Reviewerly/exHarmony huggingface dataset:
Loop

LOOP, which in Dutch means “walk,” is a physical artifact that visualizes personal step data. The daily steps made show in the seven weekday rings (dark brown) moving up, and can be compared to the step goal ring (light brown).
Front Matter for Lifelines 2022: 1971 San Fernando Earthquake and Lifeline Infrastructure

Front matter pages come before the papers or chapters in a published work. Front matter includes the title page, copyright and notices page, and table of contents. It can also include a foreword, preface, or introduction; series information; lists of contributors, sponsors or reviewers; lists of abbreviations and notations; and conversion tables.
Unified entropy entanglement with tighter constraints on multipartite systems

Monogamy and polygamy relations characterize the distributions of entanglement in multipartite systems. We provide a characterization of multiqubit entanglement constraints in terms of unified-( q , s ) entropy. A class of tighter monogamy inequalities of multiqubit entanglement based on the α -th power of unified-( q , s ) entanglement for α ≥ 1 and a class of polygamy inequalities in terms of the β -th power of unified-( q , s ) entanglement of assistance are established in this paper. Our results present a general class of the monogamy and polygamy relations for bipartite entanglement measures based on unified-( q , s ) entropy, which are tighter than the existing ones. What is more, some usual monogamy and polygamy relations, such as monogamy and polygamy relations based on entanglement of formation, Renyi- q entanglement of assistance and Tsallis- q entanglement of assistance, can be obtained from these results by choosing appropriate parameters ( q , s ) in unified-( q , s ) entropy entanglement. Typical examples are also presented for illustration.
Text data-driven new product development: a systematic mapping review

Purpose The purpose of this study/paper is conducting a Systematic mapping review, as a systematic literature review method for reviewing the literature of new product development by textmining and mapping the results of this review. Design/methodology/approach This research has been conducted with the aim of systematically reviewing the literature on the field of design and development of products based on textual data. This research wants to know, how text data and text mining methods, can use for the design and development of new products. Findings This review finds out what are the most popular algorithms in this field? What are the most popular areas in using these approaches? What types of data are used in this area? What software is used in this regard? And what are the research gaps in this area? Originality/value The contribution of this review is creating a macro and comprehensive map for research in this field of study from various aspects and identifying the pros and cons of this field of study by systematic mapping review.
Mivar’s Approach to Detailed Description of Knowledge for the Academic Subject “Rocket and Space Manufacturing Technologies”

The amount of knowledge in various industries is constantly growing and employers increase and update the requirements for a university graduate. But the restrictions on the time of training of students and the amount of the studied material remain. This leads to a contradiction between the existing student learning process and the requirements of employers. Consequently, the learning process must constantly change, which requires the development of educational programs and academic disciplines. In universities such as the Bauman Moscow State Technical University (BMSTU), which has about 30,000 students in 650 educational programs, it is impossible to support educational programs and academic disciplines without the use of automation tools. Therefore, to solve the problem of automated planning of the educational process, the article proposes a method for describing academic subjects in the formalism of mivar networks was proposed. Within the framework of this methodology, each academic subject is divided into more detailed modules of knowledge “didactic units”, which allows you to create a learning process detailed plan within the academic subject and throughout the educational program. The result of the application of this methodology is the possibility of automatic formation of an optimal curriculum and organizing an assessment of knowledge control reliability. The methodology presented in the article is used in the project to formalize educational programs to improve the University’s management at BMSTU.
Modelling of Blockchain Technology in the Electoral Process

Currently, elections have become the main method of political activity of citizens on their way to democracy. However, during elections of various scales, the cyberattacks by intruders, scammers and other attackers are increasingly occurring. They are trying, as a rule, to make changes to the databases where voter registration is carried out, to disrupt the voting mechanism. This problem is observed all over the world, and is getting worse every year. The blockchain technology considered in this paper provides an opportunity to solve the problems discussed above. The electoral systems in Russia have been constantly improving and modernizing over the past 30 years. Nevertheless, the applied technologies do not give the desired result; it is noticeable at all levels: federal, regional and local. This gives rise to the search for new technologies and methods to solve these problems. In this regard, it is advisable to use the blockchain technology, which is used successfully in the economic sector of industry already. In the last decade, Russia has paid a lot of attention to the issues of cybersecurity of the country, and large financial resources have been allocated for this. This is due to the fact that the special services of a number of countries are trying to manipulate the course of the elections.
The Development of a Cognitive-Learning Model for the Adaptive Digital Profile on the Basis of E-Portfolio

