Reviewerly/exHarmony huggingface dataset:
Reinforcing each other: strategies to advance international interlibrary loan

Purpose The purpose of this paper is to share the results of the 2019 international interlending survey conducted by the American Library Association, Reference and User Services Association (RUSA), Sharing and Transforming Access to Resources Section (STARS) International Interlibrary Loan (ILL) Committee, the fourth such survey conducted by this group since 2007. Design/methodology/approach Committee members developed the survey instrument and collected data in Qualtrics. Questions from the 2015 and 2011 instruments were reused to support longitudinal data analysis, and new questions related to improving the sharing of resources across borders were added. The survey was distributed to libraries worldwide via multiple methods, and translations in six of the seven official International Federation of Library Associations and Institutions languages were provided. Findings Results reveal challenges to engaging in global resource sharing in the areas of discovery and request methods, policies and service models, copyright and licensing, delivery and payment. Strategies to overcome these challenges, as informed by both the survey results and recent literature, are shared. Reinforcing each other, these independently and organically developed solutions illustrate ways to further advance ILL. Practical implications The findings can aid ILL practitioners and the organizations to which they belong in improving the sharing of resources across borders. By optimizing this service, libraries can better support researchers in a globalized environment. Originality/value The 2019 RUSA STARS survey is the first survey on the topic of international ILL to offer multiple translations complimenting global distribution. It contributes to the literature by offering a broad and updated perspective on current international ILL practices, challenges impeding the sharing of library materials abroad and possible solutions.
An Approach to Improving Software Security Through Access Control for Data in Programs

In a recent information system, effective management of secure information plays an important role in maintaining the system securely. To ensure the safe operation of a system, secure information in the system should be kept safely and prevented from external intrusion or information leakage. So, it is needed to protect secure information against unauthorized access to maintain a safe information system. In this paper, we present a data secure language and design an access control method for protecting secure data against unauthorized access in programs. The proposed method is designed to manage data containing secure information, and it can improve the information security of programs. In experiments, we show evaluation results and the accuracy of the proposed method.
Extraction and analysis of massive skeletal information from video data of crowded urban locations for understanding implicit gestures of road users

This work explains the possible inferable information from a long-term video acquisition with cameras installed in close proximity to pedestrian movements with an unobstructed view of the entire intersection. The main goal is detecting implicit and explicit gestures and understanding communication and interactions between different types of road users. After explaining the designs of different gesture classification approaches, we relate the qualitative approach with our classification scheme for the extracted skeletons. To this end, a sequence with selected moving entities is selected and compared with the manually annotated video sequence. Results show the limitations of the automated approach and indicate a level of subjectivity in the manual annotation procedure. Subsequently, we discuss possibilities and restrictions of our approach and reflect on the importance of the specific conditions of video acquisitions. Depending on the field of view and distance between installed video cameras and moving vulnerable road users (VRUs), we are able to define the restrictions of our approach. As a result, we are able to define a selection of suitable applications for our approach.
Assessment of the Accessibility of Digital Educational Platforms Based on Fuzzy - Logical Analysis Methods

The paper we have considered the concept of accessibility of a software system as one of the characteristics of its quality. On the basis of the international standard of the Web-accessibility WCAG we have identified the most important for the evaluation criteria. We have developed a methodology for a comprehensive assessment of the availability of educational resources based on fuzzy-multiple aggregation of accessibility estimates for certain types of violations.
Composing dinatural transformations: Towards a calculus of substitution

Dinatural transformations, which generalise the ubiquitous natural transformations to the case where the domain and codomain functors are of mixed variance, fail to compose in general; this has been known since they were discovered by Dubuc and Street in 1970. Many ad hoc solutions to this remarkable shortcoming have been found, but a general theory of compositionality was missing until Petric, in 2003, introduced the concept of g-dinatural transformations, that is, dinatural transformations together with an appropriate graph: he showed how acyclicity of the composite graph of two arbitrary dinatural transformations is a sufficient and essentially necessary condition for the composite transformation to be in turn dinatural. Here we propose an alternative, semantic rather than syntactic, proof of Petric's theorem, which the authors independently rediscovered with no knowledge of its prior existence; we then use it to define a generalised functor category, whose objects are functors of mixed variance in many variables, and whose morphisms are transformations that happen to be dinatural only in some of their variables. We also define a notion of horizontal composition for dinatural transformations, extending the well known version for natural transformations, and prove it is associative and unitary. Horizontal composition embodies substitution of functors into transformations and vice-versa, and is intuitively reflected from the string-diagram point of view by substitution of graphs into graphs.
Zero and first-degree spline multiwavelets and parallel computing

