat untuk Menurunkan Prokrastinasi Akademik pada Mahasiswa

As the purpose of this study was to investigate the effect of shalat's training to lower on student academic procrastination. The hypothesis is no effect of a decrease in academic procrastination among students who attend training shalat with students who are not trained to shalat. This study uses pretest and posttest control group design, in which the research subjects are students from various departments at "Y" University in Yogyakarta a total of 8 people, with 4 people in the experimental group and 4 people in the control group.This study uses a scale of academic procrastination compiled by Siswanto (2013). Data were analyzed using non-parametric statistics, the Wilcoxon sign rank test. The results of the research in the experimental group for differences pre and post test obtained value Z =-0.368, p = 0.713 (p>0.05). As for differences in pre-test and post-test in the control group, the value of Z = -l.826, p = 0.068 (p>0.05). It can be concluded that there is no difference in academic procrastination before and after the shalat's training. Thus, the research hypothesis was rejected. Â Keywords : Academic Procrastination, Shalat's Training
Non-Gaussian seasonal adjustment: X-12-ARIMA versus robust structural models

This study compares X-12-ARIMA and MING, two new seasonal adjustment methods designed to handle outliers and structural changes in a time series. X-12-ARIMA is a successor to the X-11-ARIMA seasonal adjustment method, and is being developed at the US Bureau of the Census. MING is a ‘Mixture based Non-Gaussian’ method for seasonal adjustment using time series structural models and is implemented as a function in the S-Plus language. The procedures are compared using 29 macroeconomic time series from the US Bureau of the Census. These series have both outliers and structural changes, providing a good testbed for comparing non-Gaussian methods. For the 29 series, the X-12-ARIMA decomposition consistently leads to smoother seasonal factors which are as or more ‘flexible’ than the MING seasonal component. On the other hand, MING is more stable, particularly in the way it handles outliers and level shifts. This study relies heavily on graphical tools for comparing seasonal adjustment methods.
Asymptotically exact streaming algorithms

We introduce a new computational model for data streams: asymptotically exact streaming algorithms. These algorithms have an approximation ratio that tends to one as the length of the stream goes to infinity while the memory used by the algorithm is restricted to polylog(n) size. Thus, the output of the algorithm is optimal in the limit. We show positive results in our model for a series of important problems that have been discussed in the streaming literature. These include computing the frequency moments, clustering problems and least squares regression. Our results also include lower bounds for problems, which have streaming algorithms in the ordinary setting but do not allow for sublinear space algorithms in our model.
Construction of robust prognostic predictors by using projective adaptive resonance theory as a gene filtering method

MOTIVATION For establishing prognostic predictors of various diseases using DNA microarray analysis technology, it is desired to find selectively significant genes for constructing the prognostic model and it is also necessary to eliminate non-specific genes or genes with error before constructing the model. RESULTS We applied projective adaptive resonance theory (PART) to gene screening for DNA microarray data. Genes selected by PART were subjected to our FNN-SWEEP modeling method for the construction of a cancer class prediction model. The model performance was evaluated through comparison with a conventional screening signal-to-noise (S2N) method or nearest shrunken centroids (NSC) method. The FNN-SWEEP predictor with PART screening could discriminate classes of acute leukemia in blinded data with 97.1% accuracy and classes of lung cancer with 90.0% accuracy, while the predictor with S2N was only 85.3 and 70.0% or the predictor with NSC was 88.2 and 90.0%, respectively. The results have proven that PART was superior for gene screening. AVAILABILITY The software is available upon request from the authors. CONTACT honda@nubio.nagoya-u.ac. jp
Unambiguous tracking of high-order BOC signals in urban environments: Channel considerations

