Reviewerly/exHarmony huggingface dataset:
Investigating the Impact of Performance Appraisal and its Dimension, Directions and Objectives to Staff, on Employees’ Job Satisfaction in INGOs in Yemen

This study aims to gain a better comprehension of how employees in INGOs in Yemen perceive performance appraisals in general and specifically directions and objectives to staff and their role in job satisfaction through exploring the impact of performance appraisal and its dimension, directions and objectives to staff, on employees' job satisfaction in INGOs in Yemen. A quantitative method was used, and relevant questionnaires were distributed to these employees. The findings of the study show that there is a positive significant relationship between job satisfaction and performance appraisal and its above-stated construct. They also reveal that performance appraisal and the aforementioned construct have significant positive impacts on employees’ job satisfaction. Moreover, the high R2 of 82% is an indication of the high explanatory power of the test. The above findings are consistent with similar studies referred to hereunder. This study suggests that decision-makers in Yemen's INGOs should improve the clarity of directions and objectives and the performance appraisal in general. Such improvements are critical since they may result in increasing the employees’ job satisfaction and consequently lead to a better humanitarian service delivered by the respective INGOs through accomplishing more and better work in less time and with fewer resources. The proposed framework is developed based on previous literature on performance appraisal emphasizing that no previous study used performance appraisal and the above construct together to measure their impacts on job satisfaction of employees working in INGOs in Yemen.
The Digital Competence of Future Teachers in Kosovo

The aim of this chapter is to identify the level of preparation of university students in using information and communication technology (ICT) in their work as future teachers in Kosovo. The collected data are based on desk research analyzing the scientific publications of authors from Kosovo relating to ICT and the digital competences of teachers. Considering that only a modest number of studies have been obtained regarding digital competence, all curricula of programs that lead to the qualification of pre-university teachers have also been analyzed. These data have been analyzed through content analysis, determining whether the courses offered manage to develop digital competences according to these three groupings: (a) competence in the generic use of computers, (b) digital teaching competence, and (c) professional digital competence. The findings indicate an average level of competence in the generic use of computers and general digital teaching competence. However, this level is higher in the faculties of education. On the other hand, professional digital competence is developed in programs at the Faculty of Mathematical and Natural Sciences.
lmForc: Linear Model Forecasting in R

Linear forecasting models are a popular option in many domains due to their simplicity and interpretability. This paper considers the tools a forecaster has for evaluating linear forecasting models and presents lmForc, a package which implements these evaluation functions. Functions in the lmForc package are built around a new S4 class, Forecast, which introduces a dedicated data structure for storing forecasts to the R language and permits the creation of complex forecasting functions. The lmForc package is designed to leverage the simplicity and interpretability of linear models so that a forecaster may easily test model specifications and understand precisely how a model arrives at a forecast.
Promoting Transfer of Robot Neuro-Motion-Controllers by Many-Objective Topology and Weight Evolution

The ability of robot motion controllers to quickly adapt to new environments is expected to extend the applications of mobile robots. Using the concept of transfer optimization, this study investigates the capabilities of neuro-motion-controllers, which were obtained by simultaneously solving several source problems, to adapt to target problems. In particular, the adaptation comparison is carried out between specialized controllers, which are optimal for a single source motion problem, and nonspecialized controllers that can solve several source motion problems. The compared types of controllers were simultaneously obtained by a many-objective evolution search that is tailored for the optimization of the topology and weights of neural networks. Based on the examined problems, it appears that nonspecialized solutions, which are “good enough” in all the source motion problems, show significantly better transfer capabilities as compared with solutions that were optimized for a single source motion problem. The proposed approach opens up new opportunities to develop controllers that have good enough performances in various environments while also exhibiting efficient adaptation capabilities to changes in the environments.
Ancient Village Culture Based on Big Data Technology

