Reviewerly/exHarmony huggingface dataset:
Printed Blank Forms of the Cyrillic Printed Letters of 1759 from Bishop of Pinsk and Turov George Bulgak in the Collection of the Russian State Library

Printed Blank Forms are poorly studied historical sources. They were published in almost every printing house, often in large editions; however, due to the specifics of the purpose, they were poorly preserved. Many of the printed editions have not reached our days. You can learn about their existence from archival documents. A number of editions are known in a single copy. The Research Department of Rare Books (Book Museum) of the Russian State Library has two Printed Blank Forms. These are the Certificates of Ordination from the Bishop of Pinsk and Turov, George Bulgak. These are the only known copies of the editions. They were issued to priest Vasily Shemetil on July 15, 1759 in Pinsk and stamped with the Episcopal seal. They came from the Vilnius Public Library. Due to the small number of complex studies of such sources, the article provides the detailed description of them, reveals the content at the level of the edition (printed blank form) and the copy (handwritten text); for the first time there is undertaken publication of the texts of the certificates. Moreover, the authors consider the actual problems of the bibliographic description of these publications: different bibliographers (F.N. Dobryansky, A.I. Milovidov, G.Y. Golenchenko, Y.A. Labyntsev) described and attributed these editions differently (Mogilev and Suprasl were mentioned as the place of publication). Being unique sources, they attracted attention of bibliographers, but were not used at all by historians and other researchers of book culture. The authors come to the conclusion that the attribution of the publication of letters in the printing house of the Annunciation Monastery in Suprasl, proposed by Y.A. Labyntsev, looks the most convincing today. The study emphasizes the importance of the considered documents that are the only known examples of printed Greek Catholic Certificates of Ordination of the 18th century. Since the life and activity of Georgy Bulgak himself, who became the Archimandrite of the Annunciation Monastery in Suprasl, remains practically unexplored in Russian historiography, the article presents his biography, focuses on this period of his activity and his great contribution to the development of book publishing in the monastery printing house, which printed books in Slavic, Polish and Latin languages.
Considerations for Translating Medical Devices Under Development From the Clinic to the Home: A Case Study in Providing Chronic Wound Care

A prototype medical device and protocol that work well in a clinic would not necessarily work reliably in the home setting. The goal of this work is to identify factors to consider when translating a device evaluated in a clinic for use by home caregivers providing chronic wound care for persons living with dementia. One must consider the current device, the associated bundle, the protocol and how they may need to be modified. Semi-structured interviews with the research assistants who have applied a prototype device were conducted to generate a list of known issues with the current clinical protocol in terms of ultrasound application The researchers performed a set of specified tasks by following the current device protocol and explaining any difficulties with use\execution. They embellished the descriptions with details about what had happened with actual patients in the clinic. Using thematic analysis, analysts identified themes and subthemes. Issues potentially relevant to translating medical devices to the home are discussed.
An Efficient Comparison between Structured Analysis and Object-Oriented Analysis

if you want to find out that which is the best suitable analysis then you have to find outs all the merits and demerits of that both analysis methods. The aspect of this paper is to describe combine both the traditional (structured) and the object-oriented approach, certain methodology for information systems development. Despite the fact that objects-oriented paradigm is actually widely adopted for software analysis, design, and implementation, there are still a large number of companies that continue to utilize the structured approach to develop software analysis and design. The empirical study that we present considers both an Uncontrolled and a controlled experiment with Master students. with the existing structured approach of developing a system there is significant way to know how appropriate the OO topics. So, in recent time, Objects-orientation analysis is largely acceptable subject. This paper discourses some basics about these two design paradigms.
An Analysis of Research Trends in AI Education based on LDA

AI thinking-based education that automatically learns rules and meanings not based on procedural thinking but based on adaptation and cognition as a framework is attracting higher attention. Many studies on “AI Thinking” based on algorithms and logical thinking are actively conducted based on the computing thinking proposed for software education purposes. In this article, specifically on what topics and in which fields the studies on AI education are conducted was examined, and in which forms such studies are utilized was examined as well. To understand the topic of the articles on “Artificial Intelligence Education”, such articles were analyzed using topic modeling techniques. To do so, a total of 1,258 articles were collected by retrieving the keyword “Artificial Intelligence Education” from the Korean academic articles provided by the Research Information Sharing Service (RISS). Of the automatically collected articles, those that were off topic were removed, and a total of 1,045 articles were finally selected as the analysis targets. Based on the analysis results acquired using the LDA, a representative algorithm of topic modeling, 8 topics were finally selected as the optimal classification standards, and the title per detailed topic was determined based on the keywords per topic. This study is expected to help teachers establish their AI education learning and research direction. The future study should be aimed at implementing web services to automate and generalize all processes.
On Solving Image Deblurring Problem via Nash Equilibrium

