Reviewerly/exHarmony huggingface dataset:
Spring Basics and Beyond

In Chapter 1 , you read about ways to persist the information in databases. Spring Data is the only one that offers the possibility to use relational and non-relational databases. Before discussing how the framework works behind the scenes, you need to know how Spring Framework works. In this chapter, you learn how to create a small project, which you use throughout this book.
Reactive Access

Most of the communication that an application establishes using HTTP connections implies a request, a specific process to obtain the response, and the response, which could be an exception or a type of information.
Transaction Management

As you saw in the previous chapters of this book, you can execute simple queries to obtain information. Still, there are other cases when the situation is complex and involves updating/inserting/deleting tables across the same database.
Research on the Reform and Practice of Online Teaching of Public Physical Education Courses in Universities in the Post-epidemic Era

The emergence of COVID-19 has brought severe challenges to all walks of life in society.At present, with the continuous promotion and development of the epidemic momentum, China has moved forward towards the post epidemic era. Under this special background, it has also brought certain impact on the development of public physical education courses in colleges and universities. Therefore, in the research, this paper mainly focuses on the online teaching ability of public sports in Colleges and universities, andanalyzes and studies it from four aspects: students, teachers, schools and platforms.Finally, through this form, college sports teaching can be better developed, and students' enthusiasm for physical exercise can be mobilized.
Innovation management in the construction industry based on an ontological model

Aim. The presented study develops an ontological model of innovation from the industrial perspective (through the example of the construction industry) reflecting the relationship between innovation and the goals of their implementation and management objects, which would ensure the optimization of the innovation management system in the long term and facilitate the introduction of a quality assessment and selection system for more efficient operation of organizations. Tasks. The authors analyze the existing terminology and types of innovations at construction enterprises; classify the objects of innovation management; develop an ontological model of innovation at construction enterprises in the form of a directed graph. Methods. This study is based on the theory of innovation management, elements of system analysis, practical foundations of the organizational life cycle, and graph theory. Results. The obtained results can be used both in building an innovation management model at construction enterprises and in applying the methodology for assessing the quality of innovation. The relationship between management goals, implemented innovations, and management objects is determined and presented in the form of an ontological model. Conclusions. Development of an ontological model makes it possible not only to determine the relationship between various elements of economic, scientific, technical, and innovation activities of a construction company, but also to develop approaches to assessing their quality and building an innovation management system. Further research should focus on the aspects of innovation implementation at construction enterprises, its technological features, and processes that can be transformed for the successful implementation of these innovations.
Exploratory Study on the Analysis of Domestic Firm Workers’ Employer Brand and Differences between Employees Those Who Recommend Their Firm and Those Who Do Not: Application of Structural Topic Modeling

This study applies Structural Topic Modeling to explore the text data from corporate reviews of the recruitment platform describing employer brands and to investigate whether such data have meaningful differences between the employees who recommend their firms and those who do not. Specifically, we examine the text data on the satisfactory factors of companies as described by incumbent employees who recommend and do not recommend their firms. Topic modeling derives 30 topics, among which 22 are considered appropriate by using a critical review. These topics are labeled considering previous research on employer brands, and the highest percentage are categorized as ‘horizontal corporate culture’. Significant differences are identified between current employees who recommend and do not recommend their firms. Most of the extracted topics are similar to the variables that are previously used in literature. However, new topics are also derived from our studies. Finally, the implications of study findings are discussed.
An Exploratory Study on Consumer Perceptions of Price, Quality, and Consumer Service for Personalized Products

In addition to technological development, the key to the success of personalized production is to increase customer acceptance by considering them as key strategic factors. However, there is a lack of attempts to approach smart factory from the end-user’s perspective, particularly with regard to personalized products that may be more expensive, of lower quality, and have lower customer service levels than existing products. These practical limitations of personalized products may be perceived as a risk by consumers. This study examines consumers’ perception of price, quality, and consumer service, as well as their preference for personalized products and types of customization. The results indicate that consumers’ willingness to pay a premium for personalized products was found, while their tolerance for quality reduction was extremely low, and their tolerance for service degradation was low. In addition, it was found that there was a preference for personalized products, and among the three customized types, option selection customized types were the most preferred. This study provides practical implications by analyzing realistic factors that companies should consider when supplying personalized products.
Probabilistic Harmony Search Algorithm: Fitness Proportionate Selection Variants

