Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Performance Prediction of Middleware Systems by Combining Analytical Modeling and Machine-Learning

Classical approaches to performance prediction rely on two, typically antithetic, techniques: Machine Learning (ML) and Analytical Modeling (AM). ML takes a black box approach, whose accuracy strongly depends on the representativeness of the dataset used during the initial training phase. Specifically, it can achieve very good accuracy in areas of the features’ space that have been sufficiently explored during the training process. Conversely, AM techniques require no or minimal training, hence exhibiting the potential for supporting prompt instantiation of the performance model of the target system. However, in order to ensure their tractability, they typically rely on a set of simplifying assumptions. Consequently, AM’s accuracy can be seriously challenged in scenarios (e.g., workload conditions) in which such assumptions are not matched. In this paper we explore several hybrid/gray box techniques that exploit AM and ML in synergy in order to get the best of the two worlds. We evaluate the proposed techniques in case studies targeting two complex and widely adopted middleware systems: a NoSQL distributed key-value store and a Total Order Broadcast (TOB) service.
An ecological analysis of task subgoals during a simulated medical emergency

Whereas classical task analysis methods consider task goals and intermediary task subgoals as formal nodes in graph formalism, cognitive work analysis has brought a new dimension by considering task goals as ascribed in the concrete work domain. Complementary to this latter approach, we suggest analysing task subgoals as the satisfaction of constraints coming from equipment and work space management. We present a classification of deficiencies in task environment constraint satisfaction based on observations in a simulated medical emergency setting.
Otizm Davranış Kontrol Listesinin (ABC) Geçerlik ve Güvenirliğinin İncelenmesi

Bu arastirmanin amaci otizmi belirlemeye yonelik bir tarama araci olan ve 57 maddeden olusan Otizm Davranis Kontrol Listesi'nin (ABC) gecerlik ve guvenirligini incelemektir. Yontem: ABC'nin psikometrik ozellikleri yaslari 3-15 arasinda degisen 479 cocuktan olusan bir orneklem uzerinde incelenmistir. Orneklem daha once otizm tanisi almis 208, zeka geriligi tanisi almis 97 ve hicbir bozuklugu olmayan 174 cocuktan olusmustur. Sonuclar: ABC'nin dogru siniflama oraninin % 88 oldugu bulunmustur. Olcekte kesme puani 39 olarak belirlenmistir. Guvenirlik bulgulari incelendiginde Cronbach alfa katsayisi ve iki yarim test guvenirligi . 92 olarak bulunmustur. Puanlayicilar arasi guvenirlige bakildiginda iki ogretmen tarafindan bagimsiz olarak degerlendirilen 100 cocuk icin . 86, bir ogretmen ve bir ebeveyn tarafindan degerlendirilen 69 cocuk icin . 59 oldugu bulunmustur. Arastirmada dissal olcutler olarak tani ve sorunun derecesi kullanilmistir. Sonuclar olcegin olcut gecerligini destekler niteliktedir. Tartisma: Tum bu bulgular olcegin Turkiye icin gecerli ve guvenilir bir arac oldugunu desteklemektedir.
Optimal artificial neural network topology for foreign exchange forecasting

Foreign exchange market is one of the highest investments markets the average daily trade volume is 1.8 trillion USD. Foreign exchange rate forecasting has been always one of the most challenging subject and area of researches. Trader around the world is relying on the technical indicators which just following the price and has emerged a lag results. When the currency market has a random move (when the market is not trending) most of the indicators gets confused because of the fact that classical linear methods are unable to react with the non linearity in the data and hence with the market behavior. This research reports empirical results that tend to confirm the applicability of a neural network model to the prediction of the foreign exchange rates market. Artificial neural networks have proven to be efficient and profitable in forecasting financial time series in particular, feed forwarded back propagation. It is important to use an optimal ANN topology that emerged great results in short term prediction and the daily predication results showed that ANN model learns well and most likely to generalize well. Weekly predication results demonstrate good results in the low prediction while failed to have a good results on the high and the close prediction while the monthly prediction did not give a satisfactory results due to a very few data samples.
Unconscious processes in emotion: The bulk of the iceberg