This article is devoted to the compilation of a special profile (ADP), studying on the basis of an automatic portfolio system and a cognitive model of student learning at the university. The development of a cognitive model of learning for electronic educational systems was stimulated by the identification of a wide range of problems in solving, suggests the existence of grounds for making managerial decisions at the university and poses the challenges of digital transformation. A software solution has been developed with the help of which the analysis of students’ achievements is carried out using an electronic portfolio and data on the states of students’ cognitive parameters. The data obtained as a result of processing allows you to form individual trajectories, as well as pay attention to problematic moments in learning. The cognitive model of a student for a digital profile takes into account, in our case, cognitive abilities, mental and socially determined characteristics, the ability to reflect, as well as the current level of personal experience and professional competence. Based on the presented model of cognitive transfer, the choice of cognitive indicators of students for use in a digital profile is substantiated. In addition, the possibilities for the formation of personalized events for students in various fields of the University activity in accordance with their learning path are considered.
Social Work: A Trajectory of Education and Employment

The article explores the trajectory of education and employment of graduates in the direction of training specialists, bachelors and masters of social work. One of the main prerequisites for their preparation is the aging of the population and the decline in the number of people of working age. The dynamics of education in undergraduate and graduate programs over the past seven years, both in Russia and in the Mari El Republic is given. Based on a comparison of the educational trajectories of social work specialists, positive and negative factors are identified. The positive factors include the growth of the practical orientation of education, the mobility of graduates, the development of social partnership, the improvement of the composition of teachers and the improvement of technologies for the quality of education. Against this background, problems such as a weak scientific base in the constituent entities of the Russian Federation for the employment of postgraduates, low wages for social workers, a high level of aspirations of graduates, etc. have been identified. In order to improve the quality of education, the following measures have been proposed: state partnership, wider involvement of students in scientific research and grant activities, partnership in the field of joint laboratories and small enterprises for social assistance to lonely and elderly citizens on the basis of a social contract.
Intelligent Stable Monitoring Algorithm of Students' Online Learning Behavior Based on User Portrait

In the monitoring of students’ online learning behavior, the accurate capture rate of learning behavior has been low due to the complex actions of students. An intelligent monitoring algorithm of students’ online learning behavior based on user portraits is designed. This paper collects student data from five aspects: demographic attributes, academic attributes, behavior habits, interests and hobbies, and psychological attributes, and constructs student user portraits. The deep perception model of online learning behavior is designed from two levels of face recognition and emotion perception to implement the deep perception of students’ online learning behavior. According to the perception results, the online learning behavior is monitored intelligently through three stages: before learning, during learning and after learning, so as to realize the intelligent monitoring of students’ online learning behavior. The algorithm is tested. The test results show that the designed algorithm has a high accurate capture rate for learning behavior of all grades, and the designed algorithm has a high learning efficiency and a good learning effect.
Effect of the Scaffolding Done by a BDI Agent on the Evolution of Learning Trajectories

Cognitive convergence between humans and agentive tools opens research opportunities to foster learning. The document presents an artificial cognitive architecture for an intelligent agent to perform educational scaffolding while the solution of the Towers of Hanoi game. The architecture’s design considers two paradigms: the theory of BDI agents (Beliefs, Desires, Intentions) and scaffolding pedagogical strategy. To validate the architecture, four game versions of the Hanoi Tower game were designed: game alone, game with graph, game with agent, game with agent and graph. Then, the game solution trajectories in the four environments were compared. 16 scholars aged 10 to 12 participated. The results suggest that the scaffolding carried out by the agent contributes to the evolution of schemes in the solution of well-structured problems.
Political Improved Invasive Weed Optimization-Driven Hybrid Exemplar Technique for Video Inpainting Process