In this article, we present a blind deconvolution method for image restoration involving an adaptive point spread function. The method is introduced by applying concurrent optimization via simulating an image deblurring game. We assign the optimal image deblurring as a Nash equilibrium image deblurring game between three players based on three criteria associated with each player. The aim of the three players is to minimize their objective functions which are image intensity, a point spread function and contour respectively. Under some suitable conditions, we have shown the existence and uniqueness of an optimal solution of the Nash equilibrium image deblurring game. Finally, we provide some numerical examples to demonstrate the computational performance of the proposed method in comparison with some state of the art methods in the
The Nuremberg Address Knowledge Graph

The research of European history across various time layers gives insights about the development of the European cultural identity. Nuremberg as one of the great European metropolises during the Middle Ages experienced a number of transformations throughout the centuries. Within the TRANSRAZ research project, Nuremberg and the development of its architecture and culture is recreated from the 17th to the 21st century. It will be available for researchers and the public by means of an interactive 3D environment. Goal of this poster paper is to discuss the ongoing work of connecting heterogeneous historical data from sources previously hidden in archives to the 3D model using knowledge graphs for a scientifically accurate exploration of Nuremberg. The contribution of this paper is the Nuremberg Address Knowledge Graph (NA-KG) which contains information of people and organizations in Nuremberg from unstructured data of Nuremberg address books.
Topology-Theoretic Approach To Address Attribute Linkage Attacks In Differential Privacy

Differential Privacy (DP) is well-known for its strong privacy guarantee. In this paper, we show that when there are correlations among attributes in the dataset, only relying on DP is not sufficient to defend against the "attribute linkage attack", which is a well-known privacy attack aiming at deducing participant's attribute information. Our contributions are 1 we show that the attribute linkage attack can be initiated with high probability even when data are protected under DP, 2 we propose an enhanced DP standard called "APL-Free ∈-DP", 3 by leveraging on topology theory, we design an algorithm "APLKiller" which satisfies this standard. Finally, experiments show that our algorithm not only eliminates the attribute linkage attack, but also achieves better data utility.
Multiple Facets of Open: A Different View on Open Science

Open – a well-known word, but with multiple facets: open, open-minded… In the publishing industry, “open” and “openness” describe a movement which has been setting the scene over the last decades, however the opening of science is not a new momentum. Writing down our thoughts and ideas is regarded as a first indicator of opening the human mind. To cope with information overload, paper slips were used as a favourite device - a precursor to modern index cards and card catalogs. The internet opens the doors to disseminate and share knowledge in a fast and easy way. Now, science is emerging in cyberspace and an innovative level of science is shaping, the evolution of Cyberscience. Science is shifting into the open, Open Science is developing as an additional form of doing research. These diverse perspectives are part of a colorful picture of an evolving scientific landscape, which will rise awareness of changing work behaviors.
On Posterior Consistency of Bayesian Factor Models in High Dimensions

As a principled dimension reduction technique, factor models have been widely adopted in applications. However, conducting a proper Bayesian factor analysis can be subtle in high-dimensional settings since it requires both a careful prescription of the prior distribution and a suitable computational strategy. We analyze issues of posterior inconsistency and sensitivity under different priors for high-dimensional sparse normal factor models, and show why adopting the n-orthonormal factor assumption can resolve these issues and lead to a more robust and efficient Bayesian analysis. We also provide an efficient Gibbs sampler to conduct the required computation, and show that it can be orders of magnitude more efficient than compared existing algorithms.
Data Analytics for Cyber Risk Analysis Utilizing Cyber Incident Datasets