This chapter reviews some of the central emotion processes and the respective role of consciousness (or its absence). Contrary to what seems to be current practice, I attemps to refrain from classifying phenomenainto conscious and unconscious, which implies a divide between two separate categories. Rather, I assume that a large majority of emotion processes functions in an unconscious mode and that only some of these processes (or their outcomes) will emerge into consciousness for some periods of time. I like Freud's metaphor of an iceberg, the bulk of which is invisible (the unconscious) with only a small tip above the surface (the conscious). If we assume that the fluid in which the emotion iceberg floats (the mental soup) can vary in consistency, buoyancy will determine the degree of emergence (the buoyant force is eqauel to the weight of the liquid that the object displaces. If the liquid is denser, the buoyant force is greater. Steel sinks in water but floats in mercury). Similarly, increases in density of mental processing may result in a greater emergence of processing intoconsciousness. The major purpose of this chapter is to analyse theoretically the conscious and unconscious processes in emotion elicitation and differenctiation. Specific research is cited as illustration, but no coherent, let alone exhaustive, review is intended.
The Transformation of Schools’ Social Networks during a Data-Based Decision Making Reform

Context Collaboration within school teams is considered to be important to build the capacity school teams need to work in a data-based way. In a school characterized by a strong collaborative culture, teachers may have more access to the knowledge and skills for analyzing data, teachers have more opportunity to discuss the performance goals to be set, and they also can share effective teaching strategies to achieve those goals. Although many studies on data-based decision making (DBDM) foreground the importance of teacher collaboration, our knowledge of what such collaboration looks like and how such collaboration may change during a DBDM reform remains limited. Objective The current study uses a social network perspective to explore how collaboration in 32 elementary schools in the Netherlands takes shape in the interactions among teachers as they engage in a DBDM reform project. Research Design Schools’ social networks were examined at the start of the intervention and after having participated 1 year in the DBDM reform. Social networks regarding three DBDM topics are examined: (1) discussing student achievement; (2) discussing achievement goals; (3) and discussing instructional strategies. The density, reciprocity, and centralization of these networks were calculated, and multivariate multiple regression analysis was used to analyze changes over time. Conclusion Findings suggest that teachers’ DBDM related networks transform during the intervention, especially regarding the discussion of student achievement data: although the number of relationships remains stable, more reciprocal relationships are formed, and this network becomes less centralized around one or a few influential staff members.
Opportunistic beamforming with beam selection in sparse networks

Exploiting spatial multiplexing and multiuser diversity in multi-antenna broadcast channels, orthogonal multiple opportunistic beamforming (OBF) is proven to achieve the same capacity scaling as dirty paper coding (DPC) when there exists a large number of users, and unfortunately, OBF yields degraded performance if the number of users is not large enough (here this scenario is called as sparse networks). To resolve the problem, propose a new OBF based on beam selection (OBFBS). For a system with base station (BS) equipped with M antennas and K single-antenna users, there exists M beam-user pairs communicating at the same time at most, select the optimal beam subset by maximizing system sum rate, consider that the strongest signal-to-interference-and-noise ratio (SINR) beam-user pair makes the greatest contribution to sum rate, rank M beam-user pairs in SINR descending order, add a pair to the optimal beam subset every time until the sum rate no longer increases or M pairs have been implemented. This study demonstrates that, OBFBS improves the sum rate performance significantly in sparse networks compared to conventional OBF.
Reinforcement Focus + Context

A technique for graph-based visualisations is introduced we nick-name Reinforcement Focus+Context (RFC), which is useful to navigate in graphs of complex structure like generated content networks or ontologies. RFC lays out prominent nodes and their surroundings as “structural primitives”, such as trees or circles that are embedded in the complete graph. This way, special aspects of the graph content become much more intelligible in focal regions.
Evaluating a Prison-Based Intervention Program: Approaches and Challenges

