Reviewerly/exHarmony huggingface dataset:
Significance of Supportive HRM Practices and Job Satisfaction towards Work Engagement

The main intention is to probe the impression of the HR practices on work engagement. The study has tested the intervening role of job satisfaction in the connection between supportive HR practices (job security and job crafting) and work engagement. In line with this, partial least square (PLS-SEM) through SmartPLS software 3.0 was used to perform the analysis. 576 instruments were disseminated, out of which 465 were received returned, whereas 85 questionnaires were discarded because they were not filled correctly. Therefore, final analysis was executed on 380 responses and the response rate of 80.729%. The current research concluded a significant affirmative link exists among supportive HRM practices towards job satisfaction and work engagement. Findings show that workers of the hotel industry in Oman found time job security and job crafting better sources of job satisfaction and work engagement. Additionally, this research also found significant intervening influence of job satisfaction on the association of job security, job crafting with work engagement. This research has valuable theoretical and practical implications.
A Constant Gain Kalman Filter for Wireless Sensor Network and Maneuvering Target Tracking

One of the well-known approaches to target tracking is the Kalman filter. The problem of applying the Kalman Filter in practice is that in the presence of unknown noise statistics, accurate results cannot be obtained. Hence the tuning of the noise covariances is of paramount importance in order to employ the filter. The difficulty involved with the tuning attracts the applicability of the concept of Constant Gain Kalman Filter (CGKF). It has been generally observed that after an initial transient the Kalman Filter gain and the State Error Covariance P settles down to steady state values. This encourages one to consider working directly with steady state or constant Kalman gain, rather than with error covariances in order to obtain efficient tracking. Since there are no covariances in CGKF, only the state equations need to be propagated and updated at a measurement, thus enormously reducing the computational load. The current work first applies the CGKF concept to heterogeneous sensor based wireless sensor network (WSN) target tracking problem. The paper considers the Standard EKF and CGKF for tracking various manoeuvring targets using nonlinear state and measurement models. Based on the numerical studies it is clearly seen that the CGKF out performs the Standard EKF. To the best of our knowledge, such a comprehensive study of the CGKF has not been carried out in its application to diverse target tracking scenarios and data fusion aspects.
Contextual matching via graph representation learning with side information

The performance of recommendation systems is highly dependent on candidate matching techniques for scoping users’ information needs. Existing candidate matching methods are based on text embedding and collaborative filtering, which base similarity primarily on semantics or co-occurrences of listings. Unfortunately, they do not leverage valuable user behavior to include recommendation impressions, clicks, or the sequences leading up to a purchase. This rich information reflects accurate user preferences, which have been widely used to enhance the ranking stage of a recommendation system. Yet integrating contextual info into the matching stage is challenging because the feature dimensionality and sparsity will be increased as well. Recently, graph representation learning (GRL) has gained much success in industrial applications like item-to-item recommendation systems. GRL learns a mapping of embedding nodes (with edges) into a low dimensional space by representing users’ behaviours into an activity graph. The goal is to optimize this mapping function so that the learnt geometric relationships can reflect the structural information of the original graph. The trained embeddings can be used as features for downstream applications like nearest neighbor search and ranking problems. Our work focuses on a GRL framework to enhance the performance of the candidate generation. Such an approach inevitably faces the cold start problem. For example, listings with fewer or none user interactions can not be learned effectively. To deal with it, side information like shop, category, price, etc. are integrated into the listing embedding by learning an integrated multi-view embedding.
Mobile Application for Student Handbook Based on the Android Operating System

This paper presents the design and implementation of the mobile student handbook for the Department of Computer and Communications Engineering (DCCE), Abubakar Tafawa Balewa University (ATBU). The book was aimed at providing students with specific information for the curriculum of the department that indicates their structures as given by the Minimum Academic Standard of the National Universities Commission (NUC-MAS) Nigeria. It provides information on the brief history of the department, entry requirement, grading system, graduation requirements among many. The Conventional student handbook is a hard copy that is not easy to carry alone all the time, hard to manage, prone to damage, and easy to being misplaced. To address this setback, a software application of a student handbook is developed using Android SDK. This will help to provide an easy-to-use, interactive, and portable means for simple access to the department’s curriculum structure that will assist students during their academic pursuits. The user evaluation process was employed to validate the application, this involved installations of the application to the mobile phone of 100 students and 20 staff. Results were recorded and shown in both graphs and tabular format. This application provides basic features of the DCCE’s curriculum like the courses and their pre-requisites for all levels, grading system, degree nomenclature, graduation requirements, and examination guidelines, etc. Moreover, it is an all-in-one offline mobile application that was meant to resolve basic issues. In the future, the link to the university website should be incorporated for further access to information.
Using technology, hands-on, and fieldwork activity for GLOBE learning in secondary school

