Reviewerly/exHarmony huggingface dataset:
The Role of Knowledge Management on Competitive Advantage in Jordan Manufacturing Companies from Employees Perspectives

The study aimed to investigate the role of knowledge management on competitive advantage in Jordan manufacturing companies from employee&amp;rsquo;s perspective. The research used the descriptive analytical methodology. In addition a self administrated questionnaire was developed according to research hypothesis and objectives for the purpose of achieve the study objectives. The research sample consisted of. 255 subjects. The self administrated questionnaires were distributed over 0 research sample, 240 questionnaire were collected, therefore the research sample is 240.. All gathered data were checked and coded then analyzed by using the social Packaging statistical System (SPSS). The study concluded that there is a relationship between knowledge management and competitive advantage in Jordanian industrial companies from the point of view of administrative employee perspectives. In addition the data also concluded that here is a relationship between knowledge generation and competitive advantage. Also there is a relationship between knowledge storage and competitive advantage and there is a relationship between knowledge sharing and competitive advantage in Jordanian industrial companies. The study revealed hat there is a relationship between knowledge application and competitive advantage.... The study recommended that Jordan manufacturing companies have to encourage knowledge management use and to notify their employees with the motives behind such use for obtaining their support.
Variable Selection in Count Data Regression Model based on Firefly Algorithm

Variable selection is a very helpful procedure for improving computational speed and prediction accuracy by identifying the most important variables that related to the response variable. Count data regression modeling has received much attention in several science fields in which the Poisson and negative binomial regression models are the most basic models. Firefly algorithm is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, firefly algorithm is proposed to perform variable selection for count data regression models. Extensive simulation studies and two real data applications are conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. Further, its performance is compared with other methods. The results proved the efficiency of our proposed methods and it outperforms other popular methods.
Speeding Up Classifier Chains in Multi-label Classification

Multi-label classification has attracted increasing attention of the scientific community in recent years, given its ability to solve problems where each of the examples simultaneously belongs to multiple labels. From all the techniques developed to solve multi-label classification problems, Classifier Chains has been demonstrated to be one of the best performing techniques. However, one of its main drawbacks is its inherently sequential definition. Although many research works aimed to reduce the runtime of multi-label classification algorithms, to the best of our knowledge, there are no proposals to specifically reduce the runtime of Classifier Chains. Therefore, in this paper we propose a method called Parallel Classifier Chains which enables the parallelization of Classifier Chain. In this way, Parallel Classifier Chains builds k binary classifiers in parallel, where each of them includes as extra input features the predictions of those labels that have been previously built. We performed an experimental evaluation over 20 datasets using 5 metrics to analyze both the runtime and the predictive performance of our proposal. The results of the experiments affirmed that our proposal was able to significantly reduce the runtime of Classifier Chains while the predictive performance was not statistically significantly harmed.
Linear Complexity of <i>d</i> ‐Ary Sequence Derived from Euler Quotients over GF( <i>q</i> )

For an odd prime p and positive integers r, d such that 0 < d ≤ pr, a generic construction of d-ary sequence based on Euler quotients is presented in this paper. Compared with the known construction, in which the support set of the sequence is fixed and d is usually required to be a prime, the support set of the proposed sequence is flexible and d could be any positive integer less then pr in our construction. Furthermore, the linear complexity of the proposed sequence over prime field GF(q) with the assumption of qp-1 ≢ 1 mod p2 is determined. An algorithm of computing the linear complexity of the sequence is also given. Our results indicate that, with some constrains on the support set, the new sequences possess large linear complexities.
Enabling A Conversation Across Scholarly Monographs through Open Annotation

The digital format opens up new possibilities for interaction with monographic publications. In particular, annotation tools make it possible to broaden the discussion on the content of a book, to suggest new ideas, to report errors or inaccuracies, and to conduct open peer reviews. However, this requires the support of the users who might not yet be familiar with the annotation of digital documents. This paper will give concrete examples and recommendations for exploiting the potential of annotation in academic research and teaching. After presenting the annotation tool of Hypothesis, the article focuses on its use in the context of HIRMEOS (High Integration of Research Monographs in the European Open Science Infrastructure), a project aimed to improve the Open Access digital monograph. The general line and the aims of a post-peer review experiment with the annotation tool, as well as its usage in didactic activities concerning monographic publications are presented and proposed as potential best practices for similar annotation activities.
Lean Agile Integration for the Development of Large Size Projects

