Reviewerly/exHarmony huggingface dataset:
The Impact of Administrative Transparency on the Relationship between Internal Marketing Policies and the Job Satisfaction of the Internal Customer: An Applied Study at Taif University - Kingdom of Saudi Arabia: أثر الشفافية الإدارية في العلاقة بين سياسات التسويق الداخلي وبين الرضا الوظيفي للعميل الداخلي - دراسة تطبيقية بجامعة الطائف، المملكة العربية السعودية -

This study aims to identify the extent of the practice of internal marketing policies and administrative transparency at Taif University, and its impact on the level of internal customer satisfaction (employee). As well as to observe the mediating effect of administrative transparency in the relationship between internal marketing policies and internal customer satisfaction. This study attempts to fill the gap in previous studies related to the impact of internal marketing policies (independent variable) and administrative transparency (intermediate variable) on enhancing internal customer satisfaction (dependent variable) in Saudi universities. A descriptive-analytical approach was used, and 254 questionnaires were distributed randomly stratified to the study sample. The number of valid questionnaires for analysis was (125), which were analysed using the statistical program (SPSS). The findings were that there was a medium degree of approval towards each of the dimensions of internal marketing, the practice of administrative transparency and internal customer satisfaction. There was also a relationship and impact between internal marketing policies and internal customer satisfaction. Similarly, there was a relationship and impact between administrative transparency and internal customer satisfaction. There were a relationship and impact between internal marketing policies and administrative transparency. Moreover, it found that administrative transparency strengthens the positive relationship between internal marketing policies and internal customer. This study recommended increasing employees’ job satisfaction by implementing an effective system of incentives and rewards, training and development and promotions. Furthermore, universities should improve the level of administrative transactions and communications, financial facilitation, and good relations among employees. Moreover, they have to adopt the concept of internal marketing by meeting the needs of employees and working on developing them. Finally, they have to tout administrative transparency policies, as well as clarifying the reasons for administrative decisions with stability and ease of understanding in order to build credibility and trust with employees.
Modelling of Blink-Related Eyelid-Induced Shunting on the Electrooculogram

Besides the traditional regression model-based techniques to estimate the gaze angles (GAs) from electrooculography (EOG) signals, more recent works have investigated the use of a battery model for GA estimation. This is a white-box, explicit and physically-driven model which relates the monopolar EOG potential to the electrode-cornea and electrode-retina distances. In this work, this model is augmented to cater for the blink-induced EOG signal characteristics, by modelling the eyelid-induced shunting effect during blinks. Specifically, a channel-dependent parameter representing the extent to which the amount of eyelid opening affects the particular EOG channel is introduced. A method to estimate these parameters is also proposed and the proposed model is validated by incorporating it in a Kalman filter to estimate the eyelid opening during blinks. The results obtained have demonstrated that the proposed model can accurately represent the blink-related eyelid-induced shunting.
HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization

Most consumer-grade digital cameras can only capture a limited range of luminance in real-world scenes due to sensor constraints. Besides, noise and quantization errors are often introduced in the imaging process. In order to obtain high dynamic range (HDR) images with excellent visual quality, the most common solution is to combine multiple images with different exposures. However, it is not always feasible to obtain multiple images of the same scene and most HDR reconstruction methods ignore the noise and quantization loss. In this work, we propose a novel learning-based approach using a spatially dynamic encoder-decoder network, HDRUNet, to learn an end-to-end mapping for single image HDR reconstruction with denoising and dequantization. The network consists of a UNet-style base network to make full use of the hierarchical multi-scale information, a condition network to perform pattern-specific modulation and a weighting network for selectively retaining information. Moreover, we propose a Tanh_L1 loss function to balance the impact of over-exposed values and well-exposed values on the network learning. Our method achieves the state-of-the-art performance in quantitative comparisons and visual quality. The proposed HDRUNet model won the second place in the single frame track of NITRE2021 High Dynamic Range Challenge.
Neural Machine Translation with Monolingual Translation Memory

Prior work has proved that Translation memory (TM) can boost the performance of Neural Machine Translation (NMT). In contrast to existing work that uses bilingual corpus as TM and employs source-side similarity search for memory retrieval, we propose a new framework that uses monolingual memory and performs learnable memory retrieval in a cross-lingual manner. Our framework has unique advantages. First, the cross-lingual memory retriever allows abundant monolingual data to be TM. Second, the memory retriever and NMT model can be jointly optimized for the ultimate translation goal. Experiments show that the proposed method obtains substantial improvements. Remarkably, it even outperforms strong TM-augmented NMT baselines using bilingual TM. Owning to the ability to leverage monolingual data, our model also demonstrates effectiveness in low-resource and domain adaptation scenarios.
Decentralized multi-client functional encryption for set intersection with improved efficiency

