Reviewerly/exHarmony huggingface dataset:
Crime and social media

Purpose The purpose of this paper is to complement the scant macroeconomic literature on the development outcomes of social media by examining the relationship between Facebook penetration and violent crime levels in a cross-section of 148 countries for the year 2012. Design/methodology/approach The empirical evidence is based on ordinary least squares (OLS), Tobit and quantile regressions. In order to respond to policy concerns on the limited evidence on the consequences of social media in developing countries, the data set is disaggregated into regions and income levels. The decomposition by income levels included: low income, lower middle income, upper middle income and high income. The corresponding regions include: Europe and Central Asia, East Asia and the Pacific, Middle East and North Africa (MENA), Sub-Saharan Africa and Latin America. Findings From OLS and Tobit regressions, there is a negative relationship between Facebook penetration and crime. However, quantile regressions reveal that the established negative relationship is noticeable exclusively in the 90th crime quantile. Further, when the data set is decomposed into regions and income levels, the negative relationship is evident in the MENA while a positive relationship is confirmed for Sub-Saharan Africa. Policy implications are discussed. Originality/value Studies on the development outcomes of social media are sparse because of a lack of reliable macroeconomic data on social media. This study primarily complemented three existing studies that have leveraged on a newly available data set on Facebook.
Construction and Utilization of a Neural Network Model to Predict Current Procedural Terminology Codes from Pathology Report Texts

Background: At our department, each specimen was assigned a tentative current procedural terminology (CPT) code at accessioning. The codes were subject to subsequent changes by pathologist assistants and pathologists. After the cases had been finalized, their CPT codes went through a final verification step by coding staff, with the aid of a keyword-based CPT code-checking web application. Greater than 97% of the initial assignments were correct. This article describes the construction of a CPT code-predicting neural network model and its incorporation into the CPT code-checking application. Materials and Methods: R programming language was used. Pathology report texts and CPT codes for the cases finalized during January 1-November 30, 2018, were retrieved from the database. The order of the specimens was randomized before the data were partitioned into training and validation set. R Keras package was used for both model training and prediction. The chosen neural network had a three-layer architecture consisting of a word-embedding layer, a bidirectional long short-term memory (LSTM) layer, and a densely connected layer. It used concatenated header-diagnosis texts as the input. Results: The model predicted CPT codes in both the validation data set and the test data set with an accuracy of 97.5% and 97.6%, respectively. Closer examination of the test data set (cases from December 1 to 27, 2018) revealed two interesting observations. First, among the specimens that had incorrect initial CPT code assignments, the model disagreed with the initial assignments in 73.6% (117/159) and agreed in 26.4% (42/159). Second, the model identified nine additional specimens with incorrect CPT codes that had evaded all steps of checking. Conclusions: A neural network model using report texts to predict CPT codes can achieve high accuracy in prediction and moderate sensitivity in error detection. Neural networks may play increasing roles in CPT coding in surgical pathology.
Dark channel prior based video dehazing algorithm with sky preservation and its embedded system realization for ADAS applications

Dark Channel Prior (DCP) is one of the significant dehazing methods based upon the observation of the key features of the haze-free images. But it has disadvantages; high computational complexity, over-enhancement in the sky region, flickering artefacts in video processing, and poor dehazing. Therefore, we propose improved solutions to solve the aforementioned drawbacks. First, we adopt the fast one-dimensional filter, look-up table, and program optimization to reduce the computational complexity. Next, we follow by using a part of the guided filter for sky detection and to preserve the sky region from noise by avoiding over recovery. Then, we propose an airlight update strategy and adjust the radius of a guided filter to reduce the flickering artifacts and also propose an airlight estimation method to produce the better dehazing result as the final step of our algorithm. The improved results from our proposed algorithm are stable and are obtained from the real-time processing suitable for ADAS, surveillance, and monitoring systems. The implementation of the proposed algorithm has yielded a processing speed of 75 fps and 23 fps respectively on an NVIDIA Jetson TX1 embedded platform and Renesas R-Car M2, both on D1 (720x480) resolution videos.
Estimating n-tetradecane/bitumen mixture viscosity in solvent-assisted oil recovery process using GEP and GMDH modeling approaches