Harmony search algorithm is a meta-heuristic, nature-inspired optimization algorithm that tries to mimic real-life improvisations that musicians use to generate a harmony that is more pleasing to hear. This paper presents and compares three different types of harmony search algorithms. We start with the implementation of the original HSA. Further, two different modifications were made to the original HSA, the first one uses fitness proportionate selection of harmonies from the HM and is known as biased Roulette harmony search algorithm (BRHSA) and the second one builds on BRHSA by adding simple mathematics to further guide the algorithm toward the desired solution and is called guided biased Roulette harmony search algorithm (GBRHSA). These three variants of HSA were applied on four benchmark test functions on the same machine, and the results obtained after 30 runs were noted down for comparison. It was observed that the results given by the three variants had no specific trend in terms of best result or computational time and the performance of a particular variant was subject to parameters like the kind of function and its search domain. The results presented in this paper can be used as a foundation for the future works that will be done on this algorithm and can help derive an apt variant of HSA for solving a particular problem.
Fuzzy Logic

This chapter describes how to control a process using fuzzy logic techniques. Up to this point in theFuzzy logic Arduino book series we have used Boolean Logic techniques. That is, the digital logic inputs and outputs to the microcontroller have either been at logic one (true) or logic zero (false). In fact, we have gone to great lengths to describe proper peripheral interface techniques to preserve these logic values.
Docker Security

This chapter, you will look at seccomp profiles, one of the security features provided by Docker, which use the seccomp feature built into the Linux kernel. Standalone Go applications can also implement seccomp security without using Docker, and you will look at how to do this using the seccomp library.
Toward Metaliteracy and Transliteracy in the History Classroom: A Case Study Among Underserved Students

ABSTRACT In the last twenty years, scholars have reimagined information literacy to better address an overly saturated world of information and the growing participatory culture of Web 2.0. Outside of library and information science (LIS), researchers have promoted transliteracy—the intersection between information, visual, digital, and other literacies—to help students find and assess information. Within the LIS discipline, metaliteracy has provided a foundation to rethink information literacy frameworks, redefining students as creators who produce and share information. Relatively few studies exist, however, on how to leverage literacies in support of student digital scholarship projects. Likewise, digital humanities professors promote metaliteracy in the classroom, yet fewer scholars create digital humanities projects or write case studies about them outside of research institutions, prestigious private colleges, and larger, well-established public history programs. This case study examines a class project for a small undergraduate Introduction to Public History course at Texas A&amp;M University–Corpus Christi (TAMU–CC), a regional university with a comparatively large population of historically underserved students. Working with one archivist, two librarians, and the professor, students established a digital home for the ongoing South Texas Stories oral history project. Through this project, students learned and practiced various aspects of primary source literacy, information literacy, visual literacy, and digital literacy. The authors argue that such digital projects promote both metaliteracy and transliteracy, offering students a holistic learning experience during which they can practice their skills and that these types of projects are feasible at all kinds of institutions, even those with largely historically underserved populations.
A study on the theoretical basis of english education and teaching in the new era

In order to promote students' subjective initiative in English education, this paper studies the theoretical basis of English education and teaching in the new era.This paper discusses the current situation of English teaching in the new era, and puts forward three disadvantages of English teaching.In order to solve the problems such as students' lack of interest in independent learning, the single teaching model of English education, and the teaching evaluation system of English education does not adapt to the new era, new requirements for education and teaching are put forward through teaching theory.It is clear that the core goal in the classroom should be to cultivate students' interest in autonomous learning, encourage students to actively participate in classroom interaction, and establish a scientific teaching evaluation system.
WaLi: Control-Flow-Based Analysis of Wasm Smart Contracts

