Reviewerly/exHarmony huggingface dataset:
The Impact of Performance Appraisal on Employee Performance in Banks Operating in the South of Jordan

This research was about performance appraisal and its effect on employees’ work performance. The major aim of the research was to investigate the effect of performance appraisal on employees’ work performance of banks in the South of Jordan. This study also aimed at investigating the major elements of performance appraisal which included: establishing performance standards, establishing communication standards, measuring actual performance with established standards, discussing the appraisal with employees and giving feedback. In conducting this study, the required data was collected through a closed ended structured questionnaire. The questionnaire was adopted and adapted from many related studies. The procedures used to determine the comprehensive sample from a total population of 260 employees was the following: First, the target population was identified; second, the researcher used a formula based-sample size determination. Basically, a total of 260 questionnaires were distributed to the sampled employees and 260 were returned which represented a 100% response rate. The 260 returned questionnaires were then analyzed using smart PLS which is specially used for Structural Equation Modeling, path analysis, and confirmatory factor analysis. It is also known as analysis of covariance or causal modeling software. A descriptive statistics and correlation analysis was performed. The results of the study showed that performance appraisal has a mainly positive effect on employee performance in the banking sector of the South of Jordan. There correlation analysis showed that there was a positive and some negative association (Very strong association) between performance appraisal and employee performance.
Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo

In the last few decades, the robotics world has seen great progress at all levels, from personal assistant robots to multi-robotic and intelligent swarm systems.Simulation platforms play a critical role in this improvement due to efficiency, flexibility, and fault tolerance they provide during the cycles of developing and testing new strategies and algorithms.In this paper, we model a new mobile robot equipped with a 2D Lidar using Robot Operating System (ROS) and use this robot model to develop a robot detection method in Gazebo simulation environment.Detecting surrounding objects and distinguishing robots from these objects (kin detection) are essential in multi-robot and swarm robotic applications.In this paper, we use Lidar to handle this task by applying the following steps: (1) acquisition of laser data and pre-processing, (2) segmentation of data using the point-distance-based segmentation method, (3) classification of segments by applying two levels of filtering: filtering by segment diameter which aims to eliminate segments that don't fit a certain size (Lidar size) using features for each segment, filtering by segment shape to check remaining segments to test if they fit the Lidar's shape (which is a circle with known radius) or not by using the circle fitting method and (4) identify the position of kin relative to the observer robot.Two different scenarios are discussed in the experiments section.In the first scenario, many cylindrical objects with radius different from the robot's Lidar were used in addition to a robot, and thus objects are distinguished from the robot in the first level of filtering without using the second one which may be a complex operation.In the second scenario, various objects with a similar radius were used, and due to the similarity in the radius between the Lidar and the objects, it was necessary to apply all the method's steps to detect the kin robots.It was noticed from experiments that the accuracy of the results depends on two main factors: the distance between the observer robot and other objects or robots and the amount of noise in the Lidar measurements.
The effect of orthography on the recognition of pronunciation variants.

In conversational speech, it is very common for words' segments to be reduced or deleted.However, previous research has consistently shown that during spoken word recognition, listeners prefer words' canonical pronunciation over their reduced pronunciations (e.g., pretty pronounced [prɪti] vs. [prɪɾi]), even when the latter are far more frequent.This surprising effect violates most current accounts of spoken word recognition.The current study tests the possibility that words' orthography may be one factor driving the advantage for canonical pronunciations during spoken word recognition.Participants learned new words presented in their reduced pronunciation (e.g.[trɒti]), paired with one of three spelling possibilities: (1) no accompanying spelling, (2) a spelling consistent with the reduced pronunciation (a reduced spelling, e.g., "troddy"), or (3) a spelling consistent with the canonical pronunciation (a canonical spelling, e.g., "trotty").When listeners were presented with the new words' canonical forms for the first time, they erroneously accepted them at a higher rate if the words had been learned with a canonical spelling.These results remained robust after a delay period of 48 hours, and after additional learning trials.Our findings suggest that orthography plays an important role in the recognition of spoken words and that it is a significant factor driving the canonical pronunciation advantage observed previously.
Temporally Consistent Segmentation of Brain Tissue From Longitudinal MR Data