In this study, the methods of group method of data handling (GMDH) and gene expression programming (GEP) were used to develop symbolic correlations for truthful viscosity estimation of n-tetradecane/bitumen mixture. A number of graphical and statistical tools were utilized to make evident the omnipotence of the proposed models as compared to the published literature correlations. It is found that the GMDH model is the best acting approach with the lowest total error of 2.51% and the highest R2 of 0.9994. Sensitivity analysis verifies that concentration of the solvent is the most influencing input parameter on the viscosity estimation of n-tetradecane/bitumen mixture.
The Development of Smart Contracts for Heterogeneous Blockchains

The advanced development of blockchain technologies at present is to have external applications that interact with the native blockchain. A smart contract allows us to insert business logic which can add transactions and share them with multiple parties. This paper discusses the development process of a smart contract platform that aims to generate smart contracts for heterogeneous blockchain technologies. We start the process of creating a blueprint design, a datalogical stereotype that employs the ontology design from a Resources-Event-Agent (REA) perspective (McCarthy in The Accounting Review, LVII(3) (1982) [1]). With the modeling approach we are using in this paper, Unified Modeling Language (UML) and Object Constraint Language (OCL), we implement the workflow and algorithm in a supply chain demo sample [2].
Graph-Regularized Discriminative Analysis-Synthesis Dictionary Pair Learning for Image Classification

Analysis-synthesis dictionary pair learning, which can provide a comprehensive view of data representation, has been applied in various computer vision tasks. Although good performance has been reported in image denoising, discriminative dictionary pair learning for image classification remains unsolved. In this paper, we propose a novel model of graph-regularized discriminative analysis-synthesis dictionary pair learning (GDASDL), in which a graph-regularized term and a discriminative term are incorporated into dictionary pair learning. By taking advantage of graph constraints, the proposed GDASDL can preserve the local geometry structure of the data. Global information is introduced by associating label information with dictionary atoms. In this paper, an iteration algorithm is presented to efficiently solve the proposed GDASDL. We extensively conduct experiments on three public image datasets and one face dataset in comparison with the existing dictionary learning approaches, and the experimental results show that the proposed model achieves superior performance using a simple linear classifier.
Recurrent neural networks with segment attention and entity description for relation extraction from clinical texts

At present, great progress has been achieved on the relation extraction for clinical texts, but we have noticed that the current models have great drawbacks when dealing with long sentences and multiple entities in a sentence. In this paper, we propose a novel neural network architecture based on Bidirectional Long Short-Term Memory Networks for relation classification. Firstly, we utilize a concat-attention mechanism for capturing the most important context words for relation extraction in a sentence. In addition, a segment attention mechanism is proposed to improve the performance of the model processing long sentences. Finally, a tensor-based entity description is used to overcome the performance degradation of the model when there are multiple entities in a sentence. The performance of the proposed model is evaluated on a part of the i2b2-2010 shared task clinical relation extraction dataset. The result indicates that our model can effectively overcome the above two problems and improve the F1-score by approximately 3% compared with baseline model.
A study on the evaluation of HPC microservices in containerized environment

Summary Containers are gaining popularity over virtual machines as they provide the advantages of virtualization with the performance of near bare metal. The uniformity of support provided by Docker containers across different cloud providers makes them a popular choice for developers. Evolution of microservice architecture allows complex applications to be structured into independent modular components making them easier to manage. High‐performance computing (HPC) applications are one such application to be deployed as microservices, placing significant resource requirements on the container framework. However, there is a possibility of interference between different microservices hosted within the same container (intracontainer) and different containers (intercontainer) on the same physical host. In this paper, we describe an extensive experimental investigation to determine the performance evaluation of Docker containers executing heterogeneous HPC microservices. We are particularly concerned with how intracontainer and intercontainer interference influences the performance. Moreover, we investigate the performance variations in Docker containers when control groups (cgroups) are used for resource limitation. For ease of presentation and reproducibility, we use Cloud Evaluation Experiment Methodology (CEEM) to conduct our comprehensive set of experiments. We expect that the results of evaluation can be used in understanding the behavior of HPC microservices in the interfering containerized environment.
Stabilizing Bell states of two separated superconducting qubits via quantum reservoir engineering

