Reviewerly/exHarmony huggingface dataset:
The Effect of Transformational Leadership on Innovation: Evidence from Lebanese Banks

Purpose: Transformational leadership (TL) has been recognized as one of the most important factor influencing innovation. It is argued that this style plays an essential role in developing the process, structure and climate for organizations to become innovative. This research aims to examine the impact of TL on two aspects of innovation namely product and process innovation. Design/Methodology/Approach: The quantitative and explanatory analysis was taken by using the Structural equations modeling (SEM) with AMOS 20 to examine the relationship between TL and innovation. Research data were collected through a survey method. The sample result was determined by the probability stratiﬁed sampling technique of about 310 employees at 27 banks in Lebanon. Findings: The ﬁndings confirmed the importance of TL in enhancing innovation in banking sector. The main implication of the research highlights that individualized consideration is the most important predictor of product and process innovation followed by, inspirational motivation and idealized influence, respectively, whereas, intellectual stimulation has insignificant influence on product and process innovation. Practical Implications: Findings point to how transformational style of leadership produce better outcomes for the banks by mobilizing employees to engage in innovative products and processes. Originality/Value: These ﬁndings extends the understanding of the processes through which transformational styles of leadership stimulate innovation, and also highlight the beneﬁts gained by cultivating more transformational styles of leadership to generate more innovative outcomes.
Deep Mutual Encode Model for Network Embedding From Structural Identity

Network Embedding (NE) is one of the most popular learning methods in complex networks.It aims at learning the low-dimensional representations of nodes in networks and has been applied in a variety of network analytic tasks.Most existing methods of NE are designed by merely using the local, high-order or global proximity to preserve the network structure; hence they are incapable of fully capturing the structural identity of nodes, which is a concept of symmetry defined by the network structure and their relationship to other nodes.There are two limitations to existing NE models.First, the local and global node dependency information is not considered simultaneously.Second, there is no adequate framework that can reveal the role property of each node.In this paper, we propose an intuitive and unified deep learning framework named DMER, short for Deep Mutual Encode for Embedding, to learn node embeddings from structural identity.In our model, Graph Convolution Network (GCN) is adopted to model the dependency relations between nodes from a global perspective.An Auto-Encoder (AE) framework is proposed to reconstruct the features of nodes, and it can conclusively reveal the structural identity from network structure.By integrating the GCN and AE components with a shared and constrained mechanism, the proposed model implements mutual enhancement for node embedding from structural identity.Experimental results based on structural role classification and visualization demonstrate that our model achieves better performance compared with the state-of-the-art methods.
A closer look at the approximation capabilities of neural networks

The universal approximation theorem, in one of its most general versions, says that if we consider only continuous activation functions σ, then a standard feedforward neural network with one hidden layer is able to approximate any continuous multivariate function f to any given approximation threshold e, if and only if σ is non-polynomial. In this paper, we give a direct algebraic proof of the theorem. Furthermore we shall explicitly quantify the number of hidden units required for approximation. Specifically, if X in R^n is compact, then a neural network with n input units, m output units, and a single hidden layer with {n+d choose d} hidden units (independent of m and e), can uniformly approximate any polynomial function f:X -> R^m whose total degree is at most d for each of its m coordinate functions. In the general case that f is any continuous function, we show there exists some N in O(e^{-n}) (independent of m), such that N hidden units would suffice to approximate f. We also show that this uniform approximation property (UAP) still holds even under seemingly strong conditions imposed on the weights. We highlight several consequences: (i) For any δ > 0, the UAP still holds if we restrict all non-bias weights w in the last layer to satisfy |w| 0 (depending only on f and σ), such that the UAP still holds if we restrict all non-bias weights w in the first layer to satisfy |w|>λ. (iii) If the non-bias weights in the first layer are *fixed* and randomly chosen from a suitable range, then the UAP holds with probability 1.
SURVEY OF THE CITIZENS DIGITAL SKILLS IN THE REGION

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 7127-7132ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.1691Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Air Surveillance Planning Approach for Large Areas

We consider an area we would like to scan using drones that can perform air surveillance, the area is too large for one drone in a mission can reach and scan. In our approach, the environment is modulated in a grid view, and each node (so called point) has a given priority, this priority define the importance of that point (the need to reach that point). The problem is processed as two sub-problems, the first part focuses on dividing the area to smaller zones which the drone can perform in a mission while meeting the required constraints. The drone has a limit energy, so a limit trajectory to perform. In the second part of the problem, we should find the best (optimal) trajectory for each zone, starting from a specific point and returning to it. The approach we present aim to maximize the gain (priorities sum) of the trajectory in each group of points that belongs to a part of the surveillance area, with the limit budget we have. Up next we detail the problem and discuss the performance of the approach.
Determinants of Micro and Small Enterprises Growth in Selected Towns of West Shoa Zone, Oromia Regional State, Ethiopia