In this paper, we propose a new 4D segmentation formulation, aiming to improve the temporal consistency of adults' brain tissue segmentation. In our method, tissue segmentation result is represented by the membership functions of the tissues, which are derived as a result of minimizing energy in a fuzzy C-means (FCM) framework. We first introduce a variational formulation with a temporal consistency constraint on the membership functions, then convert the constraint to a vector-valued function from which the membership functions are directly computed according to an analytically defined mapping. These vector-valued functions capture the bias field and intensity means of each tissue addressing the temporal variation in intensity inhomogeneities and the intensity means of the images. The effectiveness is demonstrated on the Baltimore Longitudinal Study of Aging (BLSA) benchmark dataset. Our method takes few parameters which are easy to tune and achieves 96.4% of TC factor for gray matter, 98.1% of TC factor for white matter, significantly superior to the compared methods on this research line.
Face Detection in Thermal Images with YOLOv3

The automotive industry is currently focusing on automation in their vehicles, and perceiving the surroundings of an automobile requires the ability to detect and identify objects, events and persons, not only from the outside of the vehicle but also from the inside of the cabin. This constitutes relevant information for defining intelligent responses to events happening on both environments. This work presents a new method for in-vehicle monitoring of passengers, specifically the task of real-time face detection in thermal images, by applying transfer learning with YOLOv3. Using this kind of imagery for this purpose brings some advantages, such as the possibility of detecting faces during the day and in the dark without being affected by illumination conditions, and also because it’s a completely passive sensing solution. Due to the lack of suitable datasets for this type of application, a database of in-vehicle images was created, containing images from 38 subjects performing different head poses and at varying ambient temperatures. The tests in our database show an AP50 of 99.7% and an AP of 78.5%.
An improved bound for strong unitary uncertainty relations with refined sequence

We derive the lower bound of uncertainty relations of two unitary operators for a class of states based on the geometric-arithmetic inequality and Cauchy-Schwarz inequality. Furthermore, we propose a set of uncertainty relations for three unitary operators. Compared to the known bound introduced in Phys.Rev.A.100,022116(2019), the unitary uncertainty relations bound with our method is tighter, to a certain extent. Meanwhile, some examples are given in the paper to illustrate our conclusions.
The integration of pedagogical technologies as a condition for improving the quality of education

The article presents the results of a study of the activities of educational institutions to solve an actual problemthe problem of improving the quality of education.One of the promising ways to improve the quality of education in educational institutions is the integration of pedagogical technologies.The aim of the study is the development and theoretical substantiation of the system of integration of pedagogical technologies, the advantage of which is the sum of the advantages of the constituent technologies and focused on improving the quality of education.The model of flexible integrated technology of project-module training is proposed as an example.The developed models of flexible integral pedagogical technologies were experimentally tested and widely tested in the activities of Kazan Federal University, Kazan State Power Engineering University, Samara National Research University and several schools in Kazan (Republic of Tatarstan).This approbation led to the conclusion about the effectiveness of these models.The results of the study indicate the positive impact of these models on the main parameters characterizing the effectiveness and quality of the pedagogical process in the education system.The study confirmed that one of the important ways to increase the efficiency of the pedagogical process in education is the integration of pedagogical technologies, the implementation of flexible integrated pedagogical technologies capable adapt to different levels of complexity of pedagogical conditions and effectively solve modern problems of education.The integration of pedagogical technologies, the implementation of flexible pedagogical technologies in education allows not only to improve the quality, but also the efficiency of education, ensures the operational regulation of pedagogical technologies and the conditions of the educational process, considering the type and theme of the academic discipline, the level of students' development, availability of material resources, tasks of education.
Terahertz Image Super-Resolution Reconstruction of Passive Safety Inspection Based on Generative Adversarial Network

When passive terahertz imaging technology is used in Safety inspection, it has low image resolution and is susceptible to environment. As a result, it is difficult to support the demand for high-resolution terahertz images in related projects. This paper proposes a passive terahertz image reconstruction method based on improved Generative Adversarial Networks, and compares it with other existing methods. Experimental results show that the improved SRGAN method has higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).
Short-Term Performance Metrics Forecasting for Virtual Machine to Support Anomaly Detection Using Hybrid ARIMA-WNN Model

