Reviewerly/exHarmony huggingface dataset:
Regularizing Conjunctive Features for Classification

We consider the feature-generation task wherein we are given a database with entities labeled as positive and negative examples, and the goal is to find feature queries that allow for a linear separation between the two sets of examples. We focus on conjunctive feature queries, and explore two fundamental problems: (a) deciding whether separating feature queries exist (separability), and (b) generating such queries when they exist. In the approximate versions of these problems, we allow a predefined fraction of the examples to be misclassified. To restrict the complexity of the generated classifiers, we explore various ways of regularizing (i.e., imposing simplicity constraints on) them by limiting their dimension, the number of joins in feature queries, and their generalized hypertree width (ghw). Among other results, we show that the separability problem is tractable in the case of bounded ghw; yet, the generation problem is intractable, simply because the feature queries might be too large. So, we explore a third problem: classifying new entities without necessarily generating the feature queries. Interestingly, in the case of bounded ghw we can efficiently classify without ever explicitly generating the feature queries.
Enforcing globally dependent flow policies in message-passing systems

The flow of information in a computing system is a crucial indicator for the security of the system. In a system of multiple message-passing processes, the flow of information could depend on the states of different processes. We devise a type-based verification technique for flow policies with such multi-process (global) dependencies, to provide confidentiality guarantees. In this technique, the confidentiality requirements for the presence and content of messages are dealt with separately. We develop a pair of synergetic static analyses to over-approximate the potential sets of values of the variables depended upon by the flow policies – covering global value correspondence between the variables of different processes. We significantly improve the permissiveness of security typing by exploiting information about which variables are live, and by specializing the flow policies using the conditional expressions of branching and looping constructs. We prove the soundness of our verification technique, provide a proof-of-concept implementation of it, and illustrate its effectiveness at an example system where the flow of information depends on how the headers of the messages from different processes correlate.
Unsupervised Learning Bee Swarm Optimization Metaheuristic

In this work, we investigate the use of unsupervised data mining techniques to speed up Bee Swarm Optimization metaheuristic (BSO). Knowledge is extracted dynamically during the search process in order to reduce the number of candidate solutions to be evaluated. One approach uses clustering (for grouping similar solutions) and evaluates only clusters centers considered as representatives. The second uses Frequent itemset mining for guiding the search process to promising solutions. The proposed hybrid algorithms are tested on MaxSAT instances and results show that a significant reduction in time execution can be obtained for large instances while maintaining equivalent quality compared to the original BSO.
COMPETENCE-BASED APPROACH IN EDUCATION AS A NECESSARY CONDITION FOR PROFESSIONAL TRAINING OF FUTURE SPECIALISTS WITHIN HIGHER SCHOOL

The article is devoted to one of the priority areas in the Russian education modernisation: transition from the knowledge paradigm to the competence-based one. The authors analyse the evolution of the competence notion in education and consider in detail the methodological basis for the competence-based approach developed in the framework of the European project ‘Tuning’. By the example of the ‘Business English + Knowledge Management’ educational project implemented by the authors, practical importance of using a single training complex that combines the methodology of the competence-based approach and digital technology in the process of acquiring professional competences by students is proved.
The university libraries in the social network VKontakte: The administrator’s experience

The social media have become part and parcel of the modern society, as they are a social communication channel. Using social networks for connecting to potential users have been increasingly important for the libraries. The libraries have been setting up groups and accounts in the social media to connect to their users, to draw attention to their work and to widen its scope. The university libraries, like many other libraries of educational institutions, participate in the process. The author analyzes the experience of Perm State Humanitarian Pedagogical University with VKontakte network subscribers group in 2013-2018. The general statistical data characterize group evolution during the selected period; dynamics of views, and the viral coverage and subscribers group coverage are provided. The author examines the administration mistakes that result in the loss of subscribers; she reviews the experience of attracting users, and rules for content presentation. She concludes that group success is determined by interesting and quality content relevant to the group aims and tasks; that the librarian in charge must be familiar with the principles of social group administering and content generation in accordance with the library’s goals and tasks.
Phishing susceptibility across industries: The differential impact of influence techniques