Abstract: The Quebec government considers program evaluations to be a necessity if public organizations are to monitor and improve their performances (Lalande, 2010). However, relatively few evaluations are conducted in prisons, despite the important need for programs in such an environment (Cortoni & Lafortune, 2009). The numerous challenges that face researchers who attempt such evaluations may partially explain the reluctance to undertake them. This article traces and explains the process used by a team of researchers in evaluating an addiction program in a provincial prison. Th e article describes the challenges and problems faced by the team and the strategies that were implemented to overcome these challenges. Resume : Pour le gouvernement du Quebec, l’evaluation de programme est incontournable pour que les organisations rendent compte de leurs performances et les ameliorent (Lalande, 2010). Pourtant, peu de demarches evaluatives sont realisees en contexte carceral, malgre les besoins importants (Cortoni & Lafortune, 2009), les nombreux defi s qui y guettent les chercheurs pouvant expliquer la reticence de plusieurs chercheurs. L’experience d’une equipe de recherche a travers l’evaluation d’un programme specialise en toxicomanie off ert dans un etablissement de detention quebecois est presentee afi n d’illustrer les diff erents defi s auxquels elle fut confrontee, mais surtout, de quelles facons ces defi s ont ete surmontes.
Prevalence and Predictors of Low Vitamin B6 Status in Healthy Young Adult Women in Metro Vancouver

Low periconceptional vitamin B6 (B6) status has been associated with an increased risk of preterm birth and early pregnancy loss. Given many pregnancies are unplanned; it is important for women to maintain an adequate B6 status throughout reproductive years. There is limited data on B6 status in Canadian women. This study aimed to assess the prevalence of B6 deficiency and predictors of B6 status in young adult women in Metro Vancouver. We included a convenience sample of young adult non-pregnant women (19–35 years; n = 202). Vitamin B6 status was determined using fasting plasma concentrations of pyridoxal 5’-phosphate (PLP). Mean (95% confidence interval) plasma PLP concentration was 61.0 (55.2, 67.3) nmol/L. The prevalence of B6 deficiency (plasma PLP < 20 nmol/L) was 1.5% and that of suboptimal B6 status (plasma PLP = 20–30 nmol/L) was 10.9%. Body mass index, South Asian ethnicity, relative dietary B6 intake, and the use of supplemental B6 were significant predictors of plasma PLP. The combined 12.4% prevalence of B6 deficiency and suboptimal status was lower than data reported in US populations and might be due to the high socioeconomic status of our sample. More research is warranted to determine B6 status in the general Canadian population.
Moderating Effects of Family Strength on the Relationship between Role Stress and Depression of Family Care Workers

This study examined 168 family care workers who work in the ChungnamᆞChungbuk Province about how role stress affects to depression, and verified moderating effects of family strength in this process. The results were as follows: First, it was found that role conflict of role stress was more than average level, and role overload of role stress was somewhat lower than average level. Family strength was found to be lower than average level, and depression was found to be higher than average level. Second, influential factors of depression were role overload, family strength, health status, and role conflict. Third, role overload was found to have moderating effects on the relationship between role stress and depression. Based on such empirical analysis results, this study suggested comprehensive working strategies to improve family strength and to prevent depression of family care workers. (Korean J Stress Res 2016;24:210∼219)
Wide-Locking-Range Multi-Phase-Outputs Regenerative Frequency Dividers Using Even-Harmonic Mixers and CML Loop Dividers

