Reviewerly/exHarmony huggingface dataset:
Selected Aspects of Information Security Management in Entities Performing Medical Activity

The article is devoted to the issues related to an information security management in medical entities. The healthcare entities have been amongst the prime targets for hackers for several years. According to the IBM report “The 2016 X-Force Cyber Security Intelligence Index” in 2015 most of the attacks were carried out against these entities. The years 2016 and 2017 also witnessed spectacular cyberattacks, for example: medical records breach of 3.3 million people because of an unauthorized access to a server in the US, some WannaCry ransomware attacks on the UK hospitals, some MongoDB Database Leaks in the US or NotPetya ransomware attacks in the US hospitals. Entities performing medical activity are processing personal data concerning health that is classified as a “sensitive data” and needs a special protection. The article presents the results of the survey – interviews with IT managers (or designated persons) in entities performing medical activity in Lodz Voivodeship in Poland. The aim of the research was analysis and evaluation of information security management in these entities. The interviews had been performed between December, 2017 and January, 2018. As the results of the research, the ways of information security management were identified (in particular such aspects as: characteristics of the information security teams, information security management system auditing, risk management, information security incidents, budgets for information security, training and the General Data Protection Regulation implementation). The paper also describes the types of information that should be protected in healthcare entities and characteristic of surveyed entities that subordinate to the local government of Lodz Voivodeship in Poland.
Problems of the Quality of Higher Education in the Field of Training “Jurisprudence”

The adoption of professional standards and a decrease in the level of training of university graduates raises the question of the need for systemic changes in education. There are many reasons for the decline in the quality of education, but the main factors remain the quality of recruitment and the quality of teaching. The existing system of accreditation of universities conducts a quality assessment based on the correctness of the document package and a set of requirements that the teacher does not always understand. In fact, in recent decades there has been a systemic transformation of the education system at all levels. The teacher devotes more time to paperwork and meeting various requirements, rather than to the educational process and improving his professional level. The article analyses the two components of the degree of readiness of applicants for study at the university in the light of the competent approach in teaching, as well as the quality of teaching and faculty. There is currently no continuity in education. A single state exam reveals the level of knowledge, and a university should form competencies for which a school graduate is not ready. The ageing of higher school staff, high workload and assessment of the teacher’s work according to formal criteria leads to a decrease in the quality of education.
Improving Sparse Noise Removal via <i>L</i> <sub>0</sub>-Norm Optimization for Hyperspectral Image Restoration

This letter presents a novel method for hyperspectral image (HSI) restoration, which aims to improve the removal effectiveness of the sparse noise. In contrast to the existing approaches that employ the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> -norm for tractable optimization, we apply the non-convex non-smooth <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> -norm to measure the sparsity of the impulse noise, stripes, deadlines, and other outliers accurately. By combining the low-rank and total variation (TV) priors to exploit the intrinsic properties of the clean HSI and using the patch scheme to preserve local features, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> -PLRTV restoration model is established. In order to deal with the optimization problem, we introduce an equivalent primal-dual formulation to reformulate the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> -norm term, and develop a minimization approach for the objective function based on the alternating iterative method. The simulated and real data experiments confirm that the proposed algorithm can effectively reduce the sparse noise in HSI.
MedSecureChain: Applying Blockchain for Delegated Access in Health Care

Blockchain in today’s standard lacks delegated access and proper identity and access management (IAM) support. In the medical ecosystem, there are different parties, which have access to patient’s data. However, every third party must not get all the data about the patient to preserve the user’s privacy. In this paper, we present MedSecureChain, which is implemented on a private blockchain-based OAuth type authentication to protect and give the respective user control over their data. Delegated access to different categories of users is provided thus giving the user total control over his data. Asymmetric key cryptography has been used to achieve secure delegated access, wherein each different node sharing a common data uses different private keys for accessing the data that has been encrypted using the same public key.
Named Entity Recognition on Morphologically Rich Language: Exploring the Performance of BERT with varying Training Levels

