Reviewerly/exHarmony huggingface dataset:
Component Profiling and Prediction Models for QoS-Aware Self-Adapting DSMS Framework

Quality of Service (QoS) has been identified as an important attribute of system performance of Data Stream Management Systems (DSMS). A DSMS should have the ability to allocate physical computing resources between different submitted queries and fulfil QoS specifications in a fair and square manner. System scheduling strategies need to be adjusted dynamically to utilise available physical resources to guarantee the end-to-end quality of service levels. In this paper, we present a proactive method that utilises a multi-level component profiling approach to build prediction models that anticipate several QoS violations and performance degradations. The models are constructed using several incremental machine learning algorithms that are enhanced with ensemble learning and abnormal detection techniques. The approach performs accurate predictions in near real-time with accuracy up to 85% and with abnormal detection techniques, the accuracy reaches 100%. This is a major component within a proposed QoS-Aware Self-Adapting Data Stream Management Framework.
An Empirical Study of Architectural Changes in Code Commits

The maintenance of software architecture is challenged by fast-delivery code changes since developers are rarely aware of the architectural impacts of their code changes. To ease the burdens of architects, in this work, we proposed a light-weight framework to identify changes in architectures from code commits automatically. The framework identifies architectural changes without heavy architecture recovery techniques. Instead, it only takes a code commit as input. The framework, on the one hand, can be integrated into prevalent continuous integration systems to monitor architectural changes. On the other hand, it can be plugged into code review systems to help developers realize the architectural changes they introduce. Based on the framework, we further conducted a large-scale empirical study on 368,847 commits of 16 Apache open projects to study architectural changes. Our study reveals several new findings regarding the frequency of architectural change commits, the common and risky intents under which developers introduce architectural changes, and the correlations of architectural changes with lines of code and number of modified source files in commits. Our findings provide practical implications for software contributors and shed light on potential research directions on architecture maintenance.
Metro Environmental Assessment and Analysis Based on Inclusive Design

Under the background of accelerating urbanization, increasing aging population and decreasing fertility rate in China, all sectors of society eager for an elderly-and-children friendly environment.As public transportation, the metro is at the frontline.This article takes promoting the inclusive design of the metro environment as the research goal, inclusive design as the research perspective and progressive research as the research method.It analyzes the significance of the inclusive design of metro through pyramid theory, employs personas classification to define the inclusive group, and includes user journey to explore the touch-point of metro ride experience so as to propose a metro inclusiveness evaluation kit.With the help of the toolkit, six metro lines and 42 stations in Hangzhou were evaluated and analyzed by using the method of mobile ethnography.Based on the macro and micro analysis of horizontal comparison, it offers solutions to Hangzhou Metro's existing problems and optimizes the inclusive performance, which can provide direction for the evaluation and optimization of the inclusive design of domestic metro construction.
Towards an XAI-Assisted Third-Party Evaluation of AI Systems: Illustration on Decision Trees

We explored the potential contribution of eXplainable Artificial Intelligence (XAI) for the evaluation of Artificial Intelligence (AI), in a context where such an evaluation is performed by independent third-party evaluators, for example in the objective of certification. The experimental approach of this paper is based on “explainable by design” decision trees that produce predictions on health data and bank data. Results presented in this paper show that the explanations could be used by the evaluators to identify the parameters used in decision making and their levels of importance. The explanations would thus make it possible to orient the constitution of the evaluation corpus, to explore the rules followed for decision-making and to identify potentially critical relationships between different parameters. In addition, the explanations make it possible to inspect the presence of bias in the database and in the algorithm. These first results lay the groundwork for further additional research in order to generalize the conclusions of this paper to different XAI methods.
Securely Sharing Randomized Code That Flies

Address space layout randomization was a great role model, being a light-weight defense technique that could prevent early return-oriented programming attacks. Simple yet effective, address space layout randomization was quickly widely adopted. Conversely, today only a trickle of defense techniques arebeing integrated or adopted mainstream. As code reuse attacks have evolved in complexity, defenses have strived to keep up. However, to do so, many have had to take unfavorable tradeoffs like using background threads or protecting only a subset of sensitive code. In reality, these tradeoffs were unavoidable steps necessary to improve the strength of the state of the art. In this article, we present Mardu , an on-demand system-wide runtime re-randomization technique capable of scalable protection of application as well as shared library code that most defenses have forgone. We achieve code sharing with diversification by implementing reactive and scalable rather than continuous or one-time diversification. Enabling code sharing further removes redundant computation like tracking and patching, along with memory overheads required by prior randomization techniques. In its baseline state, the code transformations needed for Mardu security hardening incur a reasonable performance overhead of 5.5% on SPEC and minimal degradation of 4.4% in NGINX, demonstrating its applicability to both compute-intensive and scalable real-world applications. Even when under attack, Mardu only adds from less than 1% to up to 15% depending on application size and complexity.
Guidance and Public Information Systems: Diagnosis and Harmonization of Tourist Signage