This paper presents a divide-by-4 (D4) and divide-by-8 (D8) regenerative frequency dividers (RFDs) with the quadrature and the octet outputs. The topologies of the D4 and D8 RFDs use the even-harmonic mixers and the current-mode-logic (CML) loop dividers to achieve high division ratio and wide locking ranges. The modified Gilbert cells with quadrature- and octet-phases local-oscillator switching transistors are adopted to perform 2 × and 4 × subharmonic mixing with good conversion efficiency. The divide-by-2 and D4 CML loop dividers are used to obtain wide frequency range and the quadrature and the octet outputs. The D4 and D8 RFDs were fabricated by using a 90-nm CMOS process. With consuming 10.8 and 17.8 mW of dc power, the measured locking ranges of the D4 and D8 dividers are 13.2-18.4 and 16.8-26 GHz, respectively. Over the whole locking range, the two dividers with the phase deviation between the quadrature and the octet signals to be less than 2.2° and 4.4°, respectively. The proposed circuits achieve wide locking ranges and accurate output phases, making them very attractive for application into a multi-phase generator.
Robust Power system stabilizer design for single machine infinite bus system with different membership functions for fuzzy logic controller

This paper presents an evaluation of membership functions on a single machine infinite bus (SMIB) system with Power system stabilizers (PSSs). The PSS is added to excitation system to enhance the damping during low frequency oscillations. In this paper the system is analyzed for conventional PSS, fuzzy PSS and without PSS. This paper presents a study of fuzzy logic power system stabilizer (FPSS) for stability enhancement of a single machine infinite bus system. The speed deviation (Δω) and acceleration (Δπ) of the rotor of synchronous generator are taken as the input to the fuzzy logic controller to improve small signal stability by improving damping. The effect of these variables on damping at the generator shaft mechanical oscillations is very significant. The stabilizing signals were computed using the different fuzzy membership functions. The general membership functions under consideration are triangular, trapezoidal, Gaussian, bell, sigmoidal and polynomial types. The performance of the fuzzy logic PSS with different membership functions is compared with the system Response without PSS and with Conventional PSS (CPSS). The simulation results obtained from the different plants over a wide range of operating conditions indicate the improved performance of FPSS over the CPSS by considering the triangular and Gaussian type of membership functions in the design of fuzzy logic controller.
Worst-case absolute regret based solution algorithms for optimization problems with interval uncertainty

Uncertain versions of three classical optimization problems are examined: the resource allocation in a complex of independent operations and two task scheduling problems Q∥ΣCi, P∥Cmax. It is assumed that some parameters of the problems are uncertain and they belong to intervals of the known bounds. The complexity of the uncertain problems is determined. The worst-case absolute regret approach is used to evaluate the quality of the solutions. The exact, approximate or heuristic solution algorithms are proposed depending on the complexity and properties of the problems.
An algorithm for identifying differentially expressed genes in multiclass RNA-seq samples

Gene expression in the whole cell can be routinely measured by microarray technologies or recently by using sequencing technologies. Using these technologies, identifying Differentially Expressed Genes (DEGs) among multiple phenotypes is one of the most important tasks in biology. Thus many methods for detecting DEGs between two groups has been developed. For example, T-test and relative entropy are used for detecting the difference between two probability distributions. When more than two phenotypes are considered, these methods are not applicable and other methods such as ANOVA F-test and Kruskal-Wallis are used for finding DEGs in the multiclass data. However, ANOVA F-test assumes a normal distribution and it is not designed to identify DEGs where gene are expressed distinctively in each of phenotypes. Kruskal-Wallis method, a non-parametric method, is more robust but sensitive to outliers. This paper proposes a non-parametric and information theoretical approach for identifying DEGs in the multiple class data and the approach is less sensitive to outliers. In extensive experiments with simulated and real data, our method outperformed existing tools. In addition, a web service is implemented for the analysis of multi-class data: http://biohealth.snu. ac.kr/software/degselection.
Affective music recommendation system using input images

Music that matches our current mood can create a deep impression, which we usually want to enjoy when we listen to music. However, we do not know which music best matches our present mood. We have to listen to each song, searching for music that matches our mood. As it is difficult to select music manually, we need a recommendation system that can operate affectively. Most recommendation methods, such as collaborative filtering or content similarity, do not target a specific mood. In addition, there may be no word exactly specifying the mood. Therefore, textual retrieval is not effective. In this paper, we assume that there exists a relationship between our mood and images because visual information affects our mood when we listen to music. We now present an affective music recommendation system using an input image without textual information.
Comparison of Staphylococcus aureus from skin and soft-tissue infections in US emergency department patients, 2004 and 2008.

