Reviewerly/exHarmony huggingface dataset:
The impacts of human resource management practices on employees’ motivation and loyalty

The research was conducted in order to identify the relationships between human resource practices, employee motivation and employee loyalty.Quantitative research method was applied with questionnaires directly delivered to 595 employees in hospitality sector in Ho Chi Minh city, Vietnam.Statistics techniques including exploratory factor analysis, reliability analysis, multiple regression and mediation analysis were used.The empirical results showed that variables of team spirit, career development, compensation, relationship with manager and working environment directly affected employee motivation and consequently, they provided indirect effects on loyalty through the mediation of employee motivation.In addition, employee loyalty was also directly affected by career development, working environment and employee motivation.It is suggested that hospitality sector should develop attractive compensation and benefit policies, comfortable workplace environment, coherent teamwork culture as well as straightforward career training and development plan for achieving higher level of employee loyalty.
Channel Invariant Speaker Embedding Learning with Joint Multi-Task and Adversarial Training

Using deep neural network to extract speaker embedding has significantly improved the speaker verification task. However, such embeddings are still vulnerable to channel variability. Previous works have used adversarial training to suppress channel information to extract channel-invariant embedding and achieved a significant improvement. Inspired by the successful joint multi-task and adversarial training with phonetic information for phonetic-invariant speaker embedding learning, in this paper, a similar methodology is developed to suppress the channel variability. By treating the recording devices or environments as the channel variability, two individual experiments are carried out, and consistent performance improvement is observed in both cases. The best performance is obtained by sequentially applying multi-task training at the statistics pooling layer and adversarial training at the embedding layer, achieving 10.77% and 9.37% relative improvements in terms of EER compared to the baselines, for the recording environments or devices level, respectively.
Lance: efficient low-precision quantized winograd convolution for neural networks based on graphics processing units 

Accelerating deep convolutional neural networks has become an active topic and sparked an interest in academia and industry. In this paper, we propose an efficient low-precision quan-tized Winograd convolution algorithm, called LANCE, which combines the advantages of fast convolution and quantization techniques. By embedding linear quantization operations into the Winograd-domain, the fast convolution can be performed efficiently under low-precision computation on graphics processing units. We test neural network models with LANCE on representative image classification datasets, including SVHN, CIFAR, and ImageNet. The experimental results show that our 8-bit quantized Winograd convolution improves the performance by up to 2.40× over the full-precision convolution with trivial accuracy loss.
Efficient Simulation of Loop Quantum Gravity: A Scalable Linear-Optical Approach

The problem of simulating complex quantum processes on classical computers gave rise to the field of quantum simulations. Quantum simulators solve problems, such as boson sampling, where classical counterparts fail. In another field of physics, the unification of general relativity and quantum theory is one of the greatest challenges of our time. One leading approach is loop quantum gravity (LQG). Here, we connect these two fields and design a linear-optical simulator such that the evolution of the optical quantum gates simulates the spin-foam amplitudes of LQG. It has been shown that computing transition amplitudes in simple quantum field theories falls into the bounded-error quantum polynomial time class, which strongly suggests that computing transition amplitudes of LQG are classically intractable. Therefore, these amplitudes are efficiently computable with universal quantum computers, which are, alas, possibly decades away. We propose here an alternative special-purpose linear-optical quantum computer that can be implemented using current technologies. This machine is capable of efficiently computing these quantities. This work opens a new way to relate quantum gravity to quantum information and will expand our understanding of the theory.
Entanglement as upper bound for the nonlocality of a general two-qubit system

Nonlocality and entanglement are not only the fundamental characteristics of quantum mechanics but also important resources for quantum information and computation applications. Exploiting the quantitative relationship between the two different resources is of both theoretical and practical significance. The common choice for quantifying the nonlocality of a two-qubit state is the maximal violation of the Clauser-Horne-Shimony-Holt inequality. That for entanglement is entanglement of formation, which is a function of the concurrence. In this paper, we systematically investigate the quantitative relationship between the entanglement and nonlocality of a general two-qubit system. We rederive a known upper bound on the nonlocality of a general two-qubit state, which depends on the state's entanglement. We investigate the condition that the nonlocality of two different two-qubit states can be optimally stimulated by the same nonlocality test setting and find the class of two-qubit state pairs that have this property. Finally, we obtain the necessary and sufficient condition that the upper bound can be reached.
Integration Colour and Texture Features for Content-based Image Retrieval

