Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of coloured lighting in the real world environment and virtual reality

Many studies have shown that colour can affect emotional and physiological responses in real environments but do the same effects occur in virtual environments? This study investigated human subjective and objective responses to different coloured lighting environments, first in a physical laboratory (PE) and then in an approximately identical virtual reality (VR) environment, using three experimental techniques. Data from a smiley-face self-reporting exercise and from electroencephalography (EEG) revealed differential effects of coloured lighting in both the PE and VR environments. However, although data from the Positive and Negative Affect Schedule (PANAS) revealed an effect of coloured lighting in the RE, the same effects were not statistically significant in the VR. We suggest that this might be because of a limited sample size. The results of this study demonstrate the potential of colour for positive emotional effect in an immersive ‘real-world’ environment for participants in a natural state, i.e. not engaged in any specific cognitive activity. Importantly, the results also indicate that in a VR replica of the same environment, participants also experienced an increase in positive emotions.
Does it really matter for Vietnamese SMEs?

Purpose – The purpose of this paper is to investigate the simultaneous relationship among ownership concentration, innovation and firm performance of the smalland medium-sized enterprises (SMEs) in Vietnam during the 2011–2015. By employing a Conditional Mixed Process (CMP) model, the findings show that: there is no impact of ownership concentration on innovation, but it has a positive impact on sales growth; innovation positively affects firm performance; and there exists a positively reverse causality from sales growth to innovation. Design/methodology/approach – In this study, the authors propose the adaption of CMP model (Roodman, 2011). The nature of the first stage dependent variable – Innovation – is a binary one while the dependent variable Performance is continuous. Therefore, a model that can adapt the binary nature of the dependent variable and perform the estimation of a system of equations such as CMP model is preferred. The CMP framework is substantially that of seemingly unrelated regression, but with application in a larger scope. This approach is based on a “simulated maximum likelihood method” suggested by Geweke–Hajivassiliou–Keane algorithm. Findings – By applying CMP method, this study examines the simultaneous relationship among ownership concentration, innovation and firm performance of the SMEs in Vietnam from 2011 to 2015. The findings indicate that: there is no impact of ownership concentration on innovation, but it has a positive impact on sales growth; innovation positively affects firm performance; and there exists a positively reverse causality from sales growth to innovation. Research limitations/implications – In spite of the efforts to explore the simultaneous relationship among ownership concentration, innovation and firm performance of the SMEs in Vietnam, the study still has some limitations which are promising further research directions. First, the SME surveys by Central Institute for Economic Management do not have much information about other types of ownership including state-owned and foreign ownership. Therefore, possible further studies with richer data sets may explore the impacts of different types of ownership on firm innovation and performance. Second, other types of innovation such as organizational innovation, marketing innovation can also be investigated in further studies in a richer data set for the case of Vietnam SMEs. Originality/value – The findings show that: there is no impact of ownership concentration on innovation, but it has a positive impact on sales growth; innovation positively affects firm performance; and there exists a positively reverse causality from sales growth to innovation. The policy implications insist on facilitating SMEs with easier access to capital via loans with preferred interest or trust loans without collateral, training programs for the labor force and SME leaders, and reduction of unnecessary administrative procedure.
Dense U-net for single image super-resolution with shuﬄe pooling layer

Recent researches have achieved great progress on single image super-resolution(SISR) due to the development of deep learning in the ﬁeld of computer vision. In these method, the high resolution input image is down-scaled to low resolution space using a single ﬁlter, commonly max-pooling, before feature extraction. This means that the feature extraction is performed in biased ﬁltered feature space. We demonstrate that this is sub-optimal and causes information loss. In this work, we proposed a state-of-the-art convolutional neural network method called Dense U-net with shuﬄe pooling. To achieve this, a modiﬁed U-net with dense blocks, called dense U-net, is proposed for SISR. Then, a new pooling strategy called shuﬄe pooling is designed, which is aimed to replace the dense U-Net for down-scale operation. By doing so, we eﬀectively replace the handcrafted ﬁlter in the SISR pipeline with more lossy down-sampling ﬁlters speciﬁcally trained for each feature map, whilst also reducing the information loss of the overall SISR operation. In addition, a mix loss function, which combined with Mean Square Error(MSE), Structural Similarity Index(SSIM) and Mean Gradient Error (MGE), comes up to reduce the perception loss and high-level information loss. Our proposed method achieves superior accuracy over previous state-of-the-art on the three benchmark datasets: SET14, BSD300, ICDAR2003. Code is available online 1 .
Scale resolving simulations of the non-cavitating and cavitating flows in an axial water jet pump