The usage of BOC modulations in GNSS provides a better code tracking accuracy than BPSK modulation at the cost of new correlation peaks appearing in the autocorrelation function. For high-order BOC modulations side peaks might be only few meters away from the main correlation peak to be tracked, so there is a risk to lock into one of those peaks, inducing therefore a bias in the pseudorange estimation. The probability of false lock can be in particular important in harsh channel conditions, where the unambiguous tracking can be difficult due to the low C/No and the presence of multipath, which impact can be relevant during fading periods. This paper presents typical channel conditions that should be considered in the design and assessment of unambiguous tracking techniques of high-order BOC signals, focusing in urban environments where the receiver might need to track the signal even at low C/No. A preliminary assessment of some state-of-the-art techniques in urban environments is presented, showing the limitations of some of the techniques, which have been designed considering very mild propagation conditions typically present in open sky environments. The multi-correlator approach is then presented as a possible way to overcome the limitations of techniques based on legacy GNSS receiver architectures. In this case the unambiguous tracking problem is presented as the minimization of a non-linear Least Squares cost function subject to restrictions and some design alternatives are discussed.
Survey of object-oriented metrics

To quantify various attributes of object-oriented software systems, numerous object-oriented metrics have been proposed by various researchers. This paper surveys existing object-oriented metrics by focusing on the two major issues-Validation and Formal Specification. This paper provides researchers with an understanding of existing object-oriented metrics in terms of validation and formal specification. Because few metrics are validated and formally specified, there is a need of both validation and formal specification of object-oriented metrics.
Multi-Dimensional Disk Array

The excellent reliability provided by RAID Level 5 data organization has been seen to be insuucient for future mass storage systems. We analyze the multi-dimensional disk array in search of the necessary improved reliability. The paper begins by introducing multi-dimensional disk array data organization schemes based on maximum distance separable error correcting codes and incorporating both strings and spares. Several gures of merit are calculated using a standard Markov failure and repair model for these organizations. Based on our results, the multi-dimensional disk array organization is an excellent approach to providing improved reliability.
Exploiting Graphic Card Processor Technology to Accelerate Data Mining Queries in SAP NetWeaver BIA

Within business Intelligence contexts, the importance of data mining algorithms is continuously increasing, particularly from the perspective of applications and users that demand novel algorithms on the one hand and an efficient implementation exploiting novel system architectures on the other hand. Within this paper, we focus on the latter issue and report our experience with the exploitation of graphic card processor technology within the SAP NetWeaver business intelligence accelerator (BIA). The BIA represents a highly distributed analytical engine that supports OLAP and data mining processing primitives. The system organizes data entities in column-wise fashion and its operation is completely main-memory-based. Since case studies have shown that classic data mining queries spend a large portion of their runtime on scanning and filtering the data as a necessary prerequisite to the actual mining step, our main goal was to speed up this expensive scanning and filtering process. In a first step, the paper outlines the basic data mining processing techniques within SAP NetWeaver BIA and illustrates the implementation of scans and filters. In a second step, we give insight into the main features of a hybrid system architecture design exploiting graphic card processor technology. Finally, we sketch the implementation and give details of our vast evaluations.
Enhanced sparse speech processing strategy for cochlear implants

To improve the performance of cochlear implants (CI) in noisy environments, a novel strategy is proposed by combining two established algorithms. By combining the Kalman Expectation-Maximize (KEM) algorithm for noise reduction and the SPARSE algorithm for information selection, the new “enhanced SPARSE” strategy gains from the benefits of both. In the new algorithm, the noisy speech is first transferred to the time-frequency domain via a 22- channel filter bank and the envelope in each frequency channel is extracted; secondly, KEM filtering is applied to the envelope in each channel; finally, the SPARSE strategy developed previously in our group is used to generate more sparse stimuli. Here we present results of objective and subjective experiments where KEM was applied to the standard CI speech strategy (Advanced Combination Encoder, ACE) and the novel SPARSE strategies for comparison. Sparseness, measured by kurtosis, of the KEM enhanced simulations was higher than the corresponding output without noise reduction. In subjective listening experiments, six normal hearing listeners were tested with sentences in noise at three signal-to-noise ratios (SNR): 0, 5, 10 dB. Speech intelligibility was better using the enhanced ACE algorithm than the original ACE for all these three SNR, enhanced SPARSE yielded better performance in low SNR (0 dB) than SPARSE, but the advantage was not obvious for the higher SNR (5, 10 dB). We conclude that the suggested strategy shows promise for achieving better speech perception for CI users in the future.
In Vivo Insulin Sensitivity and Lipoprotein Particle Size and Concentration in Black and White Children