Content-Based Image Retrieval offers an automatic way to extract visual image contents such as colour, texture, and shape so-called extracted features.Due to growing volume of digital images, Content-Based Image Retrieval is emerged to store and retrieved images from large scale databases.However, Content-Based Image Retrieval faces a challenge of meaning "Semantic gap" between machine and human conceptual.How to reduce this gap between colour and/or texture features that represent an object in the image?It is still the challenge that basically related to the effectiveness of image representation by extracted features and similarity measures between a query image features and database image features.Hence, different visual features have been proposed such as Gary Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Discrete Wavelet Transform (DWT) texture features that are extracted from gray-scale images.This paper presents an unsupervised algorithm that exploits data and score-level fusion to address the semantic gap.The algorithm first extracts mentioned features from colour images in HSV and YCbCr colour spaces to increase the effectiveness of image representation by integrating texture and colour visual information in terms of data-level fusion.Resulted similarity retrieval values are then fused in three versions of score-level fusion, summing values without weights, fixed, and adaptive weights using linear regression to raise relevant images in a ranked retrieved images list.WANG standard colour images are used to implement the algorithm.Rates of achievement in image retrievals are enhanced at both levels.
Improving robot dual-system motor learning with intrinsically motivated meta-control and latent-space experience imagination

Combining model-based and model-free learning systems has been shown to improve the sample efficiency of learning to perform complex robotic tasks. However, dual-system approaches fail to consider the reliability of the learned model when it is applied to make multiple-step predictions, resulting in a compounding of prediction errors and performance degradation. In this paper, we present a novel dual-system motor learning approach where a meta-controller arbitrates online between model-based and model-free decisions based on an estimate of the local reliability of the learned model. The reliability estimate is used in computing an intrinsic feedback signal, encouraging actions that lead to data that improves the model. Our approach also integrates arbitration with imagination where a learned latent-space model generates imagined experiences, based on its local reliability, to be used as additional training data. We evaluate our approach against baseline and state-of-the-art methods on learning vision-based robotic grasping in simulation and real world. The results show that our approach outperforms the compared methods and learns near-optimal grasping policies in dense- and sparse-reward environments.
Automatic detection of relation assertion errors and induction of relation constraints

Although the link prediction problem, where missing relation assertions are predicted, has been widely researched, error detection did not receive as much attention. In this paper, we investigate the problem of error detection in relation assertions of knowledge graphs, and we propose an error dete ction method which relies on path and type features used by a classifier for every relation in the graph exploiting local feature selection. Furthermore, we propose an approach for automatically correcting detected errors originated from confusions between entities. Moreover, we present an approach that translates decision trees trained for relation assertion error detection into SHACL-SPARQL relation constraints. We perform an extensive evaluation on a variety of datasets comparing our error detection approach with state-of-the-art error detection and knowledge completion methods, backed by a manual evaluation on DBpedia and NELL. We evaluate our error correction approach results on DBpedia and NELL and show that the relation constraint induction approach benefits from the higher expressiveness of SHACL and can detect errors which could not be found by automatically learned OWL constraints.
Proposed Model for Arabic Grammar Error Correction Based on Convolutional Neural Network

Deep learning and machine learning algorithms are widely used in Arabic Natural Language Processing (ANLP) aims to develop tools and techniques to process human languages in several forms like written and spoken context. The ANLP still lacks tools and applications to bridge the hole between Arabic and other languages. Furthermore, there are insufficient available resources such as dictionaries, grammatical rules, corpora, etc. Grammatical Error Correction (GEC) one of the NLP tasks seek to develop automatic tools to correct grammar and spelling, the input is incorrect words or sentences and the output become the corrected version of the same sentences. Recently, Neural Networks are used in GEC and had promising results but some improvement is still needed. The limitation of the previous studies are handcrafted and most often extracted from short sentences, also the whole previous Arabic neural approaches are used Recurrent Neural Networks (RNNs). This paper present work-in-progress for developing an Arabic GEC model based on multi convolutional layers with an attention mechanism. Moreover, proposed the incremental techniques and multi-round training model using parallel corpus to get more accuracy and fluently results, also to achieve human-level performance.
Texture Analysis of Ultrasound Images to Differentiate Simple Fibroadenomas From Complex Fibroadenomas and Benign Phyllodes Tumors