In this paper, the non-cavitating and cavitating flows in the AxWJ-2 axial water jet pump of Johns Hopkins University are simulated using a Large Eddy Simulation (LES) approach. The non-cavitating simulations are performed on grids with different mesh resolutions and the effect of mesh resolution on predicting the performance and capturing the structure of Tip Leakage Vortex (TLV) is investigated. Based on this investigation, it can be concluded that the main feature of the non-cavitating TLV can be well captured compared with the experiment provided that a refinement region with at least 40 cells in tip gap is used. The cavitating simulation using the same grid refinement also shows that the cavitating structures described in the experiment are predicted by the simulation. Furthermore, the structure of non-cavitating and cavitating TLVs are compared and the effect of cavitation on the structure of TLV are analysed using the simulation results.
Maximizing Persistent Memory Bandwidth Utilization for OLAP Workloads

Modern database systems for online analytical processing (OLAP) typically rely on in-memory processing. Keeping all active data in DRAM severely limits the data capacity and makes larger deployments much more expensive than disk-based alternatives. Byte-addressable persistent memory (PMEM) is an emerging storage technology that bridges the gap between slow-but-cheap SSDs and fast-but-expensive DRAM. Thus, research and industry have identified it as a promising alternative to pure in-memory data warehouses. However, recent work shows that PMEM's performance is strongly dependent on access patterns and does not always yield good results when simply treated like DRAM. To characterize PMEM's behavior in OLAP workloads, we systematically evaluate PMEM on a large, multi-socket server commonly used for OLAP workloads. Our evaluation shows that PMEM can be treated like DRAM for most read access but must be used differently when writing. To support our findings, we run the Star Schema Benchmark on PMEM and DRAM. We show that PMEM is suitable for large, read-heavy OLAP workloads with an average query runtime slowdown of 1.66x compared to DRAM. Following our evaluation, we present 7 best practices on how to maximize PMEM's bandwidth utilization in future system designs.
Multimarginal Optimal Transport with a Tree-Structured Cost and the Schrödinger Bridge Problem

The optimal transport problem has recently developed into a powerful framework for various applications in estimation and control. Many of the recent advances in the theory and application of optimal transport are based on regularizing the problem with an entropy term, which connects it to the Schrödinger bridge problem and thus to stochastic optimal control. Moreover, the entropy regularization makes the otherwise computationally demanding optimal transport problem feasible even for large scale settings. This has led to an accelerated development of optimal transport based methods in a broad range of fields. Many of these applications have an underlying graph structure, for instance information fusion and tracking problems can be described by trees. In this work we consider multi-marginal optimal transport problems with a cost function that decouples according to a tree structure. The entropy regularized multi-marginal optimal transport problem can be viewed as a generalization of the Schrödinger bridge problem with the same tree-structure, and by utilizing these connections we extend the computational methods for the classical optimal transport problem in order to solve structured multi-marginal optimal transport problems in an efficient manner. In particular, the algorithm requires only matrix-vector multiplications of relatively small dimensions. We show that the multi-marginal regularization introduces less diffusion, compared to the commonly used pairwise regularization, and is therefore more suitable for many applications. Numerical examples illustrate this, and we finally apply the proposed framework for tracking of an ensemble of indistinguishable agents.
Game Input with Delay - A Model for the Time to Select a Target with a Mouse

Computer game player performance can degrade with delays from both the local system and network. Analytic models and simulations have the potential to enable exploration of player performance with delay as an alternative to time-intensive user studies. This paper presents an analytic model for the distributions of elapsed times for players doing a common game action – selecting a moving target with a mouse with delay – derived from results from prior user studies. We develop and validate our model, then demonstrate the use of our model via simulation, exploring player performance in games with different configurations and delays.
EFFECT OF E-MARKETING AND E-CRM ON E-LOYALTY: AN EMPIRICAL STUDY ON INDONESIAN MANUFACTURES

The purpose of this research is to analyze the effect of e-marketing on e-loyalty, the effect of e-crm on eloyalty of manufacture in Banten. The method used in this research is quantitative method, data collection method by distributing questionnaires to manufactur manager. The questionnaire was distributed electronically using simple random sampling technique. The results of the questionnaire returned were 100 respondents. Based on the results of data analysis, it is concluded that e-marketing have a significant effect on e-loyalty. An increase in the e-marketing variable will be followed by an increase in e-loyalty and a decrease in the variable e-marketing will be followed by a decrease in e-loyalty. e-crm have a significant effect on e-loyalty. The novelty of this research is the variable relationship model of e-crm, e-loyalty and eloyalty. The results of this study can become a reference reference for future research to be applied in other places or countries.
DRMI: A Dataset Reduction Technology based on Mutual Information for Black-box Attacks

