Reviewerly/exHarmony huggingface dataset:
The impact of big-5 model leadership traits on team entrepreneurship: An empirical study of small businesses in the UAE

Entrepreneurs who are operating small businesses focus on faster growth of their firms through greater wealth creation.This wealth creation process requires skill and investment in terms of time and capital.However, the success or failure of entrepreneurial teams depends on personality differences among the entrepreneurs.In this context, the present study explores the impact of the Big 5 model traits on the team entrepreneurship and the performance of the ventures among the small businesses in the UAE.Further, the study encapsulates how these personality traits can help in solving business problems and achieving long term success for the business.In this regard, the researcher conducted a survey on 49 employees working in 5 entrepreneurial businesses in UAE.The findings of the study highlighted that sub-factors of big five leadership traits broad-minded, gregarious, assertive, responsible, anxious and emotionally depressed, significantly impact the team entrepreneurship of small businesses in the UAE, aiding in increased customer base, venture skills, stock values, and innovating of new products.
On the Estimation of Mutual Information

In this paper we focus on the estimation of mutual information from finite samples ( X &times; Y ) . The main concern with estimations of mutual information (MI) is their robustness under the class of transformations for which it remains invariant: i.e., type I (coordinate transformations), III (marginalizations) and special cases of type IV (embeddings, products). Estimators which fail to meet these standards are not robust in their general applicability. Since most machine learning tasks employ transformations which belong to the classes referenced in part I, the mutual information can tell us which transformations are most optimal. There are several classes of estimation methods in the literature, such as non-parametric estimators like the one developed by Kraskov et al., and its improved versions. These estimators are extremely useful, since they rely only on the geometry of the underlying sample, and circumvent estimating the probability distribution itself. We explore the robustness of this family of estimators in the context of our design criteria.
FuzzAttention on Session-based Recommender System

mender system is studied widely and has been implemented successfully into businesses and daily lives. Its primary purpose is to analyze user behaviors to predict the next item of interest. To enrich user information, a session-based recommender system considers the user's historical records in the session. This information includes the user's general and current interests as additional information to improve the performance of the model such that the next item can be recommended easily. However, the recommender system not only considers the prediction performance but also regards the interpretability as a major target. For enhancing the interpretability and performance, we propose a novel mechanism, i.e., FuzzAttention, based on the attention mechanism that is applied widely to deep-learning models. In FuzzAttention, we utilize a fuzzy neural network to build a fuzzy inference system; therefore, we adopt joint learning to learn the parameters in the session-based recommendation and the fuzzy neural network jointly. In the experiments, we used two types of session-based recommender systems and conducted them on two datasets including the session-based information to compare the model performance based on the traditional attention mechanism and FuzzAttention. The results indicate that our proposed mechanism can improve the performance to predict the next item and the model's interpretability.
Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network

Traditional brain network methods usually focus on either functional connectivity (FC) or structural connectivity (SC) for describing node interactions and only consider the interaction between paired network nodes. Therefore, the underlying relationship between FC and SC, as well as the complicated interactions among network nodes, has not been sufficiently studied and fully utilized to discover disease-related biomarkers. To tackle these problems, we propose a Diffusion-Convolution-Bilinear Neural Network (DCB-NN) framework for brain network analysis, which couples FC and SC seamlessly and considers wider interactions among network nodes. Specifically, a brain network model (graph) is first defined, whose edges are determined by neural fiber physical connections extracted from DTI and node features are governed by brain activities extracted from fMRI. Then, based on this model, we build two DCB modules to extract multi-scale features from this brain network. Each DCB module consists of diffusion, convolution and bilinear pooling. Through diffusion guided by physical connections, the network node features not only reflect the activities in their corresponding brain regions, but also are influenced by the activities from other brain regions. These enhanced node features are nonlinearly weighed through 1-D convolution, and their second-order statistics are further extracted by bilinear pooling for disease prediction. In order to capture node interactions at multi-scale, we include two DCB modules, corresponding to one-step and two-step diffusions, respectively. The whole model is trained in an end-to-end way. Experiments on a real epilepsy dataset demonstrate the effectiveness and advantages of our proposed method.
CVABS: moving object segmentation with common vector approach for videos