Scrum is a well-known agile model due to its strong management practices.It can be mingled with many software development models such as extreme programming (XP), Agile Unified Process, and Feature Driven Development (FDD).Lean is a very popular known process in the automobile industry due to its effective practices such as Kanban bard and smooth workflow.Lean development is gaining popularity in the software industry from the last few years.Lean development is rational, convenient, responsive, and team-based and it adds value to the enterprise.Scrum is useable and practical for small and medium projects but it does not render positive support for the large size projects.In order to adopt Scrum for large size projects, there is a need to integrate Lean and Scrum.It is required to inherit some properties of the Lean into Scrum, without compromising the speed, quality, efficiency, and standards, to accomplish large size projects successfully such as enterprise resource planning (ERP) systems.It is anticipated that the proposed Lean Scrum integration will make it suitable to develop large size projects.The same is accomplished by purposing an integrated LScrum model in this research.The proposed model is validated using a survey to conclude the results.The results of the survey support the proposed integration of Lean and Scrum for the development of large size projects.
The CICES Journal Governance Performance Improvement on Quality Of Current Issues (Case Study of STMIK RAHARJA)

Journal Publications in STMIK Raharja is a facility for students and lecturers in conducting research, the CICES Journal is one of the journals in STMIK Raharja, which was published 1 year 2 times in February and August, the problem that occurs is the absence of an online journal google scholar indexed, and there was no information summary to make a decision and the difficulty of the prospective writer getting information about the CICES journal. To overcome this problem, a research was carried out which developed a system for improving the governance of CICES journals on published quality and quality by using SWOT analysis and the Statistical Product and Service Solutions (SPSS) method which were felt to be able to assist in data management. then transformed in the form of iMe in STMIK Raharja, is expected to overcome the existing constraints, by providing information that supports the decision-making system in Journal Publications. This system was created in collaboration with Rinfo, Rinfo Sheet, Rinfo Form and iLearning Media which are part of the 10 IT STMIK Raharja Pillars. which is then implemented for the needs of journal publications on STMIK Raharja through web cices.ilearning.me.&#x0D; Keywords: Rinfo, Rinfo Sheet, Rinfo Form
Swarm Intelligence Based Feature Selection for High Dimensional Classification: A Literature Survey

Feature selection is an important and challenging task in machine learning and data mining techniques to avoid the curse of dimensionality and maximize the classification accuracy. Moreover, feature selection helps to reduce computational complexity of learning algorithm, improve prediction performance, better data understanding and reduce data storage space. Swarm intelligence based feature selection approach enables to find an optimal feature subset from an extremely large dimensionality of features for building the most accurate classifier model. There is still a type of researches that is not done yet in data mining. In this paper, the utilization of swarm intelligence algorithms for feature selection process in high dimensional data focusing on medical data classification is form the subject matter. The results shows that swarm intelligence algorithms reviewed based on state-of-the-art literature have a promising capability that can be applied in feature selections techniques. The significance of this work is to present the comparison and various alternatives of swarm algorithms to be applied in feature selections for high dimensional classification.
Information and Communication Systems Based on Signal Systems with Improved Properties Building Concept

The paper presents the results of solving the actual problem of improving the performance indicators of information and communication systems (ICS), in particular, information security, noise immunity, secrecy, in terms of external and internal disturbing influences, based on the models and methods development for information exchange in ICS, synthesis new classes of nonlinear discrete cryptographic signals (CS) with the necessary properties.For the first time, complex non-linear discrete СS synthesis problem was formulated and solved, based on the use of random (pseudo-random) processes.Taking into account the requirements of cryptographic robustness and the complexity of generating a cryptographic signal as a source of random (pseudo-random) processes, the choice of a symmetric block encryption algorithm with a counter is grounded.The synthesis method obtained by the authors makes it possible to form large ensembles of discrete sequences of practically any period with given, but physically realizable, side-lobe values of auto, cross and butt correlation functions, as well as statistical characteristics of correlation functions that are not inferior to those of the best linear classes of signals, in terms of correlation functions.A method for synthesizing derived signal systems based on nonlinear discrete cryptographic signals is proposed.Improved (as compared with the known linear signal classes) ensemble, correlation and structural properties of the synthesized signal system allow improving the indicators of information security, noise immunity and secrecy of the operation of ICS.
A Study on Shared Leadership and Positive Psychological Capitals of Teachers at Primary and Secondary Schools

