Reviewerly/exHarmony huggingface dataset:
Hospital Productivity After Data Breaches: Difference-in-Differences Analysis

Data breaches are an inevitable risk to hospitals operating with information technology. The financial costs associated with data breaches are also growing. The costs associated with a data breach may divert resources away from patient care, thus negatively affecting hospital productivity.After a data breach, the resulting regulatory enforcement and remediation are a shock to a hospital's patient care delivery. Exploiting this shock, this study aimed to investigate the association between hospital data breaches and productivity by using a generalized difference-in-differences model with multiple prebreach and postbreach periods.The study analyzed the hospital financial data of the California Office of Statewide Health Planning and Development from 2012 to 2016. The study sample was an unbalanced panel of hospitals with 2610 unique hospital-year observations, including general acute care hospitals. California hospital data were merged with breach data published by the US Department of Health and Human Services. The dependent variable was hospital productivity measured as value added. The difference-in-differences model was estimated using fixed effects regression.Hospital productivity did not significantly differ from the baseline for 3 years after a breach. Data breaches were not significantly associated with a reduction in hospital productivity. Before a breach, the productivity of hospitals that experienced a data breach maintained a parallel trend with control hospitals.Hospital productivity was resilient against the shocks from a data breach. Nonetheless, data breaches continue to threaten hospitals; therefore, health care workers should be trained in cybersecurity to mitigate disruptions.
Central bank digital currencies: policy and operational perspectives for India

Cryptocurrencies are an innovation that brings together computing, networking and an incentive regime to create secure, decentralized, peer-to-peer digital currency that lends itself to be used as a medium of exchange and as storage of value. Most Central Banks have been reluctant to permit cryptocurrencies considering risks to the financial system, and have veered towards Central Bank Digital Currency or ‘Digital Fiat’ that shares several advantages of cryptocurrencies but are entirely mandated and managed by the Central Bank. This paper examines provides a background to CBDCs and discusses some of the policy and operational aspects that an Indian CBDC launch should consider.
Predicate Correlation Learning for Scene Graph Generation

For a typical Scene Graph Generation (SGG) method in image understanding, there usually exists a large gap in the performance of the predicates' head classes and tail classes. This phenomenon is mainly caused by the semantic overlap between different predicates as well as the long-tailed data distribution. In this paper, a Predicate Correlation Learning (PCL) method for SGG is proposed to address the above problems by taking the correlation between predicates into consideration. To measure the semantic overlap between highly correlated predicate classes, a Predicate Correlation Matrix (PCM) is defined to quantify the relationship between predicate pairs, which is dynamically updated to remove the matrix's long-tailed bias. In addition, PCM is integrated into a predicate correlation loss function (LPC) to reduce discouraging gradients of unannotated classes. The proposed method is evaluated on several benchmarks, where the performance of the tail classes is significantly improved when built on existing methods.
University Virtual Learning in Covid Times

Abstract Online training is demanded in the ubiquitous society we live in, and this is especially true if we consider the current situation at universities due to the Government issuing a state of alarm decree which requests all citizens to remain at home. The goal of this study is to determine the opinion of university students from different Spanish campuses on e-learning platforms, by the means of a descriptive and correlational study design, with N = 431. The results reveal that there is still a long road ahead to ensure that these tools work optimally to enable professors to fully exert their teaching profession. We can conclude that the online teaching system needs to be improved regarding the technical service that the university offers.
Analysis of Machine Learning Algorithms for Anomaly Detection on Edge Devices

The Internet of Things (IoT) consists of small devices or a network of sensors, which permanently generate huge amounts of data. Usually, they have limited resources, either computing power or memory, which means that raw data are transferred to central systems or the cloud for analysis. Lately, the idea of moving intelligence to the IoT is becoming feasible, with machine learning (ML) moved to edge devices. The aim of this study is to provide an experimental analysis of processing a large imbalanced dataset (DS2OS), split into a training dataset (80%) and a test dataset (20%). The training dataset was reduced by randomly selecting a smaller number of samples to create new datasets Di (i = 1, 2, 5, 10, 15, 20, 40, 60, 80%). Afterwards, they were used with several machine learning algorithms to identify the size at which the performance metrics show saturation and classification results stop improving with an F1 score equal to 0.95 or higher, which happened at 20% of the training dataset. Further on, two solutions for the reduction of the number of samples to provide a balanced dataset are given. In the first, datasets DRi consist of all anomalous samples in seven classes and a reduced majority class ('NL') with i = 0.1, 0.2, 0.5, 1, 2, 5, 10, 15, 20 percent of randomly selected samples. In the second, datasets DCi are generated from the representative samples determined with clustering from the training dataset. All three dataset reduction methods showed comparable performance results. Further evaluation of training times and memory usage on Raspberry Pi 4 shows a possibility to run ML algorithms with limited sized datasets on edge devices.
Production of Mobile English Language Teaching Application Based on Text Interface Using Deep Learning