Abstract As technology plays a crucial role in daily life, The Institute for the Promotion of Teaching Science and Technology (IPST) has designed “GLOBE Trees around Us” workshop based on GLOBE protocols (Green-Up, Green-Down, and tree height) using learning elements in order for students to measure tree height through mobile application together with Green-Up and Green-Down protocols through lecture and hands-on activities. According to the results from 150 student’s handbooks, more than 87% of students were able to understand Green-Up, Green-Down and tree height protocol by using video clips, paper leaf mock-up, mobile application and fieldwork activity. From the interview of nine teachers from four schools about the Green-Up activity, it shows that about 80% of students concentrated on the workshop better than they did in the classroom and felt enjoyable during the hands-on activity. In terms of the learning elements, it is found that almost 90% of teachers thought they were beneficial to apply in this workshop, especially the GLOBE Observer application. All the teachers agreed that the application is easy to use, and all related materials are easy to prepare. However, the limitation of activity is that the application is not compatible with some devices. Teachers also planned to use the learning elements in classroom or science camp to promote student interest and understanding. In addition, the students may gain problem-solving, collaboration, communication, and thinking skills from attending this workshop.
3D-RadViz: Three Dimensional Radial Visualization for Large-Scale Data Visualization

This paper presents 3D-RadViz, a visualization method for high dimensional data using a three dimensional radial visualization technique. The proposed technique extends the capabilities of the classical two dimensional radial visualization (RadViz) in order to reduce overlapping data points. For evaluation purposes, the paper applies the proposed 3D-RadViz alongside with t-SNE and a recently published 3-dimensional radial visualization technique to the same real-world datasets from the UCI machine learning repository. The contribution of the work is an interactive, deterministic, and high-performance library, implemented in Python, that can be utilized to realize better high dimensional data visualization using the proposed 3D-RadViz technique. The paper also suggests some directions for future enhancements to the proposed visualization approach.
Longitudinal network models and permutation‐uniform Markov chains

Abstract Consider longitudinal networks whose edges turn on and off according to a discrete‐time Markov chain with exponential‐family transition probabilities. We characterize when their joint distributions are also exponential families with the same parameter, improving data reduction. Further we show that the permutation‐uniform subclass of these chains permit interpretation as an independent, identically distributed sequence on the same state space. We then apply these ideas to temporal exponential random graph models, for which permutation uniformity is well suited, and discuss mean‐parameter convergence, dyadic independence, and exchangeability. Our framework facilitates our introducing a new network model; simplifies analysis of some network and autoregressive models from the literature, including by permitting closed‐form expressions for maximum likelihood estimates for some models; and facilitates applying standard tools to longitudinal‐network Markov chains from either asymptotics or single‐observation exponential random graph models.
High-Resolution Neural Style Transfer for Test Data Generation for ADAS/HAD Functions

In recent years, neural networks have shown astonishing results as data generators, especially in settings of generative adversarial networks (GANs). One special application is the field of style transfer, where the goal is to transform a data sample with a certain style (e.g. from sunny to rainy weather conditions) while keeping its content unchanged. This application is particularly interesting in the field of highly autonomous driving, where a major task is to generate appropriate data sets to verify and validate various kinds of algorithms. In real-world measurements, the data is usually recorded in the form of unpaired training examples, meaning that contentequivalent samples from both domains do not exist. In this case, the required GAN arrangement is complex and limits the highest processible data resolution. In this paper, we solve this issue by processing the data on the level of overlapping subsamples and present several high-quality results on image data as well as radar point clouds.
Not a Negation? A Logico-Philosophical Perspective on the Ugaritic Particles lā/ ’al

Abstract The negative particles lā/ ’al in Ugaritic change from positive to negative in modal contexts, conditional, questions, disjunctions, etc. They have usually been studied from a Semitic and linguistic points of view. On the basis of their occurrence in Ugaritic texts, we pretend to explain their uncommon behaviour from a philosophical and logico-semantic perspective. Is it possible to translate this linguistic structure in our Modern languages? Starting from a general view of their use in Ugaritic language, we claim that this phenomenon can be more clearly understood in relation to modality. We interpret these negation as a negative evidential paradigm and we explain how they change in different contexts. Methodologically, we make use of formal tools of Dynamic Epistemic Logic in order to provide a more fine-grained understanding of these negations, and their dynamics.
Facial Recognition Software for Identification of Powered Wheelchair Users

