Reviewerly/exHarmony huggingface dataset:
Mapping the Scholarly Literature Found in Scopus on “Research Data Management”: A Bibliometric and Data Visualization Approach

INTRODUCTION Since the 2000s, interest in research data management (RDM) has grown considerably. As a result, a large body of literature has discussed a broad variety of aspects related to data management. But, few studies have examined and also interpreted from visual perception the intellectual structure and progressive development of the existing literature on RDM. METHODS Guided by five research questions, this study employed bibliometric techniques and a visualization tool (CiteSpace) to identify and analyze the patterns of the scholarly publications about RDM. RESULTS Through CiteSpace’s modeling and computing, the knowledge (or network) structures, significant studies, notable topics, and development trends in the literature of RDM were revealed. DISCUSSION The majority of the literature pertinent to RDM was published after 2002. Major research clusters within this interdisciplinary field include “scientific collaboration,” “research support service,” and “data literacy,” while the “scientific collaboration” research cluster was the most active. Topics such as “digital curation” and “information processing” appeared most frequently in the RDM literature. Additionally, there was a sharp increase in several specific topics, such as “digital library,” “big data,” and “data sharing.” CONCLUSION By looking into the “profile” of the literature on RDM, in terms of knowledge structure, evolving trends, and important topics in the domain, this work will add new information to current discussions about RDM, new service development, and future research focuses in this area.
SAR Speckle Nonlocal Filtering With Statistical Modeling of Haar Wavelet Coefficients and Stochastic Distances

Due to the coherent processing of synthetic aperture radar (SAR) systems, multiplicative speckle noise arises providing a granular appearance in SAR images. This kind of noise makes it difficult to analyze and interpret surface images from the earth. Therefore, studying alternatives to attenuate the speckle is a constant task in the image processing literature. Current state-of-the-art filters in remote sensing area explore the philosophy of similarity between patches. This paper aims to expand the traditional nonlocal means (NLM) algorithm originally proposed for the additive white Gaussian noise (AWGN) to deal with the speckle. In our research, we consider the worst scenario, i.e., the single-look speckle noise, and apply the NLM to filter intensity SAR images in the Haar wavelet domain. To accomplish this task, the Haar coefficients were described by exponential-polynomial (EP) and gamma distributions. Furthermore, stochastic distances based on these two mentioned distributions were derived and embedded in the NLM filter by replacing the Euclidean distance of the original method. This represents the main contribution of the proposed research. Finally, this paper analyzes and compares the synthetic and real experiments of the proposed method with some recent filters of the literature demonstrating its competitive performance.
The Rule of Law on Instagram: An Evaluation of the Moderation of Images Depicting Women’s Bodies

This article uses innovative digital research methods to evaluate the moderation of images that depict women’s bodies on Instagram against the Western legal ideal of the rule of law. Specifically, this article focuses on the contested rule of law values of formal equality, certainty, reason giving, transparency, participation and accountability. Female forms are the focal point for our investigation due to widespread concerns that the platform is arbitrarily removing some images of women’s bodies and, possibly, privileging certain body types. After examining whether 4944 like images depicting (a) Underweight, (b) Mid-Range and (c) Overweight women’s bodies were moderated alike, we identify an overall trend of inconsistent moderation. Our results show that up to 22 per cent of images are potentially false positives – images that do not appear to violate Instagram’s content policies and were removed. The platform’s opaque moderation processes, however, make it impossible to identify whether images were removed by Instagram or by the user. This article concludes that the apparent lack of rule of law values in the platform’s moderation processes, and Instagram’s largely unfettered power to moderate content, are significant normative concerns which pose an ongoing risk of arbitrariness for women and users more broadly. We propose ways that platforms can improve transparency, and advocate for the continued development of digital methods for empirical, legal analysis of platform governance. These improvements are crucial to help identify arbitrariness where it exists and to allay the suspicions and fears of users where it does not.
Tackling the Problem of Class Imbalance in Multi-class Sentiment Classification: An Experimental Study

