Reviewerly/exHarmony huggingface dataset:
Computer Age Statistical Inference: Algorithms, Evidence, and Data ScienceBradleyEfron and TrevorHastie Institute of Mathematical Statistics Monographs Cambridge University Press, 2016, (8th printing 2018), xix + 475 pages, $74.99, hardcover ISBN: 978‐1‐107‐14989‐2

International Statistical ReviewVolume 87, Issue 1 p. 186-188 Book Review Computer Age Statistical Inference: Algorithms, Evidence, and Data Science Bradley Efron and Trevor Hastie Institute of Mathematical Statistics Monographs Cambridge University Press, 2016, (8th printing 2018), xix + 475 pages, $74.99, hardcover ISBN: 978-1-107-14989-2 Reijo Sund, Corresponding Author Reijo Sund reijo.sund@uef.fi Institute of Clinical Medicine, University of Eastern Finland (UEF), Kuopio, FinlandSearch for more papers by this author Reijo Sund, Corresponding Author Reijo Sund reijo.sund@uef.fi Institute of Clinical Medicine, University of Eastern Finland (UEF), Kuopio, FinlandSearch for more papers by this author First published: 05 April 2019 https://doi.org/10.1111/insr.12320Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume87, Issue1April 2019Pages 186-188 RelatedInformation
The Effects of Social and Spatial Presence on Engagement in a 3D Virtual Reality Environment: An Empirical Investigation

Virtual reality (VR) provides opportunities for businesses to innovatively engage customers.Based on presence theory, a research model was developed to test the influence of two major components of presence, social presence and spatial presence, on users' perceptions of hedonic value, utilitarian value, and engagement.An experiment was conducted on two conditions of a VR application (low vs. high social presence) to test the hypotheses in the research model.The results reveal that social presence and spatial presence can improve hedonic value of VR.However, inconsistent with previous studies, our findings reveal a negative relationship between spatial presence and engagement.Theoretical and practical implications, as well as future research directions are subsequently discussed.
KupC: A Formal Tool for Modeling and Verifying Dynamic Updating of C Programs

Dynamic Software Updating (DSU) is a useful technique for updating running software without incurring any downtime. Its correctness must be guaranteed because updating a running software is a complicated and safety-critical process. In this paper, we present a formal tool called KupC for modeling and verifying dynamic updating of C programs. The tool is built on $$\mathbb {K}$$ –a formal semantic framework for programming languages. We formalize a patch-based dynamic updating mechanism in $$\mathbb {K}$$ based on the formal executable operational semantics of C. The formalization automatically yields an interpreter and several verification tools, which can be used to formally analyze the correctness of dynamic updating for C programs. To our knowledge, KupC is the first formal tool for code-level verification of dynamic software updating.
Efficient virtual data center request embedding based on row-epitaxial and batched greedy algorithms

Data centers are becoming the main backbone of and centralized repository for all cloud-accessible services in on-demand cloud computing environments. In particular, virtual data centers (VDCs) facilitate the virtualization of all data center resources such as computing, memory, storage, and networking equipment as a single unit. It is necessary to use the data center efficiently to improve its profitability. The essential factor that significantly influences efficiency is the average number of VDC requests serviced by the infrastructure provider, and the optimal allocation of requests improves the acceptance rate. In existing VDC request embedding algorithms, data center performance factors such as resource utilization rate and energy consumption are not taken into consideration. This motivated us to design a strategy for improving the resource utilization rate without increasing the energy consumption. We propose novel VDC embedding methods based on row-epitaxial and batched greedy algorithms inspired by bioinformatics. These algorithms embed new requests into the VDC while reembedding previously allocated requests. Reembedding is done to consolidate the available resources in the VDC resource pool. The experimental testbed results show that our algorithms boost the data center objectives of high resource utilization (by improving the request acceptance rate), low energy consumption, and short VDC request scheduling delay, leading to an appreciable return on investment.
Analysis of Seismic Wave Selection in Time History Analysis

By introducing a series of problems in time history analysis, such as the selection principle of input seismic wave, the amplitude and frequency characteristics of ground motion, beginners can have a preliminary understanding of the selection of input seismic wave and lay a foundation for further research.
Successful Mobile Application Development: Towards a Taxonomy of Domain-Specific Process Models and Methodologies

