Reviewerly/exHarmony huggingface dataset:
Using machine learning to identify top predictors for nurses’ willingness to report medication errors

This paper presents a novel methodology to analyze nurses’ willingness to report medication errors. Parallel Extreme Learning Machines were applied to identify the top interpersonal and organizational predictors and Self-Organizing Maps to create comprehensive visualization. The results of the data analysis were targeted to improve the likelihood of nurses reporting of medication errors. ELMs are accurate by extremely fast prediction models. Self-Organizing Maps enable us to perform non-linear dimensionality reduction to get an accurate visualization of the selected variables. Combining both techniques reduces the curse of dimensionality and improves the interpretability of the visualization.
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Huge DBMSs storing genomic information are being created and engineerized for doing large-scale, comprehensive and in-depth analysis of human beings and their diseases. This paves the way for significant new approaches in medicine, but also poses major challenges for storing, processing and transmitting such big amounts of data in compliance with recent regulations concerning user privacy. We designed and implemented ER-index, a new full-text index in minute space which was optimized for pattern-search on compressed and encrypted genomic data using a reference sequence, and that complements a previous index for reference-free genomics. Thanks to a multi-user and multiple-keys encryption model, a single ER-index can store the sequences related to a large population of individuals so that users may perform search operations directly on compressed data and only on the sequences to which they were granted access. Tests performed of three different computing platforms show that the ER-index get very good compression ratios and search times, outperforming in many cases a reference tool that was proved nearly-optimal in time and space and does not implement encryption. The ER-index C++ source code plus scripts and data to assess the tool performance are available at: https://github.com/EncryptedIndexes/erindex.
An Approach Towards Merging Grammars

Introduction: Since the introduction of Island Grammars, they have been successfully used for a variety of tasks, including impact analysis, multi-lingual parsing, and source code identification. However, there has been no attempt to automate the generation of Island Grammars. Objective: This research considers the development of a method to automate the merging of Island Grammar components. The goal of this is to facilitate the development of an approach to automate the creation of Island Grammars. The result of this is the reduction in initial effort and maintenance effort required for Island Grammar engineering. Methods: We develop an automated approach to merge the components of grammars. To evaluate this approach, we conducted two experiments, each using a factorial design of five replications each. We randomly selected pairs of grammars from each of three size categories to assess the effects of the merging process on the maintenance effort and complexity of the generated grammars. Results: We found that in nearly all cases, the application of this merging approach reduces the maintenance effort and complexity of the grammars. Limitations: The primary limitation of this research is that this approach is currently limited to grammars written in the Antlr4 grammar format. Conclusions: This work presents the initial steps towards the automated construction of Island and Tolerant Grammars. We have shown that this approach to merging grammar components follows suit with the expectations of Island and Tolerant grammars (reduction in maintenance effort and complexity).
Counterfactual Retrieval for Augmentation and Decisions

The optimal situation to make a decision is to have all variables in grasp. This however, almost never occurs. There has been research on counterfactuals as a way to provide more explainable systems and models. In furtherance of this research, this paper proposes CORFAD, Counterfactual Retrieval for Augmentation and Decisions. We explore user generated counterfactual tweets and by aggregating counterfactual statements that relate to pre-determined keywords, CORFAD simplifies data analysis by suggesting variables towards which future actions might have the greater or lesser effects towards a defined goal. This has the dual purpose of making synthetic counterfactual data generation more focused and less likely to generate non-useful explanations, while also able to stand alone to assist decision makers. This paper uses as test case, Counterfactual Statements connected with the Tesla Model 3 to explore insights that can guide decision-making in situations where multiple variables are possible and exist.
Difficulties of succession pipeline in large industrial enterprises of Nizhny Tagil in studies of tendencies of competitiveness of higher technical education

Abstract There have been significant changes in the higher education system in Russia recently. The possibility to apply for post-secondary education to several universities according to the results of the Unified State Examination (USE) and the transition to the two-level structure of higher education have significantly changed the landscape of competitiveness not only between individual universities, but also between large cities, which have become centers of mass attraction of applicants and young specialists. The number of students studying federally funded has been decreasing over the years. As a result, regional universities suffer a shortage of applicants, while local industrial enterprises suffer a shortage of young qualified personnel. The described problems are fully demonstrated in the system of higher education and training of young engineering personnel for large industrial enterprises in Nizhny Tagil. The article reviews the results of the study on the motivational field of academic migration of young people from the Nizhny Tagil city, conducted in February-March 2020.
A semi-empirical approach to quantify and handle the effect of moisture on spectral unmixing