MSEs have an important contribution to economic growth and employment creation in Ethiopia. However, due to different bottlenecks that hinder the growth, MSEs found in these three towns were not grown as it was expected. Therefore, this study aimed at identifying the major determinants of MSEs growth in West Shoa Zone, Oromia region, Ethiopia. In the study, both qualitative and quantitative research methods were used. Primary data was obtained using questionnaires. Stratified sampling was used to select proportional number of samples from the study area. The study used Chi-square and logistic regression to measure the association and determine growth probability of MSEs, respectively. The result of the study revealed, Entrepreneurial competency, managerial skills, market access, innovation, high initial investment size, access to finance and manufacturing sector variables were identified as a major determinant of growth of the MSEs in the study area. MSEs Owner/manager characteristics such as gender and age were found not to have an influence on the growth of the enterprises as the descriptive result showed. The probability of growth of MSEs were positively influenced by entrepreneurship competency, management skill, market access, initial investment size, and sector in manufacturing; however, getting access to finance and engaging in innovation activity have negative impact on the growth probability of MSEs based on the binary logit model result. The variables included in the model explain about 73.38% of the influences on the MSEs Growth so as, the model is fit. The study recommends that proper understanding of these factors constitutes an essential starting point and important for the Owner/manager of MSEs, government and non-governmental organizations to formulate policies and strategies in order to reduce unemployment, poverty and income inequality thereby promote MSEs and their growth in the country and particularly in West Shoa Zone.
A Hybrid Reversible Data Hiding for Multiple Images With High Embedding Capacity

This paper proposed a high capacity reversible data hiding (RDH) scheme using three steganographic images, on which three successive embedding phases are performed. An embedding rule is elaborately designed to guide the data embedding and three alike stego-images are generated in the first phase. In view of the similarity of three stego-images, the pixel value ordering (PVO) strategy is utilized in the second phase. In the third phase, the prediction error histogram shifting (PEHS) strategy is employed by exploiting the slight difference among three temporary stego-images. Since no complex operation is involved, the algorithm is efficient. The experimental results verify that our method is superior to other schemes in both EC and image quality. Moreover, the proposed scheme can successfully resist the RS steganalysis and provide a secure secret transmission.
Image Captioning in Turkish Language

Image captioning is one of the everlasting challenging tasks in the field of artificial intelligence that requires computer vision and natural language processing. Plenty of salient works have been proposed throughout the time for English language however, the number of studies in Turkish language is still too limited. This paper couples an encoder CNN -the component that is responsible for extracting the features of the given images-, with a decoder RNN -the component that is responsible for generating captions using the given inputs-to generate Turkish captions within human gold-standards. We conducted the experiments using the most common evaluation metrics such as BLEU, METEOR, ROUGE and CIDEr. Results show that the performance of the proposed model is satisfactory in both qualitative and quantitatively evaluations. A Web App is already deployed to allow volunteers to contribute to the improvements of the Turkish captioned dataset.
ACTOR-CRITIC REINFORCEMENT LEARNING FOR ENERGY OPTIMIZATION IN HYBRID PRODUCTION ENVIRONMENTS

This paper presents a centralized approach for energy optimization in large scale industrial production systems based on an actor-critic reinforcement learning (ACRL) framework. The objective of the on-line capable self-learning algorithm is the optimization of the energy consumption of a production process while meeting certain manufacturing constraints like a demanded throughput. Our centralized ACRL algorithm works with two artificial neural networks (ANN) for function approximation using Gaussian radial-basis functions (RBF), one for the critic and another for the actor, respectively. This kind of actorcritic design enables the handling of both, a discrete and continuous state and action space, which is essential for hybrid systems where discrete and continuous actuator behavior is combined. The ACRL algorithm is exemplary validated on a dynamic simulation model of a bulk good system for the task of supplying bulk good to a subsequent dosing section while consuming as low energy as possible. The simulation results clearly show the applicability and capability of our machine learning (ML) approach for energy optimization in hybrid production environments.
Control of a Complex of Works in Multiprocessor Real-Time ACS

Mathematical models of scheduling in real-time ACS are considered. Effective algorithms for scheduling in multiprocessor real-time automation are offered. For intermittent works heuristic algorithms are proposed. For uninterrupted work accurate pseudopolynomial algorithm is given. The synthesis problem is considered. A brief overview of a number of generalizations of the considered problems is given. The possibilities of application of the obtained results are indicated.
Semiotic Analysis of the Visual Signs of Protest on Online Jordanian Platforms: Code Choice and Language Mobility