With the rapid development of China’s economy and the continuous improvement of people’s living standards, people’s demand for material and spiritual aspects is higher and higher. Under such a background, traditional culture is facing great challenges. In recent years, China’s tourism industry has risen rapidly. At present, there is no unified standard for the development and management of ancient villages, and there are even phenomena that no one is responsible in many places. Tourists can understand the ancient village culture through various media platforms, and can also use some social software to communicate online and browse the information of scenic spots and related history on the web. This paper mainly discusses the problems existing in the construction of ancient village culture under big data technology, in order to study the ancient village culture based on big data technology and understand big data technology and its advantages. This paper uses statistical analysis and experimental research methods to study the ancient village culture based on big data technology. The survey results show that most respondents believe that the promotion effect of ancient villages is relatively good, and most foreign respondents obtain ancient village information from TV, mobile phone and the Internet. On the basis of the Internet, more people get information about ancient villages. The research on ancient village culture based on big data technology is a subject of great research value.
Deep Learning for Fast Segmentation of E-waste Devices’ Inner Parts in a Recycling Scenario

Recycling obsolete electronic devices (E-waste) is a dangerous task for human workers. Automated E-waste recycling is an area of great interest but challenging for current robotic applications. We focus on the problem of segmenting inner parts of E-waste devices into manipulable elements. First, we extend a dataset of hard-drive disk (HDD) components with labelled occluded and non-occluded points of view of the parts, in order to increase the diversity and the quality of the learning data with different angles. We then perform an extensive evaluation with three different state-of-the-art models, namely CenterMask, BlendMask and SOLOv2 (including variants) and two types of metrics: the average precision as well as the frame rate. Our results show that instance segmentation using state-of-the-art deep learning methods can precisely detect complex shapes along with their boundaries, as well as being suited for fast tracking of parts in a robotic recycling system.
Fibrous Minerals and Naturally Occurring Asbestos (Noa) in the Metacarbonate Hosted Fe Oxide-Cu-Au-Co Mineralized Rocks from the Guelb Moghrein Mine, Akjoujt, Mauritania: Implications for in Situ Hazard Assessment and Mitigation Protocols

The awareness of the potential risks associated with the environmental exposition of asbestos is on the rise and has facilitated a novel interest in the in situ identification and hazards of fibrous minerals. The mineralised metacarbonate rocks of Guelb Moghrein deposit have not been studied based on the aim of identifying and characterising the fibrous minerals occurrences. Thus, the aim of the study was to collect samples from different lithotypes having contained visually identifiable fibrous minerals and examine the geological-structural features pertaining to their mode of occurrence and formation. The mineral fibre components of the rock samples demonstrated fibrous and asbestiform mineralisation occurs via several modes including fracture fill, slip-fibre recrystallisation and replacement. The geological diversity of fibrous amphibole mineralisation is shown in this study and points to the chemistry of the rocks in this area as well as open space being the major factors controlling the presence of NOA in this deposit. The analytical variability of NOA due to different fibrous mineral intergrowths is investigated in this work by determining the bulk mineralogical and geochemical properties of the fibrous mineral content of sample 1. The confirmation and identification of the asbestiform features exhibited by siderite, anthophyllite and talc in the mineralised metacarbonate of the Guelb Moghrein deposit underlines the necessity for further mineralogical research to enhance our understanding fibrous minerals and how we assess their potential hazards to health.
GANs Based Conditional Aerial Images Generation for Imbalanced Learning

In this paper, we examine whether we can use Generative Adversarial Networks as an oversampling technique for a largely imbalanced remote sensing dataset containing solar panels, endeavoring a better generalization ability on another geographical location. To this cause, we first analyze the image data by using several clustering methods on latent feature information extracted by a fine-tuned VGG16 network. After that, we use the cluster assignments as auxiliary input for training the GANs. In our experiments we have used three types of GANs: (1) conditional vanilla GANs, (2) conditional Wasserstein GANs, and (3) conditional Self-Attention GANs. The synthetic data generated by each of these GANs is evaluated by both the Fréchet Inception Distance and a comparison of a VGG11-based classification model with and without adding the generated positive images to the original source set. We show that all models are able to generate realistic outputs as well as improving the target performance. Furthermore, using the clusters as a GAN input showed to give a more diversified feature representation, improving stability of learning and lowering the risk of mode collapse.
Blockchain Cross-Chain Research Based on Verifiable Ring Signatures

