Reviewerly/exHarmony huggingface dataset:
Development of the Wide Area Fire Support System in Korea

In the event of a large-scale disaster, Korea's wide-area fire support system (WFSS) mobilizes the same personnel and equipment regardless of the type and characteristics of the disaster. Support resources are inefficient for operation with equal mobilization without considering disaster-caused areas and the scope of support. Therefore, the status of the operation of the Emergency Fire Response Team for Disaster Response in Japan, which has developed a rational disaster response system due to frequent earthquakes, was compared and analyzed to demonstrate the direction of the development of the WFSS in Korea. The WFSS should be organized in such a way that the units suitable for the type and characteristics of disasters are mobilized and resources should be regularly provided with a registration and management system. The command of the support unit should form a command system linked to the operation of the existing emergency rescue control group; however, it should strengthen mutual training in close cooperation with related organizations in preparation for complex disasters. In principle, budgets used in response to disasters should be the responsibility of the state. The above-mentioned improvements should be made effective by legislating the “Mobilization Ordinance,” which is the nature of the current National Fire Agency's guidelines, into the Framework Act on Firefighting and Framework Act on Disaster and Safety Management.
Explainability Case Studies

Explainability is one of the key ethical concepts in the design of AI systems. However, attempts to operationalize this concept thus far have tended to focus on approaches such as new software for model interpretability or guidelines with checklists. Rarely do existing tools and guidance incentivize the designers of AI systems to think critically and strategically about the role of explanations in their systems. We present a set of case studies of a hypothetical AI-enabled product, which serves as a pedagogical tool to empower product designers, developers, students, and educators to develop a holistic explainability strategy for their own products.
On WPA2-Enterprise Privacy in High Education and Science

A plethora of organizations, companies, and foremost universities and educational institutions are using WPA2-Enterprise protocol to allow their end-users to connect to provided Wi-Fi networks. When both the provider’s and the end-user’s devices are configured properly, it is considered one of the safest Wi-Fi connection protocols with the added benefits of having a unique password for every Wi-Fi user. However, a known evil twin attack can be performed to steal users’ Wi-Fi login credentials, if the devices are not configured correctly. Considering the widespread use of Wi-Fi-enabled smartphones and rising concerns regarding users’ privacy, we focus on the privacy aspects of WPA2-Enterprise vulnerabilities mainly on the widespread Eduroam network. We show that device deanonymization is a concerning liability of many Eduroam networks. More than 87% of 1650 devices collected during a two-month test on our university are vulnerable to MAC address deanonymization attack. Furthermore, by analyzing the Eduroam Configuration Assistant Tool of 1066 different institutions around the world, 67% of exported Eduroam profiles having the Wi-Fi device reveal the user’s identity in the clear, thus linking the users with the device’s MAC address. Indeed, the analysis of the configuration profiles has been confirmed by performing the deanonymization attack on a large-scale international music festival in our country, where 70% of the devices have been vulnerable. Additionally, we showcase the psychological aspects of secure Eduroam users, where some are willing to modify secure configuration profiles to gain aspects to certain blocked features. As a result, the attacker is granted with user credentials and IMSI number and provided with access to all Eduroam-related services.
Educational Escape Games for Mixed Reality

Escape Games are new real-world entertainment formats where two to six people collaboratively solve riddles to escape a locked room in a certain fictional setting, e.g. a space station. Educational researchers have adopted the concept for teaching science subjects, e.g. in K12 teaching. However, setting up such rooms is costly because of the physical objects which are required for the riddles. In this paper, we introduce a gamified Mixed Reality environment, which was adopted to create educational Escape Rooms in an easy and fast way. We created two games for different subjects to demonstrate the feasibility of the approach by an evaluation at a high school. The games and the platform are open source software and open content, to facilitate the creation and the use of educational Escape Games on a broader base.
Long sequences having no two nonempty zero-sum subsequences of distinct lengths

Let $G$ be an additive finite abelian group. We denote by $\mathrm {disc}(G)$ the smallest positive integer $t$ such that every sequence $S$ over $G$ of length $|S|\geq t$ has two nonempty zero-sum subsequences of distinct lengths. In this paper, we first
Low Light Image Enhancement for Dark Images

Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of the image for better visual quality and show details that are hidden in darkness.Research fields that may assist us in lowlight environments, such as object detection, has glossed over this aspect even though breakthroughs-after breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO.To improve image quality, these low-light images are needed to be enhanced.For this purpose, an exclusively dark dataset comprising of images captured in visible light only is proposed.Further, dehazing technique is used for haze removal, histogram equalization (HE) technique is used for contrast enhancement and denoising technique is used for noise removal.Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancement and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.
Gradual GRAM and Secure Computation for RAM Programs

Despite the fact that the majority of applications encountered in practice today are captured more efficiently by RAM programs, the area of secure two-party computation (2PC) has seen tremendous improvement mostly when the function is represented by Boolean circuits. One of the most studied objects in this domain is garbled circuits. Analogously, garbled RAM (GRAM) provide similar security guarantees for RAM programs with applications to constant round 2PC. In this work we consider the notion of gradual GRAM which requires no memory garbling algorithm. Our approach provides several qualitative advantages over prior works due to the conceptual similarity to the analogue garbling mechanism for Boolean circuits. We next revisit the GRAM construction from [11] and improve it in two orthogonal aspects: match it directly with tree-based ORAMs and explore its consistency with gradual ORAM.
The First Scientific and Practical Conference “Letter and Digit: Libraries on the way to Digitalization” (“BiblioPiter–2020”). Review of Events

The analytic report on the First Scientific and Practical Conference “Letter and Digit: Libraries on the way to Digitalization” (“BiblioPiter–2020”) is presented. The Conference took place in St. Petersburg on February 17–19, 2020. The Conference was organized by: Russian National Public Library for Science &amp; Technology, Russian State Library, National Library Association “Libraries of the Future” (NALF), and International Association of Users and Developers of Electronic Libraries and New Information Technologies (ELNIT). The goal of the Conference was to join to get her the efforts by libraries and other academic, information and research institutions in developing new concepts, methods and technologies of the library and information sector in contemporary digital environment. The NALF School and IRBIS School “Digital Technologies and Services at Libraries” took place within the Conference program. The papers by known scientists and professionals in library science, information science, bibliography and cultural studies were delivered. The Conference also comprised the round tables “Special Patron at the Library” and “Intellects and Library Logos”, master class on patent search, special event on the Open Science issues, as well as the presentation of the monograph “Copyright at Libraries, Research and Academic Institutions” by Andrey I. Zemskov and Yakov L. Shrayberg. Totally over 30 papers were presented and over 100 participants attended the Conference. Over 600 users watched live Internet broad casts of the Conference events. The video records of every Conference event, as well as that of IRBIS School and NALF Schools are available in full on the YouTube channel of the Russian National Public Library for Science &amp; Technology. Power point presentations are available on NALF website. The decision was made to organize the Conference annually. It will be hold every year in St. Petersburg in mid-February.
Fatores relacionados ao diabetes mellitus que podem influenciar no autocuidado

Objectives: To identify the profile of individuals older than 18 years of age, with type 1 and type 2 Diabetes Meliitus (DM), of both sexes followed in the FHS of the rural area of the city of Divinópolis-MG and the types of self-care performed. Method: Descriptive, quantitative study using an adapted instrument and the Diabetes Self-Care Activities Questionnaire. Results: Among the 107 interviewees, 42.1% are over 65 years of age, 62.6% are female, with the same percentage for individuals who have incomplete elementary education and are married, 65.4% have another health problem, being 58.7% Hypertension. In the identification of self-care, responses to "medication" (96.3%) were more prevalent, as opposed to "physical activity" (18.7%), which had a lower prevalence. Conclusion: the research shows that people adhere more to medication treatment than changes in lifestyle.
Applying Instructional Design Principles on Augmented Reality Cards for Computer Science Education

Abstract In this article we describe Augmented Reality (AR) cards for computer science education that were created in the PCBuildAR project. From a technological point of view, we use marker-based AR for the cards so that students can learn and practice at any time with their smartphones. The instructional approach is based on the components content, construction and communication (3C model). The content ensures the acquisition of knowledge, which then is applied via problem-based learning activities (construction). Communication not only takes place between learners, but also with teachers. All materials will be available as open educational resources after the project is completed.
Efficient Mining of Pareto-Front High Expected Utility Patterns