We propose a quantum reservoir engineering approach for stabilizing Bell states of two superconducting qubits. The system under consideration consists of two linearly coupled superconducting transmission line resonators and two separated flux qubits, one of which is interacted with one resonator. Applying external driving fields to tailor appropriate qubit-resonator interactions, we show that dissipative photons of the resonators can be exploited to autonomously drive and stabilize the two flux qubits into an approximate Bell state at the stationary state. Because of using the dissipative dynamical process, the present approach does not need to well prepare the initial state of the system and exactly monitor the evolution time. Compared with previous schemes, the present one has the remarkable features that the generation of the entangled state is implemented at a single-photon quantum level, and all four Bell states can be generated and stabilized on demand by changing the external driving parameters. Our result may have useful applications for the realization of quantum computation with superconducting quantum circuits.
The use of bibliography enriched features for automatic citation screening

Citation screening (also called study selection) is a phase of systematic review process that has attracted a growing interest on the use of text mining (TM) methods to support it to reduce time and effort. Search results are usually imbalanced between the relevant and the irrelevant classes of returned citations. Class imbalance among other factors has been a persistent problem that impairs the performance of TM models, particularly in the context of automatic citation screening for systematic reviews. This has often caused the performance of classification models using the basic title and abstract data to ordinarily fall short of expectations. In this study, we explore the effects of using full bibliography data in addition to title and abstract on text classification performance for automatic citation screening. We experiment with binary and Word2vec feature representations and SVM models using 4 software engineering (SE) and 15 medical review datasets. We build and compare 3 types of models (binary-non-linear, Word2vec-linear and Word2vec-non-linear kernels) with each dataset using the two feature sets. The bibliography enriched data exhibited consistent improved performance in terms of recall, work saved over sampling (WSS) and Matthews correlation coefficient (MCC) in 3 of the 4 SE datasets that are fairly large in size. For the medical datasets, the results vary, however in the majority of cases the performance is the same or better. Inclusion of the bibliography data provides the potential of improving the performance of the models but to date results are inconclusive.
On Asymptotic Behaviors of Graph CNNs from Dynamical Systems Perspective

Graph Neural Networks (graph NNs) are a promising deep learning approach for analyzing graph-structured data. However, it is known that they do not improve (or sometimes worsen) their predictive performance as we pile up many layers and add non-lineality. To tackle this problem, we investigate the expressive power of graph NNs via their asymptotic behaviors as the layer size tends to infinity. Our strategy is to generalize the forward propagation of a Graph Convolutional Network (GCN), which is a popular graph NN variant, as a specific dynamical system. In the case of a GCN, we show that when its weights satisfy the conditions determined by the spectra of the (augmented) normalized Laplacian, its output exponentially approaches the set of signals that carry of the connected components and node degrees only for distinguishing nodes. Our theory enables us to relate the expressive power of GCNs with the topological of the underlying graphs inherent in the graph spectra. To demonstrate this, we characterize the asymptotic behavior of GCNs on the Erdős -- Renyi graph. We show that when the Erdős -- Renyi graph is sufficiently dense and large, a broad range of GCNs on it suffers from the information loss in the limit of infinite layers with high probability. Based on the theory, we provide a principled guideline for weight normalization of graph NNs. We experimentally confirm that the proposed weight scaling enhances the predictive performance of GCNs in real data. Code is available at this https URL.
Relation Classification Using Segment-Level Attention-based

Van-Hien Tran, Van-Thuy Phi, Hiroyuki Shindo, Yuji Matsumoto. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Benefits from morphological regularities in dyslexia are task dependent.

Are difficulties of individuals with dyslexia (IDDs) reduced or enhanced in tasks where linguistic regularities typically facilitate performance, such as vocabulary acquisition and reading? If impaired short-term memory and poor phonological decoding pose the main impediments to IDDs, then they are expected to compensate for these difficulties with a greater reliance on linguistic regularities, to reduce online load. However, if reduced benefits from regularities pose the main bottleneck, IDDs might benefit less than good readers from regularities in spite of their online difficulties. To test that, we administered two experiments. In a novel paradigm of auditory vocabulary acquisition in Hebrew, novel words were presented either with or without familiar morphological structure. Participants with dyslexia showed a reduced recall benefit from familiar structure as compared with controls. However, their recognition was facilitated by morphological structure and did not significantly differ from controls'. In the second experiment, participants read novel words with and without familiar structure. Benefit from structure familiarity for IDDs was significantly smaller than for controls, in spite of IDDs' greater potential benefit from familiar structure due to their reduced overall accuracy. However, when asked to emphasize speed in reading, structure familiarity was found to be beneficial for IDDs, without compromising accuracy. These results imply that accumulative acquisition of sublexical regularities is less efficient in dyslexia, though in some tasks this knowledge is accessible and beneficial. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Cybersecurity Metrics for Enhanced Protection of Healthcare IT Systems