This paper proposes a novel text interface using deep learning in a mobile platform environment and presents the English language teaching applications created based on our interface. First, an interface for handwriting texts is designed with a simple structure based on a touch-based input method of mobile platform applications. This input method is easier and more convenient than the existing graphical user interface (GUI), in which menu items such as buttons are selected repeatedly or step by step. Next, an interaction that intuitively facilitates a behavior and decision making from the input text is proposed. We propose an interaction technique that recognizes a text handwritten on the text interface through the Extended Modified National Institute of Standards and Technology (EMNIST) dataset and a convolutional neural network (CNN) model and connects the text to a behavior. Finally, using the proposed interface, we create English language teaching applications that can effectively facilitate learning alphabet writing and words using handwriting. Then, the satisfaction regarding the interface during the educational process is analyzed and verified through a survey experiment with users.
Data privacy preservation algorithm with k-anonymity

Abstract With growing concern of data privacy violations, privacy preservation processes become more intense. The k -anonymity method, a widely applied technique, transforms the data such that the publishing datasets must have at least k tuples to have the same link-able attribute, quasi-identifiers, values. From the observations, we found that, in a certain domain, all quasi-identifiers of the datasets, can have the same data type. This type of attribute is considered as an Identical Generalization Hierarchy ( IGH ) data. An IGH data has a particular set of characteristics that could utilize for enhancing the efficiency of heuristic privacy preservation algorithms. In this paper, we propose a data privacy preservation heuristic algorithm on IGH data. The algorithm is developed from the observations on the anonymous property of the problem structure that can eliminate the privacy constraints consideration. The experiment results are presented that the proposed algorithm could effectively preserve data privacy and also reduce the number of visited nodes for ensuring the privacy protection, which is the most time-consuming process, compared to the most efficient existing algorithm by at most 21%.
PIE: A Parallel Idiomatic Expression Corpus for Idiomatic Sentence Generation and Paraphrasing

Idiomatic expressions (IE) play an important role in natural language, and have long been a "pain in the neck" for NLP systems.Despite this, text generation tasks related to IEs remain largely under-explored.In this paper, we propose two new tasks of idiomatic sentence generation and paraphrasing to fill this research gap.We introduce a curated dataset of 823 IEs, and a parallel corpus with sentences containing them and the same sentences where the IEs were replaced by their literal paraphrases as the primary resource for our tasks.We benchmark existing deep learning models, which have state-of-the-art performance on related tasks using automated and manual evaluation with our dataset to inspire further research on our proposed tasks.By establishing baseline models, we pave the way for more comprehensive and accurate modeling of IEs, both for generation and paraphrasing. 1
PEREGRiNN: Penalized-Relaxation Greedy Neural Network Verifier

Abstract Neural Networks (NNs) have increasingly apparent safety implications commensurate with their proliferation in real-world applications: both unanticipated as well as adversarial misclassifications can result in fatal outcomes. As a consequence, techniques of formal verification have been recognized as crucial to the design and deployment of safe NNs. In this paper, we introduce a new approach to formally verify the most commonly considered safety specifications for ReLU NNs – i.e. polytopic specifications on the input and output of the network. Like some other approaches, ours uses a relaxed convex program to mitigate the combinatorial complexity of the problem. However, unique in our approach is the way we use a convex solver not only as a linear feasibility checker, but also as a means of penalizing the amount of relaxation allowed in solutions. In particular, we encode each ReLU by means of the usual linear constraints, and combine this with a convex objective function that penalizes the discrepancy between the output of each neuron and its relaxation. This convex function is further structured to force the largest relaxations to appear closest to the input layer; this provides the further benefit that the most “problematic” neurons are conditioned as early as possible, when conditioning layer by layer. This paradigm can be leveraged to create a verification algorithm that is not only faster in general than competing approaches, but is also able to verify considerably more safety properties; we evaluated PEREGRiNN on a standard MNIST robustness verification suite to substantiate these claims.
Path-based reasoning over heterogeneous networks for recommendation via bidirectional modeling