Abstract Sentiment classification is an important task which gained extensive attention both in academia and in industry. Many issues related to this task such as handling of negation or of sarcastic utterances were analyzed and accordingly addressed in previous works. However, the issue of class imbalance which often compromises the prediction capabilities of learning algorithms was scarcely studied. In this work, we aim to bridge the gap between imbalanced learning and sentiment analysis. An experimental study including twelve imbalanced learning preprocessing methods, four feature representations, and a dozen of datasets, is carried out in order to analyze the usefulness of imbalanced learning methods for sentiment classification. Moreover, the data difficulty factors — commonly studied in imbalanced learning — are investigated on sentiment corpora to evaluate the impact of class imbalance.
Mapping Neural Networks to FPGA-Based IoT Devices for Ultra-Low Latency Processing

Internet of things (IoT) infrastructure, fast access to knowledge becomes critical. In some application domains, such as robotics, autonomous driving, predictive maintenance, and anomaly detection, the response time of the system is more critical to ensure Quality of Service than the quality of the answer. In this paper, we propose a methodology, a set of predefined steps to be taken in order to map the models to hardware, especially field programmable gate arrays (FPGAs), with the main focus on latency reduction. Multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) was employed along with custom scores for sparsity, bit-width of the representation and quality of the model. Furthermore, we created a framework which enables mapping of neural models to FPGAs. The proposed solution is validated using three case studies and Xilinx Zynq UltraScale+ MPSoC 285 XCZU15EG as a platform. The results show a compression ratio for quantization and pruning in different scenarios with and without retraining procedures. Using our publicly available framework, we achieved 210 ns of latency for a single processing step for a model composed of two long short-term memory (LSTM) and a single dense layer.
Joint Sparse Learning With Nonlocal and Local Image Priors for Image Error Concealment

Joint sparse representation (JSR) model has recently emerged as a powerful technique with wide variety of applications. In this paper, the JSR model is extended to error concealment (EC) application, being effective to recover the original image from its corrupted version. This model is based on jointly learning a dictionary pair and two mapping matrices that are trained offline from external training images. Given the trained dictionaries and mappings, the restoration is done by transferring the recovery problem into the sparse representation domain with respect to the trained dictionaries, which is further transformed into a common space using the respective mapping matrices. Then, the reconstructed image is obtained by back projection into the spatial domain. In order to improve the accuracy and stability of the proposed JSR-based EC algorithm and avoid unexpected artifacts, the local and non-local priors are seamlessly integrated into the JSR model. The non-local prior is based on the self-similarity within natural images and helps to find an accurate sparse representation by taking a weighted average of similar areas throughout the image. The local prior is based on learning the local structural regularity of the natural images and helps to regularize the sparse representation, exploiting the strong correlation in the small local areas within the image. Compared with the state-of-the-art EC algorithms, the results show that the proposed method has better reconstruction performance in terms of objective and subjective evaluations.
The Effects on Innovation from Financial Sector Development: Evidence from Developing Countries

The subject of financial system development is widely studied in the finance literature as an indicator of economic growth and a driver of technological innovation supporting the mobilization of capital for growth.However, varying results are obtained when examining developed countries compared to the emerging countries.Developing countries are characterized by a lower investment in innovation, and lower levels of competitiveness in terms of research and development funding, fewer research academic institutions, but also lower incentives for private innovation by enterprises.The purpose of this paper is to examine the relationship between financial development indicators and innovation by analyzing data for 15 developing countries within the time frame of 1996-2016.The financial system is analyzed in two main pillars: credit institutions and equity markets.The number of innovation patent applications is the dependent variable of the study, i.e. as an indicator of a country's innovative performance.The results of the study show that a higher level of crediting by the banking sector will positively affect the number of patent applications.Credit mobilization by other financial intermediaries in the financial system such as equity markets or non-banking institutions show a lower efficiency in boosting innovation activities, resulting in the opposite effects regarding the number of patent applications.Countries with a higher level of education will show more efforts toward innovation and the development of innovative practices.On the other hand, higher competitiveness among companies in terms of technology innovation and processes is expected to boost the demand for funds in the financial system, leading to the improvement in system development.
Variational Implicit Processes

We introduce the implicit processes (IPs), a stochastic process that places implicitly defined multivariate distributions over any finite collections of random variables. IPs are therefore highly flexible implicit priors over functions, with examples including data simulators, Bayesian neural networks and non-linear transformations of stochastic processes. A novel and efficient approximate inference algorithm for IPs, namely the variational implicit processes (VIPs), is derived using generalised wake-sleep updates. This method returns simple update equations and allows scalable hyper-parameter learning with stochastic optimization. Experiments show that VIPs return better uncertainty estimates and lower errors over existing inference methods for challenging models such as Bayesian neural networks, and Gaussian processes.
Numerical method of distribution of labor resources by game-theoretic model