BACKGROUND In the past decade, new methicillin-resistant Staphylococcus aureus (MRSA) strains have emerged as a predominant cause of community-associated skin and soft-tissue infections (SSTIs). Little information exists regarding trends in MRSA prevalence and molecular characteristics or regarding antimicrobial susceptibility profiles of S. aureus isolates. METHODS We enrolled adults with acute, purulent SSTIs presenting to a US network of 12 emergency departments during August 2008. Cultures and clinical information were collected. S. aureus isolates were characterized by antimicrobial susceptibility testing, pulsed-field gel electrophoresis, and toxin genes detection. The prevalence of S. aureus and MRSA and isolate genetic characteristics and susceptibilities were compared with those from a similar study conducted in August 2004. RESULTS The prevalence of MRSA was 59% among all SSTIs during both study periods; however, the prevalence by site varied less in 2008 (38%-84%), compared with 2004 (15%-74%). Pulsed-field type USA300 continued to account for almost all MRSA isolates (98%). Susceptibility to trimethoprim-sulfamethoxazole, clindamycin, and tetracycline among MRSA isolates remained greater than 90% in 2008. A higher proportion of MRSA infections were treated with an agent to which the infecting isolate was susceptible in vitro in 2008 (97%), compared with 2004 (57%). CONCLUSIONS Similar to 2004, MRSA remained the most common identifiable cause of purulent SSTIs among patients presenting to a network of US emergency departments in 2008. The infecting MRSA isolates continued to be predominantly pulsed-field type USA300 and susceptible to recommended non-β-lactam oral agents. Clinician prescribing practices have shifted from MRSA-inactive to MRSA-active empirical antimicrobial regimens.
Seamless integration of mobile WiMAX in 3GPP networks

As the wireless industry makes its way to the next generation of mobile systems, it is important to engineer solutions that enable seamless integration of emerging 4G access technologies within the currently deployed and/or evolved 2G/3G infrastructures. In this article we address a specific case of such a seamless integration, that of mobile WiMAX in evolved 3GPP networks. In this context we investigate the architecture and the key procedures that enable this integration, and we also introduce a novel handover mechanism that enables seamless mobility between mobile WiMAX and legacy 3GPP access, such as UTRAN or GERAN. The core characteristic of this novel handover mechanism is that mobile terminals do not need to support simultaneous transmission on both WiMAX and 3GPP accesses; therefore, it mitigates the RF coexistence issues that exist otherwise and improves handover performance. In addition, we provide a brief overview of mobile WiMAX and the evolved 3GPP network technologies, and we set the appropriate background material before presenting our proposed handover mechanism. Our main conclusion is that integrating mobile WiMAX in evolved 3GPP networks is a compelling approach for providing wireless broadband services, and mobility across WiMAX and 3GPP access can become seamless and efficient with no need for mobile terminals to support simultaneous transmission on both types of access.
An Empirical Study on the Concentration Ratio of Stockholders Equity and the Corporate Governance Performance in Listed Companies

With the 169 listing enterprises of machinery, equipment and instrumentation industries as the samples and by means of the 2004 and 2006 data, this paper deals with the empirical relation among the first major shareholders equity ration, the second to the fifth largest shareholding ratio and Z index and the corporate governance performance. The research results indicate that, before the share reform, the first major shareholders equity ratio is not in obviously correlation with the corporate governance performance, but in remarkable correlation with the agency cost, and that the second to the fifth largest shareholding ratio is in significant correlation with the corporate governance performance. Z index is in the significant negative correlation with the corporate governance performance. After the share reform, the first major shareholders equity ratio is in significant positive correlation with TobinQ value and the agency cost, and the second to the fifth largest shareholding ratio is in significant positive correlation with the corporate governance performance, but is in significant negative correlation with agency cost; and also Z index is in significant negative correlation with the corporate governance performance.