Background modelling is a fundamental step for several real‐time computer vision applications that requires security systems and monitoring. An accurate background model helps to detect the activity of moving objects in the video. In this work, the authors have developed a new subspace‐based background‐modelling algorithm using the concept of common vector approach (CVA) with Gram–Schmidt orthogonalisation. Once the background model that involves the common characteristic of different views corresponding to the same scene is acquired, a smart foreground detection and background updating procedure is applied based on dynamic control parameters. A variety of experiments is conducted on different problem types related to dynamic backgrounds. Several types of metrics are utilised as objective measures and the obtained visual results are judged subjectively. It was observed that the proposed method stands successfully for all problem types reported on CDNet2014 dataset by updating the background frames with a self‐learning feedback mechanism.
Zero-shot transfer for implicit discourse relation classification

Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation.It becomes even more challenging by the fact that annotated training data exists only for a small number of languages, such as English and Chinese.We present a new system using zero-shot transfer learning for implicit discourse relation classification, where the only resource used for the target language is unannotated parallel text.This system is evaluated on the discourse-annotated TED-MDB parallel corpus, where it obtains good results for all seven languages using only English training data.
Radiation View Factor for Building Applications: Comparison of Computation Environments

Computation of view factors is required in several building engineering applications where radiative exchange takes place between surfaces such as ground and vertical walls or ground and sloping thermal or photovoltaics collectors. In this paper, view factor computations are performed for bifacial solar photovoltaic (PV) collectors based on the finite element method (FEM) using two programming languages known as Microsoft Excel-Visual Basic for Applications (VBA) and Python. The aim is to determine the computer response time as well as the performance of the two languages in terms of accuracy and convergence of the numerical solution. To run the simulations in Python, an open source just-in-time (JIT) compiler called Numba was used and the same program was also run as a macro in VBA. It was observed that the simulation response time significantly decreased in Python when compared to VBA. This decrease in time was due to the increase in the total number of iterations from 400 million to 250 billion for a given case. Results demonstrated that Python was 71–180 times faster than VBA and, therefore, offers a better programming platform for the view factor analysis and modelling of bifacial solar PV where computation time is a significant modelling challenge.
PQtutor, a quasi-intelligent tutoring system for quantitative problems in General Chemistry

Abstract The best tutors give a student the appropriate amount of guidance necessary for learning while helping the student stay confident, motivated and focused. So-called intelligent tutoring systems, trying to replicate the discipline-specific and the psychological dimensions of expert human tutoring, require enormous investments and are not accessible to the larger student population. PQtutor (physical quantities tutor) is a free online tutor designed to help students work out homework problems closely related to worked examples. The software is an extension of a free online calculator for science learners and uses problems from an open (free) textbook, making PQtutor accessible in terms of both technology and cost. PQtutor works by comparing student input to a model answer in order to generate prompts for finding a path to the solution and for correcting mistakes. The feedback is in the form of questions from a virtual study group suggesting problem-solving moves such as accessing relevant content knowledge, reviewing worked examples, or reflection on what their answer means. In cases where these moves have been exhausted but the problem remains unsolved, the tutoring system suggests seeking intelligent human help.
ITC: Infused Tangential Curves for Smooth 2D and 3D Navigation of Mobile Robots

Navigation is an indispensable component of ground and aerial mobile robots. Although there is a plethora of path planning algorithms, most of them generate paths that are not smooth and have angular turns. In many cases, it is not feasible for the robots to execute these sharp turns, and a smooth trajectory is desired. We present 'ITC: Infused Tangential Curves' which can generate smooth trajectories for mobile robots. The main characteristics of the proposed ITC algorithm are: (1) The curves are tangential to the path, thus maintaining G 1 continuity, (2) The curves are infused in the original global path to smooth out the turns, (3) The straight segments of the global path are kept straight and only the sharp turns are smoothed, (4) Safety is embedded in the ITC trajectories and robots are guaranteed to maintain a safe distance from the obstacles, (5) The curvature of ITC curves can easily be controlled and smooth trajectories can be generated in real-time, (6) The ITC algorithm smooths the global path on a part-by-part basis thus local smoothing at one point does not affect the global path. We compare the proposed ITC algorithm with traditional interpolation based trajectory smoothing algorithms. Results show that, in case of mobile navigation in narrow corridors, ITC paths maintain a safe distance from both walls, and are easy to generate in real-time. We test the algorithm in complex scenarios to generate curves of different curvatures, while maintaining different safety thresholds from obstacles in vicinity. We mathematically discuss smooth trajectory generation for both 2D navigation of ground robots, and 3D navigation of aerial robots. We also test the algorithm in real environments with actual robots in a complex scenario of multi-robot collision avoidance. Results show that the ITC algorithm can be generated quickly and is suitable for real-world scenarios of collision avoidance in narrow corridors.
On a Chatbot Conducting Dialogue-in-Dialogue