Mobile applications and mobile application development issues receive an increasing attention for practitioners and academics.The development of mobile applications is connected with a number of domain-specific issues and challenges (e.g., fulfilment of customer requirements or the prevention of high development costs).Consequently, the decision of the most effective process model to develop a mobile application plays a crucial role for software and mobile application development teams.With the help of a structured taxonomy-building methodology, we contribute to the extant literature by creating and presenting a taxonomy for process models and methodologies in software engineering and the mobile application development domain.The taxonomy enrich the existing knowledge base and can help mobile application developers to choose the most suitable process model or methodology.Based on our examination, our results indicate new directions for mobile application research and implications for mobile application development.
Human Interpretation of Trade-Off Diagrams in Multi-Objective Problems: Implications for Developing Interactive Decision Support Systems

The growing need for efficient and effective human decision-makers warrants a better understanding of how decision support systems (DSS) guide users to improved decisions. Decision support approaches utilize visual aids to assist decision-making, including trade-off diagrams. These visualizations help comprehension of key trade-offs among decision alternatives. However, little is known about the role of trade-off diagrams in human decision-making and the best way to present them. Here, we discuss an empirical study with two goals: 1) evaluating DSS interactivity and 2) identifying decision-making strategies with trade-off diagrams. We specifically investigate the value of interface interactivity and problem context as users make nine increasingly complex decisions. Our results suggest that problem context and interactivity separately influence ability to navigate trade-off diagrams.
Design and Development of Login Security System Using Radio Frequency Identification

System security is important in information systems to prevent unauthorized users from accessing data. Login system applies security using encrypted passwords stored on RFID cards. This research designed login security system storing encrypted password using MD5 encryption into the Mifare Tag RFID card and equipped NFC reader to read data from RFID Card. By storing encrypted password characters on RFID cards, login system security is stronger and cannot be traced by unauthorized parties to log into systems. Some stage of system design are through study of literature, designing process flow, system algorithms, designing encryption methods and system interfaces, writing card module coding, card reading module coding, implementation, and system testing. The system login applied by scanning RFID card on the NFC reader, if the password on RFID matches then the user successfully logs into the system. Based on the testing of RFID Tag readings, the maximum distance from the reading of RFID Tag cards is up to 7 cm with a reading range of 00 to 300 with a success rate of 100% authentication. By using RFID Tag cards, increase security for logging into the system, because user cannot log in without having a card with the appropriate password.
Research on Modulation Recognition Technology based on Machine learning

This paper presents a method for modulation recognition of digital signals using machine learning.This method first extracted seven characteristic parameters according to the instantaneous parameters and high-order cumulant characteristics of the signal, then combined the decision tree classifier, neural network classifier and support vector machine classifier in machine learning with these characteristic parameters, and finally realized the modulation recognition of MASK, MFSK, MPSK and MQAM signals.This method not only has low computational complexity and more recognized signals, but also improves the recognition rate at low SNR.
Increasing STEM undergraduate participation in innovative activities: Field experimental evidence

Workers trained in STEM are generally viewed as essential for innovation-led economic growth. Yet, recent statistics suggest that a majority of STEM undergraduates do not go on to pursue innovation-focused careers in their fields of study. We investigate whether STEM students who do not self-select into innovative tasks are doing so because they are less capable than their peers who do. We find that monetary inducement among STEM students increases aggregate innovative output, but that low-GPA students who were induced significantly underperform relative to low-GPA students who self-selected; however, induced and self-selected high-GPA students perform statistically the same. In contrast, words of encouragement appears to benefit those students with the lowest GPAs. Our results highlight the value of efforts to increase the pool of STEM students who pursue innovative careers and underscore the importance of interventions targeted at specific student subgroups to maximize the returns on those efforts.
The Structure of Functioning of the Curricular Design Activity from the Perspective of Computer Science