Anomaly detection is a significant functionality in most cloud monitoring applications. Time-series forecasting model could be easily used for predicting the values of the performance metrics which could be used for representing the performance status of the cloud environment. The proposed hybrid model combines both Autoregressive Integrated Moving Average (ARIMA) and Wavelet Neural Network (WNN) models. Firstly, ARIMA model is employed to firstly predict the linear component and then WNN model is used for the nonlinear residual component prediction. Finally, the results of the two parts are combined into the final prediction value of the performance metric. Finally the experimental results show that the hybrid model could produce more accurate short-term prediction than other models.
Hyperledger Fabric Blockchain as a Service for the IoT: Proof of Concept

Blockchain as a Service for the Internet of Things is an emerging topic in the blockchain research and industrial community, especially relating to increased system consistency, security, and privacy. Blockchains represent highly distributed and autonomous decision-making systems with distributed data and process management. Internet of Things systems share these characteristics, while also bringing the cyber-physical dimension and machine-to-machine interaction concept to the ecosystem of devices and users. Blockchain infrastructure setup and smart contract prototyping are cumbersome tasks. Onboarding an existing system to a blockchain network takes a significant amount of time and manual effort, and incorporating business logic requires a series of complex steps. For IoT systems, these task needs to be carried out having in mind the typical characteristics of such systems: low hardware, storage, and networking capabilities and high node churn and transaction volume. Moreover, these tasks need to be semi to fully automated in terms of workflows that support easy-to-use integration mechanisms for onboarding of diverse IoT infrastructures and on-demand business logic generation. In this paper, we present a Hyperledger Fabric-based Blockchain as a Service for addressing the identified challenges. Specifically, the framework is tailored to answer to specific requirements of IoT systems through three major services: Hyperledger Fabric Infrastructure Configuration Generator, Hyperledger Fabric Chaincode Builder and Hyperledger Fabric Operator Modules.
AnomalyDetect: An Online Distance-Based Anomaly Detection Algorithm

Anomaly detection is a key challenge in data mining, which refers to finding patterns in data that do not conform to expected behavior. It has a wide range of applications in many fields as diverse as finance, medicine, industry, and the Internet. In particular, intelligent operation has made great progress in recent years and has an urgent need for this technology. In this paper, we study the problem of anomaly detection in the context of intelligent operation and find the practical need for high-accuracy, online and universal anomaly detection algorithms in time series database. Based on the existing algorithms, we propose an innovative online distance-based anomaly detection algorithm. K-means and time-space trade-off mechanism are used to reduce the time complexity. Through the experiments on Yahoo! Web-scope S5 dataset we show that our algorithm can detect anomalies accurately. The comparative study of several anomaly detectors verifies the effectiveness and generality of the proposed algorithm.
Potential Field Based Path Planning with Predictive Tracking Control for Autonomous Vehicles

Path planning and tracking of autonomous vehicles are generally two independent tasks accomplished through traffic environment modeling, reference trajectory generation and vehicle motion control. In this paper, we proposed a unified path planning and tracking method utilizing the optimization algorithm of the model predictive control to generate optimal reference trajectory and vehicle motion control concurrently. The vehicle’s surroundings including obstacle vehicles and road marks are firstly reconstructed based on the artificial potential field approach which generates a reference trajectory, then the total potential of the traffic environment is incorporated into the cost function of the model predictive controller. Therefore, the path planning and tracking of the vehicle can be unified for collision avoidance with moving or fixed obstacles in its surrounding traffic environment. The simulation shows that this unified path planning and tracking method is capable of accomplishing the obstacle avoidance for the vehicle during severe traffic scenarios.
Optimization of deep learning features for age-invariant face recognition

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods.
Type 1 versus type 2 fuzzy logic speed controllers for brushless dc motors

&lt;span lang="EN-US"&gt;This work presented two fuzzy logic (FL) schemes for speed-controlled brushless DC motors. The first controller is a Type 1 FL controller (T1FLC), whereas the second controller is an interval Type 2 FL controller (IT2FLC). The two proposed controllers were compared in terms of system dynamics and performance. For a fair comparison, the same type and number of membership functions were used for both controllers. The effectiveness of the structures of the two FL controllers was verified through simulation in MATLAB/SIMULINK environment. Simulation result showed that IT2FLC exhibited better performance than T1FLC.&lt;/span&gt;
EFL Students’ Perspective on the Usefulness of ICT based Learning in Indonesian Higher Education

