Reviewerly/exHarmony huggingface dataset:
Strategic human resource management practices and human capital development: The role of employee commitment

This paper studied the influence of strategic human resource management on human capital development through the mediation of employee commitment. A descriptive cross-sectional study design was used to collect data from 514 participants (medical staff) from five hospitals in northern Jordan. The hospitals involved were from different sectors, including governmental, private, and university hospitals. Several analysis methods were used in the study: Confirmatory Factor Analysis (CFA), discriminant validity, and composite reliability. Direct and indirect hypothesis testing was also utilized using Structural Equation Modeling (SEM). The study showed that the practice of strategic human resource management had a direct positive impact on employee commitment; the practice of strategic human resource management had a direct positive impact on human capital development; the impact of employee commitment on human capital development was positive and direct; employee commitment has a partial mediating effect between both of them. Accordingly, HR managers in hospitals should move from “softer” responsibilities and traditional HR activities to a more strategic level (i.e., developmental strategy), where HR strategies are aligned and reinforce the hospital’s vision and mission and link organizational strategy to HR strategies. Healthcare managers should invest more in human capital through formal education and training. AcknowledgmentsThe Deanship of Research at Jordan University of Science and Technology (JUST) in Jordan is acknowledged by authors for providing facilities through the research No. 488/2020 and research environment to accomplish the goals of this work. The authors thank Professor Fareed Nusair at the Department of Health Management &amp;amp;amp; Policy, the Faculty of Medicine.
Coherently controlled quantum features in a coupled interferometric scheme

Abstract Over the last several decades, entangled photon pairs generated by spontaneous parametric down conversion processes in both second-order and third-order nonlinear optical materials have been intensively studied for various quantum features such as Bell inequality violation and anticorrelation. In an interferometric scheme, anticorrelation results from photon bunching based on randomness when entangled photon pairs coincidently impinge on a beam splitter. Compared with post-measurement-based probabilistic confirmation, a coherence version has been recently proposed using the wave nature of photons. Here, the origin of quantum features in a coupled interferometric scheme is investigated using pure coherence optics. In addition, a deterministic method of entangled photon-pair generation is proposed for on-demand coherence control of quantum processing.
Blockchain in Service Management and Service Research - Developing a Research Agenda and Managerial Implications

As blockchain technology is maturing to be confidently used in practice, its applications are becoming evident and, correspondingly, more blockchain research is being published, also extending to more domains than before. To date, scientific research in the field has predominantly focused on subject areas such as finance, computer science, and engineering, while the area of service management has largely neglected this topic. Therefore, we invited a group of renowned scholars from different academic fields to share their views on emerging topics regarding blockchain in service management and service research. Their individual commentaries and conceptual contributions refer to different theoretical and domain perspectives, including managerial implications for service companies as well as forward-looking suggestions for further research.
Verifying Reinforcement Learning up to Infinity

Formally verifying that reinforcement learning systems act safely is increasingly important, but existing methods only verify over finite time. This is of limited use for dynamical systems that run indefinitely. We introduce the first method for verifying the time-unbounded safety of neural networks controlling dynamical systems. We develop a novel abstract interpretation method which, by constructing adaptable template-based polyhedra using MILP and interval arithmetic, yields sound---safe and invariant---overapproximations of the reach set. This provides stronger safety guarantees than previous time-bounded methods and shows whether the agent has generalised beyond the length of its training episodes. Our method supports ReLU activation functions and systems with linear, piecewise linear and non-linear dynamics defined with polynomial and transcendental functions. We demonstrate its efficacy on a range of benchmark control problems.
Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic

Convolutional Neural Networks (CNNs) such as ResNet-50, DenseNet-40 and ResNeXt-56 are severely over-parameterized, necessitating a consequent increase in the computational resources required for model training which scales exponentially for increments in model depth. In this paper, we propose an Entropy-Based Convolutional Layer Estimation (EBCLE) heuristic which is robust and simple, yet effective in resolving the problem of over-parameterization with regards to network depth of CNN model. The EBCLE heuristic employs a priori knowledge of the entropic data distribution of input datasets to determine an upper bound for convolutional network depth, beyond which identity transformations are prevalent offering insignificant contributions for enhancing model performance. Restricting depth redundancies by forcing feature compression and abstraction restricts over-parameterization while decreasing training time by 24.99% - 78.59% without degradation in model performance. We present empirical evidence to emphasize the relative effectiveness of broader, yet shallower models trained using the EBCLE heuristic, which maintains or outperforms baseline classification accuracies of narrower yet deeper models. The EBCLE heuristic is architecturally agnostic and EBCLE based CNN models restrict depth redundancies resulting in enhanced utilization of the available computational resources. The proposed EBCLE heuristic is a compelling technique for researchers to analytically justify their HyperParameter (HP) choices for CNNs. Empirical validation of the EBCLE heuristic in training CNN models was established on five benchmarking datasets (ImageNet32, CIFAR-10/100, STL-10, MNIST) and four network architectures (DenseNet, ResNet, ResNeXt and EfficientNet B0-B2) with appropriate statistical tests employed to infer any conclusive claims presented in this paper.
Value-Agnostic Conversational Semantic Parsing

Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses.Existing parsers typically condition on rich representations of history that include the complete set of values and computations previously discussed.We propose a model that abstracts over values to focus prediction on type-and function-level context.This approach provides a compact encoding of dialogue histories and predicted programs, improving generalization and computational efficiency.Our model incorporates several other components, including an atomic span copy operation and structural enforcement of well-formedness constraints on predicted programs, that are particularly advantageous in the low-data regime.Trained on the SMCALFLOW and TREEDST datasets, our model outperforms prior work by 7.3% and 10.6% respectively in terms of absolute accuracy.Trained on only a thousand examples from each dataset, it outperforms strong baselines by 12.4% and 6.4%.These results indicate that simple representations are key to effective generalization in conversational semantic parsing.
A Fully Convolutional Network-Based Tube Contour Detection Method Using Multi-Exposure Images

The tube contours in two-dimensional images are important cues for optical three-dimensional reconstruction. Aiming at the practical problems encountered in the application of tube contour detection under complex background, a fully convolutional network (FCN)-based tube contour detection method is proposed. Multi-exposure (ME) images are captured as the input of FCN in order to get information of tube contours in different dynamic ranges, and the U-Net type architecture is adopted by the FCN to achieve pixel-level dense classification. In addition, we propose a new loss function that can help eliminate the adverse effects caused by the positional deviation and jagged morphology of tube contour labels. Finally, we introduce a new dataset called multi-exposure tube contour dataset (METCD) and a new evaluation metric called dilate inaccuracy at optimal dataset scale (DIA-ODS) to reach an overall evaluation of our proposed method. The experimental results show that the proposed method can effectively improve the integrity and accuracy of tube contour detection in complex scenes.
Privacy Intrusiveness in Financial-Banking Fraud Detection

Specialty literature and solutions in the market have been focusing in the last decade on collecting and aggregating significant amounts of data about transactions (and user behavior) and on refining the algorithms used to identify fraud. At the same time, legislation in the European Union has been adopted in the same direction (e.g., PSD2) in order to impose obligations on stakeholders to identify fraud. However, on the one hand, the legislation provides a high-level description of this legal obligation, and on the other hand, the solutions in the market are diversifying in terms of data collected and, especially, attempts to aggregate data in order to generate more accurate results. This leads to an issue that has not been analyzed yet deeply in specialty literature or by legislators, respectively, the privacy concerns in case of profile building and aggregation of data for fraud identification purposes and responsibility of stakeholders in the identification of frauds in the context of their obligations under data protection legislation. This article comes as a building block in this direction of research, as it contains (i) an analysis of existing fraud detection methods and approaches, together with their impact from a data protection legislation perspective and (ii) an analysis of respondents’ views toward privacy in case of fraud identification in transactions based on a questionnaire in this respect having 425 respondents. Consequently, this article assists in bridging the gap between data protection legislation and implementation of fraud detection obligations under the law, as it provides recommendations for compliance with the latter legal obligation while also complying with data protection aspects.
RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion

Static knowledge graph (SKG) embedding (SKGE) has been studied intensively in the past years.Recently, temporal knowledge graph (TKG) embedding (TKGE) has emerged.In this paper, we propose a Recursive Temporal Fact Embedding (RTFE) framework to transplant SKGE models to TKGs and to enhance the performance of existing TKGE models for TKG completion.Different from previous work which ignores the continuity of states of TKG in time evolution, we treat the sequence of graphs as a Markov chain, which transitions from the previous state to the next state.RTFE takes the SKGE to initialize the embeddings of TKG.Then it recursively tracks the state transition of TKG by passing updated parameters/features between timestamps.Specifically, at each timestamp, we approximate the state transition as the gradient update process.Since RTFE learns each timestamp recursively, it can naturally transit to future timestamps.Experiments on five TKG datasets show the effectiveness of RTFE.
A 3 Tier CNN model with deep discriminative feature extraction for discovering malignant growth in multi-scale histopathology images