Abstract Functional encryption (FE) is a new paradigm of public key encryption that can control the exposed information of plaintexts by supporting computation on encrypted data. In this paper, we propose efficient multi-client FE (MCFE) schemes that compute the set intersection of ciphertexts generated by two clients. First, we propose an MCFE scheme that calculates the set intersection cardinality (MCFE-SIC) and prove its static security under dynamic assumptions. Next, we extend our MCFE-SIC scheme to an MCFE scheme for set intersection (MCFE-SI) and prove its static security under dynamic assumptions. The decryption algorithm of our MCFE-SI scheme is more efficient than the existing MCFE-SI scheme because it requires fewer pairing operations to calculate the intersection of two clients. Finally, we propose a decentralized MCFE scheme for set intersection (DMCFE-SI) that decentralizes the generation of function keys. Our MCFE schemes can be effectively applied to a privacy-preserving contact tracing system to prevent the spread of recent infectious diseases.
Heuristic Weakly Supervised 3D Human Pose Estimation

Monocular 3D human pose estimation from RGB images has attracted significant attention in recent years. However, recent models depend on supervised training with 3D pose ground truth data or known pose priors for their target domains. 3D pose data is typically collected with motion capture devices, severely limiting their applicability. In this paper, we present a heuristic weakly supervised 3D human pose (HW-HuP) solution to estimate 3D poses in when no ground truth 3D pose data is available. HW-HuP learns partial pose priors from 3D human pose datasets and uses easy-to-access observations from the target domain to estimate 3D human pose and shape in an optimization and regression cycle. We employ depth data for weak supervision during training, but not inference. We show that HW-HuP meaningfully improves upon state-of-the-art models in two practical settings where 3D pose data can hardly be obtained: human poses in bed, and infant poses in the wild. Furthermore, we show that HW-HuP retains comparable performance to cutting-edge models on public benchmarks, even when such models train on 3D pose data.
A Hybrid Genetic XK-means++ Clustering Algorithm with Empty Cluster Reassignment

K-means is a classical clustering algorithm in many research areas, such as, document clustering, bioinformatics, image segmentation and pattern recognition. But, K-means is sensitive to the initial choice of cluster centers. A successful modification of K-means has been introduced in the literature by improving arbitrary cluster centers in the initialization stage-called K-means++. eXploratory K-means(XK-means)is another modification of K-means, which added an exploratory disturbance onto the vector of cluster centers, so as to improve the condition of sensitivity to the initial centers and jump out of the local optimum. However, the empty clusters may appear in the process of XK-means. The efficiency of the clustering result will be damaged by these empty clusters. In this paper, we try adding exploratory disturbance in K-means++ referred to as XK-means++. The same as XK-means, empty clusters also appear in the iteration process of XK-means++. Therefore, in this paper, an empty-cluster-reassignment technique is introduced and used in XK-means++(called EXK-means++). Furthermore, we combined the EXK-means++ with genetic mechanism, obtain a GEXK-means++ clustering algorithm. The data simulation results show that GEXK-means++is promising and effective.
SLGCN: Structure Learning Graph Convolutional Networks for Graphs under Heterophily.

The performances of GNNs for representation learning on the graph-structured data are generally limited to the issue that existing GNNs rely on one assumption, i.e., the original graph structure is reliable. However, since real-world graphs is inevitably noisy or incomplete, this assumption is often unrealistic. In this paper, we propose a structure learning graph convolutional networks (SLGCNs) to alleviate the issue from two aspects, and the proposed approach is applied to node classification. Specifically, the first is node features, we design a efficient-spectral-clustering with anchors (ESC-ANCH) approach to efficiently aggregate feature representationsfrom all similar nodes, no matter how far away they are. The second is edges, our approach generates a re-connected adjacency matrix according to the similarities between nodes and optimized for the downstream prediction task so as to make up for the shortcomings of original adjacency matrix, considering that the original adjacency matrix usually provides misleading information for aggregation step of GCN in the graphs with low level of homophily. Both the re-connected adjacency matrix and original adjacency matrix are applied to SLGCNs to aggregate feature representations from nearby nodes. Thus, SLGCNs can be applied to graphs with various levels of homophily. Experimental results on a wide range of benchmark datasets illustrate that the proposed SLGCNs outperform the stat-of-the-art GNN counterparts.
Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification

Recent years have witnessed significant progress in person re-identification (ReID). However, current ReID approaches still suffer from considerable performance degradation when unseen testing domains exhibit different characteristics from the source training ones, known as the domain generalization problem. Given multiple source training domains, previous Domain Generalizable ReID (DG-ReID) methods usually learn all domains together using a shared network, which can't learn sufficient knowledge from each domain. In this paper, we propose a novel Multiple Domain Experts Collaborative Learning (MECL) framework for better exploiting all training domains, which benefits from the proposed Domain-Domain Collaborative Learning (DDCL) and Universal-Domain Collaborative Learning (UDCL). DDCL utilizes domain-specific experts for fully exploiting each domain, and prevents experts from over-fitting the corresponding domain using a meta-learning strategy. In UDCL, a universal expert supervises the learning of domain experts and continuously gathers knowledge from all domain experts. Note, only the universal expert will be used for inference. Extensive experiments on DG-ReID benchmarks demonstrate the effectiveness of DDCL and UDCL, and show that the whole MECL framework significantly outperforms state-of-the-arts. Experimental results on DG-classification benchmarks also reveal the great potential of applying MECL to other DG tasks.
A Multi-Modal Joint Voice Parts Division Method Based On Deep Learning

The division of singers' voice parts is a basic problem in vocal arts medicine and vocal music teaching. Improper voice parts classification can have a negative impact on singers' health. In this work, we propose an objective method to classify the voice parts from the acoustic perspective and anatomic perspective. We first extract relevant features from singers' voice signals and laryngoscope images, then splice the features of these two modalities. After that, a voice parts division convolutional network (VDCNN) model is constructed to evaluate singers' voice categories. In order to realize this model, real data of voice signals and laryngeal images from 74 singers is applied to train and adjust this model. The analysis of experimental results shows that the characteristic parameters and network structure used in this model are significantly improved compared with similar methods, and the optimal classification accuracy is 93.17%.
THE DEVELOPMENT OF PUBLIC HEALTH SPORTS IN COLLEGES AND UNIVERSITIES BASED ON THE ANALYTIC HIERARCHY PROCESS

ABSTRACT With the increasing public attention to public health sports in colleges and universities, the development of public health sports urgently needs more scientific methods to be optimized. Based on this, this paper proposes to use AHP to optimize the design of public health sports in colleges and universities. Based on the simple analysis of the development of public health sports in colleges and universities and the application of AHP, this paper takes the healthy development of public health sports in colleges and universities as the guiding ideology, applies the analytic hierarchy process to course selection and student evaluation, gives different weights to the influencing factors of course selection and evaluation, and carries out simulation analysis with an example. The test results show that the application of AHP in public health sports can improve the effect of course selection and realize the diversity of teaching evaluation.
Finding All Bounded-Length Simple Cycles in a Directed Graph

A new efficient algorithm is presented for finding all simple cycles that satisfy a length constraint in a directed graph. When the number of vertices is non-trivial, most cycle-finding problems are of practical interest for sparse graphs only. We show that for a class of sparse graphs in which the vertex degrees are almost uniform, our algorithm can find all cycles of length less than or equal to $k$ in $O((c+n)(k-1)d^k)$ steps, where $n$ is the number of vertices, $c$ is the total number of cycles discovered, $d$ is the average degree of the graph's vertices, and $k > 1$. While our analysis for the running time addresses only a class of sparse graphs, we provide empirical and experimental evidence of the efficiency of the algorithm for general sparse graphs. This algorithm is a significant improvement over the only other deterministic algorithm for this problem known to us; it also lends itself to massive parallelism. Experimental results of a serial implementation on some large real-world graphs are presented.
Quantum mean-value approximator for hard integer-value problems

The quantum mean value (QMV) problem is a classically difficult problem that is the central part of many quantum algorithms. We show that using an approximation instead of the exact expectation results in a quadratic improvement in the efficiency of QMV and thus the underlying quantum algorithm. Together with efficient classical sampling algorithms, a quantum algorithm with minimal gate count can thus improve the efficiency of algorithms to solve general integer-value problems, such as the shortest vector problem investigated in this work.
An exploration of ingroup behaviour and social psychology in developing socially abhorrent behaviours in social media and financial systems.

The following paper provides a preliminary investigation into the growth of Cryptocurrency subgroups, the abuse of social media using automated systems, the enhancement of trolling and the ability for these activities to pose both a political and financial threat. Malicious actors have utilised technology to leverage existing psychological behaviours and create tribalistic responses that allow for the automated approach to controlling and manipulating individuals online. In this, authoritarian leaders can asymmetrically leverage sociological and psychological benefits that developed through evolutionary benefits but yet exhibit adverse effects in modern societies.
Need-Seeking: Creating, Discovering or Recovering Needs?