Heterogeneous Information Network (HIN) is a natural and general representation of data in recommender systems. Combining HIN and recommender systems can not only help model user behaviors but also make the recommendation results explainable by aligning the users/items with various types of entities in the network. Over the past few years, path-based reasoning models have shown great capacity in HIN-based recommendation. The basic idea of these models is to explore HIN with predefined path schemes. Despite their effectiveness, these models are often confronted with the following limitations: (1) Most prior path-based reasoning models only consider the influence of the predecessors on the subsequent nodes when modeling the sequences, and ignore the reciprocity between the nodes in a path; (2) The weights of nodes in the same path instance are usually assumed to be constant, whereas varied weights of nodes can bring more flexibility and lead to expressive modeling; (3) User-item interactions are noisy, but they are often indiscriminately exploited. To overcome the aforementioned issues, in this paper, we propose a novel path-based reasoning approach for recommendation over HIN. Concretely, we use a bidirectional LSTM to enable the two-way modeling of paths and capture the reciprocity between nodes. Then an attention mechanism is employed to learn the dynamical influence of nodes in different contexts. Finally, the adversarial regularization terms are imposed on the loss function of the model to mitigate the effects of noise and enhance HIN-based recommendation. Extensive experiments conducted on three public datasets show that our model outperforms the state-of-the-art baselines. The case study further demonstrates the feasibility of our model on the explainable recommendation task.
Improving prediction of surface solar irradiance variability by integrating observed cloud characteristics and machine learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.
Entity and Evidence Guided Document-Level Relation Extraction

Document-level relation extraction is a challenging task, requiring reasoning over multiple sentences to predict a set of relations in a document. In this paper, we propose a novel framework E2GRE (Entity and Evidence Guided Relation Extraction) that jointly extracts relations and the underlying evidence sentences by using large pretrained language model (LM) as input encoder. First, we propose to guide the pretrained LM’s attention mechanism to focus on relevant context by using attention probabilities as additional features for evidence prediction. Furthermore, instead of feeding the whole document into pretrained LMs to obtain entity representation, we concatenate document text with head entities to help LMs concentrate on parts of the document that are more related to the head entity. Our E2GRE jointly learns relation extraction and evidence prediction effectively, showing large gains on both these tasks, which we find are highly correlated.
Optimal Stochastic Process Optimizer: A New Metaheuristic Algorithm With Adaptive Exploration-Exploitation Property

Metaheuristic algorithms are constructed to solve optimization problems, but they cannot solve all the problems with best solutions. This work proposes a novel self-adaptive metaheuristic optimization algorithm, named Optimal Stochastic Process Optimizer (OSPO), which can solve different kinds of optimization problems with promising performance. Specifically, OSPO regards the procedure of optimization as a realization of stochastic process, and with the help of Subjective Probability Distribution Function (SPDF) and Receding Sampling Strategy proposed in this paper, OSPO can control the exploration-exploitation property online by the adaptive modification of the parameters in SPDF. This adaptive exploration-exploitation property of OSPO contributes to dealing with different kinds of problems; thus, it makes OSPO have the potential to solve at least a vast majority of optimization problems. The proposed algorithm is first benchmarked on uni-modal, multi-modal and composite test functions both in low and high dimensions. The results are verified by comparative studies with seven well-performed metaheuristic algorithms. Then, 21 real-world optimization problems are used to further investigate the effectiveness of OSPO. The winners of CEC2020 Competition on Real-World Single Objective Constrained Optimization, SASS algorithm, sCMAgES algorithm, EnMODE algorithm and COLSHADE algorithm are used as four comparative algorithms in real-world optimization problems. The analysis of simulations demonstrates that OSPO is able to provide very competitive performance compared to the comparative meta-heuristics both in benchmark functions and in real-world optimization problems; thus, the potential of OSPO to solve at least a vast majority of optimization problems is verified. A corresponding MATLAB APP demo is available on https://github.com/JiahongXu123/OSPO-algorithm.git.
Gaussian Process Monitoring of Layerwise-Dependent Imaging Data