With the proliferation of various types of blockchain applications, smart contracts are getting more and more essential in developing decentralized applications due to their powerful expressing capabilities. However, security vulnerabilities of smart contracts keep arising in recent years, which has attracted a lot of attention. Most of the related research mainly focuses on smart contracts deployed on Ethereum. But in the meantime, more and more types of smart contracts adopt Wasm as their bytecode format due to its smaller size and higher operating efficiency in blockchain. To avoid the recurrence of exploited vulnerabilities in Wasm smart contracts, we develop WaLi, a control-flow-based analyzer for security vulnerabilities. In particular, we first construct a control flow graph based on the Wasm bytecode and define a critical path identification method to locate possible paths that may contain vulnerabilities. Then we adopt a Wasm virtual machine to simulate a runtime environment when tracing the critical paths and report the result based on our vulnerability detection patterns. We choose Liquid smart contracts on FISCO-BCOS to illustrate our approach and the experimental results show that WaLi performs well in detecting the access control vulnerabilities.
RGChaser: A RL-guided Fuzz and Mutation Testing Framework for Deep Learning Systems

Due to the powerful performance of deep learning (DL) systems, they have been increasingly deployed in numerous safety-critical domains. However, DL systems are known to be vulnerable to adversarial examples. Although testing techniques have been applied to ensure the robustness of DL systems nowadays and made gratifying progress, existing fuzz testing for DL systems does not pay attention to more diverse predetermined goals, nor does it provide guidance on how to mutate the seeds. Moreover, existing mutation testing for DL systems also suffers from similar troubles. To fill this gap, we investigate fuzz testing and mutation testing guided by reinforcement learning (RL) for DL systems. Meanwhile, we propose RGChaser, a RL-Guided testing framework which combines these two techniques to generate and screen more diverse high-quality test data for chasing potential vulnerabilities. Besides, to further improve the diversity of the generated mutants, we design a set of mutation operators for convolutional neural network (CNN) and continuous mutation spaces. We have implemented the introduced techniques on the MNIST and CIFAR-10 datasets to evaluate their practicality. Experimental evidences show that: (1) our approach is useful for generating diverse test suites efficiently, by upto 71.6% reduction in required mutations and 10 more success rates; (2) our approach could provide specific guidance on how to generate goal-based mutants.
Preface: 1st International Conference on Standardization and Metrology (ICONSTAM-2021)

First Page
Probabilistic machine learning for breast cancer classification

A probabilistic neural network has been implemented to predict the malignancy of breast cancer cells, based on a data set, the features of which are used for the formulation and training of a model for a binary classification problem. The focus is placed on considerations when building the model, in order to achieve not only accuracy but also a safe quantification of the expected uncertainty of the calculated network parameters and the medical prognosis. The source code is included to make the results reproducible, also in accordance with the latest trending in machine learning research, named Papers with Code. The various steps taken for the code development are introduced in detail but also the results are visually displayed and critically analyzed also in the sense of explainable artificial intelligence. In statistical-classification problems, the decision boundary is the region of the problem space in which the classification label of the classifier is ambiguous. Problem aspects and model parameters which influence the decision boundary are a special aspect of practical investigation considered in this work. Classification results issued by technically transparent machine learning software can inspire more confidence, as regards their trustworthiness which is very important, especially in the case of medical prognosis. Furthermore, transparency allows the user to adapt models and learning processes to the specific needs of a problem and has a boosting influence on the development of new methods in relevant machine learning fields (transfer learning).
Function approaches of QOE-driven detection in automated wireless networks

Wireless network technology has experienced tremendous growth over the past few decades, ranging from the support of circuit-switched voice and messages services to the use of high-speed mobile broadband (HSPA), worldwide microwave access interoperability and the radio network Lang-Term Evolution (LTE).Mobile apps and services are increasingly used to support communication needs in day-to-day activities.Without restriction of time, location, future technologies, device and mobility restrictions, smartphone users require access to the wide range of diverse media processes. Quality of service ( qos prototype with data science in the heterogeneous network of the ns-3 Simulation Software, for predicting client QoE.In the future, hyper and green cellular networks, both organising themselves as well as self-healing, this strategy can be crucial. Process flow starts create a network, Run FTP, Dataset, Use Parametric QoE model to generate QoE score for the dataset, Implement Procedures, Metrics Analysis, and Model. The configuration parameters for the simulation analysis have been percentage of participants, eNodeBs amount, eNodeB broadband, Convey workable eNB power, transmit disorganis edeNB control and number of sensor nodes. The simulation of wireless network configuration provides respective values for its respective parameters.
Selection Game for Consensus-Based Decentralized Aggregators of Distributed Energy Resources in a Blockchain Ecosystem