Organizations face an increasing risk of phishing attacks, leading to potential financial losses, privacy breaches, and damage to reputation. While past research has focused on individual and organizational factors in phishing susceptibility, there is a lack of understanding related to industry differences and their impact on the phenomenon. Drawing on existing literature on persuasion and phishing, we propose that shared industry practices, values, and assumptions influence the effectiveness of phishing techniques. To test our hypotheses, we conducted two studies: a lab experiment (n = 259) and a field quasi-experiment (n = 10,967) using a secondary dataset comprising mock phishing attacks on 30 finance and 15 non-finance organizations. The results revealed varying susceptibility to phishing techniques based on industry. Consistent with our expectations, liking-based techniques were more effective among non-finance organizations, while social proof, reciprocity, and authority techniques were more effective in finance organizations. These findings contribute to resolving past inconsistencies in empirical phishing research and provide insights into the role of industry characteristics in shaping phishing susceptibility.
The Role and the Place of Geometry in the System of Mathematical School Olympiads

The purpose of this article is to determine the role and place of school geometry in the subject olympiad system. For this, the authors turn to the experience of Russia in organizing and conducting geometric olympiads for schoolchildren, exploring the specifics of the olympiads named after named I.F. Sharygin, named S.A. Anischenko, named A.P. Savina, Moscow and Iran olympiads. The objectives and themes of full-time, extramural, oral geometric olympiads are defined. It is revealed that the topics of topology, projective, affine, combinatorial sections of geometry constitute the content of olympiad geometry. The study showed that the tasks of the olympiad work on geometry checked mathematical skills to perform actions with geometric figures, coordinates and vectors; build and explore simple mathematical models; apply acquired knowledge and skills in practical activities. The conclusions are made about the need to include tasks of geometric content in the block of olympiad tasks for the development of spatial thinking of schoolchildren.
Inferencing the best AI service using Deep Neural Networks

Many learned inference engines have been released as cloud-based AI services. However, because these engines are easy to use, accuracy and fairness are necessary. A previous study revealed that the accuracy of cloud-based AI services varies depending on the input. Thus, we propose to infer the best AI services by learning their different output results as training data. Our model, "AI for selecting the best AI," involves meta-learning; it learns the output of the cloud-based AI service as metadata. The results of our experiments to infer face attributes (i.e., age and gender) on a face image dataset crawled from Wikipedia showed that the accuracy of our system was 2.4% higher than that of existing face classification cloud-based AI services. Notably, results on inferring age, where training data for each service showed a significant difference in the tendency for accuracy, had 2.7% higher accuracy compared to existing cloud-based AI services.
A Mobile Expert System using Fuzzy Logic for Diagnosing Kidney Diseases

This paper presents the work of developing a mobile expert system to provide information about the different kinds of kidney diseases. The mobile application developed is capable of diagnosing the user based on their health condition. The source of data used to build the expert system was collected from various related literature studies and interviews with experts in kidney diseases or Nephrology.
The Extended Dawid-Skene Model

While label fusion from multiple noisy annotations is a well understood concept in data wrangling (tackled for example by the Dawid-Skene (DS) model), we consider the extended problem of carrying out learning when the labels themselves are not consistently annotated with the same schema. We show that even if annotators use disparate, albeit related, label-sets, we can still draw inferences for the underlying full label-set. We propose the Inter-Schema AdapteR (ISAR) to translate the fully-specified label-set to the one used by each annotator, enabling learning under such heterogeneous schemas, without the need to re-annotate the data. We apply our method to a mouse behavioural dataset, achieving significant gains (compared with DS) in out-of-sample log-likelihood (-3.40 to -2.39) and F1-score (0.785 to 0.864).
다문화 배경 학습자 대상의 언어 교육 정책 변화 경향성 분석

The purpose of this study is to analyze tendencies on how language education policies for students learning multi-cultural backgrounds have been changed and to seek for the development direction afterwards. For doing this, this study examined France and Japan and America and Australia in order to figure out how foreign language education policies for them have been enforced through literature reviews. The language education for students learning multi-cultural backgrounds have started over a decade in the Korea society. Examining how language education policies have been changed can be a reference material to seek for tendencies on how language education policies should be developed. Flows of them from 2006 when multi-cultural education plans are presented to 2019 were divided into four periods and this study examined its flows. The first period was a beginning period(2006 to 2008) when there were interests in education for children of multi-cultural families. Korean education started in each school unit and seeking for the support direction in Office of Education were main. The second period was a preparation period(2009 to 2011) to seek for tendencies of standardized Korean education and to materialize the support system of Korean education under mid and long term plans. The third period was a development period(2012 to 2016) when the state-dominated standardized Korean (KSL) education course was notified and standard textbooks were developed and supplied. The fourth period was an advanced period(2017 to present) when notification on the revised education course and textbook revision and study capability reinforcement and expansion of multi-cultural education were strengthened. As this study examined these flows, it will suggest the future direction on how education policies including language education for students learning multi-cultural backgrounds should be enforced. (Hansung University)
E-libraries as a resource to support learning and scientific activities at universities