It is non-trivial to attack deep neural networks in black-box settings without any model detail disclosed. Prior studies on black-box attacks leverage a number of queries to the target model for probing the target model or generating adversarial examples. Queries are usually limited and costly so that the adversary probably fails to mount an effective attack. However, not all the queries have to be made since there exist repetitions or redundancies that induce many inefﬁcient queries. Therefore, it leaves a lot of room for data reduction and more efﬁcient queries. To this end, we ﬁrst propose to use mutual information to measure the data redundancy between two data samples, and then develop a data reduction technique based on mutual information, termed as D R M I . We implement an efﬁcient optimization algorithm in D R M I , so as to obtain a particular subset of data samples, of which the mutual information in between is minimized. We conduct extensive experiments on MNIST, CIFAR10, and ImageNet, and six types of deep neural networks, and evaluate D R M I in model extraction and adversarial attacks. The results demonstrate its high effectiveness in these attacks, surpassing a state-of-the-art approach by raising 7% of model accuracy and two times more transferability of adversarial examples. Through the comparison experiments with other three strategies, we identify what properties of data have been preserved and removed, to some extent reveal the essences of deep neural networks.
Adherence to Antiretroviral Therapy (ART) is a powerful predictor of survival for People Living With HIV/AIDS

Adherence to Antiretroviral Therapy (ART) is an important survival factor for people living with HIV/AIDS (PLWHAs). However, patients on ART in Kenya still miss routine check-ups, and get lost to follow up. Research on the factors influencing ART adherence among different populations in Kenya have yielded inconsistent results. The objective of this study was to determine the relationship between patient factors and adherence to ART by adults living with HIV/AIDS in Kisumu County, Kenya. Facility based crosssectional survey was carried out on patients receiving ART from all the seven SubCounties in the County. Systematic random sampling was used to recruit a sample of 386 patients. Data collection tools were pre-tested and piloted to ensure validity and reliability. Chi-square statistic was used to analyze the data at α=. 05. Findings indicated that the relationship between age and ART adherence was statistically significant (χ 2 = 8.064, df= 2, p = 0.034). Similarly, the relationship between alcohol use and ART adherence was statistically significant (χ 2 = 5.331, df = 1, p = 0.009). Knowledge of ART also had a statistically significant relationship with ART adherence (χ 2 = 7.799, df = 1, p = 0.0028). However, marital status, level of education, employment status, stigma, and psychosocial support did not exhibit statistically significant relationships with ART adherence. The study concludes that patient age, use of alcohol and knowledge of ART are patient factors related to ART. It is recommended that these factors should be considered in the treatment of PLWHAs to ensure ART adherence. © 2018 Elixir All rights reserved. Elixir Psychology 121 (2018) 51503-51508 Psychology Available online at www.elixirpublishers. com (Elixir International Journal) Maurine Ng’oda et al./ Elixir Psychology 121 (2018) 51503-51508 51504 factors and adherence to ART by adult PLWHAs living in Kisumu County, Kenya.
Extending the Foundations of Diﬀerential Privacy: Flexibility and Robustness

Diﬀerential Privacy (DP) is an area that has recently seen many direct and indirect applications to machine learning. While DP provides the most rigorous notions of privacy, there are many settings where its applicability is limited. In this work, we make foundational contributions to DP, thereby greatly expanding its applicability in multiple ways. Towards this, we deﬁne two complementary concepts, namely, Flexible Accuracy and Robust Privacy. Flexible Accuracy allows small distortions in the input (e.g., dropping outliers) before measuring accuracy of the output, allowing one to extend DP mechanisms to high-sensitivity functions. Robust Privacy goes beyond the notion of individual-level privacy considered by DP: It requires a mechanism to not include any “non-utile information” in the output, thereby oﬀering database-level privacy. Then, we present mechanisms that can help in achieving these notions, where previously no meaningful diﬀerentially private mechanisms were available. In particular, we illustrate an application to diﬀerentially private histograms, which in turn yields mechanisms for revealing the support of a dataset or the extremal values in the data. Analyses of our constructions exploit new versatile composition theorems that facilitate modular design. Our deﬁnitional framework, in terms of “lossy Wasserstein distance” – a 2-parameter error measure for distributions – is of independent interest. In particular, it leads us to a mechanism for diﬀerentially private sampling.
Bicontinuous Alkaline Fuel Cell Membranes from Strongly Self-Segregating Block Copolymers

Alkaline fuel cell membranes have the potential to reduce the cost and weight of current fuel cell technology, but they still have not been broadly commercialized due to poor hydroxide conductivities and mechanical properties, in addition to low chemical stability. One approach to address these mechanical and transport shortcomings is forming a morphologically bicontinuous network of an ion transporting phase and a hydrophobic phase to provide mechanical strength. In this report, membranes having bicontinuous morphologies are fabricated by cross-linking cation-containing block copolymers with hydrophobic constituents. This is accomplished in a single step and does not require postpolymerization modification. The resulting materials conduct hydroxide ions very rapidly, as high as 120 mS cm–1 in liquid water at 60 °C. The methodological changes required to obtain a bicontinuous morphology from such strongly self-segregating block copolymers, relevant to other applications in which bicontinuous structures ar.
COMMISSION E2 SOLAR ACTIVITY

