Miracle-dz/UniFAR-FTUs huggingface dataset:
Modern Algorithms for Matching in Observational Studies

Using a small example as an illustration, this article reviews multivariate matching from the perspective of a working scientist who wishes to make effective use of available methods. The several goals of multivariate matching are discussed. Matching tools are reviewed, including propensity scores, covariate distances, fine balance, and related methods such as near-fine and refined balance, exact and near-exact matching, tactics addressing missing covariate values, the entire number, and checks of covariate balance. Matching structures are described, such as matching with a variable number of controls, full matching, subset matching and risk-set matching. Software packages in R are described. A brief review is given of the theory underlying propensity scores and the associated sensitivity analysis concerning an unobserved covariate omitted from the propensity score.
Adaptive Consistency Propagation Method for Graph Clustering

Graph clustering plays an important role in data mining. Based on an input data graph, data points are partitioned into clusters. However, most existing methods keep the data graph fixed during the clustering procedure, so they are limited to exploit the implied data manifold and highly dependent on the initial graph construction. Inspired by the recent development on manifold learning, this paper proposes an Adaptive Consistency Propagation (ACP) method for graph clustering. In order to utilize the features captured from different perspectives, we further put forward the Multi-view version of the ACP model (MACP). The main contributions are threefold: (1) the manifold structure of input data is sufficiently exploited by propagating the topological connectivities between data points from near to far; (2) the optimal graph for clustering is learned by taking graph learning as a part of the optimization procedure; and (3) the negotiation among the heterogeneous features is captured by the multi-view clustering model. Extensive experiments on real-world datasets validate the effectiveness of the proposed methods on both single- and multi-view clustering, and show their superior performance over the state-of-the-arts.
Temporal and spatial variability analysis of the solar radiation in a region affected by the intertropical convergence zone

The first aim of this work was to obtain temporal variability patterns for satellite‐derived solar radiation estimations in Zambia. A principal component analysis, in Zambia and the surrounding zones, was performed and from this analysis the physical phenomena associated with these patterns were established. According to the results, two temporal variability patterns stand out: the first is associated with the regional global climate characteristics, including both the deterministic and non‐deterministic components of solar radiation, and the other is strictly associated with the influence of the intertropical convergence zone (ITCZ) responsible for the behaviour of solar radiation during October–March. The second aim of the work was to analyse the spatial variability of the irradiance in the study area. For this aim, a clustering analysis based on the interquartile range of the data was performed. The analysis leads to a spatial distribution of the radiation in agreement with the influence of the ITCZ on the territory. Indeed, those stations less affected by the ITCZ, in the south and east of the territory, show a clear diminution of the radiation around June. However, the stations of the northwest zone, the most affected by the belt of low pressure during November–April, do not present this diminution.
Harmony Search Optimization: Application to Pipe Network Design

Abstract The Pipe network design problem is considered by choosing least-cost diameter pipes to satisfy flow demands and minimum head restrictions. Harmony and innovation are brought together as in music to devise a search pattern that can identify a large number of local optima in pipe network design. Improvization is brought in by varying the pitch rate parameter, and harmony is maintained by accumulating improving solutions. Because in pipe networks optimization by its very nature removes any redundancy, a minimum power loss criterion is introduced to enhance feasibility. The efficacy of the algorithm is demonstrated by solving a test nonlinear program and a pipe network problem from the literature to global optimum. Several near-optimal solutions are reported.
Concurrent Assessment of Process Operating Performance With Joint Static and Dynamic Analysis

Assessment of operating performance for industrial processes is critical to guarantee high productivity and low cost, under routine operating condition. In the traditional research works on assessment of operating performance, the static characteristics are fully investigated, but the dynamic characteristics are seldom explored. Actually, the dynamic characteristics are important to distinguish operating performance and indicate the regulating actions of controllers. This article presents a concurrent static and dynamic assessment (ConSDA) method for operating performance in terms of industrial processes under closed-loop control. The performance levels are distinguished from both static and dynamic aspects. Canonical variate analysis and slow feature analysis are combined to fully extract the static and dynamic features of a process to well characterize each performance level. An efficient assessing scheme using the Bayesian inference based criterion is developed to provide meticulous assessing result with meaningful physical interpretability and sensitive switching identification for performance levels. The efficacy is demonstrated through application to a numerical example and a three-phase flow process. The rates of accurately distinguishing the performance levels for ConSDA is over 95% for the two applications with strong dynamic properties. Meanwhile, the highest average accuracy rates of four other assessing methods is 87.0%. The comparison illustrates the superiority of ConSDA.
Perceived Image Quality Assessment for Stereoscopic Vision