The university libraries are to support traditional and digital access to the learning, academic, research and information resources and to maintain modern information and library space. According to the Federal State Educational Standard (FGOS), and due to the growth of distance learning forms, the students must have access to the wide range of digital resources. Thus, along with subscribing to external digital library systems and databases, Novosibirsk State Pedagogical University Library maintains the University’s digital library system. The author discusses new approaches and solutions for designing repositories and organization of information resources, methods and instruments for users to access them. As the case study of Novosibirsk State Pedagogical University, acquisition of the University’s Digital Library System collection is discussed as well as the Interuniveristy E-library which is the integrated union database of universities’ digital documents providing general access to the digital resources of the member universities. The membership in the Interuniveristy E-library enables universities to support educational programs with electronic resources; enables students, post-graduates and the faculty to keep track of digital publications by the member universities; provides the platform for the faculty to place on and to promote their works.
Fabrication, 3D Printing, and Making

This chapter presents an introduction to digital fabrication and the exciting opportunities it offers in the accessibility and assistive technology domain. Digital fabrication tools are more efficient and accessible than manual fabrication tools, allowing for efficient and affordable creation or modification of existing assistive technologies, or novel solutions. This chapter provides an overview of relevant digital fabrication tools (with an emphasis on 3D printing technology) and current online communities to share ideas and designs, and discusses exciting research and practitioner contributions that leverage 3D printing. The chapter concludes with the author’s recommendations for how to successfully apply this technology in accessibility projects and areas for future inquiry.
Enhancing the Local Search Ability of the Brain Storm Optimization Algorithm by Covariance Matrix Adaptation

Recently, the Brain Storm Optimization (BSO) algorithm has attracted many researchers and practitioners attention from the evolutionary computation community. However, like many other population based algorithms, BSO shows good performance at global exploration but not good enough at local exploitation. To alleviate this issue, in this chapter, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is utilized in the Global-best BSO (GBSO), with the aim to combine the exploration ability of BSO and local ability of CMA-ES and to design an improved version of BSO. The performance of the proposed algorithm is tested by solving 28 classical optimization problems and the proposed algorithm is shown to perform better than GBSO.
Robust Echo State Network for Recursive System Identification

The use of recurrent neural networks in online system identification is very limited in real-world applications, mainly due to the propagation of errors caused by the iterative nature of the prediction task over multiple steps ahead. Bearing this in mind, in this paper, we revisit design issues regarding the robustness of the echo state network (ESN) model in such online learning scenarios using a recursive estimation algorithm and an outlier robust-variant of it. By means of a comprehensive set of experiments, we show that the performance of the ESN is dependent on the adequate choice of the feedback pathways and that the prediction instability is amplified by the norm of the output weight vector, an often neglected issue in related studies.
Exploring Classification, Clustering, and Its Limits in a Compressed Hidden Space of a Single Layer Neural Network with Random Weights

Classification in the hidden layer of a single layer neural network with random weights has shown high accuracy in recent experimental studies. We further explore its classification and clustering performance in a compressed hidden space on a large cohort of datasets from the UCI machine learning archive. We compress the hidden layer with a simple bit-encoding that yields a comparable error to the original hidden layer thus reducing memory requirements and allowing to study up to a million random nodes. In comparison to the uncompressed hidden space we find classification error with the linear support vector machine to be statistically indistinguishable from that of the network’s compressed layer. We see that test error of the linear support vector machine in the compressed hidden layer improves marginally after 10,000 nodes and even rises when we reach one million nodes. We show that k-means clustering has an improved adjusted rand index and purity in the compressed hidden space compared to the original input space but only the latter by a statistically significant margin. We also see that semi-supervised k-nearest neighbor improves by a statistically significant margin when only 10% of labels are available. Finally we show that different classifiers have statistically significant lower error in the compressed hidden layer than the original space with the linear support vector machine reaching the lowest error. Overall our experiments show that while classification in our compressed hidden layer can achieve a low error competitive to the original space there is a saturation point beyond which the error does not improve, and that clustering and semi-supervised is better in the compressed hidden layer by a small yet statistically significant margin.
Real-Time Automatic Calculation of Euro Coins and Banknotes in a Cash Drawer