The Convolutional Neural Network (CNN) is intended to generalize and automatically learn spatial hierarchies of features, using stacked convolution-pooling layers with Backpropagation of errors. Two predominant challenges confronted while applying the CNN model for cancer classification tasks are limited size of dataset and model overfitting. The proposed 3 Tier CNN model employs- (i) spatial attention and attention across channels that potentially improves input representation power of the network, (ii) enforces Separable Convolutions for training that alleviates data overfitting concerns and shrinks network complexity, (iii) uses skip connections in some sub-networks to boost gradient flow during backpropagation and (iv) the model additionally combines spatial and semantic information present in early, intermediate and final layers which are passed to dense layers for producing better classification results. The results of the proposed model when applied to Breast Cancer histopathological imaging modality yielded the best overall performance with 98.5% average accuracy for 200X scaled images. When various images of different scales (40X/100X/200X/400X) were integrated to deliberately expand the input dataset making it appropriate for multiclass classification, the model gave an average accuracy of 95.5% and 92.8% for benign and malignant subclass classification. As the model considerably reduces the domain know-how of a specific problem, it can be used to attain better classification results for similar problems.
Multitask Learning with Knowledge Base for Joint Intent Detection and Slot Filling

Intent detection and slot filling are important modules in task-oriented dialog systems. In order to make full use of the relationship between different modules and resource sharing, solving the problem of a lack of semantics, this paper proposes a multitasking learning intent-detection system, based on the knowledge-base and slot-filling joint model. The approach has been used to share information and rich external utility between intent and slot modules in a three-part process. First, this model obtains shared parameters and features between the two modules based on long short-term memory and convolutional neural networks. Second, a knowledge base is introduced into the model to improve its performance. Finally, a weighted-loss function is built to optimize the joint model. Experimental results demonstrate that our model achieves better performance compared with state-of-the-art algorithms on a benchmark Airline Travel Information System (ATIS) dataset and the Snips dataset. Our joint model achieves state-of-the-art results on the benchmark ATIS dataset with a 1.33% intent-detection accuracy improvement, a 0.94% slot filling F value improvement, and with 0.19% and 0.31% improvements respectively on the Snips dataset.
Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory

The Moral Foundation Theory suggests five moral foundations that can capture the view of a user on a particular issue.It is widely used to identify sentence-level sentiment.In this paper, we study the nuanced stances and partisan sentiment towards entities of US politicians using Moral Foundation Theory, on two politically divisive issues -Gun Control and Immigration.We define the nuanced stances of the US politicians on these two topics by the grades given by related organizations to the politicians.To conduct this study, we first filter out 74k and 87k tweets on the topics Gun Control and Immigration, respectively, from an existing tweet corpus authored by US parliament members.Then, we identify moral foundations in these tweets using deep relational learning.Finally, doing qualitative and quantitative evaluations on this dataset, we found out that there is a strong correlation between moral foundation usage and politicians' nuanced stances on a particular topic.We also found notable differences in moral foundation usage by different political parties when they address different entities.
A Big Data Text Coverless Information Hiding Based on Topic Distribution and TF-IDF

Coverless information hiding has become a hot topic in recent years. The existing steganalysis tools are invalidated due to coverless steganography without any modification to the carrier. However, for the text coverless has relatively low hiding capacity, this paper proposed a big data text coverless information hiding method based on LDA (latent Dirichlet allocation) topic distribution and keyword TF-IDF (term frequency-inverse document frequency). Firstly, the sender and receiver build codebook, including word segmentation, word frequency and TF-IDF features, LDA topic model clustering. The sender then shreds the secret information, converts it into keyword ID through the keywords-index table, and searches the text containing the secret information keywords. Secondly, the searched text is taken as the index tag according to the topic distribution and TF-IDF features. At the same time, random numbers are introduced to control the keyword order of secret information.
LATEX-Numeric: Language Agnostic Text Attribute Extraction for Numeric Attributes