OBJECTIVE To examine sex-specific black/white differences in lipoprotein profile and the role of visceral adiposity and to assess the relationship between insulin sensitivity and lipoprotein profiles in each group. RESEARCH DESIGN AND METHODS Fasting lipoprotein particle size and concentration and visceral adipose tissue (VAT) were determined in 226 children (117 black, 101 male) aged 8 to <18 years. The relationship between lipoproteins and insulin sensitivity was evaluated in a subset of 194 children (100 black, 88 male) who underwent a hyperinsulinemic-euglycemic clamp. RESULTS Black male children had smaller VLDL and black female children had larger HDL size than their white counterparts. Overall, blacks had larger LDL size with no sex-specific race differences. After adjusting for VAT and sex, only VLDL size and concentrations remained significantly favorable in blacks. Analysis of lipoprotein particle size and concentration across insulin sensitivity quartiles revealed that in both racial groups, the most insulin-resistant children had higher concentrations of small dense LDL, small HDL, and large VLDL and smaller LDL and HDL sizes than their more insulin-sensitive counterparts. CONCLUSIONS The previously reported favorable lipoprotein profiles in black versus white children is partly due to race differences in VAT. In both groups, however, the most insulin-resistant youths have a high-risk atherogenic profile of small dense LDL, small HDL, and large VLDL, akin to the atherogenic lipoprotein pattern in adults with coronary artery disease.
A hybrid analysis framework for detecting web application vulnerabilities

Increasingly, web applications handle sensitive data and interface with critical back-end components, but are often written by poorly experienced programmers with low security skills. The majority of vulnerabilities that affect web applications can be ascribed to the lack of proper validation of user's input, before it is used as argument of an output function. Several program analysis techniques were proposed to automatically spot these vulnerabilities. One particularly effective is dynamic taint analysis. Unfortunately, this approach introduces a significant run-time penalty.
Curve Synthesis from Learned Refinement Models

We present a method for generating refined 2D illustrations from hand drawn outlines consisting of only curve strokes. The system controllably synthesizes novel illustrations by augmenting the hand drawn curves’ shape, thickness, color and surrounding texture. These refinements are learned from a training ensemble. Users can select several examples that depict the desired idealized look and train the system for that type of refinement. Further, given several types of refinements, our system automatically recognizes the curve and applies the appropriate refinement. Recognition is accomplished by evaluating the likelihood the curve belongs to a particular class based on both its shape and context in the illustration.
Cures for the heart: a poetic approach to healing after loss

ABSTRACT Writing creatively, expressively, and reflectively to aid the grieving process is founded on the idea that in order to survive and thrive after loss, personal meaning must be made of what has been suffered. The individualisation and secularisation of society has put the onus of meaning making on the individual while an abiding reservation about speaking openly about death in Western culture complicates this task. In this article we examine how metaphors are used to make sense of loss and its ensuing emotions and how they help the writer move from a ‘first’ (i.e. anguish, pain) to a ‘second’ story (i.e. healing). We argue also that evoking the beloved through writing is part of healthy grieving. We conclude that nudging, thread and crystalising metaphors as well as ‘poetic conversations’ – that help locate and position oneself in relation to the loss – together make constructing new life-giving (i.e. second) stories possible. The safety and privacy of writing is also key in making space for the voice of the bereaved. We note that grieving individuals ‘write about’ and ‘write to’ and thus evoke the person who has died. In this paradoxical way the bereaved face the reality of death by retaining the loved one.
Pedestrian detection based on LIDAR-driven sliding window and relational parts-based detection