Abstract The paper considers the Haar-like multiwavelets of zero degree splines (step functions) and first-degree splines (broken lines). In contrast to the use of the wavelet transforms based on the Hermitian spline-multiwavelets, the approach proposed does not require the calculation of approximate values of derivatives. On the other hand, the parallelization effect does not rigidly relate to the degree of a spline. This can lead to a significant computation speedup due to the parallel processing of the measured values by several (instead of one) filters. The results of numerical calculations are compared to the known analogs.
The Identical Data in Cloud Storage with ADJDUP Technique

Data deduplication is necessary for corporations to minimize the hidden charges linked with backing up their data using Public Cloud platform. Incapable data storage on its own can become improvident, and such problem are enlarging in the Public Cloud at other and scattered satisfied confirmed storage structure are creating multiple clone of single account for collating or other purposes. Deduplication is friendly in cost shrinking by lengthening the benefit of a precise volume of data. Miserably, data duplicity having several safety constraints, so more than one encoding is appropriate to validate the details. There is a system for dynamic Information-Locking and Encoding with Convergent Encoding. In this Information-Locking and Encoding with Convergent Encoding, the data is coded first and then the cipher text is encoded once more. Chunk volume is used for deduplication to diminish disk capacity. The same segments would still be encrypted in the same cipher message. The format of the key neither be abbreviated from encrypted chunk data by the hacker. The comprehension is also guarded from the cloud server. The center of attention of this document is to reducing disk storage and provides protection for online cloud deduplication.
LIORI at the FinCausal 2020 Shared task

In this paper, we describe the results of team LIORI at the FinCausal 2020 Shared task held as a part of the 1st Joint Workshop on Financial Narrative Processing and MultiLingual Financial Summarisation. The shared task consisted of two subtasks: classifying whether a sentence contains any causality and labelling phrases that indicate causes and consequences. Our team ranked 1st in the first subtask and 4th in the second one. We used Transformer-based models with joint-task learning and their ensembles.
The Effectiveness of the Picture and Picture Learning Model to Improve the Teeth Brushing Ability of Intellectual Disability

This study aims to describe the influence of the use of picture and picture learning models on the ability to attend (brushing teeth) for Class 3 SDLB C This study uses quasi-experimental research methods (quasi experiment) a form of Time Series Design. The analysis technique used is hypothesis testing using the Wilcoxon test. The results of the data analysis show that the average learning outcomes of students of ten people are five people whose values are below the class average, it can be concluded that the picture and picture learning model can improve the ability of self-brushing teeth to brush third grade students of SDLB C
An Evaluation into Deep Learning Capabilities, Functions and Its Analysis

Deep learning (DL) is a rising examination space in Machine Learning (ML) and pattern recognition. Deep learning alludes to Machine Learning methods that utilize administered or unsupervised approaches to precisely learn gradable portrayals in profound structures for arrangement. The objective is to locate extra unique choices inside the larger amounts of the representation, by utilizing neural systems that basically isolate the changed educational factors inside the data. Inside the ongoing years, it’s pulled in inexhaustible consideration in light of its dynamic execution in different regions like object perception, speech recognition, computer vision, cooperative filtering, and natural language process. Since the data continues getting bigger, deep learning is going to assume a key job in giving immense information prophetical examination arrangements. It is proposed to advance a brisk outline of deep learning, strategies, present examination tries, and furthermore the difficulties worried in it through this paper.
Regression learning on patches

Neural networks often do poorly at representing dis-continuous functions, or even just functions with rapid transitions in the response surface between closely-spaced points in feature space. However, such `edges' in the data can be a useful way to partition the feature space in order to train specialised learners for individual regions. This is particularly beneficial where these regions are relatively simple, and hence low-complexity learners can be used successfully on them. Another benefit of such an approach is that it is easily parallelisable: the specialised learners use independent partitions of the data, and so they can be trained in parallel, while output prediction is based on the output of just one network, so there is no need to combine predictions. We introduce an algorithm to partition the data that is inspired by Finite Element Tearing and Interconnecting. Using an implementation based on a decision tree with neural networks at the leaves, we demonstrate our approach for regression learning on patches of the feature space. We use both artificial and real-world datasets to show that, in some use cases, this method can outperform conventional neural networks that see the entire feature set in the original training.
An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks

Graph neural networks and graph kernels have achieved great success in solving machine learning problems on graphs. Recently, there has been considerable interest in determining the expressive power mainly of graph neural networks and of graph kernels, to a lesser extent. Most studies have focused on the ability of these approaches to distinguish non-isomorphic graphs or to identify specific graph properties. However, there is often a need for algorithms whose produced graph representations can accurately capture similarity/distance of graphs. This paper studies the expressive power of graph neural networks and graph kernels from an empirical perspective. Specifically, we compare the graph representations and similarities produced by these algorithms against those generated by a well-accepted, but intractable graph similarity function. We also investigate the impact of node attributes on the performance of the different models and kernels. Our results reveal interesting findings. For instance, we find that theoretically more powerful models do not necessarily yield higher-quality representations, while graph kernels are shown to be very competitive with graph neural networks.
Evaluation of LMT and DNN Algorithms in Software Defect Prediction for Open-Source Software

Software defect prediction has achieved considerable salability in the last years. Defect prediction is the first important step in dependability assessment of complex software systems, because it can immediately be impacted on the dependability of those systems, improves performance, and decreases the software cost. Among the most important approaches used primarily in software defect prediction are the algorithms of machine learning classification. In this research, LMT machine learning and deep learning algorithms are presented for the software bug prediction model process on a general dataset obtained by a combination of several datasets for four open-source software and applications. These are Linux kernel, MySQL DBMS, Apache HTTPD web server, and Apache AXIS WS. A new dataset has represented four defect classes: Bohrbug, Mandelbug, Aging-Related Bug, and Unknown bugs. The new classification approach depends on defect location in OSSs. The system behavior is categorized to assess system dependability in the future. Performance measurement is implemented in JAVA using NETBEANS version 8.0.2 for LMT classifier and in Python for Deep Learning classifier. In the results of experiments, fault predictors using the DNN classifier perform better than LMT, where accuracy weighted average for each class using the LMT classifier is 0.849, while accuracy weighted average for each class using DNN is 0.87.
Machine Learning Explainability and Transferability for Path Navigation

Deep neural networks are powerful tools for machine perception. Unfortunately their decisions are difficult to explain due to the complexity and size of the networks. Previously we have alleviated this issue by using the representational portion of a deep neural network and combining it with a k-nearest neighbor (KNN) classifier. Through inspection of the decisions made by the KNN, we can directly see the training data responsible for the decisions, allowing us to determine the quality of the overall decision and the quality of the representational layer of the deep NN. While the technique worked well, it requires tens of thousands of latent vectors to be stored for classification. In addition, it lacks the ability to show how parts of an image influence the classification decision. Here we address these issues by 1) Using a radial basis function network (RBFN) in place of the KNN allowing far fewer images to be used in deployment and 2) Using an autoencoder network for explainability. In addition to these techniques, we examine the effects of transfer learning to determine that results are robust. All results are tested on a domain where an unmanned aerial vehicle (UAV) navigates a forest trail through a single camera.
Code-Switching in Advertising to Ethnic Bilingual Minorities: The Case of Health Care Services

Although demographic trends indicate the increasing importance of bilingual consumers, only few studies have examined the usage of code-switched messages in advertising to ethnic bilingual minorities. Besides, these studies focused on the general effects of mixed-language advertising. However, some industries are unique in terms of appropriateness of code-switched messages and require an individual approach. Drawing on Speech Accommodation Theory and the Markedness Model, we have partially addressed this gap by developing three theoretical propositions about the applicability of code-switched advertising to bilingual minorities in the health care sector. Firstly, in the health care industry, code-switched advertising may not be a universally successful advertising approach. Secondly, if such messages are to work in this industry, greater persuasiveness might be attained by switching to the language that conveys more professional credibility with regard to medical care. Thirdly, in case of the majority and minority languages conveying a relatively equal degree of professional credibility, the effect of code-switching might be primarily determined by sociocultural factors. Managerial implications and avenues for future research are provided.
The role of information sensitivity in adoption of E2EE communication software

This poster reports on an exploratory study of E2EE communication software adoption factors among academics at five Slovenian universities (N = 367). The results of the study suggest that information sensitivity plays an important role in adoption of E2EE communication software. Privacy concerns and fear of government intrusions affect adoption only when information sensitivity is high. Protection motivation may not be applicable to this context as only perceived vulnerability affected adoption for high information sensitivity.
Digital Forensic Management System using Facial Recognition for Student's Participation and Registration on School Activities