A very interesting and useful complement to classical cash-registers is presented in this paper, coming up with a real-time auto-counting solution for the money inside a cash drawer. The system allows knowing not only the total amount of money but also how many coins and banknotes there are of each value. The embedded solution developed has been intended to become a low-cost solution, allowing better control over the money and helping both owners and workers in the establishments. By using this system, new utilities including automatic final balancing, instant error handling when making operations, and the lack of certain types of banknotes or coins inside the drawer or the excess of some in a certain compartment, could be implemented. Coins-counting solution is based on their weight, and small individual scales made by load cells have been integrated in each coin compartment. With respect to the banknotes, an innovative alternative based on the electrical properties of capacitors is presented. Additionally, considering the relevance of interoperability in today's systems, a Bluetooth module has been integrated into the system, allowing for data to be accessed remotely from any smartphone, tablet or computer within the range of the module. In this work, an Android application to both control and interact with the system has also been designed.
Dirichlet densifier bounds: Densifying beyond the spectral gap constraint

In this paper, we characterize the universal bounds of our recently reported Dirichlet Densifier. In particular we aim to study the impact of densification on the bounding of intra-class node similarities. To this end we derive a new bound for commute time estimation. This bound does not rely on the spectral gap, but on graph densification (or graph rewiring). Firstly, we explain how our densifier works and we motivate the bound by showing that implicitly constraining the spectral gap through graph densification cannot fully explain the cluster structure in real-world datasets. Then, we pose our hypothesis about densification: a graph densifier can only deal with a moderate degradation of the spectral gap if the inter-cluster commute distances are significantly shrunk. This points to a more detailed bound which explicitly accounts for the shrinking effect of densification. Finally, we formally develop this bound, thus revealing the deeper implications of graph densification in commute time estimation.
Advanced Image Processing Algorithms for Breast Cancer Decision Support and Information Management System

This paper reviews image analysis approaches developed within the DESIREE European project for the implementation of a decision support system (DSS) for breast cancer. These include robust algorithms for image segmentation, classification and imaging biomarker extraction, as well as a solution for the simulation of surgery outcomes, including a breast reconstruction module and a mechanical and healing model. These algorithms are integrated into the DSS to assist clinicians dealing with the heterogeneous information generated during the course of the disease.
Inertia Constant strategy on Mean Grey Wolf Optimizer Algorithm for Optimization functions

Mean grey wolf algorithm is a crowd based technique which mimics the leadership hierarchy of wolves are very known for their group hunting. It is very interesting approach or execute most effortless and there are several constants adjust. Performance of the algorithm depends significantly on the suitable parameter value selection strategies for fine tuning its constants. Weight has been applied on the position update mathematical equations of Mean GWO to create a balance amid the exploration and exploitation characteristics of Mean GWO. In this text, has been developed a newly inertial weight based algorithm is called Inertia Constant Mena Grey Wolf Optimizer Algorithm (ICMGWO). The efficiency of the existing method has been verify on the well-known functions during to the comparison of the algorithms. Also existing variant is compared with least number of iterations, best score, standard deviation, mean, convergence rate and best time varying. Statistical analysis and experimental solutions reveals that existing variant improves the search accuracy in terms of convergence rate as well as solution quality.
The Tyranny of Urgency

Many lament accelerations wrought by technology, while others revel in it. Commerce, however, has measurably sped up, outdating long-term initiatives. As technology continuously speeds up and algorithms become increasingly nonlinear, however, forensic shortterm preventative cybersecurity runs the risk of being overrun. In response, cybersecurity must focus on long-term design for prevention strategies with provisions for life-cycle countermeasures.
System of catalogs. How we built it for a long time and how quickly we closed up it

An article about the system of catalogs – the concept, composition, functions of different catalogs in the system, about the standards and regulatory documents on the basis of which the system of catalogs was built in libraries. The system of catalogs was created and improved in libraries for a long time. Thirty years ago, an ideal system of catalogs developed in the country, effectively providing information retrieval. Terminological problems were solved. Standard documents are approved. Libraries were preparing for the introduction of OPACs. But development has gone the wrong way. Professional library specialists were not involved in the work, world experience was not studied. The author shows that the technological revolution and library automation has destroyed the principles of upbuilding a system of catalogs. The communications are lost where an OPACs has come to replace it, there are no references, there are no dialogue with readers. These shortcomings are associated with unsuccessful programs that were developed without taking into account the opinions of professional librarians. There was no complete replacement of card catalogs with an OPACs. However, this did not prevent the closure of card catalogs in a number of libraries in the country.