Miracle-dz/UniFAR-FTUs huggingface dataset:
The pivotal factors of IT projects' success – Insights for the case of organizations from the Federation of Bosnia and Herzegovina

This research aims to investigate the circumstances and possible reasons for a very high and rather unexpected success rate of Information Technology (IT) projects implemented in the Federation of Bosnia and Herzegovina (F BiH). For that purpose, the existing literature was reviewed thoroughly, and appropriate research design was formulated. In order to answer the research questions posed, a questionnaire was developed and sent to 400 companies in the F BiH that meet the defined business profile, yielding 62 valid responses. For the purpose of data analysis, a multivariate analysis of variance (MANOVA) was employed. The obtained results show that keeping the project size small significantly increases the odds for achieving IT project success, regardless of the organizational maturity level in project management. In addition, the higher the organizational maturity level in project management, the higher IT projects success ratio. Results also revealed that the differences between IT projects’ success ratio of different groups of organizations are primarily induced by the time and costs project constraints, but not with the project scope.
The Relationship between Job Characteristics and Transformational Leadership on Organization and Altruism Commitments and its Effects on Nurses’ Satisfaction and Performance in Private Hospitals of Samarinda City

The purpose of this study was to analyze and prove the relationship of Job Characteristics and Transformational Leadership on Organizational Commitment and Altruism and Its Effect on Nurses’ Satisfaction and Performance in Private Hospitals in Samarinda City. The design of this study was explanatory research. The type of this research was observational study using a cross-sectional research design. The method used to collect the data in this study was survey method. The population of this study was nurses at Private Hospitals in Samarinda City. In this study, Slovin formula was used as the sampling method obtaining total of 225 respondents, while the data collection process used simple random sampling. This study used Structural Equation Modeling Partial Least Square (SEM-PLS) approach to analyze the data. The results of this study can be summarized as follows: Job characteristics provide a positive and significant effect on nurses’ organizational commitment. Job characteristics provide a positive but not significant effect on nurses’ satisfaction. Job characteristics have a positive but not significant effect on nurses’ performance. Job characteristics have a positive and significant effect on nurses’ altruism. Transformational leadership has a positive and significant effect on nurses’ organizational commitment. Transformational leadership has a negative but not significant effect on nurses’ satisfaction. Transformational leadership has a positive and significant effect on nurses’ performance. Transformational leadership has a positive but not significant effect on nurses’ altruism. Organizational commitment has a positive and significant effect on nurses’ satisfaction. Organizational commitment has a positive and significant effect on nurses’ performance. Altruism has a negative and insignificant effect on nurses’ satisfaction. Altruism has a positive and significant effect on nurses’ performance. Nurses’ satisfaction provides a positive and significant effect on the performance of executive nurses at Private Hospitals in the City Samarinda.
Remarks on Japanese /w/

Articulatory phonetics is the basis of all phonetic sciences, including phonology. It is known, however, that articulatory phonetic descriptions of speech often involve uncertainty due to the subjective nature of observation. Since 2017, the author has been involved in a research project, “Reconstruction of articulatory phonetics by means of real-time MRI. ” The main goal of this project is to design and construct a novel database of the articulatory movements of the Japanese consonants and vowels, thereby providing a firm quantitative basis for the articulatory phonetics of Japanese (前川他2018, 浅井他 2018, 能田他2019, Maekawa 2019, Saito, Yurong & Maeawa 2019, Takemoto et al. 2019, 前川2019, Asai, Kikuchi & Maekawa 2019, Maekawa submitted). The issues of Japanese phonetics so far analyzed in this project include consonant palatalization, the place of articulation of the utterancefinal moraic nasal /N/, and the place and manner of Japanese /r/. In the current study, the articulatory characteristics of Japanese /w/ will be analyzed: hereafter, the symbol /w/ stands for the consonant that appears in the Japanese mora written with the kana わ or ワ, unless specified otherwise. In the literature of Japanese linguistics, the /w/ sound is often transcribed with the IPA (or seemingly IPA) symbols [w] (i.e., a voiced bilabial-velar double articulation approximant) or [ɰ] (a voiced velar approximant), but it is often difficult to know precisely the intentions with which these symbols are used by the authors. Most studies of the Japanese language use [w] without explanation, like Shibatani (1990). In the illustration of Japanese in the Handbook of the International Phonetic Association (IPA 1999), the symbol [w] appears in the column for voiced velar approximant (which is usually occupied by the [ɰ] symbol in the IPA consonant chart) without explanation. In this case, the readers will thus be left with at least two competing interpretations: either there is no bilabial constriction, or there is bilabial constriction but its degree is secondary to the primary dorso-velar constriction. On the other hand, Vance (2008) stated clearly why he used the symbol [ɰ] as follows.
It Doesn’t Have to Be So Hard: Efficient Symbolic Reasoning for CRCs