The imperative factors of cybersecurity within institutions have become prevalent due to the rise of cyber-attacks. Cybercriminals strategically choose their targets and develop several different techniques and tactics that are used to exploit vulnerabilities throughout an entire institution. With the thorough analysis practices being used in recent policy and regulation of cyber incident reports, it has been claimed that data breaches have increased at alarming rates rapidly. Thus, capturing the trends of cyber-attacks strategies, exploited vulnerabilities, and reoccurring patterns as insight to better cybersecurity. This paper seeks to discover the possible threats that influence the relationship between the human component and cybersecurity posture. Along with this, we use the Vocabulary for Event Recording and Incident Sharing (VERIS) database to analyze previous cyber incidents to advance risk management that will benefit the institutional level of cybersecurity. We elaborate on the rising concerns of external versus internal factors that potentially put institutions at risk for exploiting vulnerabilities and conducting an exploratory data analysis that articulates the understanding of detrimental monetary and data loss in recent cyber incidents. The human component of this research attributes to the perceptive of the most common cause within cyber incidents, human error. With these concerns on the rise, we found contributing factors with the use of a risk-based approach and thorough analysis of databases, which will be used to improve the practical consensus of cybersecurity. Our findings can be of use to all institutions in search of useful insight to better their risk-management planning skills and failing elements of their cybersecurity.
Mapping Usability Heuristics to Medical Ethical Principles in Clinical Decision Support Design

Human factors professionals voice concerns about the slow and uneven adoption of human-centered design (HCD) principles in healthcare. With good intentions and best efforts, human factors work in healthcare continues, but to understand and to be understood we must share a language that speaks to one another. One way to facilitate this understanding is by bringing attention to how we talk about decision-making approaches in medical ethics and HCD. Both disciplines suggest efforts should be participatory, guided by a set of principles applied thoughtfully, with critical consideration to context and trade-offs. Considering the connections between usability heuristics for design and medical ethical principles may offer a bridge between disciplines and their associated dialects. In this paper, we provide three representations mapping ethical principles to design principles, including usability heuristics: a word tree visualization, narrative passages, and a table format. We believe this mapping demonstrates both that ethical design is inherently human-centric and that human-centered design is ethical design.
Designing an Efficient Gradient Descent Based Heuristic for Clusterwise Linear Regression for Large Datasets

Multiple linear regression is the method of quantifying the effects of a set of independent variables on a dependent variable. In clusterwise linear regression problems, the data points with similar regression estimates are grouped into the same cluster either due to a business need or to increase the statistical significance of the resulting regression estimates. In this paper, we consider an extension of this problem where data points belonging to the same category should belong to the same partition. For large datasets, finding the exact solution is not possible and many heuristics requires an exponentially increasing amount of time in the number of categories. We propose variants of gradient descent based heuristic to provide high-quality solutions within a reasonable time. The performances of our heuristics are evaluated across 1014 simulated datasets. We find that the comparative performance of the base gradient descent based heuristic is quite good with an average percentage gap of $$0.17\%$$ when the number of categories is less than 60. However, starting with a fixed initial partition and restricting cluster assignment changes to be one-directional speed up heuristic dramatically with a moderate decrease in solution quality, especially for datasets with a multiple number of predictors and a large number of datasets. For example, one could generate solutions with an average percentage gap of $$2.81\%$$ in one-tenth of the time for datasets with 400 categories.
Human-Machine Learning with Mental Map

Many cognitive processes can be represented as a graph that is visual and computational. Graph search used to be a classic AI method. Here we present a dynamic graph, called “Mental Map” with a set of timestamps, nodes, edges, attributes, and operators for extracting experts’ knowledge and incorporating other AI models such as Association Rule Learning and Decision Tree. Mental Map is written in Javascript and it can run on any platform that has a web browser. Three case studies are presented: suspicious behavior detection, email phishing, and malware detection in embedded systems. The tool can be used interactively and automatically. Through human-machine collaborative learning, Mental Map provides more explainability and flexibility than prevailing semantic webs and machine learning algorithms.
Study for phase retrieval based on dual frequency grating projection

The dual frequency grating projection based on structured light is very important in the phase retrieval for the fusing of the advantages of dual frequency grating, it must satisfy a basic condition to better obtain the phase information. Firstly, the relationship between phase information and height information of object measured is given, and the basic principle of phase retrieval based on dual frequency grating projection is analyzed in detailed. Secondly, the corresponding phase retrieval condition is proposed, that is to say, the high frequency must be at least twice as large as the low frequency to satisfy the spectra separation between the two frequencies, the maximal allowable phase error of low frequency unwrapped phase must be less than π/2. Finally, to demonstrate the validity and correctness of the proposed basic principle, both of simulation and experiment are carried out, the expected results are obtained, when the phase recovery condition is satisfied, the phase errors are smaller, and vice versa.
The Construction and Application of an Emotion Knowledge Base for Prose Reading Comprehension