Face recognition technology is widely used in applications. But in some activities it may be too difficult to install the device and the registration boot. That requires both manpower and time, such as enrolling students to attend university activities. If you will use the face scanning system, one by one will waste a lot of time. The other method. It may be easy to falsify. Using digital imagery in student participation to solve problems by developing a system that can detect participants' faces in digital photographs obtained by taking still images and videos from several photographers. And collecting detailed pictures and videos throughout the event it is a digital proof to find the participants to verify their faces match with any student in the database. Who participate in that activity, the system will have Finding and comparing data of pre-recorded students' photographs and the algorithm would checks for duplicate data and records the activity in the database. Where users can specify category or activity name for later inspection
On Stability with Respect to a Part of the Variables for Nonlinear Discrete-Time Systems with a Random Disturbances

Nonlinear discrete (finite-difference) system of equations subject to the influence of a random disturbances of the "white" noise type, which is a difference analog of systems of stochastic differential equations in the Ito form, is considered. The increased interest in such systems is associated with the use of digital control systems, financial mathematics, as well as with the numerical solution of systems of stochastic differential equations. Stability problems are among the main problems of qualitative analysis and synthesis of the systems under consideration. In this case, we mainly study the general problem of stability of the zero equilibrium position, within the framework of which stability is analyzed with respect to all variables that determine the state of the system. To solve it, a discrete-stochastic version of the method of Lyapunov functions has been developed. The central point here is the introduction by N. N. Krasovskii, the concept of the averaged finite difference of a Lyapunov function, for the calculation of which it is sufficient to know only the right-hand sides of the system and the probabilistic characteristics of a random process. In this paper, for the class of systems under consideration, a statement of a more general problem of stability of the zero equilibrium position is given: not for all, but for a given part of the variables defining it. For the case of deterministic systems of ordinary differential equations, the formulation of this problem goes back to the classical works of A. M. Lyapunov and V. V. Rumyantsev. To solve the problem posed, a discrete-stochastic version of the method of Lyapunov functions is used with a corresponding specification of the requirements for Lyapunov functions. In order to expand the capabilities of the method used, along with the main Lyapunov function, an additional (vector, generally speaking) auxiliary function is considered for correcting the region in which the main Lyapunov function is constructed.
Estimating the $$F_1$$ Score for Learning from Positive and Unlabeled Examples

Semi-supervised learning can be applied to datasets that contain both labeled and unlabeled instances and can result in more accurate predictions compared to fully supervised or unsupervised learning in case limited labeled data is available. A subclass of problems, called Positive-Unlabeled (PU) learning, focuses on cases in which the labeled instances contain only positive examples. Given the lack of negatively labeled data, estimating the general performance is difficult. In this paper, we propose a new approach to approximate the $$F_1$$ score for PU learning. It requires an estimate of what fraction of the total number of positive instances is available in the labeled set. We derive theoretical properties of the approach and apply it to several datasets to study its empirical behavior and to compare it to the most well-known score in the field, LL score. Results show that even when the estimate is quite off compared to the real fraction of positive labels the approximation of the $$F_1$$ score is significantly better compared with the LL score.
ALTERNATIVE IMAGE STABILIZATION METHOD FOR COASTAL VIDEO MONITORING SYSTEMS

The use of shore-based video systems has become a very popular and accessible low-cost tool for coastal monitoring given their capability to deliver continuous and high-resolution temporal data over large enough spatial scales. However, the reliability of the final image products can be compromised by external factors, sometimes overlooked, that can alter the image geometry over time. In particular, unwanted camera movement, produced either by thermal or mechanical effects, can lead to significant geo-rectification errors if not properly corrected. This study addresses an alternative straightforward method to stabilize an either continuous or subsampled image sequence based on state-of-the-art techniques and available routines.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/xX1CrvPQpK8
Enriching Graph Representations of Text: Application to Medical Text Classification

Graph based representations have been utilized to achieve state-of-the-art performance in text classification tasks. The same basic structure underlies knowledge graphs, large knowledge bases that contain rich information about the world. This paper capitalises on the graph of words model and enriches it with concepts from knowledge graphs, resulting in more powerful hybrid representations of a corpus. We focus on the domain of medical text classification and medical ontologies in order to test our proposed methods and analyze different alternatives in terms of text representation models and knowledge injection techniques. The method we present produces text representations that are both explainable and effective in improving the accuracy on the OHSUMED classification task, surpassing neural network architectures such as GraphStar and Text GCN.