This thesis describes an automatic evaluation approach for estimating the quality of stereo displays and vision systems using image features. The method is inspired by the human visual system. Display of stereo images is widely used to enhance the viewing experience of three-dimensional (3D) visual displays and communication systems. Applications are numerous and range from entertainment to more specialized applications such as: 3D visualization and broadcasting, robot tele-operation, object recognition, body exploration, 3D teleconferencing, and therapeutic purposes. Consequently, perceived image quality is important for assessing the performance of 3D imaging applications. There is no doubt that subjective testing (i.e., asking human viewers to rank the quality of stereo images) is the most accurate method for quality evaluation. It reflects true human perception. However, these assessments are time consuming and expensive. Furthermore, they cannot be done in real time. Therefore, the goal of this research is to develop an objective quality evaluation methods (computational models that can automatically predict perceived image quality) correlating well with subjective predictions that are required in the field of quality assessment. I believe that the perceived distortion and disparity of any stereoscopic display ii
Potential benefits of arrival time assignment: Dynamic programming trajectory optimization applied to the Tokyo international airport

Increasing the efficiency and capacity of flights arriving at a congested airport is one of the most challenging problems in air traffic management research. The efficiency of commercial jet airliner flights arriving at the Tokyo International Airport, the busiest airport in Japan, is analyzed using integrated Air Route Surveillance Radar information. Fuel consumption is estimated for each flight from the surveillance data using meteorological and aircraft performance data. The actual flight is compared with the optimized trajectory in terms of fuel consumption and flight time, which introduces the potential benefits of optimizing the flight. A total performance index, which comprises the fuel consumption and flight times of each flight, is proposed to optimize all flights arriving at the airport under the constraint of safe time separation at the terminal point. Dynamic programming is used to optimize not only each trajectory but also the arrival time assignment, where arrival times are assigned to each flight to minimize the total performance index. The results show that a rational arrival sequence and time assignment are generated by the optimization method, and the potential benefit deterioration due to imposing the arrival time separation constraint is limited. Keywords-arrival management; arrival time assignment; potential benefit; trajectory optimization
Effects of gloss type on text recall and incidental vocabulary learning in mobile-assisted L2 listening

Abstract This study investigates the effects of multimedia glosses on text recall and incidental vocabulary learning in a mobile-assisted L2 listening task. A total of 88 participants with a low level of proficiency in English were randomly assigned to one of four conditions that involved single channel (textual-only, pictorial-only) and dual-channel (textual-plus-pictorial) glosses as well as a control condition where no glosses were provided. The participants listened to a story through their mobile phones and were engaged in an immediate free recall task and unannounced vocabulary tests after listening. The findings indicated that access to glosses facilitated recognition and production of vocabulary with the type of gloss having no effect. On the other hand, glosses had no effect on text recall.
Chapter 2 Intonation of pronominal subjects in Porteño Spanish : Analysis of spontaneous speech

Based on spontaneous data obtained in face-to-face free conversations, the present paper discusses the impact different information-structural functions have on intonational realizations of pronominal subjects (PS) in BuenosAires Spanish (Porteño). The study applies the Spanish ToBI labeling system and examines its applicability to spontaneous speech. One of the questions addressed is whether PS with different functions have clear phonological correlates. It will be shown that intonation plays an important role in distinguishing topics from focus, but not in the interpretation of different types of topics. By means of an acoustic-phonetic analysis, the research also demonstrates that overt PS are not always emphatic or contrastive, as commonly asserted in previous, mostly theoretical, studies. Despite a high degree of variability found in the data, the paper argues for the need to use spontaneous material as well as further laboratory phonology techniques in the study of grammatical variation.
INFLUENCE OF SOIL NONLINEARITY AND LIQUEFACTION ON DYNAMIC RESPONSE OF PILE GROUPS