Video inpainting aspires to fill the Spatio-temporal holes in videos with probable and coherent content. This process recovers the missing content of corrupted video effectively, which is useful in many fields, including removal of watermarking and video restoration. The difficulties of creating video contents with exquisite detail while maintaining spatiotemporal coherence in the missing areas is the main difficulty in the video inpainting process. Modern studies ignore semantic structural coherence maintenance between frames in favor of using flow information to synthesize temporally smooth pixels. In this paper, Political Improved Invasive Weed Optimization (PIIWO)-based optimal exemplar is designed for the productive video inpainting process. Accordingly, the developed PIIWO algorithm is newly designed by combining Political Optimizer (PO) and Improved Invasive Weed Optimization (IIWO). Here, the inpainting results obtained from context-aware Ant Lion Gray Wolf Optimization (ALGWO)-based Markov Random Field (MRF) modeling, Whale Monarch Butterfly Optimization (Whale MBO)-based Deep Convolutional Neural Network (DCNN), K-Nearest Neighbors (KNN) with Bhattacharya distance, Bi-harmonic function modules and developed PIIWO-based exemplar model are fused using Bayes probabilistic fusion for producing the final result. Three metrics, peak signal-to-noise ratio (PSNR), second derivative like the measure of enhancement (SDME) and structural similarity (SSIM) of 40.19[Formula: see text]dB, 78.07[Formula: see text]dB and 0.9857, respectively, are used to assess the performance of the developed video inpainting technique.
Speed-Up Single Shot Detector on GPU with CUDA

Nowadays, most of the current research on object detection is to improve the whole framework, in order to improve the accuracy of detection, but another problem of object detection is the detection speed. The more complex the architecture, the slower the speed. This time, we implemented a Single Shot Multibox Detector(SSD) using GPU with CUDA. We have improved the object detection speed of SSD, which is one of the most regularly used objects detection frameworks. The most time-consuming part, the VGG16 network, is rewritten by using cuDNN, which is made faster by about 9%. The second time-consuming part is post-processing, where non-maximum-suppression (NMS) is performed. We accelerated NMS by implementing our new algorithms that are suitable for GPUs, which is about 52% faster than the original PyTorch version [16]. We also ported those parts that were originally executed on CPU to GPU. In total, our GPU-accelerated SSD can detect objects 22.5% faster than the original version. We demonstrate that using GPUs to accelerate existing frameworks is a viable approach.
Semantic Knowledge Representation for Long-Range Action Anticipation

Action anticipation is an important capability for systems interacting with humans in their everyday environments, such as robots or digital assistants. Action anticipation already works remarkably well for short periods of time; however, it is still an unsolved challenge for larger time gaps. In this paper, we propose a semantic representation of previous actions for the prediction of a distribution across possible future actions. We show that this approach is able to beat a baseline prediction for as much as 5 minutes into the future.
SpackNVD: A Vulnerability Audit Tool for Spack Packages

Security models for Linux distro package security and interoperability have traditionally emphasized the use of more recent (more secure) versions at the occasional expense of execution reproducibility. A complementary approach (e.g., Lmod) allows access to multiple sysadmin-approved package versions. Another approach (e.g., Spack) enables a purely user space process for package selection without system administrator oversight. While maximizing reproducibility, there is no user feedback regarding potential security vulnerabilities. We introduce a general security model for package management and our implementation of SpackNVD, a security auditing tool for Spack. Users may query reported vulnerabilities for specific package versions and can prevent installation where the severity score exceeds a threshold. We emphasize this is a tool, not a solution: Spack users are not expected to be security professionals. However, this information may influence Spack concretizer decisions, and enable users to ask support staff about whether specific package versions are appropriate for use.
Pelatihan Pengembangan Media Pembelajaran Digital E-book Menggunakan Flipbook PDF Professional bagi Guru di Kota Banjarmasin

Currently, teaching materials are needed that can be accessed offline and online to make it easier for students and teachers such as e-books. e-books can be further developed with interactive features. The results of observations on a number of teachers show that teachers have not been able to create digital learning media for them to use in learning. This community service aims to improve the ability of teachers to develop digital learning media using the Flipbook PDF Professional software which will be hosted on Google Drive. This activity was carried out at the Madani Islamic High School, Banjarmasin City. The methods used in the training include stages (1) planning, (2) implementation, and (3) evaluation of activities. The results of the implementation and evaluation of activities show that training on the development of digital learning media using flipbook PDF Professional software for teachers in the city of Banjarmasin has had a positive impact on participants in the form of increasing their ability to develop learning media digitally. The positive response to the implementation of the training shows that training on the development of digital learning media into e-books is considered useful for use in the learning process in the digital era at this time.
Clustering-Based Recommender Systems

Recommender systems based on unsupervised machine learning algorithms are very popular because they overcome many challenges that collaborative, hybrid, and classification-based systems face. A clustering technique is used to recommend the products/items based on the patterns and behaviors captured within each segment/cluster. This technique is good when data is limited, and there is no labeled data to work with.
Influence of Technological Factors in Teaching and Communication in Higher Education During the COVID-19 Health Crisis