The political discourse of protesting which comprises carrying signs for clarifying demands and expressing feelings constitutes a significant area of study in the signs of online platforms within the linguistic landscape field. Taking as a case in point the Jordanian protest on May 30, 2018, a few examples of the signs of protest are analyzed using some aspects of visual semiotics, particularly the code choice. The study is grounded on both quantitative and qualitative data culled from online sources. The analysis of the data finds a variety of linguistic codes used in attaining different readerships: the standard form of Arabic as the official language in the country and in other Arab countries; Jordanian Arabic investigated as the device of speaking out the voice of the local audience; English viewed as the language of addressing the global audience; and the multilingualism occurrence as a significant feature in the corpus for achieving further readerships. These codes are largely motivated by other significant semiotic resources, including multimodality, font size, color relevance, and materiality practices. The study further views the signs of protest as a new trend of mobility, often considered a challenging notion to the territoriality of fixed signs in most linguistic landscape studies.
Enabling Domain Expertise in Scientific Visualization With CinemaScience

Scientific users present unique challenges to visualization researchers. Their high-level tasks require them to apply domain-specific expertise. We introduce a broader audience to the CinemaScience project and demonstrate how CinemaScience enables efficient visualization workflows that can bring in scientist expertise and drive scientific insight.
Blind Data Detection With Unknown Channel Coding

Blind data detection is widely investigated since it finds many crucial applications in the interference cancelation, cognitive radio, and military interception, etc. In this letter, we propose a blind iterative algorithm that simultaneously improves the performance of channel coding identification and data detection in the communication system with the unknown channel coding and the unknown fading channels. First, the likelihood-based channel estimator and the soft detector and regenerator are adopted in an iterative manner, which estimates the unknown parameters and detects the information bits. Then, an average log-likelihood ratio (LLR) classifier is adopted to identify the unknown channel coding and determine the information bits. Numerical results show that the proposed algorithm outperforms the existing scheme in terms of correct identification and data detection.
CACTUS: cancer image annotating, calibrating, testing, understanding and sharing in breast cancer histopathology

Develop CACTUS (cancer image annotating, calibrating, testing, understanding and sharing) as a novel web application for image archiving, annotation, grading, distribution, networking and evaluation. This helps pathologists to avoid unintended mistakes leading to quality assurance, teaching and evaluation in anatomical pathology. Effectiveness of the tool has been demonstrated by assessing pathologists performance in the grading of breast carcinoma and by comparing inter/intra-observer assessment of grading criteria amongst pathologists reviewing digital breast cancer images. Reproducibility has been assessed by inter-observer (kappa statistics) and intra-observer (intraclass correlation coefficient) concordance rates.CACTUS has been evaluated using a surgical pathology application-the assessment of breast cancer grade. We used CACTUS to present standardized images to four pathologists of differing experience. They were asked to evaluate all images to determine their assessment of Nottingham grade of a series of breast carcinoma cases. For each image, they were asked for their overall grade impression. CACTUS helps and guides pathologists to improve disease diagnosis with higher confidence and thereby reduces their workload and bias. CACTUS can be useful for both disseminating anatomical pathology images for teaching, as well as for evaluating agreement amongst pathologists or against a gold standard for evaluation or quality assurance.
Scene Mapping-based Video Registration Using Frame Similarity Measurement and Feature Tracking

Video registration is a technique that obtains pairs of frames showing the same scene to synchronize two videos. This paper presents a video registration algorithm based on scene mapping using frame similarity measurement and feature point tracking. The proposed algorithm consists of four steps: i) frame-to-frame matching based on similarity measurement to set the start frame, ii) feature tracking using feature points of the start frame to obtain a candidate matching scene, iii) end-frame matching for scene mapping, and iv) synchronization of two videos using a linear equation. In the proposed algorithm, frame-to-frame matching is performed using traditional feature detection and matching. Additionally, the proposed algorithm measures the similarity between frames to register videos with the most similar structural information. Feature point tracking is performed to track and collect similar structures in a candidate scene. We demonstrate that the proposed algorithm can synchronize frames from registered videos for some experiments. Therefore, the proposed algorithm can be applied to various areas.
PROFESSIONAL DEVELOPMENT OF MEDICAL UNIVERSITY ACADEMIC STAFF: EXPERIENCE OF LEARNING PLATFORM MOODLECLOUD APPLICATION