—Cyclic redundancy check functions (CRCs) are commonly used as checksums in hardware and software systems. Though some previous work has treated CRCs as if they would be difﬁcult for automated reasoning or proposed using them for obfuscation, we argue that CRCs need not be difﬁcult for symbolic execution. CRCs have a strong algebraic structure, and the key to efﬁcient reasoning about them is exposing this structure. Common CRC implementation techniques use per-bit branching or lookup tables, but these code structures can be compactly summarized by symbolic execution engines that perform path merging or create formulas reﬂecting the contents of a table, respectively. To evaluate the effectiveness of these techniques, we test the binary symbolic execution tools angr and FuzzBALL, and the Java bytecode tool Java Ranger, on symbolically ﬁnding CRC preimages for output sizes of 32 and 64 bits and input sizes up to 8192 bytes. The best conﬁgurations can ﬁnd short CRC preimages in just a fraction of a second and long ones in under 10 seconds, demonstrating that constraint solving with CRCs is not difﬁcult.
Model of coping strategies, resilience, psychological well-being, and perceived health among military personnel

Background: Military personnel are confronted with physiological and psychological changes caused by stress and exposure to trauma. Although resilience may be protective against psychopathology, very few studies have explored the relationships between the resilience and coping strategies. The study aims to assess how different coping strategies affect resilience, psychological well-being (PWB), and perceived health among military personnel. Subjects and Methods: This study was a cross-sectional survey. Nuclear, biological, and chemical (NBC) soldiers and nurses in the military medical center were recruited in Taiwan in November 2015. The survey comprised the Brief COPE Scale, Ryff's PWB Scale, and the Resilience Scale for Adults, which examined the relationships among coping strategies, PWB, resilience, and perceived health. Path analysis was applied. Results: We recruited 200 participants (145 male and 177 single) aged 24.6 ± 4.7 years (range, 18–46 years). Resilience (coefficient = 0.60, P < 0.001) and PWB (coefficient = 0.33, P < 0.001) were better when using more approach-oriented coping strategies and fewer avoidant coping strategies, whereas the opposite pattern was seen when using avoidant coping (coefficient = −0.31, P < 0.001 for resilience and coefficient = −.20, P < 0.1 for PWB). PWB significantly predicted perceived health (coefficient = 0.45, P < 0.001). Conclusions: Resilience is higher when positive approach-oriented coping strategies are used, which directly affects PWB, and in turn, predicts better-perceived health. Our conceptual model indicates that interventions designed to promote approach-oriented coping strategies may help military personnel develop improved resilience, PWB, and perceived health status.
Guidelines for the selection, configuration, and use of Transport Layer Security (TLS) implementations

Transport Layer Security (TLS) provides mechanisms to protect sensitive data during electronic dissemination across the Internet. This Special Publication provides guidance to the selection and configuration of TLS protocol implementations while making effective use of Federal Information Processing Standards (FIPS) and NISTrecommended cryptographic algorithms, and requires that TLS 1.1 configured with FIPSbased cipher suites as the minimum appropriate secure transport protocol and recommends that agencies develop migration plans to TLS 1.2 by January 1, 2015. This Special Publication also identifies TLS extensions for which mandatory support must be provided and other recommended extensions.
Closing the STEM gender gap: Choice, access, and policies in Mexico City high schools

The gender gap in science, technology, engineering, and mathematics (STEM) widens during high school, due both to differences in student choices and institutional barriers to accessing STEM education. Using rich data on high school choice from Mexico City, we show that most of the gap is due to gendered choices but exam-based assignment also restricts access to selective STEM schools. General equilibrium simulations reveal the potential for policies at the margin to have large effects. Policies such as STEM affirmative action or inducing females to move their highest ranked STEM school up by one rank can reduce the gap significantly. ∗E-mail addresses: dngo@oxy.edu, andrew.dustan@vanderbilt.edu. Author names are in reverse alphabetical order. We thank Mexico’s Secretariat of Public Education for providing the data and participants at the Liberal Arts College Development Conference, Association for Education Finance and Policy Conference, and Western Economics Association Conference for useful feedback. Aleeza Toribio provided excellent research assistance.
Performance Evaluation of Decomposition Methods in Perturbed Walking

