Reviewerly/exHarmony huggingface dataset:
Tigris: A DSL and framework for monitoring software systems at runtime

The understanding of the behavioral aspects of a software system is an essential enabler for many software engineering activities, such as adaptation. This involves collecting runtime data from the system so that it is possible to analyze the collected data to guide actions upon the system. Consequently, software monitoring imposes practical challenges because it is often done by intercepting the system execution and recording gathered information. Such monitoring may degrade the performance and disrupt the system execution to unacceptable levels. In this paper, we introduce a two-phase monitoring approach to support the monitoring step in adaptive systems. The first phase collects lightweight coarse-grained information and identifies relevant parts of the software that should be monitored in detail based on a provided domain-specific language. This language is informed by a systematic literature review. The second phase collects relevant and fine-grained information needed for deciding whether and how to adapt the managed system. Our approach is implemented as a framework, called Tigris, that can be seamlessly integrated into existing software systems to support monitoring-based activities. To validate our proposal, we instantiated Tigris to support an application-level caching approach, which adapts caching decisions of a software system at runtime to improve its performance.
Synthetic Emotions for Empathic Building

Empathic buildings are intelligent ones that aim to measure and execute the best user experience. A smoother and intuitive environment leads to a better mood. The system gathers data from sensors that measure things like air quality, occupancy, noise and analyse it for the better experience of the users. This research proposes an artificial intelligence-based approach to detect synthetic emotions based on Thayer’s emotional model and Fuzzy Cognitive Maps. This emotional model is based on a biopsychological approach to the analysis of the humans’ emotional state. In this research, Fuzzy Grey Cognitive Maps are used, which are an extension of the fuzzy cognitive maps using the grey systems theory to model uncertainty. Fuzzy Cognitive Grey Maps (FGCMs) have become a very valuable theory for modeling high-uncertainty systems when small and incomplete discrete data sets are available. This research includes experiments with a couple of synthetic case studies for testing this proposal. This proposal provides an innovative way for simulating synthetic emotions and designing an empathic building.
Integrated, Speckle-Based Displacement Measurement for Lateral Scanning White Light Interferometry

Lateral scanning white light interferometry (LSWLI) is a promising technique for high-resolution topography measurements on moving surfaces. To achieve resolutions typically associated with white light interferometry, accurate information on the lateral displacement of the measured surface is essential. Since the uncertainty requirement for a respective displacement measurement is currently not known, Monte Carlo simulations of LSWLI measurements are carried out at first to assess the impact of the displacement uncertainty on the topography measurement. The simulation shows that the uncertainty of the displacement measurement has a larger influence on the total height uncertainty than the uncertainty of the displacing motion itself. Secondly, a sufficiently precise displacement measurement by means of digital speckle correlation (DSC) is proposed that is fully integrated into the field of view of the interferometer. In contrast to externally applied displacement measurement systems, the integrated combination of DSC with LSWLI needs no synchronization and calibration, and it is applicable for translatory as well as rotatory scans. To demonstrate the findings, an LSWLI setup with integrated DSC measurements is realized and tested on a rotating cylindrical object with a surface made of a linear encoder strip.
Relay protection system of transmission line based on AI

With the development of modern power systems, higher requirements are imposed on relay protection technology. Traditional relay protection and fault diagnosis technologies have been unable to meet the requirements of the continuous development of power systems, and relay protection systems based on artificial intelligence(AI) technology have received increasing attention. Therefore, this document first analyses the weaknesses of traditional broadcast line protection and uses the adaptability and self-learning of artificial intelligence(AI); to propose the concept of protection of a relay line based on AI. In combination with the artificial nervous network, the AI-based relay protection system shall be studied and the experimental model shall be developed. This paper validates it with simulation experiments. The research results show that for the analysis of the ANN test results of the subnetwork, the actual output of the subnetwork is very close to the ideal output, and the error does not exceed 0.2%. The system has good performance and high reliability.
Compliance with security guidelines in teenagers