The theoretical-game model of the optimal distribution of labor resources is considered. The proposed model can be used to develop an employment strategy. It will allow to properly balance the costs associated with hiring and firing workers.
Detection of medical text semantic similarity based on convolutional neural network

Imaging examinations, such as ultrasonography, magnetic resonance imaging and computed tomography scans, play key roles in healthcare settings. To assess and improve the quality of imaging diagnosis, we need to manually find and compare the pre-existing reports of imaging and pathology examinations which contain overlapping exam body sites from electrical medical records (EMRs). The process of retrieving those reports is time-consuming. In this paper, we propose a convolutional neural network (CNN) based method which can better utilize semantic information contained in report texts to accelerate the retrieving process.We included 16,354 imaging and pathology report-pairs from 1926 patients who admitted to Shanghai Tongren Hospital and had ultrasonic examinations between 1st May 2017 and 31st July 2017. We adapted the CNN model to calculate the similarities among the report-pairs to identify target report-pairs with overlapping body sites, and compared the performance with other six conventional models, including keyword mapping, latent semantic analysis (LSA), latent Dirichlet allocation (LDA), Doc2Vec, Siamese long short term memory (LSTM) and a model based on named entity recognition (NER). We also utilized graph embedding method to enhance the word representation by capturing the semantic relations information from medical ontologies. Additionally, we used LIME algorithm to identify which features (or words) are decisive for the prediction results and improved the model interpretability.Experiment results showed that our CNN model gained significant improvement compared to all other conventional models on area under the receiver operating characteristic (AUROC), precision, recall and F1-score in our test dataset. The AUROC of our CNN models gained approximately 3-7% improvement. The AUROC of CNN model with graph-embedding and ontology based medical concept vectors was 0.8% higher than the model with randomly initialized vectors and 1.5% higher than the one with pre-trained word vectors.Our study demonstrates that CNN model with pre-trained medical concept vectors could accurately identify target report-pairs with overlapping body sites and potentially accelerate the retrieving process for imaging diagnosis quality measurement.
Citation Recommendation Based on Weighted Heterogeneous Information Network Containing Semantic Linking

Citation recommendation is of great value in scientific research during the era of big scholarly data. The task deals with different object types (e.g., paper, author, etc.) and relation types, which naturally constitute a heterogeneous information network (HIN). In order to capture semantic relations and attribute values on relations, we propose a Weighted Heterogeneous Information Network Containing Semantic Linking algorithm (WHIN-CSL) to recommend references. Firstly, we construct a weighted HIN consisting of two kinds of vertexes (papers and authors) and four kinds of relations (semantic linking, citing, writing and co-author). Secondly, we use the network representation learning method to obtain feature representation of each vertex, then we can compute the similarity between vertexes. Finally, we recommend references through linear combination among multimodal similarities. Experimental results on two real-world datasets show that WHIN-CSL achieves better performance because of the flexibly integrating information with the help of weighted HIN containing semantic linking.
Predictions of World Population Life Expectancy Using Cyclical Order Weight / Bias

Abstract Life expectancy is the average number of years of life that is still lived by someone who has reached a certain age. Life Expectancy is a tool to evaluate government performance in improving the welfare of the population in general and improving health status in particular. The purpose of this paper is to estimate the life expectancy of the world population so that the government has a benchmark in determining policies to further improve the health and health of the people in their respective countries. The estimation stated in this paper will use the Cyclical Order Weight Neural Network method. The data used in this paper is the number of world population expectations. Data sources come from the United Nations: “World Population Prospect: The 2010 Revision Population Database”. The results of this study are expected to be a reference for the governments of each country to pay more attention to the level of health and welfare of its population so that the life expectancy of the population will be higher. This study uses 5 architectural models. Of these 5 models, the best architectural model is 3-5-10-1 with an accuracy of 97% and an MSE value of 0,0008358919.
Edge Computing Assisted Joint Quality Adaptation for Mobile Video Streaming

