Miracle-dz/UniFAR-FTUs huggingface dataset:
Experiences from knowledge management implementations in companies of the software sector

The Know‐Net knowledge management solution, that includes a theoretical framework, a consulting method and a software tool, is based on a knowledge asset‐centric design that innovatively fuses the process or human approach with the product or content approach of knowledge management. This paper describes how Know‐Net was applied in four companies of the software sector. It outlines how specific business areas such as an R&D unit, the bid management process and collaboration between geographically‐dispersed teams can benefit from knowledge management. It outlines the role specific Know‐Net components played within the transformation of existing business processes and structures, and provides key recommendations based on this experience.
A new secant method for unconstrained optimization

We present a gradient-based algorithm for unconstrained minimization derived from iterated linear change of basis. The new method is equivalent to linear conjugate gradient in the case of a quadratic objective function. In the case of exact line search it is a secant method. In practice, it performs comparably to BFGS and DFP and is sometimes more robust.
The impact of advance care planning on end of life care in elderly patients: randomised controlled trial

Objective To investigate the impact of advance care planning on end of life care in elderly patients. Design Prospective randomised controlled trial. Setting Single centre study in a university hospital in Melbourne, Australia. Participants 309 legally competent medical inpatients aged 80 or more and followed for six months or until death. Interventions Participants were randomised to receive usual care or usual care plus facilitated advance care planning. Advance care planning aimed to assist patients to reflect on their goals, values, and beliefs; to consider future medical treatment preferences; to appoint a surrogate; and to document their wishes. Main outcome measures The primary outcome was whether a patient’s end of life wishes were known and respected. Other outcomes included patient and family satisfaction with hospital stay and levels of stress, anxiety, and depression in relatives of patients who died. Results 154 of the 309 patients were randomised to advance care planning, 125 (81%) received advance care planning, and 108 (84%) expressed wishes or appointed a surrogate, or both. Of the 56 patients who died by six months, end of life wishes were much more likely to be known and followed in the intervention group (25/29, 86%) compared with the control group (8/27, 30%; P<0.001). In the intervention group, family members of patients who died had significantly less stress (intervention 5, control 15; P<0.001), anxiety (intervention 0, control 3; P=0.02), and depression (intervention 0, control 5; P=0.002) than those of the control patients. Patient and family satisfaction was higher in the intervention group. Conclusions Advance care planning improves end of life care and patient and family satisfaction and reduces stress, anxiety, and depression in surviving relatives. Trial registration Australian New Zealand clinical trials registry ACTRN12608000539336.
A novel method for minimizing loss of accuracy in Naive Bayes classifier

In Data Mining classification plays prominent role in predicting outcomes. One of the best supervised classification techniques in Data Mining is Naive Bayes Classification. Naive Bayes Classification is good at predicting outcomes and often outperforms other classification techniques. One of the reasons behind the strong performance of Naive Bayes Classification is due to the assumption of conditional Independence among predictors. However, this very strong assumption leads to loss of accuracy. In this paper, the authors are proposing a novel method for improving accuracy in Naive Bayes Classifier. The proposed novel technique used in NBC gave better accuracy even with Conditional Independence.
ANALISIS PERILAKU PENGGUNA E-LEARNING SCHOOLOGY MENGGUNAKAN MODEL UTAUT (Studi Kasus: SMAS Babussalam Pekanbaru)