We demo a chatbot that delivers content in the form of virtual dialogues automatically produced from plain texts extracted and selected from documents. This virtual dialogue content is provided in the form of answers derived from the found and selected documents split into fragments, and questions are automatically generated for these answers.
Improving GAN-Based Calligraphy Character Generation using Graph Matching

The Chinese character generation with specific style is the key of personal calligraphy font generation. In recent years, with the development of artificial intelligence, researchers have used deep learning to solve the calligraphy generation problem, which can automatically generate personal calligraphy fonts. However, the end-to-end approaches possibly generate wrong results in terms of glyph structure of Chinese character because of lack of constraints on glyph structures. This paper proposes a method that adds constraints of glyph structure to deep neural network in order to improve the correctness of generation. This paper represents the glyph structure of Chinese character with glyph nodes extracted by object detection method. We compute the glyph structure loss between inputs and the generated results by the deep learning-based graph matching method. Experiments show that our method significantly increases the accuracy of Chinese character generation. For showing our method more clearly, we use the print as the original style samples in this paper.
Phoenixmap: An Abstract Approach to Visualize 2D Spatial Distributions

The multidimensional nature of spatial data poses a challenge for visualization. In this paper, we introduce Phoenixmap, a simple abstract visualization method to address the issue of visualizing multiple spatial distributions at once. The Phoenixmap approach starts by identifying the enclosed outline of the point collection, then assigns different widths to outline segments according to the segments' corresponding inside regions. Thus, one 2D distribution is represented as an outline with varied thicknesses. Phoenixmap is capable of overlaying multiple outlines and comparing them across categories of objects in a 2D space. We chose heatmap as a benchmark spatial visualization method and conducted user studies to compare performances among Phoenixmap, heatmap, and dot distribution map. Based on the analysis and participant feedback, we demonstrate that Phoenixmap 1) allows users to perceive and compare spatial distribution data efficiently; 2) frees up graphics space with a concise form that can provide visualization design possibilities like overlapping; and 3) provides a good quantitative perceptual estimating capability given the proper legends. Finally, we discuss several possible applications of Phoenixmap and present one visualization of multiple species of birds' active regions in a nature preserve.
Aplikasi Sistem Pakar Untuk Mendiagnosa Gangguan Kepribadian Pada Remaja Dengan Menggunakan Metode Certainty Factor (CF)

Adolescence is a period of increased activity, maturity of sexuality, growth of awareness, and recognition that the time free from problems experienced in childhood is lost forever. So they experience emotional instability as a result of adjusting themselves to new patterns of behavior and social environment. Personality and behavior disorders in adolescents are problems faced by parents and their environment. In this study, this knowledge was obtained from experts, namely psychologists. This study uses an interview method with a Psychologist. The expert system is a system designed to assist in detecting diseases with a dynamic knowledge base. This expert system can deduce the user's illness based on the symptoms of adolescent personality disorders that are entered into the system. As for the purpose of the research is to produce an expert system to assist psychologists in documenting their knowledge. The expert system will display the amount of confidence the symptom has on the possibility of the user's illness. The amount of the trust value is the result of calculations using the Certainty Factor (CF) method. The results obtained from this study are the application of an expert system to diagnose adolescent personality disorders with the certainty factor method by using a computer as a tool to access data&#x0D; Teenagers, Adolescent Personality Disorders, Expert System, Certainty Factor
Predicting State Uncertainty for GNSS-based UAV Path Planning using Stochastic Reachability

An important component of path planning algorithms is predicting state uncertainty in order to ensure probabilistic collision-free paths. While predicting state uncertainty, path planning algorithms generally assume zero mean Gaussian distributions for motion and measurement error models. However, a zero mean Gaussian assumption is not applicable for Global Navigation Satellite System (GNSS) positioning measurements, which typically contain uncertain biases in urban areas due to multipath and non-line-of-sight (NLOS) effects. In this paper, we propose a method to predict the state uncertainty of a UAV in the presence of uncertain GNSS positioning biases using stochastic reachability analysis. We enclose all possible GNSS positioning error distributions arising due to uncertain biases with a probabilistic zonotope. For the on-board state estimation filter, we choose a Kalman filter (KF) and fix a hypothesis for the GNSS positioning error distribution. We first compute the stochastic set of estimation errors resulting from the above hypothesis. We then compute the stochastic reachable sets for a linear motion model of the UAV with linear state feedback control along the candidate path. Finally, we validate the stochastic reachability analysis by evaluating the predicted state uncertainty along candidate paths in a simulated 3D urban environment.
Short Text Mining: State of the Art and Research Opportunities