The most standard image object detectors are usually comprised of one or multiple feature extractors or classifiers within a sliding window framework. Nevertheless, this type of approach has demonstrated a very limited performance under datasets of cluttered scenes and real life situations. To tackle these issues, LIDAR space is exploited here in order to detect 2D objects in 3D space, avoiding all the inherent problems of regular sliding window techniques. Additionally, we propose a relational parts-based pedestrian detection in a probabilistic non-iid framework. With the proposed framework, we have achieved state-of-the-art performance in a pedestrian dataset gathered in a challenging urban scenario. The proposed system demonstrated superior performance in comparison with pure sliding-window-based image detectors.
Bounding buffer space requirements for real-time priority-aware networks

One implementation alternative for network interconnects in modern chip-multiprocessor systems is priority-aware arbitration networks. To enable the deployment of real-time applications to priority-aware networks, recent research proposes worst-case latency (WCL) analyses for such networks. Buffer space requirements in priority-aware networks, however, are seldom addressed. In this work, we bound the buffer space required for valid WCL analyses and consequently optimize router design for application specifications by computing the required buffer space at each virtual channel in priority-aware routers. In addition to the obvious advantage of bounding buffer space while providing valid WCL bounds, buffer space reduction decreases chip area and saves energy in priority-aware networks. Our experiments show that the proposed buffer space computation reduces the number of unfeasible implementations by 42% compared to an existing buffer space analysis technique. It also reduces the required buffer space in priority-aware routers by up to 79%.
A study on the trade-off among wirelength, number of TSV and placement with different size of TSV

Through-Silicon Via (TSV) is a promising technology to reduce the length of interconnect in a three dimensional integrated circuit (3D-IC). However, the area overhead of TSV also poses a negative impact on a 3D-IC. Using too many TSVs will increase the die size and cancel out the benefit brought by TSV. Therefore, in this paper we will analyze the trade-off among wirelength and the number of TSVs with different TSV sizes. Since the number of TSVs is determined by placement, we also investigate how placement affects the wirelength and the number of TSVs. The experimental result shows that, in our study cases, the average maximum TSV area is 25.30% of the cell area. Beyond this value, virtually no wirelength reduction can be obtained.
Mesh Segmentation via Recursive and Visually Salient Spectral Cuts

We develop a new mesh segmentation algorithm via recursive spectral 2-way cut and Nystrom approximation. The cut is performed on 1-D spectral embeddings, which are efficiently computed from appropriately defined distances between the set of mesh faces and only two sample faces. By using a novel sampling scheme based on shape context and a line search over the 1-D embeddings to locate the most perceptually salient cut, our algorithm achieves robust and intuitive segmentation results.
Extending an Algorithm for Clustering Gene Expression Time Series

The development of microarray technology has enabled simultaneous expression measurements from tens of thousands of genes [1]. Many gene expression experiments produce time series data with only a few (around 5) time points, due to the high measurement costs. The time series usually represent the dynamic response of an organism to a change in conditions, e.g. application of some drug or other treatment. Here we share some of the experiences we gained while analyzing such data sets, originating from a collaborative project. More information about that work can be found in [2]. We focus on a particular clustering algorithm designed for short time series data [3]. We found that some inaccuracies in the original presentation of the algorithm need to be addressed. In addition to providing corrections for the problems, we also present an extension to the algorithm.
How Do People Perceive and Trust a Lifelike Robot

 Abstract—This paper reports the results from an experiment examining people's perception and trust when interacting with an android robot. Also, they engaged in an economic trust game with the robot. We used proxemics, the physical distance to the robot, and questionnaires to measure the participants` character and their perception of the robot. We found influences of the subject's character onto the amount sent in the trust game and distance changes over the 3 interaction tasks. The perception of the robot changed after the interaction trials towards less anthropomorph and less intelligent, but safer. This study would enable future researches to compare different robot types, personality traits and cross-cultural effects.