Objectives American College of Radiology Breast Imaging Reporting and Data System (BI‐RADS) category 4A lesions can be distinguished from BI‐RADS 3 lesions with main ultrasound (US) findings such as a well‐defined contour, round/oval shape, and parallel orientation with a homogeneous echo pattern. Breast Imaging Reporting and Data System 4A solid masses might be diagnosed as simple fibroadenomas (SFAs), complex fibroadenomas (CFAs), or benign phyllodes tumors (BPTs). Complex fibroadenomas have an increased risk of invasive cancer development than SFAs, and BPTs have a risk of borderline‐malignant phyllodes tumor transformation; both of them are surgically treated, whereas follow‐up procedures are applied in SFAs. It is essential to differentiate SFAs from CFAs and BPTs. Grayscale features of these lesions include a prominent overlap. Texture analyses in breast lesions have contributions in benign‐malignant lesion differentiation. In this study, we aimed to use texture analysis of US images to differentiate these benign lesions. Methods Grayscale US features of lesions (32 SFAs, 31 CFAs, and 32 BPTs) were classified according to the BI‐RADS. Texture analysis of US images with LIFEx software ( http://www.lifexsoft.org ) was performed retrospectively. First‐ and second‐order histogram parameters were evaluated. Results In grayscale US, the shape, orientation, and posterior acoustic characteristics had statistical significance ( P &lt; .05). In the statistical analysis, skewness, kurtosis, excess kurtosis, gray‐level co‐occurrence matrix (GLCM)‐energy, GLCM‐entropy log 2, and GLCM‐entropy log 10 revealed significant differences among all 3 groups ( P &lt; .05). Conclusions As grayscale US features show prominent intersections, and treatment options differ, correct diagnosis is essential in SFAs, CFAs, and BPTs. In this study, we concluded that texture analysis of US images can discriminate SFAs from CFAs and BPTs. Texture analyses of US images is a potential candidate diagnostic tool for these lesions, and accurate diagnoses will preclude patients from undergoing unnecessary biopsies.
Impact of an educational program on earthquake awareness and preparedness in Nepal

Abstract. Scientific education of local communities is key in helping to reduce the risk associated with natural disasters such as earthquakes. Western Nepal has a history of major seismic events and is highly prone to further earthquakes; however, the majority of the population is not aware about or prepared for them. To increase earthquake awareness and improve preparedness, a seismology education program was established at 22 schools in Nepal. At each school, educational activities were performed by teaching earthquake-related topics in classrooms, offering training to teachers and through installing a low-cost seismometer network which supported both teaching and awareness objectives. To test the effects of this program, we conducted two surveys with school children, one before and one after the initiation of the program, with several hundred participants in each. The survey findings highlighted that educational activities implemented at schools are effective in raising the awareness levels of children, promoting broader social learning in the community, thus improving the adaptive capacities and preparedness for future earthquakes. However, perceptions of risk did not change very much. The high and positive impact of the program on the students and the community is encouraging for the continuation and expansion of the program.
Gender Classification Based on Face Images of Local Binary Pattern Using Support Vector Machine and Back Propagation Neural Networks

In this paper, presented a Gender classification through Support Vector Machine (SVM) and Scaled Conjugate Gradient Back Propagation Neural Network (SCGBPNN) from face images using Local Binary Patterns.To achieve better classification performance, need to be applied pre-processing technique first and then extracted the features on facial images from Local Binary Pattern Histogram (LBPH) method.These extracted features were stored into a vector called feature vector.Later, the feature vector is inputted to Polynomial SVM and SCG Back Propagation Neural Network classification methods along with labelled target vector.The performance of the both classifiers is measured by the labelled AT&T face database and Nottingham Scan Database.
An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network

The developments in the fields of industrial Internet of Things (IIoT) and big data technologies have made it possible to collect a lot of meaningful industrial process and quality-based data. The gathered data are analyzed using contemporary statistical methods and machine learning techniques. Then, the extracted knowledge can be used for predictive maintenance or prognostic health management. However, it is difficult to gather complete data due to several issues in IIoT, such as devices breaking down, running out of battery, or undergoing scheduled maintenance. Data with missing values are often ignored, as they may contain insufficient information from which to draw conclusions. In order to overcome these issues, we propose a novel, effective missing data handling mechanism for the concepts of symmetry principles. While other existing methods only attempt to estimate missing parts, the proposed method generates a whole set of data set using Gaussian process regression and a generative adversarial network. In order to prove the effectiveness of the proposed framework, we examine a real-world, industrial case involving an air pressure system (APS), where we use the proposed method to make quality predictions and compare the results with existing state-of-the-art estimation methods.
Near-Duplicate Image Detection System Using Coarse-to-Fine Matching Scheme Based on Global and Local CNN Features