Along with the spurt of blockchain technology, the problem of blockchain’s closedness is getting more attention, leading to cross-chain technology. Cross-chain technology solves the problem that blockchains cannot be interconnected and opens up the value circulation between blockchains. With the development of blockchain technology, cross-chain technology is getting more and more attention. This paper introduces the development of blockchain cross-chain technology and the mainstream schemes of cross-chain, such as hash locking, notary model, side-chain relay, distributed secret key, etc. Since there is a situation of who initiates who has the advantage in the cross-chain solution without introducing a third party, therefore, this paper proposes a new smart contract lock cross-chain solution based on ring signature ambiguity and verifiable ring signature that can remove ring signature ambiguity after announcing some secret information, which can satisfy the situation of ensuring fair and just value exchange between chains without third-party participation.
Analysis of Covid-19 News Using Text Mining Techniques

COVID-19, which has taken the whole world under its influence, has been a remarkable period to investigate the emotions and behaviors of people in extraordinary situations. The findings addressed during this defining moment to the fluctuation of the number of cases and certain turning points. It is a matter of debate how much these important moments are affected by the attitudes of the authorities directing the public. The aim of this study is to determine in which periods and what expressions the news in the newspapers is used by using text mining techniques such as word clouds, clustering and sentiment analysis. In order to do this, COVID-19 news published in the last two years from three respected newspapers were used. Results reveal that, there are significant changes in the main themes of the COVID-19 related news with the release of the vaccine. Moreover, when all periods are inspected, it has been observed that different topics like herd immunity, vaccination, variants and human rights have come to the forefront in various print media sources.
Study of Effective Storage System for MPEG-7 Document Clustering

To use multimedia data in restricted resources of mobile environment, any management method of MPEG-7 documents is needed. At this time, some XML clustering methods can be used. But, to improve the performance efficiency better, a new clustering method which uses the characteristics of MPEG-7 documents is needed. A new clustering improved query processing speed at multimedia search and it possible document storage about various application suitably. In this paper, we suggest a new clustering method of MPEG-7 documents for effective management in multimedia data of large capacity, which uses some semantic relationships among elements of MPEG-7 documents. And also we compared it to the existed clustering methods.
Neural networks in R

Neural networks are complex algorithms that can be used for supervised classification and regression problems, as well as unsupervised models. Supervised neural networks take in the input data through input nodes (neurons), pass it through hidden layer node(s) while assigning weights to each node, and then represent it through the output layer. Various factors determine the structure of a neural network and its output, including but not limited to the number of hidden layers, number of nodes in each hidden layer, and activation function. In this chapter, we will briefly introduce some of the supervised and unsupervised neural networks algorithms, and compare effects of changing model parameters on its performance.
Stride Length Estimation Model Based on Machine Learning Algorithms

The accuracy and feasibility of the stride length estimation model are important for pedestrian dead reckoning (PDR). In the current algorithms, the linear frequency models or nonlinear models consider few variable factors, and need to determine the coefficients previously for different pedestrians, so these models are not universal. The paper proposes to use machine learning algorithm (MLAs) to try to establish a general stride length estimation model by using a consumer-grade micro inertial measurement unit (MIMU) to collect data. Two MLAs including back propagation artificial neural network (BP-ANN) and least squares support vector machine (LSSVM) are adopted for this model. The relationship between input parameters and output values is trained by data of 13 subjects, then 3 new test subjects’ stride length estimation using the trained net is discussed. The experiment shows that the stride length estimation algorithm using the two MLAs both can meet the requirements of pedestrian distance estimation, and does not need to determine the coefficient in advance, which verifies the feasibility of the proposed methods.
A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions

Many communications and sensing applications hinge on the detection of a signal in a noisy, interference-heavy environment. Signal processing theory yields techniques such as the generalized likelihood ratio test (GLRT) to perform detection when the received samples correspond to a linear observation model. Numerous practical applications exist, however, where the received signal has passed through a nonlinearity, causing significant performance degradation of the GLRT. In this work, we propose prepending the GLRT detector with a neural network classifier capable of identifying the particular nonlinear time samples in a received signal. We show that pre-processing received nonlinear signals using our trained classifier to eliminate excessively nonlinear samples (i) improves the detection performance of the GLRT on nonlinear signals and (ii) retains the theoretical guarantees provided by the GLRT on linear observation models for accurate signal detection.
Creating the Original Model of a Digital Pedagogical Activity Simulator (Using the Example of a Mathematics Lesson)