Nonlinearity of soil media plays an important role in soil-pile interaction problems. Moreover, during strong excitations, the soil may liquefy and this drastically affects the response of pile groups. In this study, a simple elasto-plastic constitutive model (the Drucker-Prager soil model) has been used to investigate the effect of soil nonlinearity in a three-dimensional soil-pile system. The model for the soil-pile system is idealized using 3-D finite elements. Kelvin elements are used at the boundary, simulating radiation conditions at infinity. The model and process of computation is verified using established results in literature. To investigate the effect of liquefaction, effective stress approach has been adopted. The soil medium has been considered undrained so that the pore pressure may develop. The study has been performed for single pile as well as for pile groups. First, the effect of nonlinearity on the dynamic stiffness of the soil-pile system has been examined. Next the study has been extended for probable liquefaction in the soil medium.
The Relationship between Emotional Intelligence and Coping Styles Against Stress Among Nurses

 Abstract — Emotional intelligence is commonly understood as one’s ability to recognize own emotions and the emotions of others, and to use this understanding to successfully navigate important interactions. By means of emotional intelligence, thoughts will be facilitated and it could link excitement and cognitive abilities. This study is intended to investigate the relation between emotional intelligence and coping style against stress among nurses working in the hospitals of Kermanshah city in Iran. The study is cross correlation. The samples included 350 male and female nurses working in hospitals of Kermanshah city in Iran who were selected by quota. For data collection the questionnaires of demographic information, emotional intelligence, and coping styles were utilized. Data were analyzed by using SPSS version 12. The results showed that there was a meaningful statistical difference between the nurses' emotional intelligence and coping styles (P<0.001). the score of emotional intelligence in nurses with problem-focused coping style was more than the ones who uses emotional-focused and avoidance coping style. There was a meaningful statistical difference between emotional intelligence and demographic information such as age, level of education, and taking tranquilizers (P<0.001). However, there was no meaningful statistical difference between emotional intelligence and gender, work experience, location of work, marital status and shift work. The results showed that emotional intelligence could increase the application of problem-focused coping style against stress. Therefore, it is recommended to use emotional intelligence as a training priority in both academic and clinical fields in order to increase the utilizing of problem-focused coping style.
A CROSS-CULTURAL COMPARISON OF LEARNING STYLE AND SELF-EFFICACY BETWEEN EMPLOYEES IN JAPAN AND INDONESIA

This study compared Japanese and Indonesian employees with regard to learning style and two types of self-efficacy beliefs: training and skill utilization self-efficacy. It also examined how learning style affects each of these self-efficacy beliefs after controlling for the demographic characteristics of age, gender, and country. We relied on Kolb’s experiential learning theory and Bandura’s self-efficacy paradigm in theorizing a relationship between learning style and efficacy beliefs. Participants included 801 employees who worked for an automobile parts maker: 398 from Japan and 403 from Indonesia. Results of the independent t-test revealed significant differences in learning style and the two specific efficacy beliefs. Japanese employees were more concrete and active than Indonesian employees and were more likely to possess a diverging learning style, which consists of concrete experience and reflective observation. In contrast, Indonesian employees were more likely to have an assimilating learning style, which is composed of abstract conceptualization and reflective observation. Indonesian employees showed a higher level of the two self-efficacy beliefs, suggesting that Indonesian employees tend to have more self-confidence in specific situations of training and skill utilization. Results of regression analysis revealed that the two learning style variables of concrete experience vs. abstract conceptualization and reflective observation vs. active experimentation were significantly related to training and skill utilization self-efficacy beliefs. Specifically, those who learn by thinking tended to possess a higher level of the two self-efficacy beliefs than those who learn by feeling. Similarly, those who learn by acting were likely to have a higher level of the self-efficacy beliefs than those who learn by reflecting. This study confirms that learning style and self-efficacy differ by country. It also supports the perspective that learning style relates to self-efficacy beliefs, especially concerning specific situations. We discuss the implications of these results.
Electroludic Experience-Integrating Narrative To Digital Game Design

A common dilemma in the digital gaming media is the conciliation between rigid narrative structures and player control over the virtual world. Based on this issue, the following paper proposes an evolution to the MDA Model [11], integrating narrative to the process of designing a digital game. Thus, as part of the evolutionary process of any media, this research agrees that the development of formal elements is important for media consolidation. For this, this research provides a theoretical basis on narratives, narratives in games, the conflict between dramatization and agency and concludes designing a new framework model.
A P ] 1 3 M ay 2 01 9 FEFFERMAN ’ S INEQUALITY AND UNIQUE CONTINUATION PROPERTY OF ELLIPTIC PARTIAL DIFFERENTIAL EQUATIONS