Our study analyzes an epistemological and practically interdisciplinary issue at the intersection line between curriculum theory and Computer Science.We will consider an ideal model of curricular design that can be computer-aided at system level and educational process.The general aim is to value the educational backgrounds of the curriculum, identified and developed at the level of education's endowments, by means of appropriate informational means: a) macrostructural, valid at the scale of the entire education system; b) microstructural, valid for the entire educational process.The specific objectives aim at the pedagogical and informatic analysis of the correlation between: a) the macrostructural finality of education and the curriculum project of the reform of education; b) microstructural finalities of instruction and curriculum projects promoted at all disciplines and levels of education.The fulfillment of these two specific objectives implies the capitalization of adequate informational means fixed normative and methodological at the level of fundamental concepts: data, information, database, algorithm, networks, external and internal feedback.At the level of the curriculum, the training must ensure: a) the deduction of the concrete objectives from the learning objectives of the unit, the chapter, etc. -validated by computer, by fixing the database; b) logical ordering of contents -computer-validated by the positive formative generated effects; c) permanent pedagogical correlation between objectives -basic contents -methodsevaluation, validated by using efficient algorithms under existing network conditions or created
A fast discovery algorithm for large common connected induced subgraphs

We present a fast algorithm for the classical problem of finding common subgraphs, which are useful for detecting structural relationships between biological macromolecules. Although the cost is potentially high for this hard problem, we improve performance by several orders of magnitude compared to known algorithms. We validate our findings with experiments on proteins with thousands of atoms.
Regression with Support Vector Machines and VGG Neural Networks

In the area of machine learning, classification tasks have been well studied, while another important application of regression is not of the same level. In this paper, we propose parameterization and settings obtained from multiple experiments for traditional supervised machine learning of Support Vector Machine (SVM) and recently widely used deep unsupervised learning technology of Convolutional Neural Networks (CNN) based on Visual Geometry Group (VGGNet). In this study, different dataset used for regression task have been adopted. We have experimented on six data types obtained from the UCI Machine Learning Repository, and one converted handwritten image dataset from the MNIST. Accuracy of the regression results generated by the proposed models are validated with statistical methods of Mean Absolute Error (MAE) and R_square, i.e. coefficient of determination. Experimental results demonstrate that VGG has clear advantages over SVM in the cases of image recognition and attributes with strong correlation, and SVM performs better in the cases of discrete, irregular and weak correlation data than. By comparing the three kernel functions of SVM, it is found that in most cases, Rbt kernel function performs more effectively than Linear and Poly ones.
Research on Chaotic Time Series Prediction Model for Building Energy Consumption

Aiming the problem that the accuracy of current building energy consumption prediction method depends heavily on environmental parameters (air temperature, air pressure, humidity, etc.), a short-term prediction model of time series building energy consumption based on Chaos-BP is proposed. Firstly, the optimal delay time and embedding dimension of data samples are obtained by CC method. And the small data amount method is used to prove that the building energy consumption has chaotic characteristics. Secondly, the input structure of BP neural network is determined by phase space reconstruction, and again the middle school building is the research object. Finally, the Matlab software is used as the simulation tool to simulate the Chaos-BP prediction model and the BP prediction model respectively. The experimental results show that the building energy consumption has chaotic characteristics. Compared with the BP neural network prediction model, the Chaos-BP model can accurately predict the building energy consumption and provide a scientific basis for the development of building energy conservation work.
Teaching Vocabulary Techniques Through Letterland in Star Kiddy Preschool Semarang

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Application Software Cybersecurity Scanning

Scanning software applications for cybersecurity vulnerabilities is a crucial step is assessing the overall health of the application, but how can this kind of scan be performed to give development teams the information they need to make informed design decisions?Two pilot cybersecurity scans were conducted in an attempt to answer this question.A scanning team composed of various subject matter experts was established and worked closely with the development teams to perform these scans and capture metrics throughout the process.These interactions and metrics indicate that these scans can be performed in an unobtrusive way and still provide valuable information to development teams regarding the health of their application.This work is not definitive in nature but serves as a foundation for future work.
Risk Factors for Software Requirements Change Implementation