The purpose of this study was to measure shared leadership and positive psychological capital levels of teachers at schools and to examine the relationship between these two variables.Descriptive survey and relational research models were used in the study.The sample group of the study was comprised of 550 teachers working at primary and secondary schools at the Battalgazi and Yeşilyurt central districts of the province of Turkey during the 2017-2018 academic year.The data were acquired via Shared Leadership Scale and Positive Psychological Capital.Descriptive statistics were calculated during the data analysis stage, correlation analysis and regression analysis were used.According to the results of the study, there is a correlation between shared leadership and the positive psychological capitals of teachers at primary and secondary schools.Shared leadership at primary and secondary schools predict the positive psychological capitals of teachers and all its sub-dimensions in a statistically significant manner.It was observed as a result of the study that shared leadership and positive psychological capitals of teachers are at a high level in primary and secondary schools.It was observed that shared leadership at schools increases the positive perspectives of teachers, their expertise, sense of trust, social characteristics, their resolve to struggle against difficulties and their hope for the future.It was observed that creating a school environment based on sharing, supporting the efforts of teachers, including them in decision making processes, ensuring that they effectively contribute to all development and change efforts in the school or the solution stages of a problem as well as working in cooperation to reach the goals of the school are important so that teachers as one of the most important elements of education develop a positive psychology.
An Applied e-Mentoring Model For Academic Development, Reflection And Growth

Effective mentoring brings positive outcomes for mentees, mentors and their organizations. Modern mentoring is developing through employment of technology and thus it is important to better understand these new opportunities and their limitations. Termed as “e-mentoring”, the field remains under-researched and sub-optimally theorized.
The use of virtual reality for public health education with reference to Syrian refugee camps

In this study we used Virtual Reality (VR) technology to provide an immersive interactive learning experience for undergraduate public health students in the UK and Lebanon.Students carried out a problem-based learning exercise around public health challenges faced by Syrian refugees in Lebanon.In the wider context of a Virtual Student Exchange programme small, mixed nationality groups were connected by Skype, WhatsApp and other technologies to research specific healthcare requirements for refugee camps (e.g.sanitation) and constructed an interview guide for the Lebanese students to use on a visit to a camp at the end of the programme.Lebanese students captured 360-degree videos to allow UK peers to 'join' them on their camp visit using VR.Findings from post-hoc video interviews focusing on the use of VR indicated that students felt closer to the subject of their research than before it was used.Participants' emotions were affected by what they saw in the camps, providing a broader cognitive experience in which sight, sound, and emotions were linked to the camps, deepening learning about the refugees' conditions.Faculty were able to move beyond the use of simple text-based scenarios, facilitating students' learning about a real-world situation that they would not have been able to access through other means.This approach has potential for wider use in education, providing virtual access to locations it can be difficult for learners to visit by other means.
Hic Sunt Proxies: Unveiling Proxy Phenomena in Mobile Networks

Over the years middleboxes have established themselves as a solution to a wide range of networking issues, progressively changing network landscape and turning the end-to-end principle into a reminder of an Arcadian age of the Internet. Among them, proxies have found breeding ground especially in mobile networks that, moreover, have become the most popular way to access the Internet. In this paper, we present Mobile Tracebox, an Android measurement tool, and describe how its methodology, coping with the lack of privileges of mobile devices, can not only detect proxies but also characterize different facets, from their transport layer behavior to their location inside the network. Data collected from a crowdsourced deployment over more than 90 carriers and 350 Wi-Fi networks contributes to describe the potential of the tool and to draw a panorama of proxies across mobile networks. Our study confirms their prevalence and reveals that their scope is not limited to HTTP but can include several TCP services and even non standard ports. We detail the different implementations observed and delve into specific aspects of their configuration, like the initial Receive Window, the Window Scale factor or the set of Options supported, to understand how proxies can affect performance or obstruct extensions. Finally, we focus on fingerprinting and attempt to draw a dividing line between packet modifications performed by proxies and those performed by other classes of middleboxes.
Artificial Neural Networks: The Missing Link Between Curiosity and Accuracy

Artificial Neural Networks, as the name itself suggests, are biologically inspired algorithms designed to simulate the way in which the human brain processes information. Like neurons, which consist of a cell nucleus that receives input from other neurons through a web of input terminals, an Artificial Neural Network includes hundreds of single units, artificial neurons or processing elements, connected with coefficients (weights), and are organized in layers. The power of neural computations comes from connecting neurons in a network: in fact, in an Artificial Neural Network it is possible to manage a different number of information at the same time. What is not fully understood is which is the most efficient way to train an Artificial Neural Network, and in particular what is the best mini-batch size for maximize accuracy while minimizing training time. The idea that will be developed in this study has its roots in the biological world, that inspired the creation of Artificial Neural Network in the first place. Humans have altered the face of the world through extraordinary adaptive and technological advances: those changes were made possible by our cognitive structure, particularly the ability to reasoning and build causal models of external events. This dynamism is made possible by a high degree of curiosity. In the biological world, and especially in human beings, curiosity arises from the constant search of knowledge and information: behaviours that support the information sampling mechanism range from the very small (initial mini-batch size) to the very elaborate sustained (increasing mini-batch size). The goal of this project is to train an Artificial Neural Network by increasing dynamically, in an adaptive manner (with validation set), the mini-batch size; our hypothesis is that this training method will be more efficient (in terms of time and costs) compared to the ones implemented so far.
DOPdefenderPlus: A Data-Oriented Programming Attack Mitigation Technique for Complex Software