Named Entity Recognition (NER) is an information extraction task that aims to automatically identify named entities in a given text. Named entities are special types of nouns or noun groups that refer to specific entities including as person, location, organization, date, time, money and percentage. NER also facilitates various other Natural Language Processing (NLP) related tasks such as summarization and question answering. It is a vastly studied problem especially on English texts, however number of NER studies on Turkish is very limited. Being a morphologically rich language, Turkish has an agglutinative structure and hence automated analysis and information extraction performance is generally lower than those on English. The previous studies mostly use conventional supervised learning and sequence tagging methods, such as Conditional Random Fields (CRF). Only few studies use deep neural models for NER problem on Turkish texts. In this work, we particularly focus on the recent neural model, Google BERT, and analyze its performance on Turkish texts. In addition to fully trained BERT model, we investigate the performance of different training levels from fully trained to fully pre-trained.
Neural Network Method and Algorithm for Document Detection Based on Signaling Analysis

This presents a sequence article of key steps in the information processing process in an attack detection system. In order to detect attacks on information in the enterprise, the neuronal non-obscure classifier structure was selected, and a neural network method and algorithm for document detection was developed on the basis of signature analysis.
Video Interpretation for Cost-Effective Remote Proctoring to Prevent Cheating

In the rising era of globalization and digitizationKamble, Kiran P.Ghorpade, Vijay R., remote education continues in gaining popularity and reach. Efficiently proctoring online remote examination is an important limiting factor to sustain the integrity of the exam as well as provide unprejudiced results. Currently human proctoring is the customer perspective to maintain integrity, either manually with the help of a test taker or by overseeing them visually through webcams. Online exams provide the examiner the choice, to choose the environment and the tools they wish to use during the exam. In response to this, our research proposes an application to detect fraudulent activities during online examination in real-time through the video recorded by the webcam of the examiner’s system. The application provides four features that continuously estimate the integrity of the exam: (1) User verification for checking impersonation by the examiner. (2) Multiple people together solving the exam. (3) Absence of examiner. (4) Detecting the use of mobile phones. The extensive experiment depicts accuracy of our cost-effective remote proctoring.
Image-Based Visual Servoing of Rotationally Invariant Objects Using a U-Net Prediction

In this article an image-based visual servoing for the armature of electric motors is presented. For a calibrated monocular eye-in-hand camera system our goal is to move the camera to the desired position with respect to the armature. For this purpose we minimize the error between a corresponding feature vector and a measured feature vector. In this paper we derived various features from the output of a U-Net. The variety leads to the fact that we can decouple the features in the control process. The prediction of the U-Net is stabilized by strong augmentation, an armature model and an adaptive digital zoom. We can show that our U-Net control approach converges and is robust against noise and multiple objects.
Modernization of Higher Education in Russia: New Challenges and Approaches

From the beginning of the 21st century the Russian education system is in the process of transformation. Higher education has undergone the greatest changes within the framework of Russia’s integration into the Bologna process. Nowadays, priority projects are being realized to increase the openness, accessibility and competitiveness of Russian higher education at the local and international levels. Nevertheless, there is a period of stagnation of modernization caused by the instability of progressive development and the progress of reforms. According to the authors’ opinion, this situation is caused by a significant lag in the level of educational activities of universities. The article analyzes the rating presence of Russian educational institutions, analyzes the introduction of modern trends that exist in the global educational space. The authors draw conclusions about the discrepancy between educational activities in Russian universities of modern educational standards, which do not have a synergistic effect that would allow universities to reach a new level in the realization of programs and competitiveness.
Public Verifiable Private Decision Tree Prediction

Decision tree is a favored prediction model in machine learning and data mining. With the fast development and wide application of machine learning, the privacy of decision tree prediction is a rising concern. In this paper, We construct a specific purpose NIZK for privacy-preserving decision tree prediction. The protocol allows the server who holds a decision tree model to convince others the result of the decision tree on an encrypted data sample, without leaking private information about the decision tree. Our protocol has high efficiency in both prover time and verifier time, and the proof size is only several KBs. With such NIZK, we can build a public verifiable private decision tree prediction system. In this system, a client can query the result of the server’s decision tree on its encrypted feature vector, and anyone who has only the access to public information can verify the validity of the result.
Visualization of Decision Making in Digital Pathology as Educational Tool