The article outlines the main directions of research of domestic and foreign scholars regarding the problem of cloud technologies in higher education. The technical characteristics, psychological, pedagogical and didactic conditions for using the MoodleCloud learning platform for the professional development of medical university academic staff are determined. Findings from this study include information about the international quality standards of e-learning and online courses for the professional development of educators (UNIQUE і ECB CHECK; the Quality Matters Continuing &amp; Professional ED Rubric Standards, the USA etc.). The article describes the experience of Donetsk National Medical University concerning implementation of MoodleCloud learning platform in the professional development of academic staff. The emphasis is on the main components of E-learning content «Pedagogy of a higher medical school», which are characterised in the article: simple learning resources, interactive e-lessons, electronic simulations and job aids. The possibilities of cloud hosting for the professional development of teachers of higher medical educational institutions are analyzed on the example of the program «Pedagogy of a higher medical school» developed on the Moodle learning platform. The quality of an e-learning course is enhanced by: learner-centred content, granularity, engaging content, interactivity, personalization. The audit parameters (quality criteria) of the academic staff development program on the Moodle learning platform are presented: information about organization of program; target audience orientation; quality of content; program course design; media Design; technology and evaluation &amp; review). From the material presented in this paper, the following theme may require further attention: examining international quality frameworks and standards in E-Learning Systems.
WebIDE Cloud Server Resource Allocation With Task Pre-Scheduling in IoT Application Development

WebIDE is leveraged for IoT application development, which could adapt to the rapid growth of IoT applications and meanwhile facilitate the rapid development.Resource allocation is of vital significance in the WebIDE cloud service system.Existing resource allocation approaches may encounter issues such as unbalanced resource assignments, which could lead to the reduced system resource utilization or extended system response time.Existing methods are typically on the basis of predetermined resource demands for each task, and not applicable to the case that the resource demands are dynamic and unknown.This article predicts the tasks to be performed by the WebIDE cloud service through task pre-scheduling, and then applies the existing resource allocation methods.Firstly, all tasks are classified, based on the execution state, execution operations and WebIDE cloud server resource requirements.Secondly, the grouped tasks are mapped to different system states, with the Markov state transition probability matrix leveraged to model the transition probability between tasks, followed by the prediction model constructed.Finally, integrating task pre-scheduling with ant colony algorithm, WebIDE cloud server resource allocation is carried out.Experiment results show that adding the task prediction model could significantly not only reduce the task response time, but also improve the cloud server resource utilization.
Applications and Use Cases for the Quantum Internet

The Quantum Internet has the potential to improve Internet applicationfunctionality by incorporating quantum information technology into theinfrastructure of the overall Internet. In this document, we providean overview of some applications expected to be used on the QuantumInternet, and then categorize them using various classificationschemes. The intent of this document is to provide a commonunderstanding and framework of applications and use cases for theQuantum Internet.
On the Cryptographic Hardness of Local Search

We show new hardness results for the class of Polynomial Local Search problems (PLS): - Hardness of PLS based on a falsifiable assumption on bilinear groups introduced by Kalai, Paneth, and Yang (STOC 2019), and the Exponential Time Hypothesis for randomized algorithms. Previous standard model constructions relied on non-falsifiable and non-standard assumptions. - Hardness of PLS relative to random oracles. The construction is essentially different than previous constructions, and in particular is unconditionally secure. The construction also demonstrates the hardness of parallelizing local search. The core observation behind the results is that the unique proofs property of incrementally-verifiable computations previously used to demonstrate hardness in PLS can be traded with a simple incremental completeness property.
Methodology for the Selection and Sizing of an Isolated MicroGrid Based on Economic Criteria

The present work proposes a methodology for the selection and sizing of rural microgrids in small communities improving the traditional solution of the generator group with the incorporation of renewable energies. The methodology includes four stages, the data collection, the definition of the system, the optimization and the selection of the best option. The HOMER Pro software is used to perform economic optimization. Finally, the rural school of Santa Barbara in the Puertas del Sol area in the department of San Martin of the Province of San Luis, Argentina, is presented as a case study.
Cleveree: an artificially intelligent web service for Jacob voice chatbot

Jacob is a voice chatbot that use Wit.ai to get the context of the question and give an answer based on that context. However, Jacob has no variation in answer and could not recognize the context well if it has not been learned previously by the Wit.ai. Thus, this paper proposes two features of artificial intelligence (AI) built as a web service: the paraphrase of answers using the Stacked Residual LSTM model and the question summarization using Cosine Similarity with pre-trained Word2Vec and TextRank algorithm. These two features are novel designs that are tailored to Jacob, this AI module is called Cleveree. The evaluation of Cleveree is carried out using the technology acceptance model (TAM) method and interview with Jacob admins. The results show that 79.17% of respondents strongly agree that both features are useful and 72.57% of respondents strongly agree that both features are easy to use.