The greater influx of tourists to certain territories raised the need to inform, guide and communicate basic messages, with a universal language, expressed through various analog and digital supports. This proliferation of means has led to a profusion of guidance and public information systems, some of which are regulated (such as the traffic signs system, signaling of routes and paths or the signaling of protected areas) and other unregulated systems (such as the signaling of religious, equestrian, municipal, historical, heritage and cultural routes, etc.). This article is the result of an ongoing research project, which aims to systematize tourist signage in Portugal, in order to satisfy and induce the potential or real demand for guidance, direction and information of tourists, in a clear and precise way, in addition to transmitting a unique and homogeneous image of the tourist product. Its scope will provide a clear reference framework, which facilitates the implementation of a normalized system by the organizations responsible for the standardization and implementation of tourist signage in Portugal.
Combination Ultrasound and Mammography for Breast Cancer Classification using Deep Learning

The most widely used methods for early detection of breast cancer are Ultrasound and Mammography. However, single ultrasound or single mammography shows false classification that causes unnecessary biopsy. Therefore, the combination approach is proposed to improve breast cancer classification using the deep learning technique. The proposed method has been divided into two steps. First, images are randomly combined using the k-combination method. Second, deep learning based on MobileNet is used to classify breast tumors. The result demonstrated that the combination approach produces a variety of patterns and a large image dataset and improves the accuracy. In addition, the false positive tend to reduce by 13% and the false negative tend to reduce by 14%. It is useful to avoid unnecessary surgery and to plan aggressive treatment.
Integrating University Computing Laboratories with AWS for Better Resource Utilization

Computing and storage are prime requirements for running and storing the business processes or other applications on cloud. With the emergence of Industry 4.0, all applications are moving to cloud infrastructure because of the continuously increasing demand for more computing power and storage offered by individual machines. Cloud computing also offers better security, management and costing. Generally, a lot of computer laboratories of a university sit idle during the night time, and hence, it can be utilized effectively by cloud infrastructure, especially during the day time of other countries. In this paper, a model is suggested to enhance the utilization of the computer laboratory resources of various universities located in different countries by integrating them with AWS cloud computing platforms. This paper discusses implementation and deployment of models using VMware vSphere ESXI, vCenter server which is configured in computer laboratory and AWS.
The Security Situation Prediction of Network Mathematical Neural Model Based on Neural Network

With the gradual development of the modern neural network field, the construction of network mathematical models related to neural networks has become more important, but when using network mathematical models, it is often impossible to achieve strict and accurate theoretical support and data analysis. For the security situation prediction, specific strategies to improve the neural model of network mathematics should be proposed to improve the status quo that network mathematics cannot meet the status quo of scientific research. In the quantitative evaluation of the factors to be considered when selecting the target, the mathematical security situation prediction method will be used efficiently, and the membership of each factor will be determined according to the method and principle of the network mathematical neural model. Through functional analysis and quantitative selection, a mathematical model is established, and the BP algorithm is combined with the neural network algorithm. This paper takes neural network as the theoretical basis of the research, uses BP fuzzy neural algorithm as the main research algorithm, and integrates its important content to analyze and research the optimization of adaptive enhancement algorithm. This paper takes the classical network mathematical neural model as the research object, and optimizes and improves the security situation prediction of the network mathematical neural model. The BP fuzzy neural algorithm in the neural network can be regarded as a kind of node sorting algorithm, so it can be used to construct the network mathematical neural model. The experimental results show that this research uses the network mathematical neural model and the BP fuzzy neural algorithm parameters to estimate the neural network prediction value and calculate the safety situation prediction, and uses the neural network method to test the network mathematical neural model from the safety situation prediction error of the BP fuzzy neural algorithm. The method in the target selection is correct and feasible.
КОНФЛІКТ ОБИВАТЕЛІВ І ТВОРЦІВ У РОМАНІ ДЖ. ФАУЛЗА «КОЛЕКЦІОНЕР»: ПРОБЛЕМНО-ТЕМАТИЧНИЙ АНАЛІЗ