Cybersecurity incidents are on the rise in the health-care sector and it is becoming a growing concern for the senior executives. The attack surface is expanding due to the large number of connected medical devices and the proliferation of portable devices such as smart phones, tablets and USB devices. In this paper, we will discuss some of the security challenges facing this sector and propose a set of cybersecurity metrics that could be used to enhance the protection of the IT systems.
Visually analyzing eye movements on natural language texts and source code snippets

In this paper, we analyze eye movement data of 26 participants using a quantitative and qualitative approach to investigate how people read natural language text in comparison to source code. In particular, we use the radial transition graph visualization to explore strategies of participants during these reading tasks and extract common patterns amongst participants. We illustrate via examples how visualization can play a role at uncovering behavior of people while reading natural language text versus source code. Our results show that the linear reading order of natural text is only partially applicable to source code reading. We found patterns representing a linear order and also patterns that represent reading of the source code in execution order. Participants also focus more on those areas that are important to comprehend core functionality and we found that they skip unimportant constructs such as brackets.
Deep Semi-Supervised Anomaly Detection

Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets. Typically anomaly detection is treated as an unsupervised learning problem. In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of labeled samples, e.g. a subset verified by some domain expert as being normal or anomalous. Semi-supervised approaches to anomaly detection aim to utilize such labeled samples, but most proposed methods are limited to merely including labeled normal samples. Only a few methods take advantage of labeled anomalies, with existing deep approaches being domain-specific. In this work we present Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection. We further introduce an information-theoretic framework for deep anomaly detection based on the idea that the entropy of the latent distribution for normal data should be lower than the entropy of the anomalous distribution, which can serve as a theoretical interpretation for our method. In extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10, along with other anomaly detection benchmark datasets, we demonstrate that our method is on par or outperforms shallow, hybrid, and deep competitors, yielding appreciable performance improvements even when provided with only little labeled data.
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction). Several recent works suggest that convolutional neural network (CNN) based models generate richer and more expressive feature embeddings and hence also perform well on relation prediction. However, we observe that these KG embeddings treat triples independently and thus fail to cover the complex and hidden information that is inherently implicit in the local neighborhood surrounding a triple. To this effect, our paper proposes a novel attention based feature embedding that captures both entity and relation features in any given entity's neighborhood. Additionally, we also encapsulate relation clusters and multihop relations in our model. Our empirical study offers insights into the efficacy of our attention based model and we show marked performance gains in comparison to state of the art methods on all datasets.
LanczosNet: Multi-Scale Deep Graph Convolutional Networks

We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution. Relying on the tridiagonal decomposition of the Lanczos algorithm, we not only efficiently exploit multi-scale information via fast approximated computation of matrix power but also design learnable spectral filters. Being fully differentiable, LanczosNet facilitates both graph kernel learning as well as learning node embeddings. We show the connection between our LanczosNet and graph based manifold learning methods, especially the diffusion maps. We benchmark our model against several recent deep graph networks on citation networks and QM8 quantum chemistry dataset. Experimental results show that our model achieves the state-of-the-art performance in most tasks. Code is released at: \url{this https URL}.
Genetic programming performance prediction and its application for symbolic regression problems

Predicting the performance of Genetic Programming (GP) helps us identify whether it is an appropriate approach to solve the problem at hand. However, previous studies show that measuring the difficulty of a problem for GP and predicting GP performance are challenging issues. This paper presents a theoretical analysis of GP performance prediction problem and suggests an upper bound for GP performance. It means that the error of the best solution that is found by GP for a given problem is less than the proposed upper bound. To evaluate the proposed upper bound experimentally, a wide range of synthetic and real symbolic regression problems with different dimensions are solved by GP and consequently, a lot of actual GP performances are collected. Comparing the actual GP performances with their corresponding upper bounds shows that the proposed upper bounds are not violated for both synthetic and real symbolic regression problems. Then, the proposed upper bound is used to guide GP search. The results show that the proposed approach can find better results in comparison to Multi Gene Genetic Programming (MGGP).