Training in the medical field depends on the teaching of theoretical and practical skills. The acquisition of theoretical basics can be usually achieved by studying various documents, while the training of practical skills requires more intensive discussion and a special setup, such as an appropriate laboratory environment or a mentor-mentee connection. Aside from the conditions for transferring and gaining practical skills, teaching methods mostly concentrate on the sharing of explicit knowledge that can be designated and transferred easily.Therefore, the presented work focuses on the tracking of how to approach the diagnosis in pathological examinations and the visualization of any interaction with the specimen by recording microscopical exploration of an experienced pathologist. Those interactions include the full navigation path through a specimen, the panning and magnification of areas of interest, the observation duration as well as the spoken comments. Each maneuver and annotation that is relevant for approaching the diagnosis is documented, processed and visually prepared for trainees/residents to be able to comprehend argumentation via the look through the eyes of an expert.The collected data serve as a base for various medical applications like learning management systems to support education in the medical field, as well as machine learning and artificial intelligence applications in the area of digital pathology.
Graph Based CIA in Requirements Engineering

We developed a novel Vertical Breadth-First Search All-path graph algorithm that utilizes vertical data structures to find all-length paths (including shortest paths) for all pairs of vertices in a graph. In the current research we propose an approach to apply our All-path algorithm to perform Change Impact Analysis during Requirements Engineering. This article presents a roadmap of our future research plan to apply our All-path algorithm for software quality improvement.
Serious Game: Confined Space Rescue Based on Virtual Reality Technology

When rescuing individuals from confined spaces, blind entry to a space often results in casualties or severe injuries in rescuers. Emergency drills are indispensable for safeguarding rescuers and enhancing rescue outcomes in such situations. The virtual reality (VR) serious game (SG) has become a popular approach to emergency drills. A confined space rescue SG based on VR technology (CSRVRSG) was established in this study. The rescue process in a confined-space incident is modeled in 3D, with texture stitching, skinned mesh animation, and other technologies to achieve a low-cost, efficient VR SG experience for effective emergency drills. Confined space workers and normal workers are invited to test the CSRVRSG. It was showed that this game is well-designed and applicable; Moreover, it was proved that the confined space rescue knowledge of participants can be greatly improved using VR SG.
Review of Hybrid Combinations of Metaheuristics for Problem Solving Optimization

This article describes a review of the state of the art of some of the different metaheuristic combinations that exist for solving problems, using two or more methods in combination or hybrid form. There are different nature-inspired or metaheuristic algorithms, which have been classified to solve certain types of problems, although currently, trying to solve problems with a single method has been somewhat neglected, and modifications, hybridizations, among others, have been carried out. Combinations to solve a common problem, provides an opportunity to exploit the methods in different scenarios to be solved, focusing not only on the solution but also on the use of algorithms.
Estimation of age from speech using excitation source features

Extraction of various distinct features from speech, Gaussian mixture models (GMMs) have been employed as classifiers. Age estimation performance has been inferred by employing excitation source features (LPCCs). The detected age of a speaker is deemed to belong to any of the 9 age groups of 5–10, 10–15, 15–20, 20–25, 25–30, 30–35, 35–40, 40–45 and 45–50. An age group speech corpus has been collected in this work by recording voice of speakers of Hindi language of different age groups and dialects using ten text prompts in neutral speech. Textually neutral Hindi words have been used to construct text prompts which have been recorded in neutral emotion. Different age group has been characterized by using these texts prompt. The average age performance of multispeaker (male + female) is around 94%. In this research, classification of different group of speakers has been done on the basis of excitation source in human speech.
Reptile: Aggregation-level Explanations for Hierarchical Data

Users often can see from overview-level statistics that some results look "off", but are rarely able to characterize even the type of error. Reptile is an iterative human-in-the-loop explanation and cleaning system for errors in hierarchical data. Users specify an anomalous distributive aggregation result (a complaint), and Reptile recommends drill-down operations to help the user "zoom-in" on the underlying errors. Unlike prior explanation systems that intervene on raw records, Reptile intervenes by learning a group's expected statistics, and ranks drill-down sub-groups by how much the intervention fixes the complaint. This group-level formulation supports a wide range of error types (missing, duplicates, value errors) and uniquely leverages the distributive properties of the user complaint. Further, the learning-based intervention lets users provide domain expertise that Reptile learns from.
Exploration and Research on the Path of Cultivating the Craftsman Spirit of Computer Talents in Application-oriented Universities

The following topics are dealt with: computer aided instruction; teaching; educational institutions; educational courses; Internet; further education; Big Data; computer science education; learning (artificial intelligence); and pattern clustering.
A family of standards for software and systems product lines