The article deals with the confrontation of two worlds – the world of ordinary people and creators in the novel "The Collector" by the English writer-postmodernist J. Fowles. The aim of this study is to prove that the conflict between consumers and enthusiastic creators is one of the main themes of the novel "Collector", as well as to emphasize the writer's hidden appeals and warnings about the need to protect the vulnerable and vulnerable world of creators from the external correct and mechanistic world. consumers. Within the topic, it is important to generalize and adapt scientific and theoretical material on this issue for students of philology. The research technique consists in extrapolating the method of "practical criticism" (A.A. Richards, S. Johnson, M. Arnold, T.S. Eliot, F.R. Lewis) to the literary text of J. Fowles' novel "The Collector". In particular, it is assumed to read the text in accordance with the moral criteria and analyze the problems of content (clarification of the moral guidelines of the author). Results of the research. It is proved that J. Fowles in the novel "The Collector" depicted two opposite worlds – ordinary people and creators. The world of creative enthusiasts symbolizes a full life with all the richness of ideas, emotions, contradictory and complex feelings inherent in human search, and the world of ordinary people embodies a dim imitation of real life, "mechanized" existence, in which there are no creative impulses, creative initiative. Miranda is found to represent the world of creators, she is a man of search, she lives, choking on emotions and feelings, intuitively realizing that this is the meaning of life. Clegg summarizes the world of the townspeople. He is an ambitious, limited tyrant, full of hidden malice and hatred for those who are spiritually richer and smarter. At the same time, the writer showed the vulnerability and insecurity of the world of beauty and culture, recalling the eternity of the confrontation of love and hate, good and evil, creative living beginnings and hard, mundane existence. The practical significance of the research results lies in the possibility of their use in the further study of the literary heritage of J. Fowles, as well as in the preparation of students of philology for practical and seminar classes.
Part of Speech and Gramset Tagging Algorithms for Unknown Words Based on Morphological Dictionaries of the Veps and Karelian Languages

This research devoted to the low-resource Veps and Karelian languages. Algorithms for assigning part of speech tags to words and grammatical properties to words are presented in the article. These algorithms use our morphological dictionaries, where the lemma, part of speech and a set of grammatical features (gramset) are known for each word form. The algorithms are based on the analogy hypothesis that words with the same suffixes are likely to have the same inflectional models, the same part of speech and gramset. The accuracy of these algorithms were evaluated and compared. 66 thousand Karelian and 313 thousand Vepsian words were used to verify the accuracy of these algorithms. The special functions were designed to assess the quality of results of the developed algorithms. 86.8% of Karelian words and 92.4% of Vepsian words were assigned a correct part of speech by the developed algorithm. 90.7% of Karelian words and 95.3% of Vepsian words were assigned a correct gramset by our algorithm. Morphological and semantic tagging of texts, which are closely related and inseparable in our corpus processes, are described in the paper.
Cardiac function review by machine learning approaches

Hardware limitations imposed by medical imaging systems can hinder the diagnosis and treatment of cardiac-related pathologies. In the last two decades, computer-aided systems for image processing have been developed to promote these procedures and provide improved clinical outcome. This chapter offers a brief description of these theoretical structures and their applications in segmental cardiac imaging for segmentation, image enhancement, and multimodal image alignment. These analytical methods share common goals, such as time efficiency, quantitative objective evaluation, enhancement, and analysis of multimodal image data. In this survey, we concentrate on how the learning of image representation will lead to these goals and improve the accuracy and robustness of the techniques applied. Our main emphasis is especially on anatomical priors in the segmentation of medical images and also on intermediate feature spaces used in multimodal image registration and super-resolution problems. In addition, this chapter addresses computational strategies focused on machine learning (ML), which have recently gained popularity in the medical imaging community with the advent of annotated datasets. We present two widely used models in this regard and address their aspects in terms of design versatility, model parameterization, and functional aspects (e.g., specifications for computation). Later, an overview of ML applications in cardiac imaging is given, which also includes other areas of application such as landmark localization and control of image quality.
Teaching Schoolchildren to Program in Python Environment in Online Communities