With the development of research on noncontrol data attacks and defense, the threat of data-oriented programming (DOP) attacks has attracted increasing attention from the security research community. DOP attacks can manipulate security-critical noncontrol data to alter program behavior without violating control-flow integrity (CFI) and can circumvent the most effective defenses against control-data attacks. Among DOP attacks, the misuse of user input data is a major contributor. Moreover, existing defense methods, e.g., DOPdefender, currently lack security protection for user input data. To effectively defend against DOP attacks, we propose a novel technique, DOPdefenderPlus, which draws on the idea of divide-and-conquer and uses the modular authentication technique to make DOPdefender scalable for complex software that is designed modularly, as well as introduce the Inputguard technique to protect the program input data. The DOPdefenderPlus is an enhanced version of DOPdefender, which overcomes some limitations of DOPdefender. We implement DOPdefenderPlus on a Linux operating system and use it to defend against multiple realistic DOP attacks. We also evaluate the performance of our method, and all the results show that DOPdefenderPlus can overcome the two limitations of DOPdefender while introducing a moderate runtime overhead.
Modelling of Non-Linguistic Utterances for Machine to Human Communication in Dialogue

Non-Linguistic Utterances (NLUs) present a potentially useful alternative communication channel between humans and machines. NLUs are potentially cheaper, and easier to implement, and not limited to the constraints of natural language and therefore may be appropriate in situations such as assisting tourists with various language backgrounds and needs. An experiment was done to establish ranges for NLU parameters such as pitch, duration, amplitude, and timbre. Subjects listened to randomly produced NLUs and selected applicable dialogue descriptors from: Positive, Negative, Greeting, Apology, Thanking, Hesitation, Question, Approval, Disapproval, Hushing, None of the Above. Factor analysis yielded 3 major factors, which were labeled as follows: 1) Affirmative vs Negative, 2) Questioning, and 3) Meaningful vs Indeterminate. NLUs with lower pitches, downward pitch patterns, and simpler timbres were found to be more Negative. Those with upward pitch patterns were more likely to be identified as a Question. In future work, experiments will be used to develop a model of NLU inference and interpretation within a Dialogue in terms of the dominant descriptors and to test the model in applications for tourist support systems.
High-Speed FPGA-based of the Particle Swarm Optimization using HLS Tool

The Particle Swarm Optimization (PSO) is a heuristic search method inspired by different biological populations on their swarming or collaborative behavior.This novel work has implemented PSO for the Travelling Salesman Problem (TSP) in high-level synthesis to reduce the computational time latency.The high-level synthesis design generates an estimation of the hardware resources needed to implement the PSO algorithm for TSP on FPGA.The targeted FPGA of this algorithm is the Xilinx Zynq family.The algorithm has been implemented for getting the best route between 5 given cities with given distances.The research has used 7 number of particles for a different number of iterations for generating the best route between those 5 cities.The overall latency has been reduced due to the applied optimization techniques.This paper also implemented and parallelized the same algorithm on CPU Intel I7 Processor; the result shows the FPGA implementation gives better results than CPU on the comparison of performance.
Chinese Text Similarity Algorithm Based on Part-of-Speech Tagging and Word Vector Model

Based on the unique structure of Chinese text data in the field of building materials, this paper proposes a Chinese text similarity calculation method based on part-of-speech tagging and word vector model.By analyzing the special structure of Chines text data in the building materials field, the method realizes specific part-of-speech tagging by using the machine, which saves a lot of manpower and time consumption required for manual labeling.Then, combined with the word vector model, the text similarity calculation is realized by these steps: Chinese word segmentation, syntactic analysis, and similar matching of the annotated text.This paper comprehensively compares the word level similarity calculation method based on the vector space model.Through analysis and experimental comparison, the algorithm in this paper obtains an average F value of 72.0%, which is improved by 20.67% compared with the method based on vector space model, and has achieved better test results.