With the growing number of connected online users producing a tremendous amount of unstructured short-texts daily, understanding and mining these data becomes very useful for individuals, governments and companies for identifying the public users' attitudes towards different entities, such as products, services, events, places, organizations and topics.However, analyzing these short-texts using traditional methods becomes a significant challenge due to the shortness and sparsity nature of short-texts.To address such challenges, the literature introduced a broad spectrum of short-texts mining approaches and applications.Hence, this paper provides a comprehensive survey of this spectrum based on a criterion-based research strategy.The different mining techniques and approaches utilized in shorttexts were highlighted along with their related issues and challenges.This paper surveyed a total of 1575 research papers published in the refereed conferences and journals in the area of short-texts mining were sur-veyed from 2006 until 2017, from which 187 primary studies were included and analyzed to constitute the source of the present paper.After a careful review of these articles, it is obvious that there are research gaps in other languages than English and Chinese, multi-languages, and in specific domain studies.
Teaching Writing to EFL Student Teachers: Teachers' Intervention and No Teachers' Intervention through Peer Feedback Writing Techniques

The purpose of this study was to find out the impacts of teachers' intervention and no teachers' intervention peer feedback writing techniques (PFWT) on English as a foreign language (EFL) student teachers' essay writing ability and their perceptions of learning activities viewed on their level of writing anxiety.The study was conducted in one English education study program at an Indonesian state Islamic university.Writing tests were used to find out whether or not the treatments had the impact on student teachers' essay writing achievement while a questionnaire was used to gain student teachers' perceptions of writing activities.Two groups of student teachers taking a writing course were selected as participants.Data were analyzed by using Two Way ANOVA, Descriptive statistics analysis, and non-parametric test.The findings indicated that no teachers' intervention PFWT had a significant impact on student teachers' writing ability and the impact was influenced by the different levels of students' writing anxiety.Additionally, teachers' intervention PFWT had a significant effect on both students' having low and high writing anxiety.Finally, students having low writing anxiety perceived teachers' intervention PFWT more positively compared to those having high writing anxiety.
A Smart-hand Movement-based System to Control a Wheelchair Wirelessly

The number of elderly and disabled people worldwide has increased and their day-to-day activities depend on others' help.Improving the quality of life of these people has become the most important responsibility of society and it is the role of technology specialists to make their life as normal as possible and easy so that they can do their day-to-day activities at the right time without othersʼ help.Many researchers have proposed several solutions, but they have limitations such as poor performance and usability.In this paper, we propose a smart wirelessbased wheelchair system that completely controls the motion of a wheelchair wirelessly through hand movements to help partially quadriplegic people to perform their daily activities easily.In the proposed approach, sensors with relevant materials and technologies have been integrated with microcontrollers to capture hand movement signals and process them to the fully control the wheelchair wirelessly.An accumulator sensor is placed on the user's hand to acquire the directions of hand movements and translate them into movement commands using an Arduino microcontroller that is directly connected to the wheelchair and moves it.The proposed system has been simulated, and the obtained results show the effectiveness of the proposed system and its applicability for use by most physically disabled persons.
Upgrading a high performance computing environment for massive data processing

Abstract High-performance computing (HPC) and massive data processing (Big Data) are two trends that are beginning to converge. In that process, aspects of hardware architectures, systems support and programming paradigms are being revisited from both perspectives. This paper presents our experience on this path of convergence with the proposal of a framework that addresses some of the programming issues derived from such integration. Our contribution is the development of an integrated environment that integretes ( i ) COMPSs, a programming framework for the development and execution of parallel applications for distributed infrastructures; ( ii ) Lemonade, a data mining and analysis tool; and ( iii ) HDFS, the most widely used distributed file system for Big Data systems. To validate our framework, we used Lemonade to create COMPSs applications that access data through HDFS, and compared them with equivalent applications built with Spark, a popular Big Data framework. The results show that the HDFS integration benefits COMPSs by simplifying data access and by rearranging data transfer, reducing execution time. The integration with Lemonade facilitates COMPSs’s use and may help its popularization in the Data Science community, by providing efficient algorithm implementations for experts from the data domain that want to develop applications with a higher level abstraction.