Sekolah Menengah Atas Swasta (SMAS) Babussalam telah memanfaatkan e-learning Schoology sejak tahun 2010 yang di danai dari program hibah Dinas Pendidikan Kota Pekanbaru. E-Learning Schoology digunakan untuk memberikan materi pelajaran, kuis, tugas dan penilaian langsung kepada peserta didik secara online . Selama lima tahun penggunaan e-learningSchoology , SMAS Babussalam masih mengalami banyak hambatan seperti ketidaktersediaan training penggunaan e-learning Schoology dan tidak adanya Standard Operational Procedure (SOP) sistem membuat beberapa guru mengalami kesulitan dalam mengakses e-learning Schoology sehingga e-learning Schoology jarang digunakan. Penelitian ini bertujuan untuk menganalisis perilaku pengguna e-learning Schoology menggunakan model Unified Teory of Acceptance and Use of Technology (UTAUT). Dalam penelitian ini menggunaka 4 variabel independen yaitu harapan kinerja ( Performance Expectancy ), hasil usaha ( Effort Expectancy ), faktor sosial ( Social Influence ), kondisi yang memfasilitasi ( Facilitating condition ), dan satu variabel dependen yaitu niat berperilaku ( Behavioral Intention ). Teknik pengambilan sampel menggunakan sampel jenuh, dengan jumlah responden 25 orang. Untuk teknik analisis data menggunakan teknik regresi ganda dengan jenis penelitian deskriptif kuantitatif, serta alat pengolahan data menggunakan software SPSS 16.0 for window . Hasil penelitian menunjukkan bahwa secara deskriptif pengguna masih ragu apakah hasil usaha berpengaruh terhadap niat berperilaku pengguna e-learning Schoology
Automatic Music Mood Detection through Musical Structure Analysis

Since music has a close relation with people emotion, many researchers have studied on detecting music mood. Usually, they manually selected the representative segment of music and classified its mood. Although such manual approaches correctly detect the mood, their manual intervention makes their application for new music difficult. Moreover, the mood variation in music makes their application more difficult. To cope with these problems, we present an automatic method to classify the music mood. After the music is segmented into groups having similar characteristics by structural information, the support vector regressors are constructed with musical features from pitch, beat, and spectrum of each segment. The mood of unknown music is predicted by the regressors. Experimental results show that performance of the proposed approach is comparable to the performances of manual approaches.
Detection of Masses in Mammograms Using Cellular Neural Networks, Hidden Markov Models and Ripley's K Function

Breast cancer shows high frequency and its psychological effects affect the female's perception of sexuality and their personal image. The mammographic images processing has contributed to the detection and diagnosis of breast nodules, and contributes as an important tool, reducing the diagnosis uncertainty. This work presents a computational methodology that helps the expert in the task of mass detection based on mammographic images. To achieve this, hidden Markov model and Ripley's K function were used to detect masses, segmented by cellular neural networks. In the tests methodology demonstrate a sensitivity of 94.62%, with 92.57% of specificity, 93.60% of accuracy rate and an average of 0.53 false positives per image.
Towards understanding the implications of social role manipulation in online tasks

This paper provides an initial discussion of the ethical issues arising when people are asked to assume a role and, from the perspective of that role, asked to carry out an online task. We identify the following considerations: (a) People’s responses when playing a role can reveal personal information about themselves. (b) When people are asked to review the contributions of others who have a particular role, their behavior might indicate how they feel about these roles in their own life. (c) It is difficult to explain to people what they reveal about themselves when reporting their perception of the views of others. (d) People’s own view might change when they play a certain role for an extended period of time.
The Structure of Goal Contents Revisited. A Verification of the Model in Polish Samples