Received: 3 November 2019 Accepted: 20 January 2020 Mobile phones cannot be separated from our daily lives. Most of young adults use their mobile phones while walking and this potentially cause accidents. The current study aimed to evaluate the performance of decomposition methods to detect perturbed walking. Ten young adults were asked to perform normal walking and walking while playing game on their phones. The vertical acceleration data were decomposed using 4, 6, and 8 levels of ensemble empirical mode decomposition (EEMD), discrete wavelet transform (DWT), and wavelet packet decomposition (WPD) before the calculation of the step stability index (SSI) to detect the perturbation during walking. Machine learning techniques were used to evaluate the performance of the decomposition methods. The evaluation results showed that DWT with 6 levels of decomposition outperformed other decomposition methods. In conclusion, the DWT can be used to increase the sensitivity of the SSI method in detecting walking perturbation.
Characterizing and Predicting the Future Popularity of Online Videos

The big size of video content has been generated on the online web, and user keeping track of all the details requires your complete attention among those videos is spatial arrangement. With the likes hit comment or views. Hence, understanding the popularity characteristics of online videos and predicting the future popularity of individual videos are of great importance. They have direct implications in various contexts, such as service design, advertisement planning, network management, and so on. In this paper, we address those two problems head-on based on data collected from a leading online video service provider in India, namely YouTube. We firstly analyze the characteristics of YouTube video popularity from four key aspects: long-term popularity, video lifetime and early stage popularity. According to this study such as advertisement planning, network management and also view count up to 15 days. Daily growth rate of view count up to 15th days. Aspects: popularity of long-term, hit’s video, in public open popularity as well as it demand or reaction on the video.
Exploring the potential for collective leadership in a newly established hospital network.

PURPOSE Achieving integrated care is a key focus for health systems and has resulted in various structures between and within organisations. The reorganisation of the Irish health system into hospital networks/groups aims to encourage work across hospitals to integrate care. This study evaluated if collective leadership emerged over time through increased interaction and collaboration following the organisation of hospitals into a network. A secondary aim was to elucidate the potential for collective leadership, through understanding the barriers and enablers perceived by participants. DESIGN/METHODOLOGY/APPROACH This study employed social network analysis and qualitative interviews. Leaders across the hospital group were invited to participate in an online network survey and interviews (analysed using thematic analysis) at three time points over an 18-month period. FINDINGS Although there was evidence that some parts of network were beginning to operate collectively, the structures observed were more typical of a hierarchical network. Disruption in the network and uncertainty regarding permanence of the organisational structure had a negative impact on the potential for collective leadership. Yet, progress was evident in terms of establishing building blocks for collective leadership and integration, including developing trust, mutual understanding and creating space for change. PRACTICAL IMPLICATIONS This study contributes to the literature by reflecting on the mechanisms and initiatives perceived as enabling/inhibiting collective leadership. Based on this research, it is important to communicate a clear and consistent message about the plans for the organisations involved and be clear regarding the roles and expectations for those involved in introducing new approaches to leadership and integration. Honest collaboration, openness and certainty in communication will likely be important in order to help create the contextual conditions to enable collective and system approaches to introduce "stepping stones" to change. These conditions include developing interpersonal relationships between leaders, creating time and space for deep and shared reflection, and enhancing trust among colleagues. ORIGINALITY/VALUE A key strength of this study is the linking of leadership-as-networks theory with social network methods to investigate collective leadership in practice. This study contributes to the literature by reflecting on the mechanisms and initiatives perceived as enabling/inhibiting collective leadership.
Supply-Demand Prediction of DiDi Based on Points of Interests Selection in Extreme Gradient Boosting Algorithm

Received: 5 August 2019 Accepted: 10 November 2019 In recent years, DiDi, an online car-hailing (OCH) service provider, has emerged as a leader in the sharing economy. To improve user experience, the company must minimize the waiting time and optimize car utilization based on accurate estimation of supply-demand gap. This paper aims to develop a desirable model to select the most significant factors for OCH supply-demand estimation. Firstly, the correlation between the points of interest (POIs) and the supply-demand gap was proved through statistical analysis. Next, the number and type of POIs were found to have a slight impact on the estimation results. On this basis, the authors put forward a method called POI principal component extraction based on supply-demand gap (PPCE-SDG) to select the most significant POIs. The PPCESDG involves four steps: k-means clustering (KMC) of blocks based on supply-demand gap; creating a data vector of POIs after counting the POIs in each cluster; extracting the significant POIs through principal component analysis (PCA) of the data vector; importing the extracted POIs to extreme gradient boosting (XGBoost) for OCH supply-demand prediction. Finally, the POIs selected by the PPCE-SDG were proved superior than those collected by other methods in OCH supply-demand estimation, indicating that our model is a desirable tool for significant POIs selection. The research results lay a good basis for the optimization of OCH services.
An in-depth Study of Neural Machine Translation Performance