Spectral unmixing techniques aim to retrieve the abundances of the materials contributing to a spectral measurement by inverting the underlying spectral mixture occurring at the surface level. In environment and Earth sciences, the darkening effect of moisture on soils and rocks is a common challenge that may lead to major errors when neglected. In this study, a linear function was calibrated to relate a single moisture index to its spectral effect. The function was then combined with the Neighbor-Band Ratio Unmixing (NBRU) and the Linear Spectral Unmixing (LSU) approaches and applied on spectra of a crafted mixture and a rock sample acquired in laboratory conditions, and on a WorldView-3 (WV3) superspectral image of the Canadian Malartic open pit mine. In laboratory conditions, the addition of the moisture function led to a higher stability and a lower deviation of the abundances retrieved with varying water contents (WC). The combined method also obtained a moisture map of the Mine Canadian Malartic consistent with the known systematic watering performed by the mining company.
Bounding game temperature using confusion intervals

We consider bounds for the temperatures of combinatorial games. Our first result gives an upper bound on the temperatures of the positions of a ruleset in terms of the lengths of the confusion intervals of these positions. We give an example to show that this bound is tight. Our second main result is a method to find a bound for the lengths of the confusion intervals. This pair of results constitutes the first general technique to bound temperatures. As examples of the bound and the method, we consider the temperature of subsets of positions in Domineering and Snort.
Low and High Resonance Components Restoration in Multichannel Data

A technique for the restoration of low resonance component and high resonance component of K independently measured signals is presented. The definition of low and high resonance component is given by the Rational Dilatation Wavelet Transform (RADWT), a particular kind of finite frame that provides sparse representation of functions with different oscillations persistence. It is assumed that the signals are measured simultaneously on several independent channels and in each channel the underlying signal is the sum of two components: the low resonance component and the high resonance component, both sharing some common characteristic between the channels. Components restoration is performed by means of the lasso-type penalty and backfitting algorithm. Numerical experiments show the performance of the proposed method in different synthetic scenarios highlighting the advantage of estimating the two components separately rather than together.
Boosting Answer Set Optimization with Weighted Comparator Networks

Answer set programming (ASP) is a paradigm for modeling knowledge intensive domains and solving challenging reasoning problems. In ASP solving, a typical strategy is to preprocess problem instances by rewriting complex rules into simpler ones. Normalization is a rewriting process that removes extended rule types altogether in favor of normal rules. Recently, such techniques led to optimization rewriting in ASP, where the goal is to boost answer set optimization by refactoring the optimization criteria of interest. In this paper, we present a novel, general, and effective technique for optimization rewriting based on comparator networks, which are specific kinds of circuits for reordering the elements of vectors. The idea is to connect an ASP encoding of a comparator network to the literals being optimized and to redistribute the weights of these literals over the structure of the network. The encoding captures information about the weight of an answer set in auxiliary atoms in a structured way that is proven to yield exponential improvements during branch-and-bound optimization on an infinite family of example programs. The used comparator network can be tuned freely, e.g., to find the best size for a given benchmark class. Experiments show accelerated optimization performance on several benchmark problems.
PCN-Based Secure Energy Trading in Industrial Internet of Things

The application of blockchain cryptocurrency in the field of energy transactions has attracted wide attention because it supports complex communication interactions of a large number of distributed energy entities. Due to the high degree of autonomy of distributed systems, it is very important to use trusted encryption technology to ensure the security of user information in the network. In order to avoid the problem of user privacy leakage in the Peer-to-Peer (P2P) energy trading scheme in the Industrial Internet of Things (IIOT), this paper adopts the signature scheme based on Elliptic Curve Cryptography-based Threshold Cryptography (ECC-TC) to complete the anonymous process of energy nodes and ensure the traceability of pseudonyms. In addition, in order to solve the problem of transaction restrictions caused by high transaction costs and long confirmation time of energy block chain, we proposed a payment scheme based on payment channel network (PCN) to support fast and frequent small energy transactions. We conducted security analysis and feasibility assessment in the context of security and privacy protection requirements and the conclusion proves that proposed scheme enables peer to communicate anonymously in a P2P manner and quickly complete small energy transactions while using blockchain technology to protect basic transactions. In addition, the cost of communication and calculation of the scheme is much less than that of traditional transaction mode which proves that proposed scheme can be applied in practice.
TRUVIN: Lightweight Detection of Data Oriented Attacks Through Trusted Value Integrity