Most studies on adaptive streaming over Hypertext Transport Protocol (HTTP) have focused on improving the quality of experience (QoE) of clients by running the rate adaptation algorithm on the client side. In a cellular environment, this leads to inefficient resource utilization because of the lack of coordination between the competing clients. In cellular networks, the key challenge for HTTP adaptive streaming (HAS) is to optimize the conflicting video quality objectives. Edge cloud-assisted adaptive streaming presents an opportunity to optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. HAS algorithms select the video quality based on the estimated throughput and playback buffer level. In this paper, we first present a joint throughput estimation method for HAS by taking advantage of mobile edge computing. Next, we present an optimized solution for multi-access edge computing (MEC)-assisted HAS by using edge cloud capabilities. Due to the non-deterministic polynomial-time hardness of the problem, we design a heuristic rate adaptation algorithm to jointly enhance the quality metrics of the competing clients. Our extension simulation results show that the proposed edge cloud-assisted rate adaptation algorithm outperforms the existing strategies under different client-side and server-side settings. Furthermore, we show that the proposed algorithm is promising under slow-moving and fast-moving environments.
Simultaneous 3D measurement of deformation and its first derivative with speckle pattern interferometry and shearography

Digital speckle pattern interferometry and digital shearography are widely used in nondestructive testing due to their advantages of high speed, full field, and high sensitivity. However, traditional speckle pattern interferometry and shearography can only measure a single variable of deformation or strain. This study presents a modified common measurement setup that can simultaneously measure deformation and its first derivative. In the optical setup, a reference beam is introduced behind the shearing device to interfere with the object beams, thereby simplifying the spectrum and improving the quality of the phase maps. Then the spatial carrier technology is used to extract phase and achieve dynamic measurements. The proposed system also expands the measurement range using a ${4}f$4f system. This study also presents a 3D optical setup based on the 1D system, which is more suitable for practical measurement applications in industrial areas. Theoretical derivation and experimental results are described and presented.
Management of educational and cognitive activity of students within the framework of psychological and pedagogical aspect

Educational and cognitive activity of students is the key component of the modern educational process. Management of the learning process becomes effective when it is grounded on the basic laws of general theory of management and knowledge of the psychological foundations and cognitive processes patterns, the development and consolidation of certain personal qualities and mental formations that ensure the achievement of educational goals. In this article the main psychological and pedagogical review of management problems is presented; the general requirements for effective learning process management are identified. Particular attention is paid to monitoring the learning process and its correction as a necessary managerial component of the educational and cognitive activity of students.
NOVA - A tool for eXplainable Cooperative Machine Learning

In this paper, we introduce a next-generation annotation tool called NOVA, which implements a workflow that interactively incorporates the 'human in the loop'.In particular, NOVA offers a collaborative annotation backend where multiple annotators join their workforce.A main aspect of NOVA is the possibility of applying semi-supervised active learning where Machine Learning techniques are used already during the annotation process by giving the possibility to pre-label data automatically.Furthermore, NOVA implements recent eXplainable AI (XAI) techniques to provide users with both, a confidence value of the automatically predicted annotations, as well as visual explanation.This way, annotators get to understand whether they can trust their ML models, or more annotated data is necessary.
A Closer Look at Deep Policy Gradients

We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. To this end, we propose a fine-grained analysis of state-of-the-art methods based on key elements of this framework: gradient estimation, value prediction, and optimization landscapes. Our results show that the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict: surrogate rewards do not match the reward landscape, learned value estimators fail to fit the value function, and gradient estimates poorly correlate with the true gradient. The mismatch between predicted and empirical behavior we uncover highlights our poor understanding of current methods, and indicates the need to move beyond current benchmark-centric evaluation methods.
Consistent Video Style Transfer via Compound Regularization

Recently, neural style transfer has drawn many attentions and significant progresses have been made, especially for image style transfer. However, flexible and consistent style transfer for videos remains a challenging problem. Existing training strategies, either using a significant amount of video data with optical flows or introducing single-frame regularizers, have limited performance on real videos. In this paper, we propose a novel interpretation of temporal consistency, based on which we analyze the drawbacks of existing training strategies; and then derive a new compound regularization. Experimental results show that the proposed regularization can better balance the spatial and temporal performance, which supports our modeling. Combining with the new cost formula, we design a zero-shot video style transfer framework. Moreover, for better feature migration, we introduce a new module to dynamically adjust inter-channel distributions. Quantitative and qualitative results demonstrate the superiority of our method over other state-of-the-art style transfer methods. Our project is publicly available at: https://daooshee.github.io/CompoundVST/.