The article discusses the findings obtained as a result of Mathematics lesson modeling in a simulation environment. The digital simulator of pedagogical activity is a virtual platform that enables university students to have various options of making pedagogical decisions at a Mathematics lesson. The simulator trains the university students in the professional skills that are established by the Teachers’ Professional Standard. The digital simulator helps to form and assess the operation related to planning and delivering lessons. The article describes the procedure of working and the technology of creating the original model of the simulator. It also gives the methodological foundation of modeling a lesson loaded in the simulator. The article gives special attention to creating the content of a virtual lesson and describing the indicators to be assessed. The developed model can be used as a simulator and a diagnostic tool that determines the level of the labor operations performed by university students. The experience of creating digital simulators is of great importance for the practice of education aimed at acquiring the reflexive competence, forming the methodological labor operations prior to the work in the real classroom.
Combinatorial Structures Behind Binary Generalized NTU Sequences

This paper concentrates on a class of pseudorandom sequences generated by combining q-ary m-sequences and quadratic characters over a finite field of odd order, called binary generalized NTU sequences. It is shown that the relationship among the sub-sequences of binary generalized NTU sequences can be formulated as combinatorial structures called Hadamard designs. As a consequence, the combinatorial structures generalize the group structure discovered by Kodera et al. (IEICE Trans. Fundamentals, vol.E102-A, no.12, pp.1659-1667, 2019) and lead to a finite-geometric explanation for the investigated group structure.
Offline Text-Independent Writer Identification Using a Codebook with Structural Features

Understanding handwritten documents is a vital and challenging problem that attracts many researchers in the fields of forensic and authentication science. This paper presents an offline system for text-independent writer identification of handwritten documents. The system extracts a handwritten connected component contour, which in turn is divided into segments of specific length. The system utilizes the concept of a bag of features in the writer recognition domain and considers handwritten contour segments to extract two conceptually simple and effective structural features. These features are the contour point curve angle and the CONtour point CONcavity/CONvexity. The system uses the proposed features to train a k-means clustering algorithm to construct a codebook of size K. The method then uses occurrence histograms of the extracted features in the codebook to create a final feature vector for each handwritten document. The effectiveness of the proposed features is evaluated in the writer identification domain using two widely used classification methods: the nearest neighbor and the support vector machine techniques. The proposed writer identification is evaluated on two large and public datasets from different language domains, the Arabic KHATT and English IAM datasets. The experimental results show that the proposed system outperforms state-of-the-art methods on the IAM dataset and provides competitive results on the KHATT dataset with respect to the identification rate.
Medical image translation using an edge-guided generative adversarial network with global-to-local feature fusion

In this paper, we propose a framework based deep learning for medical image translation using paired and unpaired training data. Initially, a deep neural network with an encoder-decoder structure is proposed for image-to-image translation using paired training data. A multi-scale context aggregation approach is then used to extract various features from different levels of encoding, which are used during the corresponding network decoding stage. At this point, we further propose an edge-guided generative adversarial network for image-to-image translation based on unpaired training data. An edge constraint loss function is used to improve network performance in tissue boundaries. To analyze framework performance, we conducted five different medical image translation tasks. The assessment demonstrates that the proposed deep learning framework brings significant improvement beyond state-of-the-arts.
I Mplementation of Machine Learning Techniques for Face Mask Detection

The global pandemic's pervasiveness has impacted human life severely. The COVID-19 pandemic had a devastating effect which resulted in a huge amount of loss for people financially, mentally as well as physically. This is because the disease rapidly disseminated all over the globe which in turn resulted in the rise of the infection rates. Many techniques have been devised to control the spread of the virus such as washing the hands frequently, maintaining a distance from people in public places, wearing mask. Out of all the techniques mentioned above, wearing a mask has been proven as the most efficient technique. Thus, the administrative agencies have been insisting the citizens to wear masks, and law enforcement agencies have been consistently keeping a check on the citizens so that each individual follows this instruction. As a result, there is a need to provide solution in the form of techniques for automatically detecting masks on individual faces which in turn will also help the law-enforcement agencies and governments so as to reduce the spread of the pandemic. This research makes use of image processing approaches to achieve the goal of providing a solution for face mask detection. In this paper, the methodology employs a convolution neural network and a decision tree to achieve accurate facial mask detection. Frame Capturing, Pre- processing, Region of Interest, Convolutional Neural Network, and Decision Tree are the five modules included in the proposed methodology.