Additive Manufacturing (AM) enables the direct production of complex geometries from computer-aided designs (CAD).The AM fabrication process is often executed in a layerby-layer manner, whereby minute printing errors in one layer can manifest significant defects in the final part.In-situ quality monitoring and control are currently limited for AM processes and cause low repeatability.Recently.advanced imaging is increasingly invested in AM and leads to the proliferation of layerwise imaging data, which provides an opportunity to transform quality control of AM from post-build inspection to in-situ quality monitoring.However, existing methodologies for in-situ inspection primarily focus on key characteristics of image profiles that tend to be limited in the ability to analyze the variance components, as well as root causes and failure patterns that are critical to process improvement.This paper presents an Additive Gaussian Process with dependent layerwise correlation (AGP-D) to model the spatio-temporal correlation of layerwise imaging data for AM quality monitoring.The AGP-D consists of three independent GP modules.The first GP approximates the base profile, whereas the second and third GP capture the correlation within the same layer and among layers, respectively.Based on posterior predictions of new layers, Hotelling and generalized likelihood ratio (GLR) control tests are formulated to detect process shifts in the newly fabricated layer and analyze root causes.The proposed methodology is evaluated and validated using both simulation data and real-world case study of a cylinder build fabricated by a laser powder bed fusion (LPBF) machine.Experimental results show the proposed AGP-D is effective for real-time modeling and monitoring of layerwise-correlated imaging data.
High Throughput Implementation of Post-Quantum Key Encapsulation and Decapsulation on GPU for Internet of Things Applications

Internet of Things (IoT) sensor nodes are placed ubiquitously to collect information, which is then vulnerable to malicious attacks. For instance, adversaries can perform side channel attack on the sensor nodes to recover the symmetric key for encrypting IoT data. Refreshing the symmetric key frequently can reduce the risk of compromised keys. However, the number of sensor nodes connected to the gateway and cloud server is massive. Refreshed symmetric keys need to be sent to gateway devices and cloud server frequently with a secure key encapsulation mechanism (KEM), which is time-consuming. In this article, novel and efficient implementation techniques are proposed to accelerate Kyber, a post-quantum KEM, on a Graphics Processing Unit (GPU). Fully parallel implementation of number theoretic transform (NTT) with combined levels is presented, which is 2.65× faster than state-of-the-art result on a GPU. Other proposed techniques include parallel rejection sampling, central binomial distribution with coalesced memory access and parallel fine-grain AES-256. These techniques enable high throughput performance with 162760 encapsulations/second and 107631 decapsulations/second on an RTX2060 GPU. This is also the first fine grain implementation of post-quantum KEM (Kyber) on a GPU, which can be used to offer key encapsulation/decapsulation as a service to reduce the burden on IoT systems.
TextGTL: Graph-based Transductive Learning for Semi-supervised Text Classification via Structure-Sensitive Interpolation

Compared with traditional sequential learning models, graph-based neural networks exhibit excellent properties when encoding text, such as the capacity of capturing global and local information simultaneously. Especially in the semi-supervised scenario, propagating information along the edge can effectively alleviate the sparsity of labeled data. In this paper, beyond the existing architecture of heterogeneous word-document graphs, for the first time, we investigate how to construct lightweight non-heterogeneous graphs based on different linguistic information to better serve free text representation learning. Then, a novel semi-supervised framework for text classification that refines graph topology under theoretical guidance and shares information across different text graphs, namely Text-oriented Graph-based Transductive Learning (TextGTL), is proposed. TextGTL also performs attribute space interpolation based on dense substructure in graphs to predict low-entropy labels with high-quality feature nodes for data augmentation. To verify the effectiveness of TextGTL, we conduct extensive experiments on various benchmark datasets, observing significant performance gains over conventional heterogeneous graphs. In addition, we also design ablation studies to dive deep into the validity of components in TextTGL.
Transprofessional competences of school teachers in the digital environment: education employers’ perspective

The shift towards digitalization in education, intensified by the COVID-19 pandemic-related issues, has led to the emergence of a need to expand the borders of the teaching profession. To be effective in the digital environment, school teachers have to master new transprofessional competences (TPCs), enabling them to use the skill sets of other careers, gain new skills and abilities and find non-standard solutions to professional and pedagogical problems using digital technologies. This study investigated education employers' opinions (61 principals and deputy principals from 31 Russian secondary schools) about the comparative importance of TPCs for school teachers working in the digital environment and their perceptions of what barriers prevent teachers from developing TPCs. The survey used a three-item questionnaire asking the respondents to rank the TPCs and the TPC clusters in order of importance for teachers working with the digital and to describe the obstacles to teachers' TPC development. The findings revealed that the employers attached the greatest importance to psychological TPCs while assigning the least importance to digital citizenship TPCs. The major barriers to TPC development, in the respondents' opinion, lay in teachers' fear of new trends and their excessive workload. Based on the survey results, we designed a model of school teachers' transprofessional competences in digital teaching and learning that can be used as an assessment tool in different professional settings. The paper concludes with suggestions on how to overcome the barriers and help improve school teachers' TPC development.