Due to the great success of convolutional neural networks (CNNs) in the area of computer vision, the existing methods tend to match the global or local CNN features between images for near-duplicate image detection. However, global CNN features are not robust enough to combat background clutter and partial occlusion, while local CNN features lead to high computational complexity in the step of feature matching. To achieve high efficiency while maintaining good accuracy, we propose a coarse-to-fine feature matching scheme using both global and local CNN features for real-time near-duplicate image detection. In the coarse matching stage, we implement the sum-pooling operation on convolutional feature maps (CFMs) to generate the global CNN features, and match these global CNN features between a given query image and database images to efficiently filter most of irrelevant images of the query. In the fine matching stage, the local CNN features are extracted by using maximum values of the CFMs and the saliency map generated by the graph-based visual saliency detection (GBVS) algorithm. These local CNN features are then matched between images to detect the near-duplicate versions of the query. Experimental results demonstrate that our proposed method not only achieves a real-time detection, but also provides higher accuracy than the state-of-the-art methods.
A Proposed Model to Forecast Hourly Global Solar Irradiation Based on Satellite Derived Data, Deep Learning and Machine Learning Approaches

An accurate short-term global solar irradiation (GHI) forecast is essential for integrating the photovoltaic systems into the electricity grid by reducing some of the problems caused by the intermittency of solar energy, including rapid fluctuations in energy, management storage, and the high costs of electricity.In this paper, the authors proposed a new hybrid approach to forecast hourly GHI for the Al-Hoceima city, Morocco.For this purpose, a deep long short-term memory network is trained on a combination of the hourly GHI ground measurements from the meteorological station of Al-Hoceima and the satellite-derived GHI from the neighbouring pixels of the point of interest.Xgboost, Random Forest, and Recursive Feature Elimination with cross-validation were used to select the most relevant features, the lagged satellite-derived GHI around the point of interest, as input to the proposed model where the best forecasting model is selected using the Grid Search algorithm.The simulation and results showed that the proposed approach gives high performance and outperformed other benchmark approaches.
Teleportation of an Unknown Four-Qubit Cluster State Based on Cluster States With Minimum Resource

In this paper, we first present a scheme for teleporting an unknown four-qubit cluster state via a cluster state chain between two distant nodes, which do not share entanglement pairs directly. Adjacent nodes are linked by a partially entangled four-qubit cluster state with each other. In our scheme, we deduce the relationship between the coefficients of the entangled cluster states and the success probability of teleportation. Moreover, we derive the unitary matrixes for establishing direct channel between two distant nodes, which reduce the computational complexity and resource consumption significantly. By performing entanglement swapping simultaneously, our scheme is more flexible and efficient than most existing schemes.
Towards a Chatbot for educational and vocational guidance in Morocco: Chatbot E-Orientation

Chatbots are currently used in various online applications; often for shopping or as a personal assistant.These chatbots offer a range of potential benefits, including personalization and 24/7 instant availability.These positive aspects of chatbots lend to applications in the educational sector.they represent a new type of human-machine interface in natural language.However, chatbots in academia have received only limited attention, for example by providing organizational support for studies or courses and exams.This branch of research is just emerging in the scientific community, therefore, in our article, we set up a chatbot in the field of educational and professional guidance which is based on the theory of John Holland and the RIASEC questionnaire in order to determine the dominant type of personality of undergraduate and graduate students that wants to enter the job market.
A Rule-Based Method for Table Detection in Website Images

Table detection is an essential part of a document analysis because tables are among the most efficient methods for systematically summarizing information. Therefore, numerous studies on detecting tables not only from documents but also from websites have been conducted. Although, the number of websites has been growing explosively recently, most of these studies suffer from detecting tables which are image types rather than tagging due to the variability of size, contents, color, and shapes. In this paper, we propose an efficient yet robust method for detecting tables in image formats, which can apply to both documents and websites. Instead of employing recently developed deep learning methods, which require extensive training for diversity, we apply a rule-based detection method by using key features of many tables, namely, the grid format of the text provided in the tables. The proposed method consists of two stages: a feature extraction stage and a grid pattern recognition stage. In the first stage, we extract the features of the contents in the tables. We then remove the features of non-text objects and texts not included in tables. In the second stage, we build tree structures from the features and apply a novel algorithm for determining the grid pattern. When we applied our method to a website dataset, the experimental results showed a precision, recall, and F1-measure of 84.5%, 72%, and 0.778, which are improvements of 3.6%, 24.16%, and 0.276 over a previous method, respectively, while also achieving the fastest processing time. In addition, the proposed rule-based method allows the structure of the contents in the table to be easily restored.