With the rise of deep learning and rapidly increasing popularity of it, neural machine translation (NMT) has become one of the major research areas. Sequence-to-sequence models are widely used in NTM tasks, and one of the state-of-the-art models, the Transformer, has also encoder-decoder architecture with an additional attention mechanism. Despite a substantial amount of research in improving NMT models’ translation qualities and speeds, to the best of our knowledge, none of them gives a detailed performance analysis of each step in a model. In this paper we analyze the Transformer model’s performance and translation quality in diﬀerent settings. We conclude that beam search is the bottleneck of the NMT inference and analyze beam search’s eﬀect on the performance and quality in detail. We observe that the beam size is one of the largest contributors to the Transformer’s execution time. Additionally, we observe that the beam size only aﬀects BLEU score at word level, and not at token level, indicating a mismatch between the beam search internal evaluation metrics and the end-to-end metrics used to evaluate models. We also show that the vocabulary size has a major role in the performance of the beam search.
Enterprise Architecture Evaluation: A Case Study on the Purposes of Enterprise Architecture for E-Business

Enterprise Architecture (EA) is an ill-understood concept, especially in the context of e-business. The aim of this paper is to explore the elements that make up EA and to classify the kinds of purposes EA could serve for the realization of e-busi - ness. Using literature research, we derive three elements of EA frameworks. We applied the EA elements on a running case at a large multinational firm, which is currently migrating towards an e-business platform. In the case study, EA was only useable for descriptive purposes, making EA a valuable instrument for com - munication and analysis. EA was not useable for prescriptive purposes such as the design or redesign of information systems in relation to changing business processes. The main cause of this is the lack of comprehensive tooling support, something that will hamper using EA for developing and deploying e-business solutions in the future.
Developing Data-based Bayesian Network for Human Operation Analysis during Hot Work

Hot work has caused numerous accidents during the inspection and maintenance process in recent years in China. This paper collected the hot work accidents in China since 2007 and integrated them into a database. The database contained the information such as the time, site, sequence, source, type, cause and consequence. Through analysing the database, most accidents were relevant to human operations. We classified unsafe and irregular operations caused by human factors during hot work. The causes of hot work accidents involved a series of problems during the approval, supervision and construction. Integrating hot work procedures, we established the main safety evaluation framework containing three factors, i.e., approval, execution environment, and protective measures. The method of Bayesian Network (BN) aiming at solving uncertain problems was utilized to identify unsafe operations quantitatively during hot work. Eventually, eight key proposals were presented to avoid hot work accidents. The proposed framework was developed to assess human operations in chemical plants during hot work. Causes of hot work accidents in chemical plants mainly consisted of failure to handle permits of hot work, production system without isolation, unremoved flammable materials, and failure to install fire prevention measures. The results can provide compelling support for human management and training during hot work.
Investigation of Factors Influencing On Customer Satisfaction of Products Manufactured By Pharmaceutical Companies

The purpose of this research is to investigate factors influencing on customer satisfaction of products manufactured by pharmaceutical companies. This study is a descriptive research by classification based on data collection method, or study plan that describes sample features and then it's generalization with statistical population. Descriptive researches are divided in several groups and in this study scalable type applied. In scalable type, the relation between variables described, predicted and analyzed. The result of this study indicated that there is a significant relation in a positive and direct type between received quality independent variable and customer satisfaction dependent variable in an acceptable level. There is also positive and direct significant relation between received quality independent variable and dependent variable of received value in an acceptable level. The finding revealed that there is a positive and direct significant relation between customer expectation independent and received value dependent variables in an acceptable level and customer expectations independent and revived quality variable. This indicated that high customer complaints relating to Dr. Abidi pharmaceutical products will influence probably 33 percent of loyalty rate with the products and customers satisfaction rate of customer satisfaction index is higher than 3.
NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages

Social media messages, such as tweets, are frequently used by people during natural disasters to share real‐time information and to report incidents. Within these messages, geographic locations are often described. Accurate recognition and geolocation of these locations are critical for reaching those in need. This article focuses on the first part of this process, namely recognizing locations from social media messages. While general named entity recognition tools are often used to recognize locations, their performance is limited due to the various language irregularities associated with social media text, such as informal sentence structures, inconsistent letter cases, name abbreviations, and misspellings. We present NeuroTPR, which is a Neuro‐net ToPonym Recognition model designed specifically with these linguistic irregularities in mind. Our approach extends a general bidirectional recurrent neural network model with a number of features designed to address the task of location recognition in social media messages. We also propose an automatic workflow for generating annotated data sets from Wikipedia articles for training toponym recognition models. We demonstrate NeuroTPR by applying it to three test data sets, including a Twitter data set from Hurricane Harvey, and comparing its performance with those of six baseline models.