In this paper, we present a model called MHEUPM to efficiently mine the interesting high expected utility patterns (HEUPs) by employing the multi-objective evolutionary framework. The model considers both uncertainty and utility factors to discover meaningful HEUPMs without requiring pre-defined threshold values (such as minimum utility and minimum uncertainty). The effectiveness of the model is validated using two encoding methodologies. The proposed MHEUPM model can discover a set of HEUPs within a limited period. The efficiency of the proposed model is determined through rigorous analysis and compared to the standard pattern-mining methods in terms of hypervolume, convergence, and number of the discovered patterns.
The Application of Discovery Learning Models in Learning to Write Descriptive Texts

The purpose of this study was to describe the application of discovery learning models in learning to write descriptive texts. The research method uses descriptive methods. Data collection techniques using observation techniques, test writing descriptive text, and documentation. Data analysis techniques by reducing data, data presentation, data interpretation, conclusions. The results showed that the steps of the discovery learning model in learning to write descriptive texts are to describe topics, ask questions, formulate hypotheses, carry out investigations, conduct observations, organize or analyze, and present the results of investigations. Then, the students' ability to write descriptive text obtained an average value of 76.8 with good criteria after applying the discovery learning model. The learning to write descriptive text is carried out to instill the value of responsibility to students and improve the ability to write text descriptive students based on text, images, and places, so students are not passive.
An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.
Nonclassicality of bright Greenberger-Horne-Zeilinger–like radiation of an optical parametric source

With the emerging possibility to obtain emissions of triples of GHZ-entangled photons via a direct parametric generation we study here bright emissions of this kind which involve higher order emissions of two triples, three triples, etc. Such states would constitute a natural generalization of the four mode (two beams plus polarization) squeezed vacuum. We have three beam process of emission generalized bright GHZ states, which a are superpositions of one, two, three, and so on GHZ entangled triples of photons. We show how to avoid technical difficulties related with straight ahead generalization of the usual description of parametric down conversion. Using Pade approximation we turn first terms of the non-converging perturbation expansion into elements of conversing series. This allows us to study non-classicality of the new bright generalized GHZ states.
Diffusion of Covid-19 in the Northern Metropolis in Northeast Brazil: territorial dynamics and risks associated with Social Vulnerability

Throughout history, humankind has lived through many crises arising from pandemics, and it is currently in the midst of one. Coronavirus disease, caused by the severe acute respiratory syndrome coronavirus 2, is causing serious problems related to public health and socioeconomic status across the globe. Having started in Asia, the disease has already reached Europe, Africa, and more recently the Americas; hence, it is necessary to understand the context in which it spreads in regions that carry high risks associated with social vulnerability, as in the case of Latin America. Brazil is the South American country most affected by the pandemic—its first case was confirmed at the end of February 2020, and in less than two months, its total number of cases has already crossed 25,000. Among its worst-hit states, Ceará in Northeast Brazil worries health agencies as there have already been more than 2,000 cases in the 30 days of the outbreak in the state. Even more worrying is its capital Fortaleza, which has become the epicenter of the spread of the disease in Ceará, accounting for 86.5% cases and 67% deaths in the state—95% of the neighborhoods in Fortaleza have confirmed cases of the disease. An aspect that particularly causes concern is the disease’s diffusion, from neighborhoods with the highest family income to those with the lowest, directly affecting the socially vulnerable populations in territories with a majority of precarious settlements; in Fortaleza, favelas are the main type of precarious settlements (73.6%). The analyses presented as part of this research can assist in identifying priority areas for state intervention through family assistance resources.
Statistical Analysis of the Performance of Rank Fusion Methods Applied to a Homogeneous Ensemble Feature Ranking