Need-seeker approach, which orients new product development towards the satisfaction of future needs, has been recognized as one of the most efficient innovation strategies to date. But finding future needs to address remains a challenge for companies, entrepreneurs and practitioners, as they lack a methodological framework to structure their approach. In this chapter, we first elaborate on three paradigms for need-seeking: discovery of future needs, creation of new needs, and recovery of fundamental needs. We then provide examples of methods supporting each paradigm, and tentatively position them in terms of reliability and affordability, so that innovation teams can make informed choices in their application. Thereby, we expect to contribute to the field of prospective ergonomics and its concrete implementation, as well as to the promotion of radical innovation based on needs and uses rather than based solely on technology.
HiPaR: Hierarchical Pattern-Aided Regression

We introduce HiPaR, a novel pattern-aided regression method for data with both categorical and numerical attributes. HiPaR mines hybrid rules of the form \(p \Rightarrow y = f(X)\) where p is the characterization of a data region and f(X) is a linear regression model on a variable of interest y. The novelty of the method lies in the combination of an enumerative approach to explore the space of regions and efficient heuristics that guide the search. Such a strategy provides more flexibility when selecting a small set of jointly accurate and human-readable hybrid rules that explain the entire dataset. As our experiments shows, HiPaR mines fewer rules than existing pattern-based regression methods while still attaining state-of-the-art prediction performance.
Using Early-Learning Regularization to Classify Real-World Noisy Data

The memorization problem is well-known in the field of computer vision. Liu et al. propose a technique called Early-Learning Regularization, which improves accuracy on the CIFAR datasets when label noise is present. This project replicates their experiments and investigates the performance on a real-world dataset with intrinsic noise. Results show that their experimental results are consistent. We also explore Sharpness-Aware Minimization in addition to SGD and observed a further 14.6 percentage points improvement. Future work includes using all 6 million images and manually clean a fraction of the images to fine-tune a transfer learning model. Last but not the least, having access to clean data for testing would also improve the measurement of accuracy.
Integration of Knowledge in Education

Based on the citation records, this study for the first time examines the bibliometric characteristics of integration of knowledge in education literature from the year 1985 to June 2020. This study reviews total 102 documents published in the Web of Science index journals. For the visualization and analysis, VOSviewer software was applied. The bibliometric results identified the most productive and impactful authors and journals in this literature. After the science mapping of bibliometric data, this paper identifies the major underlying research themes integration of knowledge in education. The key themes in this literature include the knowledge transfer, higher education, university, innovation and management. Among these keywords, knowledge transfer, higher education and model show the strongest correlation, followed by innovation, commercialization; performance and management; multiple intelligence and information. However, there is no significant correlation among keywords like knowledge transfer, knowledge integration, healthcare, and multiple intelligence. Therefore, future research can be directed focusing on these keywords to expand the existing knowledge in this literature.
Generating Enriched Synthetic German Hospital Claims Data – A Use Case Driven Approach

Clinical data and above all individual patient data are highly sensitive. All the more it is important to protect these critical information while analyzing and exploring their specifics for further research. However, in order to enable students and other researchers to develop decision support systems and to use modern data analysis methods such as intelligent pattern recognition, the provision of clinical data is essential. In order to allow this while completely protecting the privacy of a patient, we present a mixed approach to generate semantically and clinically realistic data: (1) We use available synthetic data, extract information on patient visits and diagnoses and adapt them to the encoding systems of German claims data; (2) based on a statistical analysis of real German hospital data, we identify distributions of procedures, laboratory data and other measurements and transfer them to the synthetic patient’s visits and diagnoses in a semi-automated way. This enabled us to provide students a data set that is as semantically and clinically realistic as possible to apply patient-level prediction algorithms within the development of clinical decision support systems without putting patient data at any risk.
Improving Generalization in Mountain Car Through the Partitioned Parameterized Policy Approach via Quasi-Stochastic Gradient Descent

The reinforcement learning problem of finding a control policy that minimizes the minimum time objective for the Mountain Car environment is considered. Particularly, a class of parameterized nonlinear feedback policies is optimized over to reach the top of the highest mountain peak in minimum time. The optimization is carried out using quasi-Stochastic Gradient Descent (qSGD) methods. In attempting to find the optimal minimum time policy, a new parameterized policy approach is considered that seeks to learn an optimal policy parameter for different regions of the state space, rather than rely on a single macroscopic policy parameter for the entire state space. This partitioned parameterized policy approach is shown to outperform the uniform parameterized policy approach and lead to greater generalization than prior methods, where the Mountain Car became trapped in circular trajectories in the state space.