Due to COVID-19, the way we use digital tools and the way we communicate with students have changed in higher education, it is so that the present investigation of mixed type carried out through: bibliographic review, interview with ICT managers, application of a virtual survey to teachers, and processing of the academic database, aimed to determine the degree of influence of technological factors in the form of communication and teaching in higher education, finding that the pandemic had positive effects, mastery of digital skills in teachers increased, digitization and updating of online courses, synchronous and asynchronous modalities were integrated, the use of digital tools was diversified and in terms of communication, and institutional mail was used massively for administrative and academic purposes. Nevertheless, in the negative effects noted, that feedback and motivation were not adequate.
Enhancement of Non‐Gaussianity and Nonclassicality of Photon‐Added Displaced Fock State: A Quantitative Approach

Abstract Non‐Gaussian and nonclassical states and processes are already found to be important resources for performing various tasks related to quantum gravity and quantum information processing. Considering these facts, a quantitative analysis of the nonclassical and non‐Gaussian features is performed here for photon added displaced Fock state, as a test case, using a set of measures, namely entanglement potential, Wigner–Yanese skew information, Wigner logarithmic negativity, and relative entropy of non‐Gaussianity. It is observed that Fock parameter always increases the amount of nonclassicality and non‐Gaussianity, while photon addition is effective only for small values of the displacement parameter. Further, the nonclassical and non‐Gaussian effects decrease initially with an increase in the displacement parameter before increasing for the large displacement to saturate to the corresponding Fock state (equivalently displaced Fock state) value. Finally, dynamics of the Wigner function under the effect of photon loss channel is used to show that only highly efficient detectors are able to detect Wigner negativity.
Shape Recognition for Industrial Robot Manipulation with the Bees Algorithm

Fitting primitive shapes to point cloudPoint Cloud scenes is a challenging but necessary step for many roboticRobotic manipulation operations. State-of-the-art primitive fittingPrimitive Fitting methods rely on geometric shapeGeometric Shapes estimation or iterative procedures. They are often computationally complex and sensitive to algorithm parameterisation. This study tackles primitive fittingPrimitive Fitting as a parameterParameters optimisation problem, solving it using the Bees AlgorithmBees Algorithm, THE. The performance of the Bees AlgorithmBees Algorithm is evaluated on three sets of artificial scenes of varying degrees of blurriness and benchmarked against an evolutionary algorithmEvolutionary Algorithm. Experimental results proved the precision and consistency of the Bees Algorithm. Primitive fitting times were compatible with real-timeReal-Time application.
Diesseits und jenseits der Pandemie – Digitalität und Deutschunterricht

No AccessDiesseits und jenseits der Pandemie – Digitalität und DeutschunterrichtProf. Dr. Michael RödelProf. Dr. Michael RödelAuthors email:[email protected] Ludwig-Maximilians-Universität München Deutschland Search for more papers by this authorPublished Online:Nov 2022https://doi.org/10.14220/mdge.2022.69.4.334SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous article Next article FiguresReferencesRelatedDetails Download book coverVolume 69Issue 4November 2022 ISSN: 0418-9426eISSN: 2196-8756HistoryPublished online:November 2022 PDF download
Sequence Rule Mining for Insulin Dose Prediction Using Temporal Dataset

The objective of pattern mining is to mine sensitive and non-critical information hidden from databases. Sequential rule mining is a pattern mining technique that retrieves rules in order. It has number of applications in different area including healthcare. Healthcare is the prime concern for human’s life. Insulin dependent diabetes mellitus (IDDM), which is also known as type-1 diabetes is a kind of chronic disease and patients of these disease needs to get insulin as a supplement or medicine. Technologies that predict the right amount of insulin dose for a patient is needed for correct prognosis of patients. It facilitates to better health in patients to maintain the glucose level in a specific range which produces the right energy for human cells. Our objective is to find insulin therapy plan for diabetes patients. In this work, we applied some sequential mining algorithms to predict the dosage values of regular, NPH and ultraLente insulin at three different time frames, i.e., at prebreakfast, prelunch, and presupper. Sequential rules are generated to predict the dosage ranges for different time frames with support and confidence metrics. These rules are used to recommend particular type of insulin with dosage amounts. This work also compares different sequential rule mining algorithms such as ERMiner, CMRules, CMDeo, and RuleGrowth with their memory taken and execution speeds. We concluded that by making sequences of blood glucose ranges with insulin dose ranges we can generate rules for insulin prediction as well as diabetes predictions. We can also conclude that after applying different sequential rule mining algorithms, ERMiner is faster among the algorithms but it takes more memory to execute.