Data-oriented attacks, where the adversary corrupts critical program data in memory, remain one of the most challenging security threats to address. Because the attacker does not touch any code or code pointers, data-oriented attacks are able to circumvent common defence strategies such as data execution prevention or control-flow protection. Dataflow integrity (DFI) techniques can mitigate these attacks by detecting corruption of any program data. However, due to high performance costs, these techniques are not widely adopted in practice. This paper presents TRUVIN, a lightweight scheme that addresses data-oriented attacks by focusing on only those variables which are crucial to the integrity assurance. Instead of checking every memory operation, TRUVIN selectively instruments program data originating from only trusted agents (e.g., the programmer), as they are considered critical to the runtime integrity. Our scheme analyses the program at compile time, and generates instrumentation only for the necessary operations. TRUVIN reduces the performance cost by a factor of 4.3 on average with 28% overhead compared to full instrumentation (121%), while retaining the security guarantees.
Automatische Erkennung und Klassifikation von Formularbestandteilen in Königsurkunden

No AccessAutomatische Erkennung und Klassifikation von Formularbestandteilen in KönigsurkundenZur Aufbereitung digitaler Urkundenkorpora in der MediävistikAlina OstrowskiAlina OstrowskiSearch for more papers by this authorhttps://doi.org/10.7788/9783412520663.139SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book cover 1. AuflageISBN: 978-3-412-52064-9 eISBN: 978-3-412-52066-3HistoryPublished online:November 2020 PDF download
Improving Mongolian-Chinese Machine Translation with Automatic Post-editing

Fluency and faithfulness are the main criteria to evaluate the quality of machine translation. In order to acquire excellent translation results, the common method is to learn semantic-rich word embeddings by fine-tuning or pre-processing. However, there is no human intervention when generating translation due to the black-box prediction characteristics of neural network, which limits the generation of higher quality translation. Therefore, this paper proposes a translation automatic post-editing method combined with copying-rewriting (CoRe) network and introduces a double-ended attention module to realize the interaction between the source sentences and the machine translation. Meanwhile, we utilize interaction result (copy scores) to determine which fragments in the translation will be copied or be rewritten. Our method is verified on the CWMT2018 Mongolian-Chinese translation task and has obtained significant results.
Object Tracking with Multi-sample Correlation Filters

Due to the real-time tracking and location accuracy of the kernel correlation filtering (KCF) algorithm, this method is now widely used in object tracking tasks. However, whether KCF or its improved algorithm, the filter parameter training is achieved by the feature of the current frame, in other words, the training sample is single. If the samples of multiple frames in the previous sequence can be integrated to the filter parameters training, the trained filter parameters should be more reliable. In this paper, we propose an object tracking algorithm based on multi-sample kernel correlation filtering (MSKCF). Meanwhile, In order to select better samples, the average peak correlation energy (APCE) is introduced to measure the stability of tracking effect, and is applied as weight of sample. The frames with higher APCE value are chosen as multi-sample, and then are used to train filter parameters. Experimental results show that the tracking effect of the proposed method is superior to compared state-of-the-art algorithms.
A GIS-Based Artificial Neural Network Model to Assess Building Location Potential to Harvest Solar Energy

Dependency upon non-renewable energy sources has created challenges related to climate change, wars over energy supplies, famine, and cycles of deforestation concerns. As populations increase and economic development progress, energy demand grows, and ultimately the scalability of the problems associated with non-renewable energy resources. Solar energy plays a promising role to solve the problem and it is foreseen as the most promising renewable energy source due to its availability and benefits. Despite the promising effects, only a limited amount of electricity is currently produced globally from solar power. In order to realize the importance of tapping into solar energy, it is crucial to reveal the potential amount of electricity that could be thus produced. Many criteria should be taken into consideration before switching to solar energy. One of the most important criteria is the building location and surrounding environment. For example, a house on a hill or less shaded area will get more direct sunlight and generate more solar energy compared to a house in a shaded area. A multi-criteria methodology based on geographic information systems (GIS) and artificial neural network (ANN) is used in this research to assess community benefits of switching to solar energy by evaluating houses location and surrounding environment. The methodology presented in this research uses a tool for automated extraction of photography from 360° video data at locations of interest. The extracted imagery provides a data set to train a deep learning neural network to predict whether a house location is a good fit for solar energy. This research presents a state-of-the-art methodology to assess community benefits of switching to solar energy by using GIS and deep learning to automate the assessment of buildings location potential to harvest solar energy.
Multi-level Feature Extraction in Time-Weighted Graphical Session-Based Recommendation