In this paper, we present LaTeX-Numeric -a high-precision fully-automated scalable framework for extracting E-commerce numeric attributes from product text like product description.Most of the past work on attribute extraction is not scalable as they rely on manually curated training data, either with or without the use of active learning.We rely on distant supervision for training data generation, removing dependency on manual labels.One issue with distant supervision is that it leads to incomplete training annotation due to missing attribute values while matching.We propose a multi-task learning architecture to deal with missing labels in the training data, leading to F1 improvement of 9.2% for numeric attributes over single-task architecture.While multi-task architecture benefits both numeric and non-numeric attributes, we present automated techniques to further improve the numeric attributes extraction models.Numeric attributes require a list of units (or aliases) for better matching with distant supervision.We propose an automated algorithm for alias creation using product text and attribute values, leading to a 20.2% F1 improvement.Extensive experiments on real world dataset for 20 numeric attributes across 5 product categories and 3 English marketplaces show that LaTeXnumeric achieves a high F1-score, without any manual intervention, making it suitable for practical applications.Finally, we show that the improvements are language-agnostic and LaTeX-Numeric achieves 13.9% F1 improvement for 3 Romance languages 1 .
Text Mining an Automatic Short Answer Grading (ASAG), Comparison of Three Methods of Cosine Similarity, Jaccard Similarity and Dice's Coefficient

This study aims to find correlation assessment of Automatic Short Answer Grading (ASAG) by comparing three methods of Cosine Similarity, Jaccard Similarity and Dice Coefficient by providing one reference answer. From the results of computing using Python programming language and data processing using spreadsheets, it was obtained that the Dice Coefficient method had the highest correlation average value of 0.76, followed by Cosine Similarity with an average correlation value of 0.76, and the lowest correlation average value was the Jaccard method with a value of 0.69. The contribution to this study is the use of three methods in one data, whereas the previous research only used 1 method for 1 data or 2 methods for 1 data. So, the value in this study resulted in a more complete comparison and accuracy of data.
Attention Temperature Matters in Abstractive Summarization Distillation

Recent progress of abstractive text summarization largely relies on large pre-trained sequence-to-sequence Transformer models, which are computationally expensive.This paper aims to distill these large models into smaller ones for faster inference and with minimal performance loss.Pseudo-labeling based methods are popular in sequence-tosequence model distillation.In this paper, we find simply manipulating attention temperatures in Transformers can make pseudo labels easier to learn for student models.Our experiments on three summarization datasets show our proposed method consistently improves vanilla pseudo-labeling based methods.Further empirical analysis shows that both pseudo labels and summaries produced by our students are shorter and more abstractive.
ReS2tAC—UAV-Borne Real-Time SGM Stereo Optimized for Embedded ARM and CUDA Devices

With the emergence of low-cost robotic systems, such as unmanned aerial vehicle, the importance of embedded high-performance image processing has increased. For a long time, FPGAs were the only processing hardware that were capable of high-performance computing, while at the same time preserving a low power consumption, essential for embedded systems. However, the recently increasing availability of embedded GPU-based systems, such as the NVIDIA Jetson series, comprised of an ARM CPU and a NVIDIA Tegra GPU, allows for massively parallel embedded computing on graphics hardware. With this in mind, we propose an approach for real-time embedded stereo processing on ARM and CUDA-enabled devices, which is based on the popular and widely used Semi-Global Matching algorithm. In this, we propose an optimization of the algorithm for embedded CUDA GPUs, by using massively parallel computing, as well as using the NEON intrinsics to optimize the algorithm for vectorized SIMD processing on embedded ARM CPUs. We have evaluated our approach with different configurations on two public stereo benchmark datasets to demonstrate that they can reach an error rate as low as 3.3%. Furthermore, our experiments show that the fastest configuration of our approach reaches up to 46 FPS on VGA image resolution. Finally, in a use-case specific qualitative evaluation, we have evaluated the power consumption of our approach and deployed it on the DJI Manifold 2-G attached to a DJI Matrix 210v2 RTK unmanned aerial vehicle (UAV), demonstrating its suitability for real-time stereo processing onboard a UAV.
Improving Hate Speech Type and Target Detection with Hateful Metaphor Features

We study the usefulness of hateful metaphors as features for the identification of the type and target of hate speech in Dutch Facebook comments.For this purpose, all hateful metaphors in the Dutch LiLaH corpus were annotated and interpreted in line with Conceptual Metaphor Theory and Critical Metaphor Analysis.We provide SVM and BERT/RoBERTa results, and investigate the effect of different metaphor information encoding methods on hate speech type and target detection accuracy.The results of the conducted experiments show that hateful metaphor features improve model performance for the both tasks.To our knowledge, it is the first time that the effectiveness of hateful metaphors as an information source for hate speech classification is investigated.