What drives teenagers to comply with computer password guidelines? Using an extended form of protection motivation theory (PMT) (Rogers, 1983), we found that even if teenage computer users believe they are susceptible to being hacked, or that being hacked would be detrimental, it has no bearing on their password choices. Other motives outside of PMT also drive teenage security behaviour. Personal norms fully mediate the relationship between the perceived severity of threat and compliance intentions such that perceived severity is not sufficient to encourage compliance. Teenagers must actually feel obligated to comply. While personal norms may encourage compliance, concerns about feeling embarrassed or ashamed if their social media accounts are hacked into actually encourages compliance. On the other hand, peer influence, such as the fear of being teased about someone hacking into their account, discourages compliance. Our study contributes to understanding early security practices and highlights potential differences between adult and teenage behaviours to consider in future studies. For example, our findings suggest that password security guidelines alone will not suffice to ensure teenage compliance; they may need enforced password rules at the authentication level to eliminate any opportunity to violate password rules. Our study will benefit children and parents as well as organizations that have changed work practices to enable employees to work from home, but which places children in danger of clicking on malicious links on their parents’ computers. To our knowledge, this is the first password security study that applies PMT to examine computer-based security behaviours in teenagers.
A Switch Based Resource Management Method for Energy Optimization in Cloud Data Center

Proliferation of large number of cloud users steered the exponential increase in number and size of the data centers. These data centers are energy hungry and put burden for cloud service provider in terms of electricity bills. There is environmental concern too, due to large carbon foot print. A lot of work has been done on reducing the energy requirement of data centers using optimal use of CPUs. Virtualization has been used as the core technology for optimal use of computing resources using VM migration. However, networking devices also contribute significantly to the responsible for the energy dissipation. We have proposed a two level energy optimization method for the data center to reduce energy consumption by keeping SLA. VM migration has been performed for optimal use of physical machines as well as switches used to connect physical machines in data center. Results of experiments conducted in CloudSim on PlanetLab data confirm superiority of the proposed method over existing methods using only single level optimization.
3D reconstruction of the specular surface using an iterative stereoscopic deflectometry method

Phase measuring deflectometry (PMD) is an effective technique for three-dimensional measurement of specular surfaces. However, the ambiguity of monoscopic PMD and the time-consuming searching process of stereoscopic PMD are challenges for specular surface reconstruction. To solve it, we propose an iterative reconstruction algorithm for the stereoscopic phase measuring deflectometry system free of the time-consuming searching process for each pixel. An arbitrary seed point on the specular surface is accurately obtained via a coarse-to-fine optimization means without any other expensive and complicate auxiliaries. Then, a plane with the height of seed point is set as the initial surface form for the iteration, in which the pinhole model is used to find the linear relation to update the surface form. The converging height is the output as the final result. Simulations and experiments verify the feasibility and efficiency of our proposed method based on the stereoscopic phase measuring deflectometry system. The accuracy and robustness are comprehensively evaluated as well.
Trends in data mining research: A two-decade review using topic analysis

This work analyses the intellectual structure of data mining as a scientific discipline. To do this, we use topic analysis (namely, latent Dirichlet allocation, DLA) applied to the proceedings of the International Conference on Data Mining (ICDM) for 2001–2019. Using this technique, we identified the nine most significant research flows. For each topic, we analyse the dynamics of its popularity (number of publications) and influence (number of citations). The central topic, which unites all other direction, is General Learning, which includes machine learning algorithms. About 20% of the research efforts were spent on the development of this direction for the entire time under review, however, its influence has declined most recently. The analysis also showed that attention to topics such as Pattern Mining (detecting associations) and Segmentation (object separation algorithms such as clustering) is decreasing. At the same time, the popularity of research related to Recommender Systems, Network Analysis, and Human Behaviour Analysis is growing, which is most likely due to the increasing availability of data and the practical value of these topics. The research direction related to practical Applications of data mining also shows a tendency to grow. The last two topics, Text Mining and Data Streams have attracted steady interest from researchers. The results presented here shed light on the structure and trends of data mining over the past twenty years and allow us to expand our understanding of this scientific discipline. We can argue that in the last five years a new research agenda has been formed, which is characterized by a shift in interest from algorithms to practical applications that affect all aspects of human activity.
Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Feature selection has been widely used in data mining and machine learning. Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly. In thi... | Find, read and cite all the research you need on Tech Science Press
Classifying Scientific Publications with BERT - Is Self-attention a Feature Selection Method?