(to experience a variety of sensual pleasure) and spirituality (to find a true meaning in life). These goals seemed to extend from the self-transcendent to physical pursuits. The second dimension was supposed to differentiate and define more precisely the intrinsic and extrinsic goals. As the authors stated, among intrinsic goals community feeling would be seen as more self-transcendent than health, the latter being more “physical”, and among extrinsic – financial success would be more “physical” than popularity and image, both being more self-transcendent. However, the intrinsic – extrinsic dimension might also differentiate the goals from the other dimension. Conformity was assumed to be closer to the extrinsic pole, whereas community feeling and safety to the intrinsic. The authors assumed also that these two-dimensional organization of goals and aspirations would construct Abstract : The article presents an attempt to confirm the circumplex structure of goal contents, identified in 15 cultures around the world (Grouzet et al., 2005), in nine Polish samples. The procedure followed steps from the original study and included testing the assumed 11-factor goal structure and the two-dimensional circular organization of the goal contents. None of the analyses showed outcomes that would explicitly confirm the results attained in the original study. The CFA showed rather poor fits. Results of the MDS generally supported the assumption about the two-dimensional goal contents structure, however ipsative distance analysis reproduced only one of the two assumed dimensions. Finally, although the CIRCUM analysis showed in principle that in the Polish sample the organization of goal contents on the circumference was quite similar to original, the RMSEA indicated poor fit. Methodological and conceptual reasons for the replication failure are analyzed and discussed.
Simultaneous Robust Regulation and Robust Stabilization with Degree Constraint

This paper characterizes all controllers to a problem of simultaneous robust regulation and robust stabilization, which was left open by Cevik and Schumacher. The characterization is based on a combination of their results, i.e., the controller parameterization of robust regulation with nominal internal stability and the stability margin given by an internally stabilizing controller. The controller set will be represented in terms of a solution set to the boundary Nevanlinna-Pick interpolation problem. It is shown that a certain degree restriction on the solution set leads to a reasonable degree bound of controllers. With this degree bound, an efficient algorithm for calculating each controller is available. Using the freedom in the controller set, other performance specifications than robust regulation and robust stability may be satisfied without increasing the controller degree.
SINAI at Twitter-Normalization 2013

espanolEste articulo presenta el sistema de normalizacion de tweets desarrollado por el grupo SINAI. Realizamos una serie de conversiones a partir de lexicones de traduccion y un corrector ortografico. Nuestro sistema obtiene un resultado de accuracy bajo, un 37.6%, y analizando los resultados necesita mejorarse en varios aspectos tales como diminutivos y superlativos, tratamiento de entidades o abreviaturas. EnglishIn this paper, we present the Twitter-normalization system developed by the SINAI group. Our system performs a series of conversions on the text by the use of translation lexicons and a spell checker. We obtain a poor result, only 37.6% of accuracy, and after the analysis of these results our system should be improved in areas such as the treatment of diminutives and superlatives, entities or abbreviations.
The Perils of Detecting Measurement Faults in Environmental Monitoring Networks

Scientists deploy environmental monitoring net-works to discover previously unobservable phenomena and quantify subtle spatial and temporal differences in the physical quantities they measure. Our experience, shared by others, has shown that measurements gathered by such networks are perturbed by sensor faults. In response, multiple fault detection techniques have been proposed in the literature. However, in this paper we argue that these techniques may misclassify events (e.g. rain events for soil moisture measurements) as faults, potentially discarding the most interesting measurements. We support this argument by applying two commonly used fault detection techniques on data collected from a soil monitoring network. Our results show that in this case, up to 45% of the event measurements are misclassified as faults. Furthermore, tuning the fault detection algorithms to avoid event misclassification, causes them to miss the majority of actual faults. In addition to exposing the tension between fault and event detection, our findings motivate the need to develop novel fault detection mechanisms which incorporate knowledge of the underlying events and are customized to the sensing modality they monitor.
CSI feedback reduction by checking its validity period: poster

Multi-user MIMO (MU-MIMO) is proposed in 802.11ac to achieve more than 3x faster than 802.11n. In the real world no-one gets close to theoretical speeds. The primary reason for this anomaly are the various overheads of channel access and channel state information (CSI) feedback. In order to achieve concurrent data transmission, (CSI) feedback from users is required. However, this overhead can easily overwhelm the actual channel time spent on data transmission in large-scale network. Moreover, due to spontaneous uplink traffic, which makes the problem even more challenging. In this paper, we propose a smart CSI feedback protocol called Gabriel. Firstly, Gabriel achieves alterable CSI feedback by evaluating its validity period to eliminate the unnecessary CSI feedback operation. Secondly, we introduce a new concept named 'large time slice' to solve fairness problem. Software-radio based implementation and testbed experimentation show that Gabriel significantly outperforms state-of-the-art CSI feedback reduction methods under various traffic patterns and node mobility.
Near infrared face recognition based on wavelet transform and 2DPCA