In natural language processing, emotion analysis research in prose are relatively few for the lack of relevant knowledge base. This study establishes an category system of emotions in prose and builds up an emotion knowledge base for prose reading comprehension. In addition, because the proposed emotion categories are associated with topics, we introduce topic information into emotion knowledge base, label emotion types and topic elements. The constructed knowledge base will help to provide data resources for the solution of emotion type problems in prose reading comprehension.
A Framework for Leveraging Contextual Information in Automated Domain Specific Comprehension

When it comes to information, Enterprises today are seen as a black hole, a mass of it goes in but gets difficult to extract the practical knowledge out of it. An automated system that has the ability to consume this large mass of information and provide specific, knowledgeable, domain-oriented responses back, will go a long way in unlocking the value of this large-scale unstructured information. In a bid to enrich the answering system's accuracy in Machine Reading Comprehension (MRC), we propose a domain-specific Question Answers (QuAns) framework that specifically aims to auto-generate questions from a domain-based document using an improvised Sequence to Sequence (Seq2Seq) technique equipped with Attention and Copy mechanism. The generated questions are conditioned on a set of candidate answers, derived using a combination of heuristic-driven and graph-based techniques. Further, it also leverages the contextual information by pooling strategy to build an automated response system using a deep custom fine-tuned Bidirectional Encoder Representations from Transformers (BERT) framework and retrieving the top-k contexts for a user query. The evaluation of the QuAns architecture is performed in combination with human supervision as at times, the automated metrics like BLEU, Exact Match (EM), F1 score, etc. fail to gauge the diverse semantic and structural aspects of a generated response. Primarily, the proffered ensemble technique has leveraged the augmented domain knowledge to enrich the answering response efficacy and improving the EM and F1 score by 14.86% and 12.76% respectively over Vanilla BERT architecture. To enhance the user experience, the conversational system is equipped with Natural Language Generation (NLG) to present a human-readable response. Our architectural pipeline aims to provide a one-stop solution for the organizations in processing huge volumes of multidisciplinary data by significantly reducing the human introspection and the overhead cost.
PrivInferVis: Towards Enhancing Transparency over Attribute Inference in Online Social Networks

The European GDPR calls, besides other things, for innovative tools to empower online social networks (OSN) users with transparency over risks of attribute inferences. In this work, we propose a novel transparency-enhancing framework for OSN, PrivInferVis, to help people assess and visualize their individual risks of attribute inference derived from public details from their social graphs in different OSN domains. We propose a weighted Bayesian model as the underlying method for attribute inference. A preliminary evaluation shows that our proposal outperforms baseline algorithms on several evaluation metrics significantly. PrivInferVis provides visual interfaces that allow users to explore details about their (inferred and self-disclosed) data and to understand how inference estimates and related scores are derived.
Studying the influence of aerosol types on solar radiation prediction

The influence of aerosol types on Solar radiation prediction was studied in this paper. According to Chinese population density, China administration districts were divided into areas with urban aerosol type and rural aerosol type based on Arcgis. The Solar radiation all over China was simulated by the Mesoscale numerical Weather Prediction Model (WRF). The forecasting was compared with the data of “Clouds and the Earth’s Radiant Energy System” released by NASA. The results showed that there was a big improvement when the different aerosol types in different areas were taken into consideration.
Optimization of Systems-Development Life Cycle Through Automation Using Ansible

In order to sustain oneself in this era of cutting-edge technology, companies need to adopt different technologies for the efficient execution of tasks. In pursuit of accomplishing the targets, the companies install a number of software or add lines of code. DevOps is a large number of programming improvement repetitions that consolidate software development (Dev) and information-technology operations (Ops) to shorten the lifecycle of system development while delivering patches, updates and features from time to time in close collaboration with business objectives. Time is what everybody wants to save and this where Automation comes into practice. This paper throws light on how companies can adopt simple automation by creating HA proxy servers, web servers, and Hadoop-cluster with the help of an automation tool called Ansible. Ansible is an open-source mechanization platform, used for IT jobs, for example, framework management, application installment, intra-service coordination and accoutering. Here, YAML and Jinja languages are used to create and automate both the HA proxy server and Hadoop cluster.