The study explores the perception of the EFL students in English Language Teaching through ICT based learning at Universitas Negeri Makassar. It employs a descriptive study using purposive sampling techniques by taking 57 respondents from university students. The online questionnaire used to collect data on the perception of students on the ICT based Learning of English Language Teaching. The method of descriptive in terms of percentage has been used for data processing, and the deduction method has been used to analyze and interpret data. The result of this study indicates the positive perception those university students hold on the use of the Learning Management System in English Language Teaching. Further study can refer to this Acceptance Model in determining a suitable ICT integrated learning program between the reality and user expectations of the program used.
Cooperation and Codenames: Understanding Natural Language Processing via Codenames

Codenames – a board game by Vlaada Chvátil – is a game that requires deep, multi-modal language understanding. One player, the codemaster, gives a clue to another set of players, the guessers, and the guessers must determine which of 25 possible words on the board correspond to the clue. The nature of the game requires understanding language in a multi-modal manner – e.g., the clue ‘cold’ could refer to temperature or disease. The recently proposed Codenames AI Competition seeks to advance natural language processing, by using Codenames as a testbed for multi-modal language understanding. In this work, we evaluate a number of different natural language processing techniques (ranging from neural approaches to classical knowledge-base methods) in the context of the Codenames AI framework, attempting to determine how different approaches perform. The agents are evaluated when working with identical agents, as well as evaluated with all other approaches – i.e., when they have no knowledge about their partner.
Multi-view representation learning in multi-task scene

Over recent decades have witnessed considerable progress in whether multi-task learning or multi-view learning, but the situation that considers both learning scenes simultaneously has received not too much attention. How to utilize multiple views’ latent representation of each single task to improve each learning task’s performance is a challenge problem. Based on this, we proposed a novel semi-supervised algorithm, termed as multi-task multi-view learning based on common and special features (MTMVCSF). In general, multi-views are the different aspects of an object and every view includes the underlying common or special information of this object. As a consequence, we will mine multiple views’ jointly latent factor of each learning task, jointly latent factor is consisted of each view’s special feature and the common feature of all views. By this way, the original multi-task multi-view data have degenerated into multi-task data, and exploring the correlations among multiple tasks enables to make an improvement on the performance of learning algorithm. Another obvious advantage of this approach is that we get latent representation of the set of unlabeled instances by the constraint of regression task with labeled instances. In classification and semi-supervised clustering tasks, using implicit representation as input peforms much better than using raw data. Furthermore, an anti-noise multi-task multi-view algorithm called AN-MTMVCSF is proposed, which has a strong adaptability to noise labels. The effectiveness of these algorithms is proved by a series of well-designed experiments on both real-world and synthetic data.
Relationship Profiling over Social Networks: Reverse Smoothness from Similarity to Closeness

On social networks, while nodes bear rich attributes, we often lack the ‘semantics’ of why each link is formed–and thus we are missing the ‘road signs’ to navigate and organize the complex social universe. How to identify relationship semantics without labeled links? Founded on the prevalent homophily principle, we propose the novel problem of Attribute-based Relationship Profiling (ARP), to profile the closeness w.r.t. the underlying relationships (e.g., schoolmate) between users based on their similarity in the corresponding attributes (e.g., schools) and, as output, learn a set of social affinity graphs, where each link is weighted by its probabilities of carrying the relationships. As requirements, ARP should be systematic and complete to profile every link for every relationship– our challenges lie in effectively modeling homophily. We propose a novel reverse smoothness principle by observing that the similarity-closeness duality of homophily is consistent with the well-known smoothness assumption in graph-based semi-supervised learning– only the direction of inference is reversed. To realize smoothness over noisy social graphs, we further propose a novel holistic closeness modeling approach to capture ‘high-order’ smoothness by extending closeness from edges to paths. Extensive experiments on three real-world datasets demonstrate the efficacy of our proposed algorithm for ARP.