We investigate the self-attention mechanism of BERT in a fine-tuning scenario for the classification of scientific articles over a taxonomy of research disciplines. We observe how self-attention focuses on words that are highly related to the domain of the article. Particularly, a small subset of vocabulary words tends to receive most of the attention. We compare and evaluate the subset of the most attended words with feature selection methods normally used for text classification in order to characterize self-attention as a possible feature selection approach. Using ConceptNet as ground truth, we also find that attended words are more related to the research fields of the articles. However, conventional feature selection methods are still a better option to learn classifiers from scratch. This result suggests that, while self-attention identifies domain-relevant terms, the discriminatory information in BERT is encoded in the contextualized outputs and the classification layer. It also raises the question whether injecting feature selection methods in the self-attention mechanism could further optimize single sequence classification using transformers.
Smartphone-Based Learning Media on Microscope Topic for High School Students

One of the learning media with mobile technology that can be used for education is 'smartphone.'This study developed a smartphone-based learning media capable of supporting physics learning on Microscope topics for XI grade high school students with a specialization in Mathematics and Natural Sciences (MIPA).This development product is in the form of teaching materials that can be accessed through an application on a smartphone.This learning media is validated by media experts and content experts.The development model used is modified Borg & Gall with seven stages, namely the preliminary research stage and information gathering, the planning stage, the preliminary product development stage, the design validation, the main product revision, the main field testing, and the operational product revision stage.The data obtained were analysed using equations and interpreted in the interpretation of the validation results assessment score.This development results reveal the validity value of smartphone-based learning media by content experts and media experts.The validity of media expert's states that it is valid for product development and the validity of material expert's states that the content developed in learning media is very valid.The accumulated student response to the application of smartphonebased learning media is very good.
Blockchain is dead! Long live Blockchain!

A decade on since Satoshi's Bitcoin paper, Blockchain is now considered to be sliding into the trough of Gartner's hype cycle.Claims in regard to Blockchain and Cryptocurrencies being dead are on the rise, whilst at the same time many claim the contrary.The vague statement encapsulates many different aspects and perspectives of a myriad of use cases, technology and platforms including both the technique as a whole and as individual instantiations.In this paper, we unpack the statement, break it down and investigate objectively concrete factors which provide indication in regard to whether Blockchain is dead.We examine metrics including budgets and investment; company registries and data; community engagement, projects and source code repositories; academic research and programmes; social media posts; and public interest.We individually demonstrate metrics that indicate the respective measures' healthy activity and come to the conclusion that the collective statement 'Blockchain is dead' does not hold.A clear message extracted from the work proposed herein is that success is achieved where the community comes together rather than works in isolation.
Theoretical and Practical Aspects of the Implementation of Project Training in the University

The article discusses the theoretical and practical aspects of implementing the project-based teaching method in universities.There is a slight and ambiguous difference in effectiveness between the traditional and projectbased teaching methods, which depends on the specific educational direction.The research problem is the implementation of a project-based teaching technology as a set of methods for solving problems posed within the framework of a certain activity.Such activity should be a complex of interrelated activities aimed at solving the assigned tasks, which in the design method coincide with the pedagogical tasks.The purpose of this study is to identify facts that make it possible to represent the effectiveness of the implementation of project-based education in the practice of the university.Research methods -a comprehensive analysis of scientific articles and statistical data on the practice of implementing project-based education in universities.The effectiveness of the design method was investigated for undergraduates of the technological faculty of the university.There have also been four comprehensive reviews of the effectiveness of the project method in medical education.Interdisciplinary projects with the active participation of students and teachers are considered.Most of the results of research on the effectiveness of project-based learning in higher education agree that one of the key components of the project-based method is group orientation.The main results of the study indicate that the practice of introducing project-based education in universities shows a predominantly positive result.The novelty of the research is expressed by an objective examination of various problematic aspects in the practice of implementing the project method in universities, taking into account the experience of foreign countries.The significance of the research lies in obtaining objective data that serve as the basis for further research in this direction.
Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.
Development of a web-based contact tracing and point-of-care-testing workflow for SARS-CoV-2 at a German University Hospital