Requirements change has been regarded as a substantial risk in software development projects.The factors that contribute to the risk are identified through impact analysis, which later determine the planning of the change implementation.The analysis is however not straightforward as the risk factors that constitute requirements change implementation is currently not much explored.This paper identifies the risk factors by firstly collating them qualitatively through a review of related work and a focus group study.The factors are then confirmed quantitatively through a survey in which data is analysed by using Partial Least Squares Structural Equation Modelling (PLS-SEM).The survey comprise of 276 practitioners from software industry who are involved in the impact analysis.The results indicate that User, Project Team, Top Management, Third Party, Organisation, Identification of Change, Existing Product and Planning of Change Implementation are the significant risk factors in planning of requirements change implementation.
Methodical Training System Enhancements of Future Biology Teachers at Pedagogical Universities

Background. Methodical training plays a leading role in future biology specialists’ professional education; it is assumed to be a backbone component of future educators’ professional readiness in modern scientific research. The methodical training of future biology teachers is interpreted as a determined acquisition of methodological knowledge and skills in the context of methodical tasks related to school course of biology and their solving. New tendencies of school biology education development, implementation of innovative pedagogical technologies into teaching process, development of modern training resources provided the necessity to modernize methodical training of future biology educators in pedagogical universities.The objective of this research is giving a scientific ground to the methodical training system of future biology teachers and checking the effectiveness of its implementation at pedagogical universities.Methods. During the implementation of this research the complex of general scientific methods has been used: theoretical ones (scientific literature analysis, terminological analysis, comparison, systematization and generalization of scientific results), empirical ones (observation, discussions, questionnaire, testing for the determination of future biology educators’ method competence level, pedagogical experiment with qualitative and quantitative analysis of results), mathematical statistics method. The total number of 482 students of Ukrainian pedagogical universities were involved in the experiment.Results. After the implementation of pedagogical experiment the number of students with high level of methodical readiness has significantly increased (according to motivation component – by 9,7% cognitive one – by 5,8%, active one – by 6,2%, reflexive one – by 10,5%), with satisfactory level (according to motivation component – by 12,9%, cognitive one – by 25,2%, active one – by 19,3%, reflexive one – by 8,8%); the number of students with low level has decreased respectively (according to motivation component – by 15,9%, cognitive one – by 24,3%, active one – by 20,5%, reflexive – by 11,3%).Conclusion. According to the results of experiment it can be claimed that implementation of future biology teachers’ experimental methodical training system provides the enhancement of students’ methodical proficiency level and higher quality of their professional readiness at pedagogical universities of Ukraine.
Blockchain: A mechanism of “unorthodox” trust

Blockchain is a revolutionary technology which is expected to cut off proxies, reduce cost and increase speed and range. It offers transparency, safety and traceability and considerable high levels of trust. In contrast with traditional hierarchical structures and central data-bases where trust lies within institutions, blockchain’s trust is technology. It could possibly constitute the core of a new type of internet. The fact that it is developing rapidly in various fields simultaneously, will inevitably change the way of communication in the future. Although, since its onset, it was mainly used for financial exchange; it becomes evident that it is applicable in every form of exchange. Thus the new digital trust through encryption could have a determining role in redeveloping higher education. In such a context, students, instructors and teachers are in direct relationship with one another; as a result new forms of interaction are generated. The main objective is to examine on the one hand blockchain’s application in the educational sector, focusing on its potential to change educational mechanisms for the better and on the other hand its reception and the influence this technology could have on the socio-psychological reality of the involved subjects. Its application on the field of education will undoubtedly be beneficial but it is an imperative that we inspect any potential dangers as well. Since people have the need of an institution that they can trust, the creation of a world of decentralised trust is a challenge.
A Latent Dirichlet Allocation Approach using Mixed Graph of Terms for Sentiment Analysis

The spread of generic (as Twitter, Facebook or Google+) or specialized (as LinkedIn or Viadeo) social networks allows to millions of users to share opinions on different aspects of life every day.Therefore this information is a rich source of data for opinion mining and sentiment analysis.This paper presents a novel approach to the sentiment analysis based on the Latent Dirichlet Allocation (LDA) approach.The proposed methodology aims to identify a word-based graphical model (we call it a mixed graph of terms) for depicting a positive or negative attitude towards a topic.By the use of this model it will be possible to automatically mine from documents positive and negative sentiments.Experimental evaluation, on standard and real datasets, shows that the proposed approach is effective and furnishes good and reliable results.