The feature ranking as a subcategory of the feature selection is an essential preprocessing technique that ranks all features of a dataset such that many important features denote a lot of information. The ensemble learning has two advantages. First, it has been based on the assumption that combining different model’s output can lead to a better outcome than the output of any individual models. Second, scalability is an intrinsic characteristic that is so crucial in coping with a large scale dataset. In this paper, a homogeneous ensemble feature ranking algorithm is considered, and the nine rank fusion methods used in this algorithm are analyzed comparatively. The experimental studies are performed on real six medium datasets, and the area under the feature-forward-addition curve criterion is assessed. Finally, the statistical analysis by repeated-measures analysis of variance results reveals that there is no big difference in the performance of the rank fusion methods applied in a homogeneous ensemble feature ranking; however, this difference is a statistical significance, and the B-Min method has a little better performance.
FORMALIZED EVALUATION OF SCENARIO DEVELOPMENT OF THE NATIONAL ECONOMY IN TERMS OF PENETRATION OF BLOCKCHAIN TECHNOLOGIES IN THE FINANCIAL SECTOR

In the coming years, blockchain technologies may become one of the breakthrough innovations in the financial sector of the economy, optimizing and simplifying transaction operations in a number of areas, reducing their cost. Having a very significant potential for transforming the established algorithms for the interaction of financial market participants, it is important to understand what are the boundaries of these changes, what new opportunities are presented by blockchain technologies and, finally, what are the expected consequences for the development of the financial sector itself and the other sectors of the national economic system associated with it. In this regard, important and urgent task is to minimize risks for financial and other organizations in the face of impending opportunities and threats under the pressure of the process of integrating blockchain platforms into the economic environment. This research is devoted to the solution of the issues posed. The most important component of the paper is the proposed methodological toolkit, which allows you to identify in a formalized form the patterns and relationships between the level of penetration of blockchain technologies into the financial environment and the dynamics of gross domestic product ( GDP).
Automated Fine Grained Traceability Links Recovery between High Level Requirements and Source Code Implementations

Software Traceability has been a matter of discussion in the Software Engineering community for a long time. The process of keeping and recover traces among software artifacts in any system represents a fundamental aspect to properly perform software maintenance tasks and requirements compliance verification. Furthermore, there exist application contexts where this becomes a mandatory process, for instance, banking and healthcare. Software traceability has dedicated efforts in proposing alternatives to recover lost traceability links in a coarse-grained and middle-grained detail by so far, however, proposed techniques are not enough to meet the desired levels of granularity in critical contexts. In this work we propose a fine-grained traceability algorithm designed to recover traces between high level requirements written in human natural language and source code statements where they are implemented. We tested our approach in four open-source healthcare systems to trace constraints requirements specified by the HIPAA law, and we evaluated the results as presented is this paper.
Peculiarities of Math Students Adaptation to Temporary Forced Isolation or Quarantine

The relevance of this article is determined by the need to study the difficulties facing young people in the process of social adaptation during quarantine on the back of the coronavirus pandemic. Due to their worldview that is often in conflict with the surrounding social environment, as well as their life attitudes and inability to adapt to the situation, young people may not be able to cope with all the processes of social adaptation that occur every now and then in everyday life. In the context of the quarantine announced to curb the spread of coronavirus, some young people may experience increased social maladaptation and develop psychosocial fears. This article is aimed at identifying the specifics of the relationship between adaptation strategies and disorders caused by social stresses in college students in the situation of temporary involuntary isolation (under quarantine). We used a questionnaire survey as a research methodology to identify both social adaptation strategies of math students and disorders associated with social stresses that they may develop in the situation of temporary involuntary isolation. The article has revealed strategies for the social adaptation of math students in the situation of quarantine. The article analyses the relationship between the social adaptation strategy and social stress disorders of math students during the coronavirus pandemic. The data obtained in this research can be used in social psychology, pedagogy, age psychology, medical psychology, as well as for further theoretical development of this problem.
Pay Attention when Required

Transformer-based models consist of interleaved feed-forward blocks - that capture content meaning, and relatively more expensive self-attention blocks - that capture context meaning. In this paper, we explored trade-offs and ordering of the blocks to improve upon the current Transformer architecture and proposed PAR Transformer. It needs 35% lower compute time than Transformer-XL achieved by replacing ~63% of the self-attention blocks with feed-forward blocks, and retains the perplexity on WikiText-103 language modelling benchmark. We further validated our results on text8 and enwiki8 datasets, as well as on the BERT model.