A feature extraction algorithm of near-infrared human face based on wavelet transform and 2DPCA (two-dimension principal component analysis) was proposed. This algorithm firstly applied wavelet transform to the near-infrared face images, obtained the low frequency components of face images, and removed high frequency components. Secondly applied 2DPCA to the low frequency components of the face images for feature extraction. Finally completed the face recognition using Euclidean distance. The experimental results based on near-infrared face database clearly showed that the proposed algorithm could get higher recognition rate than the traditional PCA and 2DPCA algorithm, which demonstrated the efficiency of the proposed method.
TorusBFS : A Novel Message-passing Parallel Breadth-First Search Architecture on FPGAs

Graphs are a fundamental data structure used extensively in numerous domains. In graph-based applications, Breadth-First Search (BFS) is a key component which suffers from long latency of memory accesses. In this paper, we present a novel message passing parallel BFS architecture namely TorusBFS on field-programmable gate array (FPGA). By utilizing the on-chip memories to store the visitation status of vertices and to implement the current/next queue, our architecture reduces the accesses to the off-chip memories. We also present a on-chip 2-D torus message passing structure to reduce latencies of exchanging information among processing elements (PEs). Limited to the inefficient random write accesses to the off-chip memories, the experimental results show that our architecture on a single FPGA achieves relative lower performance compared with related works based on Convey HC1/HC-2 platforms. Nevertheless, our TorusBFS is the first architecture that can be easily extended to multiple FPGAs in a distributed environment. Keywords-Breadth-First Search; Graph500; FPGA; Message Passing
Sentiment Analysis to Understand User Context in Web Document

In today’s time we give lot of preferences to our f riends opinions, skillful person in particular doma in for making important decision in our day today’s life. Whenever we need to make s ome judgment,we want to hear other’s opinions. For example which brand of cell phone have a better camera quality, whether th e movie is worth watching, whether product gives be tter performance or not. Opinion mining, also known as Sentiment analysis plays a crucial role in all these process. Sentiment analysis is the computational study of people’s opinions, sentiments, point of vi ew, temperament and emotions expressed in natural language. It is the study of emotions i.e. Sentiments. It is one of the most eff ective research areas in natural language processin g and text mining in current years. It presents many challenging analysis probl ems, which had never been tackled before the year 2 000.The reason for lack of study before was that there was limited Opinionated text in digital forms.
A compilation based approach to inference in dynamic evidential networks

A wide variety of compilation techniques have been proposed in the literature for inference in static graphical models. One of the most widely used approaches is the arithmetic circuit method. Compiling a graphical model into an arithmetic circuit provides a compact representation of the polynomial that it induces. This representation allows linear time exact propagation in the size of the circuit. In this paper, we consider the problem of performing exact inference on dynamic evidential models. We extend the standard arithmetic circuit based approach to inference in dynamic evidential networks.
A predictive coding approach on microvessel identification via Single-Opponent signals

In this paper, we propose a predictive coding approach to identify microvessel subimages using Single-Opponent (SO) signal. First, we simulate the signal from SO neurons in the positive red and negative cyan channel to segment microvessel regions. Second, the SO signal is convolved with a Laplacian of Gaussian (LoG) filter and then processed in a predictive coding and biased competition (PC/BC) neural network. The texture patterns from SO signal is learnt and represented by the PC/BC model. Finally, we employ Support Vector Machine (SVM) with Spatial Pyramid Matching (SPM) scheme to classify the microvessel subimages on the learnt texture patterns. We have carried out extensive experiments on the identification of microvessel subimages. The proposed method is about 10% superior in F1-measure compared with direct classification on subimages in RGB channel.
Dissociable Roles for the Nucleus Accumbens Core and Shell in Regulating Set Shifting