Session-based recommendation aims to simulate users' behavior through a series of anonymous sessions. Recent research work mainly introduces deep learning into the recommender systems, and has achieved relatively good results. Previous research only focused on the clicked item thus ignoring the time information, that is dwell time for each item. It is undeniable that the length of dwell time on an item can reflect the user's preferences to a certain extent. And they lack the mining latent features of items. In this paper, we propose to explore multi-level feature extraction in time-weighted graphical session-based recommendation, abbreviated as F-TGNN. In F-TGNN, we first construct graphs for session sequences, in which the dwell time between two items is used as the weight of the corresponding edge. Then we use gated Graph Neural Network (GNN) to learn the transitions of items in the session sequence and obtain the embedding of each item. After that, we propose a Feature Extraction Module (FEM) to mine sequential patterns from item-level and contextual information between items from sequence-level. Finally, the predicted score for each item to be the next click is calculated. Extensive experiments conducted on two real datasets show that F-TGNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.
A Hybrid Graph Centrality Based Feature Selection Approach for Supervised Learning

Graph-based approaches of feature selection have found their application in diverse areas owing to their efficacy to detect potential connections among the features. In this paper, we propose a hybrid graph-based approach for selecting a subset of features in the context of supervised learning. The novelty of this approach lies in the utilisation of graph-theoretic concepts of degree centrality as well as eigenvector centrality to assess the relative importance of features. Additionally, entropy-based measures have been used. The performance of the proposed approach is compared to that of the existing benchmark algorithms using publicly available datasets. The results have come out to be quite encouraging.
Learning Interactions at Multiple Levels for Abstractive Multi-document Summarization

The biggest obstacles facing multi-document summarization include much more complicated input and excessive redundancy in source contents. Most state-of-the-art systems have attempted to tackle the redundancy problem, treating the entire input as a flat sequence. However, correlations among documents are often neglected. In this paper, we propose an end-to-end summarization model called MLT, which can effectively learn interactions at multiple levels and avoid redundant information. Specifically, we utilize a word-level transformer layer to encode contextual information within each sentence. Also, we design a sentence-level transformer layer for learning relations between sentences within a single document, as well as a document-level layer for learning interactions among input documents. Moreover, we use a neural method to enhance Max Marginal Relevance (MMR), a powerful algorithm for redundancy reduction. We incorporate MMR into our model and measure the redundancy quantitively based on the sentence representations. On benchmark datasets, our system compares favorably to strong summarization baselines judged by automatic metrics and human evaluators.
LinkedPipes Applications - Automated Discovery of Configurable Linked Data Applications

Consumption of Linked Data (LD) is a far less explored problem than its production. LinkedPipes Applications (LP-APPs) is a platform enabling data analysts and data journalists to easily create LD based applications such as, but not limited to, visualizations. It builds on our previous research regarding the automatic discovery of possible visualizations of LD. The approach was based on the matching of classes and predicates used in the data, e.g. in a form of a data sample, to what an application or visualization expects, e.g. in a form of a SPARQL query, solving potential mismatches in data by dynamically applying data transformers. In this demo, we present a platform that allows a data analyst to automatically discover possible visualizations of a given LD data source using this method and the applications contained in the platform. Next, the data analyst is able to configure the discovered visualization application and publish it or embed it in an arbitrary web page. Thanks to the configuration being stored in their Solid POD, multiple analysts are able to collaborate on a single application in a decentralized fashion. The resulting visualization application can be kept up to date via scheduling an ETL pipeline, regularly refreshing the underlying data.
Historical Document Image Denoising by Ising Model

In this paper, we propose a historical document image denoising method based on Ising-like model and Anisotropic filter. Physical statistics and morphological smooth process are combined together to improve image particulars. We first apply an Ising model to the 8-th bit plane of the original historical document image, then the image energy field and density is through Ising dynamic evolution. After the image is processed, it has been smoothed by Anisotropic morphological for several times. At last, we get the restored image document. We compare the visual quality changes before and after Ising evolution in different Anisotropic morphological condition respectively. Experimental results demonstrate that the Ising evolved image can better restore the local area details of the degraded historical document image.