The research presented in this paper investigates the use of facial recognition software as a potential system to identify powered wheelchair users. Facial recognition offers advantages over other biometric systems where wheelchair users have disabilities. Facial recognition systems scan an image or video feed for a face, and compare the detected face to previously detected data. This paper reviews the software development kits and the libraries available for creating such as systems and discusses the technologies chosen to create a prototype facial recognition system. The new prototype system was trained with 262 identification pictures and confidence ratings were produced from the system for video feeds from twelve users. The results from the trials and variance in confidence ratings are discussed with respect to gender, presence of glasses and make up. The results demonstrated the system to be 95% efficient in its ability to identify users.
Research on Personalized Recommendation Method Based on Multi-source Information Learning

Personalized recommendation can effectively solve the negative impact of information overload on users and improve user experience in the big data environment. How to build an effective personalized recommendation system has become a common concern of industry and academia. Based on the basic idea of multi-layer perceptron, this paper constructs a personalized recommendation model of multi-source information. By introducing the relevant information of users and recommended items, iterative learning is carried out to improve the accuracy of user preference prediction. Combined with multi-layer perceptron method, the extended model is constructed. Based on TensorFlow framework, the batch data flow method is used to train the model. The implementation framework of the method is built, and the effectiveness of the method is verified by movielens data set. Experimental results show that the proposed method can effectively improve the accuracy of user preference prediction.
LRRA:A Transparent Neural-Symbolic Reasoning Framework for Real-World Visual Question Answering

The predominant approach of visual question answering (VQA) relies on encoding the image and question with a “black box" neural encoder and decoding a single token into answers such as “yes” or “no”. Despite this approach’s strong quantitative results, it struggles to come up with human-readable forms of justification for the prediction process. To address this insufficiency, we propose LRRA [Look, Read, Reasoning,Answer], a transparent neural-symbolic framework for visual question answering that solves the complicated problem in the real world step-by-step like humans and provides human-readable form of justification at each step. Specifically, LRRA learns to first convert an image into a scene graph and parse a question into multiple reasoning instructions. It then executes the reasoning instructions one at a time by traversing the scene graph using a recurrent neural-symbolic execution module. Finally, it generates answers to the given questions and makes corresponding marks on the image. Furthermore, we believe that the relations between objects in the question is of great significance for obtaining the correct answer, so we create a perturbed GQA test set by removing linguistic cues (attributes and relations) in the questions to analyze which part of the question contributes more to the answer. Our experiments on the GQA dataset show that LRRA is significantly better than the existing representative model (57.12% vs. 56.39%). Our experiments on the perturbed GQA test set show that the relations between objects is more important for answering complicated questions than the attributes of objects.
Designing an Adaptive Embodied Conversational Agent for Health Literacy

Access to healthcare advice is crucial to promote healthy societies. Many factors shape how access might be constrained, such as economic status, education or, as the COVID-19 pandemic has shown, remote consultations with health practitioners. Our work focuses on providing pre/post-natal advice to maternal women. A salient factor of our work concerns the design and deployment of embodied conversation agents (ECAs) which can sense the (health) literacy of users and adapt to scaffold user engagement in this setting. We present an account of a Wizard of Oz user study of 'ALTCAI', an ECA with three modes of interaction (i.e., adaptive speech and text, adaptive ECA, and non-adaptive ECA). We compare reported engagement with these modes from 44 maternal women who have differing levels of literacy. The study shows that a combination of embodiment and adaptivity scaffolds reported engagement, but matters of health-literacy and language introduce nuanced considerations for the design of ECAs.
A speech recognition approach for an industrial training station

This paper presents a speech recognition service used in the context of commanding and guiding the activities around an industrial training station. The entire concept is built on a decentralized microservice architecture and one of the many hardware and software components is the speech recognition engine. This engine grants users the possibility to interact seamlessly with other components in order to ensure a gradual and productive learning process. By working with different API’s for both English and Romanian languages, the presented approach manages to obtain good speech recognition for defining task phrases aiding the training procedure and to reduce the recognition required time by almost 50%.
Fuzzy time series for projecting school enrolment in Malaysia / Nor Hayati Shafii … [et al.]