The ability to behave in a flexible manner is an executive function mediated in part by different regions of the prefrontal cortex. The present study investigated the role of two major efferents of the prefrontal cortex, the nucleus accumbens (NAc) core and shell, in behavioral flexibility using a maze-based strategy set-shifting task. During initial discrimination training, rats learned to use either an egocentric response or a visual-cue discrimination strategy to obtain food reward. During the set shift, animals had to shift from the previously acquired response or visual-cue-based strategy and learn the alternate discrimination. Inactivation of the NAc core, induced by infusion of the GABA agonists baclofen and muscimol, did not impair initial acquisition of either a response or visual-cue discrimination but severely disrupted shifting from one strategy to another. Analysis of the type of errors revealed that impairments in set shifting were not attributable to increased perseveration but to a disruption of the acquisition and maintenance of a new strategy. In contrast, inactivation of the NAc shell did not impair acquisition of either a response or a visual-cue discrimination, or shifting from one strategy to another. However, inactivation of the NAc shell before initial discrimination training improved performance during the set shift relative to control animals. These data indicate that the NAc core and shell make dissociable contributions to behavioral flexibility during set shifting. The NAc core facilitates the acquisition and maintenance of novel behavioral strategies and elimination of inappropriate response options, whereas the shell may mediate learning about irrelevant stimuli.
Between language as problem and resource: Examining teachers’ language orientations in dual-language programs

ABSTRACT Using a case study methodology, the article examines the language practices of two third-grade bilingual, dual-language education teachers as they prepare their students for their state’s standardized assessment. Findings revealed that both teachers taught in between the contradicting tensions of the language-as-problem and -resource orientations that simultaneously valued bilingual language practices but also positioned bilingual education and development as problematic to success on an English state test. Without a more concerted effort toward Bartolomé and Balderrama’s “ideological clarity” among educators and recentering of a language-as-right orientation in bilingual education, there is a danger that the benefits of bilingualism and the opportunity for bilingual development may not be extended to subjugated language communities.
Analyzing Hands to Recognize Social Interactions with a Large-scale Egocentric Hands Dataset

Hands appear very often in egocentric video, and their appearance and pose give important cues about what people are doing and what they are paying attention to. But existing work in hand detection has made strong assumptions that work well only in simple scenarios, such as with limited interaction with other people. We develop methods to locate hands and distinguish between hand-types in egocentric video using an efficient bounding-box proposal method and strong appearance models based on Convolutional Neural Networks. We show how these high-quality bounding boxes can be used to create accurate pixelwise hand regions, and as an application, we investigate the extent to which hand pose segmentation alone can distinguish between different social interactions from an egocentric view. We evaluate these techniques on a new dataset of 48 first-person videos of people interacting in realistic environments, with pixel-level ground truth for over 15,000 hand instances.
Tentative detection of the gravitational magnification of Type Ia supernovae

The flux from distant Type Ia supernovae (SNe) is likely to be amplified or de-amplified by gravitational lensing due to matter distributions along the line of sight. A gravitationally lensed supernova would appear brighter or fainter than the average SN at a particular redshift. We estimate the magnification of 26 SNe in the GOODS fields and search for a correlation with the residual magnitudes of the SNe. The residual magnitude, i.e. the difference between observed and average magnitude predicted by the ‘concordance model’ of the Universe, indicates the deviation in flux from the average SN. The linear correlation coefficient for this sample is r = 0.29. For a similar, but uncorrelated sample, the probability of obtaining a correlation coefficient equal to or higher than this value is ∼10%, i.e. a tentative detection of lensing at ∼90% confidence level. Although the evidence for a correlation is weak, our result is in accordance with what could be expected given the small size of the sample.