Abstract Introduction In late 2019, a novel coronavirus was detected in China. Supported by its respiratory transmissibility, even by people infected without symptomatic disease, this coronavirus soon began to rapidly spread worldwide. Background Many countries have implemented different infection control and containment strategies due to ongoing community transmission. In this context, contact tracing as well as adequate testing and consequent quarantining of high-risk contacts play leading roles in containing the virus by interrupting infection chains. This approach is especially important in the hospital setting where contacts often cannot be avoided and physical distance is usually not possible. Furthermore, health care workers (HCWs) usually have contact with a variety of vulnerable people, making it essential to identify infections among hospital employees as soon as possible to interrupt the rapid spread of SARS-CoV-2 in the facility. Several electronic tools for contact tracing, such as specific software or mobile phone apps, are available for the public health sector. In contrast, contact tracing in hospitals often has to be carried out without helpful electronic tools, and an enormous amount of human resources is typically required. Aim For rapid contact tracing and effective infection control and management measures for HCWs in hospitals, adapted technical solutions are needed. Methods In this study, we report the development of our containment strategy to a web-based contact tracing and rapid point-of-care-testing workflow. Results/conclusion Our workflow yielded efficient control of the rapidly evolving situation during the SARS-CoV-2 pandemic from May 2020 until January 2021 at a German University Hospital.
Four‐Photon Greenberger–Horne–Zeilinger Entanglement via Collective Two‐Photon Coherence in Doppler‐Broadened Atoms

Abstract Bright, entangled multiphoton sources based on atom–photon interactions are an essential requirement in the realization of several quantum information and quantum computation schemes based on photonic quantum systems. Here, a four‐photon polarization‐entangled Greenberger–Horne–Zeilinger (GHZ) state obtained from Doppler‐broadened atomic ensembles of 87 Rb atoms is experimentally demonstrated. Owing to collective two‐photon coherence in the Doppler‐broadened cascade‐type atomic system, the setup enables the generation of robust four‐photon GHZ states with a fidelity of 82.1% and a measured four‐photon average coincidence rate of 0.58 s −1 . It is believed that the generation of such bright and stable multiphoton GHZ states from atomic media is an important step toward realizing photonic quantum computation and practical quantum networks based on atom–photon interactions.
Meningkatkan Rasa Percaya Diri Mahasiswa dalam Speaking English Melalui Kegiatan English BootCamp

In today's era of globalization, English competence is an essential aspect for the young generation to collaborate and compete globally. This activity is necessary because students often feel reluctant and insecure when communicating by using English in and outside the campus because they are afraid of making mistakes in pronunciation or grammatically. This community service activity aimed to increase students' motivation and self-confidence in Speaking English through the English BootCamp. English BootCamp is an outdoor activity that aims to give students a new atmosphere through more exciting activities. This activity was attended by 60 students who are the new members of Stikes Banyuwangi English Club. The expected outcome was that the students are more motivated to sharpen their skills and confidence in speaking English even though they majored in medicine. The methods used were games, discussion, songs, role play, and Free Talking. Students are very enthusiastic about joining this program because they provide a new experience with an outdoor atmosphere that is more relaxed, not boring, and not monotonous with various fun activity methods. Students become more courageous and have high self-confidence in Speaking English. They are more active in asking questions about vocabulary/expressions they do not understand, and they show their efforts by being actively involved in every activity. English BootCamp activity can change the students' mindset if Speaking English is not as difficult as imagined. This activity not only increases students' motivation and confidence but also their English skills.