There is a drastic rise and evolution of Software and Systems Product Lines (SSPL) as a potential solution to deliver quality software products in a shorter time and with less effort. This has led to a plethora of diversified tools across the entire life cycle of SSPL, making it difficult for product line organizations to select the right set of tools for product line realization and tool vendors to develop interoperable tools. In this paper, we motivate the need for standardization of tools and methods for SSPL. The ISO/IEC JTC1/SC7 (Software and Systems Engineering) of the International Organization for Standardization (ISO) has proposed a set of interrelated standards in the area of SSPL to cater to the emerging demands of software and systems industry. We present the foundation of SSPL family of standards that are driven by market needs and discuss the core patterns and processes of SSPL. We then present an overview of ISO/IEC 26550, which is the reference model for SSPL family of standards and provide examples from current and ongoing standards. We highlight a few examples of industry adoption of SSPL and standards and emphasize the need for further research for standards in SSPL. We see this paper as a first step to bring standards knowledge into the academic world in the field of SSPL.
An Evaluation of Backpropagation Interpretability for Graph Classification with Deep Learning

The end-to-end learning in convolutional neural networks (CNNs) and their ability to extract localized deep features, have turned them into powerful tools for learning from a large corpus of data including graphs. Deep neural networks such as CNNs are "black-box", therefore various interpretability methods have been developed to understand which aspects of the input data drive the decisions of the network. However, interpretability for graph convolutional neural networks (GC-NNs) is an open area of research. To this end, we investigate three backpropagation-based interpretability methods: saliency map with contrastive gradients (CG), gradient-weighted class activation mapping (Grad-CAM), and deep learning important features (DeepLIFT) in conjunction with three state-of-the-art GCNNs: GCNN+GAP, DGCNN, and DIFFPOOL, as well as with their variants, for the graph classification problem. We discuss novel challenges and our solutions towards integrating these deep learning frameworks, measuring their efficiency and performance both qualitatively and quantitatively. With our extensive empirical analysis over five real-world graph datasets from different categories, we report their quantitative, visualization, and active subgraph based performance, compare them with the classic significant subgraph mining results, summarize their trade-offs and surprising findings. We conclude by discussing our recommendations on the road ahead.
Semantic Guided Filtering Strategy for Best-effort Subgraph Matching in Knowledge Graphs

Subgraph Matching is one of the fundamental problems in network analysis, with a wide range of applications ranging from drug repurposing and discovery to programming language analysis. Due to the increasing prevalence of knowledge graphs (KGs), there has been growing interests in extending existing subgraph matching algorithms to the KG domain. One of the main challenges here lies on the structural gap, which refers to the difference between the query pattern and the corresponding subgraph instance due to variations in semantic expression. To address this challenge, we propose a semantic guided subgraph matching method for knowledge graphs, which extends our prior filtering-based method. Specifically, our approach leverages an external semantic ontology to estimate the overall fitness/quality of subgraph candidates with respect to the query template. In addition, our approach incorporates effective query decomposition strategies to reduce the overall query cost. Furthermore, we develop a distributed implementation of the algorithm such that it can be scaled up to handle knowledge graphs with a large number of entities and relations. We demonstrate the effectiveness of the proposed approach on a variety of semantic networks provided in the DARPA Modeling Adversarial Activity (MAA) program.
Research on Alarm Causality Filtering Based on Association Mining

Mining the association rules in the alarm data generated by network is an important method for operations to monitor and manage the network equipment. Analyzing the correlation of alarms through association rule mining algorithms can effectively simplify alarms and help locate network faults. Since the network alarm data has an obvious chronological relationship, it needs to be processed by the association rule mining algorithm based on time series. Through investigation, it is found that current association rule algorithms based on time series lack the determination of the realistic cause-and-effect relationship between successive alarms. Therefore, in order to improve the effectiveness of the association algorithm, this paper adopts an association mining algorithm based on the existing time series, which supports filtering the useless sequential associated items that have no causal relationship in the results. The experimental and analytical results show that the proposed method is effective.
Agile Project Management ― What is It?

We present a qualitative study in the form of a literature review concentrated on agile project management methods for the management of product development. The demand for managing projects with agile methods is increasing, especially in the sector of information technology. The accurate method and approach to project management are crucial; however, proper project management can lower the stress level and increase the constant efficiency to keep it distributed during the project continuation. Therefore, this article is focused on a comprehensive evaluation of agile methods, adjusted on its elementary description via a literature review. In the state of the literature review, we describe the main identification signs of each method, followed by its application.