Abstract. In this paper we prove a Fefferman’s inequality for potentials belonging to a generalized Morrey space L and a Stummel class S̃α,p. Our result extends the previous Fefferman’s inequality that was obtained in [3, 7] for the case of Morrey spaces, and that in [22] for the case of Stummel classes, which was restated recently in [1]. Using this inequality, we prove a strong unique continuation property of a second order elliptic partial differential equation that generalizes the result in [1] and [22].
Multi‐band Approach to Deep Learning‐Based Artificial Stereo Extension

In this paper, an artificial stereo extension method that creates stereophonic sound from a mono sound source is proposed. The proposed method first trains deep neural networks (DNNs) that model the nonlinear relationship between the dominant and residual signals of the stereo channel. In the training stage, the band‐wise log spectral magnitude and unwrapped phase of both the dominant and residual signals are utilized to model the nonlinearities of each sub‐band through deep architecture. From that point, stereo extension is conducted by estimating the residual signal that corresponds to the input mono channel signal with the trained DNN model in a sub‐band domain. The performance of the proposed method was evaluated using a log spectral distortion (LSD) measure and multiple stimuli with a hidden reference and anchor (MUSHRA) test. The results showed that the proposed method provided a lower LSD and higher MUSHRA score than conventional methods that use hidden Markov models and DNN with full‐band processing.
Profile of mathematical reasoning ability of 8th grade students seen from communicational ability, basic skills, connection, and logical thinking

This research aims to understand the students’ weaknesses in mathematical reasoning ability in junior secondary school. A set of multiple choice tests were used to measure this ability involve components mathematical communication, basic skills, connection, and logical thinking. A total of 259 respondents were determined by stratified cluster random sampling. Data were analyzed using one-way Anova test with Fobs = 109.5760 and F = 3.0000. The results show that students’ ability from schools with high National Exam in mathematics category was the best and followed by medium and low category. Mathematical connection is the most difficult component performed by students. In addition, most students also have difficulty in expressing ideas and developing logical arguments.
Chapter 5 Decentralized Model Predictive Control

Decentralized and distributed model predictive control (DMPC) addresses the problem of controlling a multivariable dynamical process, composed by several interacting subsystems and subject to constraints, in a computation and communication efficient way. Compared to a centralized MPC setup, where a global optimal control problem must be solved on-line with respect to all actuator commands given the entire set of states, in DMPC the control problem is divided into a set of local MPCs of smaller size, that cooperate by communicating each other a certain information set, such as local state measurements, local decisions, optimal local predictions. Each controller is based on a partial (local) model of the overall dynamics, possibly neglecting existing dynamical interactions. The global performance objective is suitably mapped into a local objective for each of the local MPC problems. This chapter surveys some of the main contributions to DMPC, with an emphasis on a method developed by the authors, by illustrating the ideas on motivating examples. Some novel ideas to address the problem of hierarchical MPC design are also included in the chapter.
CHALLENGES OF ONLINE BANKING

The research presented here is the first part of my ongoing study examining the main challenges of online banking, focusing particularly on cyber security and related issues. The Technology Acceptance Model (TAM) and its main features of perceived usefulness and usage intentions are put to a test. It also uses the modified TAM model to further test the adaptation of online banking by different generations of users. The present research contributes to the existing literature on the subject and enhances knowledge and understanding of the users’ behaviour from a generational perspective.
Deep learning approach to hologram based cellular classification

With the rise of data intensive classification algorithms the need for high throughput imagery methods has increased. Lens-free imagery provides a new high throughput technique for imaging cells through hologram measurements. One acquisition of a Petri dish can provide between one thousand and ten thousand samples which do not need to be annotated if the biological properties of the Petri dish are known. Previously, hologram classification was addressed using feature extraction and non-linear classifier. In this work a deep learning approach to cellular classification using holograms is introduced. Because deep learning approaches do not require hand tailored features they are quicker to develop and the framework is easier to generalize to other hologram classification tasks. A dataset containing alive and dead cell holograms was used to judge the feasibility of the approach. Although the deep learning classifier was successful in classifying simulated holograms (over 97% test accuracy), the experimental dataset showed some key flaws which limited test performance. In an effort to improve deep learning approach the necessary improvements to create a better experimental dataset suited for deep learning have also been identified.