There are a variety of approaches to the problem of predicting educational enrolment. However, none of them can be used when the historical data are linguistic values. Fuzzy time series is an efficient and effective tool to deal with such problems. In this paper, the forecast of the enrolment of pre-primary, primary, secondary, and tertiary schools in Malaysia is carried out using fuzzy time series approaches. A fuzzy time series model is developed using historical dataset collected from the United Nations Educational, Scientific, and Cultural Organization (UNESCO) from the year 1981 to 2018. A complete procedure is proposed which includes: fuzzifying the historical dataset, developing a fuzzy time series model, and calculating and interpreting the outputs. The accuracy of the model are also examined to evaluate how good the developed forecasting model is. It is tested based on the value of the mean squared error (MSE), Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD). The lower the value of error measure, the higher the accuracy of the model. The result shows that fuzzy time series model developed for primary school enrollments is the most accurate with the lowest error measure, with the MSE value being 0.38, MAPE 0.43 and MAD 0.43 respectively.
A lasting connection: colonial ties and scientific capabilities in sub-Saharan Africa

To what extent do colonial ties and knowledge spillovers continue to influence the development of scientific capabilities in sub-Saharan Africa? Using a new database of publication output of 46 sub-Saharan African countries over the period 1976–2016 and a production function approach, this paper gives evidence that knowledge produced in former colonizing countries continues to have a large and persistent effect on publication output in Africa. This relationship is growing stronger over time. Furthermore, the publication output of smaller countries and those at more advanced stages of scientific, economic and political development is more closely related to the publication output of their former colonizer. Overall, these findings have implications for programs and policies aiming to facilitate the development of scientific capacity in the region.
Supply Chain Using Smart Contract Blockchain Technology in Organizational Business

The key distinction among a smart contract and most other blockchain platforms is that it can comprehend a general-purpose programming language. With the introduction of smart contracts and a blockchain platform capable of executing them, the possibilities for their application are endless. A smart contract is an electronic transaction protocol that is intended to facilitate, verify, and enforce the negotiation and execution of the terms of an underlying legal contract to satisfy common contractual conditions such as payments, legal obligations, and enforcement without the involvement of third parties. As a result, smart contracts attempt to reduce transaction costs, such as arbitration and enforcement, by establishing trackable and irreversible transactions on distributed databases through blockchain technology. On the other hand, smart contracts have the potential to go far beyond cost reductions by enabling the entrepreneurial collaboration of cross-organizational business activities that are prevalent in smart supply chains. A closer examination of current or continuing smart contract projects reveals that the most prevalent smart contract applications in business are supply chain management, the Internet of Things, and Industry solutions. The author completed many European initiatives involving transnational entrepreneurial networks and supply chains. As a result, the study examines how and to what degree smart contracts and blockchain technology might aid in the establishment of collaborative company structures for long-term entrepreneurial activity in supply chains. The study was performed using expert online interviews, questionnaires.
Large-Scale Scientific Research Instrument Resource Information Sharing Platform through O2O Mode

Abstract Through the collection and integration of scientific instrument resources and scientific research services across the province, a scientific instrument public service system based on the O2O model will be constructed. The system takes the one-stop retrieval of scientific and technological resources as the hub, and provides accurate and convenient intelligent services for scientific and technological information for universities, scientific research institutes, key laboratories, high-tech enterprises, incubators and other units, realizing the co-construction and sharing of scientific instrument resources, Improve the utilization rate of scientific and technological resources, effectively improve the level of comprehensive sharing and efficient use of scientific instruments.
Aerial Thermal Image based Convolutional Neural Networks for Human Detection in SubT Environments

This article proposes a novel strategy for detecting humans in harsh Sub-terranean (SubT) environments, with a thermal camera mounted on an aerial platform, based on the AlexNet Convolutional Neural Network (CNN). A transfer learning framework will be utilized for detecting the humans, where the aerial thermal images are fed to the trained network, which binary classifies them image content into two categories: a) human, and b) no human. Moreover, the AlexNet based framework is compared with two related popular CNN approaches as the GoogleNet and the Inception3Net. The efficacy of the proposed scheme has been experimentally evaluated through multiple data-sets, collected from a FLIR thermal camera during flights on an underground mining environment, fully demonstrating the performance and merits of the proposed module.
Screw Thread Measurement by Camera Inspection System

This article describes measurement of the internal screw thread by the camera system. Especially thread handedness detection is focused. For this purpose, a geometric transformation on camera image was used, followed by the FFT method to detect thread direction. Possible issues of the measurement precision are also discussed.