Solar activity is the business of Commission E2. It originates with the dynamo in the Sun’s interior, resulting from a complex interplay of differential rotation, convection and magnetic field, and gives rise to the 22-yr sunspot cycle. Manifestations of activity range in size from coronal holes covering large fractions of the surface, through active regions with their powerful sunspots, coronal mass ejections, coronal loops, flares and nanoflares, network, etc., right down to small magnetic elements at the resolution limits of current instruments, and presumably below. As we emerge from Cycle 24, the weakest in over a century, we look back at the last three years of low solar activity but high human activity in the field. New spaceand ground-based facilities are on the cusp of revolutionizing the discipline. This is a pivotal time in solar activity physics.
Working Papers / Documents de travail

Higher uncertainty about government spending generates a persistent decline in the economic activity in the Euro Area. This paper emphasizes the transmission channels explaining this empirical fact. First, a Stochastic Volatility model is estimated on European government consumption to build a measure of government spending uncertainty. Plugging this measure into a SVAR model, we stress that government spending uncertainty shocks have recessionary, persistent and humped-shaped effects. Second, we develop a New Keynesian model with financial frictions applying to a portfolio of equity and long-term government bonds. We argue that a portfolio effect – resulting from the imperfect substitutability among both assets – acts as a critical amplifier of the usual transmission channels. JEL classification: E62; E52.
Exploring the Vocabulary Learning Strategies of EFL learners

Research in vocabulary learning and its relationship to reading has gained much interest within the field of investigation in second language acquisition. Since reading requires accurate and automatic word recognition skills, learners would need to be equipped with sufficient vocabulary knowledge to read fluently. Yet, for many EFL (English as a Foreign Language) learners, reading is a ‘suffocating slow process’. One reason cited and most often experienced by these learners is their lack of vocabulary knowledge. Due to this limitation, they often give up trying to understand the meaning of the text or skip reading if sentences or an entire paragraph contain a small number of unknown words. These encounters suggest that some learners might not have the knowledge to handle words they do not understand. Although vocabulary knowledge has been recognized as a critical determinant of one’s reading ability (Grabe, 1991), yet not much research has been focused on investigating the strategies learners employ to learn vocabulary. This study explores the strategy use of vocabulary learning among a group of EFL learners. A sample of 35 respondents participated in the study. Data collection was carried out through questionnaires. The results revealed that there were distinctive strategy uses to vocabulary learning among the learners. To conclude, the paper will discuss the implications of the findings and some pedagogical considerations for the teaching and learning of vocabulary within an EFL context. Introduction The recognition of the role of vocabulary in language learning has continued to grow in recent years. This area of learning was previously neglected due to certain dominant teaching approaches in the 1940’s until the 1960’s. The theories underlying these approaches such as structural linguistics (Fries, 1945) and generative transformational linguistics (Chomsky, 1957) focused on teaching grammatical and phonological structures as well as emphasized on grammatical rules respectively. Learners were assumed that once they have learned the structural frames and the grammatical rules, they will then be able to fill in the lexical items as needed. The
Self-Leadership as Antecedent of Organizational Commitment and Intention to Leave among Data Scientists

Data scientists are new knowledge workers representing the knowledge economy era. Knowledge workers perform unstandardized works that solve ambiguity-intensive problems. Therefore, self-leadership, which emphasizes self-motivated, autonomous judgment and execution, significantly influences their work-related outcomes. Even knowledge workers have high occupational commitment, they usually show low organizational commitment. Knowledge workers’ intention to leave is also relatively high due to this reason. This study focused on data scientists' self-leadership, predicted that self-leadership would increase an organization's commitment and intention to leave. Based on the trait activation theory(TAT), the author also confirmed how perceived job autonomy enhances self-leadership influences. Results showed that data scientists' self-leadership significantly lowered intention to leave through organizational commitment and this mediating effect was moderated by perceived job autonomy. This study broadened the theoretical understanding the effects of knowledge workers' self-leadership and presented practical implications for managing data scientists. ■ keyword :∣Data Scientist∣Knowledge Worker∣Self-Leadership∣Perceived Job Autonomy∣Intention to Leave∣ 접수일자 : 2021년 03월 16일 수정일자 : 2021년 04월 13일 심사완료일 : 2021년 04월 16일 교신저자 : 정창모, e-mail : changmo.jung@mobis.co.kr 한국콘텐츠학회논문지 '21 Vol. 21 No. 5 48