The article deals with the issues of teaching the schoolchildren of the online communities of computer programming in Python environment with the use of the systems of Learning Management Systems and the services of the Internet: Google Docs and Google Colabratory.
Value of communication as an integral feature of a modern society and a condition for its sustainable development

The relevance of our research is due to the processes taking place in a modern Russian society. Value orientation is the most important component of a human life, the basis for the life goals formation and, as a result, it acts as a motivation encouraging a person to behave according to his expectations. The values of a person of each epoch differ from each other as the external environment influences each of us including our spiritual world. Interpersonal communication and communication through various technological means is of great importance to a modern society. It is difficult for modern people to imagine themselves without a cellphone with dozens of apps installed and connected to the Internet. And this digital approach changes even the attitude towards a communication value, reconsiders it. The authors attempted to define a phenomenon of a communication value, classify the communication processes in a modern society, describe an impact of the communication processes within its value into an individual and explain a process of a revaluing under certain circumstances taking in account fundamental factors for the sustainable development of the world.
Research on Fault Diagnosis Method Based on Diffusion Map and Extreme Learning Machine

There is a non-linear relationship between the vibration signal characteristics of the bearing equipment and the bearing health status, and the single feature of the bearing cannot fully reflect the bearing health status, and its multi-dimensional features have information redundancy, which affects the performance of the classifier. In response to this problem, Diffusion Maps (DM) and Extreme Learning Machine (ELM) are applied to bearing fault diagnosis, and the signal is denoised by Complementary Ensemble Empirical Mode Decomposition (CEEMD); then, the DM algorithm is used to perform high-dimensional features Dimensionality reduction processing, and establishment of an ELM fault diagnosis model to identify the health status of the bearing. Bearing experiments show that this method can effectively identify the health status of bearings and reduce the redundancy of high-dimensional information.
Research on Factors Influencing Users’ Technology Acceptance of Virtual Museums

With the development of information technology and experience economy, virtual museums has become the digital development and extension of traditional museums. At the beginning of this year (2020), virtual museums have been rapidly popularized in the form of "cloud exhibition" in the epidemic situation, and have shown great market development potential. This study aims to analyze the factors influencing users' technology acceptance on virtual museums. 15 related factors have been defined through desktop research, among which the key elements and their internal association have been summarized through the method of DEMATEL. The results show that "Improving the quality of the tour", "Having a multi-sensory experience" and "Accessing to rich information" are the key influencing factors, while "Positive media comment" and "Assurance of the brand" are the main influenced ones. In the internal mechanism of users' technology acceptance of virtual museums, the perceived ease of use encompasses the main cause factors and has a strong impact on the dimensions of perceived pleasure, perceived usefulness and perceived safety. Based on the above findings, several suggestions have been proposed to help practitioners further enhance the use experience of virtual museums.
Public Perceptions and Attitudes Towards Driverless Technologies in the United States: A Text Mining of Twitter Data

Driverless technology offers the promise of providing safer and more efficient transportation in US cities. To promote public adoption of driverless vehicles, governments need to understand the public perceptions and attitudes towards driverless technologies and the factors that influence them. Twitter data offers a means to capture these insights. In this study, we performed text mining of tweets about driverless technology in the U.S. through topic modeling and sentiment analysis. We uncovered a set of five latent themes embedded in the tweets, consisting of Safety Perception, Technology Development, Industrial/System Integration, Design and Functionality, and Ethics and Policy, and the public’s attitudes of positive and negative towards each of them. The findings indicate that Ethics and Policy, Safety, and Design and Functionality are of major concern that may prohibit the acceptance of driverless vehicles. Insights gained from this analysis would support decision-making, implementation, and prompt communication strategies about driverless vehicles.
Research on Construction of Network Teaching System for Adult College

The network teaching system is an important part of the information system of adult college. In order to build an efficient network teaching system, this paper takes the system of Jingan branch campus of Shanghai Open University as an example to introduce the requirement analysis, architecture design, function models and implementation of the network teaching system. The results show that the system can help teachers has good adaptability and efficiency. The research results can provide reference for the construction of network teaching system of other adult colleges in the future.
Discussion on Data Mining Behavior of Search Engine Under Robots Protocol

With the rapid development of cloud computing, the phenomenon of temporary replication is becoming more and more common. The definition of its nature and legal regulation has become the focus of debate at home and abroad. Temporary reproduction belongs to the reproduction in the sense of copyright law, which should be included in the scope of copy right regulation and restricted by joint use system.