Aggregators can be effective in organizing distributed energy resources (DERs) for electricity markets and power systems. The recent development of blockchain and peer-to-peer (P2P) networks provides a new ecosystem for aggregating DERs. Initial studies have mainly used off-the-shelf consensuses, which may struggle to balance node sizes and computational intensities. Moreover, the dynamics of DERs changing their selection among multiple aggregators over time are rarely considered in most related literature. This freedom of selection, which is encouraged by the electricity market, can be better activated by a blockchain. In this study, a game-dynamic-based selection framework for multiple aggregators of DERs is proposed in a decentralized blockchain ecosystem. First, a proof-of-dual-credibility (Po2C) protocol is established so that DERs in such an aggregator can reach consensus. At the same time, for one DER node, both an unavoidable objective credit and a malicious subjective credit constitute its credibility with different weights. Then, a function with triple payoffs motivates DERs in terms of both the physical characteristics of being power supply devices and P2P nodes, the latter including consensus winning and data propagation. Third, the selection game of DERs among aggregators is modeled as an evolutionary game under replicator dynamics to find equilibrium. Numerical simulations with two and four aggregators show general stability in the selection game of DERs. Performance achieved with different consensuses and incentives are compared as well. The framework shows its great potential to organize DERs in a decentralized but aggregated mechanism in open electricity markets.
Research on intelligent recognition method for self-blast state of glass insulator based on mixed data augmentation

Automatically and accurately detecting the self-blast state of glass insulators is of great significance to operation and maintenance of transmission lines. To solve the shortcomings of the existing open-loop cognitive models to detect the self-blast state of glass insulators, this paper explores a mixed data augmentation-based intelligent recognition method to detect self-blast state of glass insulator, by imitating the human cognitive mode. Firstly, the mixed data augmentation is utilized to obtain the high-quality and rich sample features. Secondly, feature maps with strong semantics and adaptive multi-scale fusion are extracted using the feature pyramid network with adaptive hierarchy (FPN-A). Then, the extracted feature maps are transmitted to a two-dimensional stochastic configuration network (2DSCNs) to develop the self-blast state classification criteria with universal approximation capability. Thirdly, based on the generalized error and entropy theory, the self-optimizing adjustment and reconstruction of the feature map space with strong semantics and multi-scale fusion and its classification criteria are realized. Finally, the stacking method is applied to integrate the recognition results of the feature pyramid network with adaptive hierarchy convolutional networks to improve the robustness of the recognition model. Results of experimental comparison with other machine learning and deep learning methods verify the feasibility and effectiveness of the proposed method.
A reflective XSS vulnerability detection method based on fuzzing test

Aiming at the problems of low efficiency,high false negative rate and high false positive rate in the current cross site scripting (XSS) vulnerability detection technology for web applications,this paper proposes a reflective XSS vulnerability detection method based on fuzzing test.First,Web crawler technology was used to crawl and analyze the web links of the specified depth of the whole site.so as to extract the potential user injection points;Secondly,fuzzy test cases was constructed according to the grammatical form of attack payload.and an initial weights was set for each element.obtain the output point type according to the injected probe vector.the corresponding attack grammatical form was selected to construct more potential attack payloads,and it was mutated to form a mutated attack payload as the request parameter;Then,the website response was analyzed and the weights of elements was adjusted to generate a more efficient attack payload.Finally,this method was compared with two mainstream XSS vulnerability detection tools,AWvsand W3AF.The experimental results show that the number of potential user injection points that can be found by the proposed system increases by 11. 5%, the false positive rate of vulnerability decreased to 0.38%,and the false negative rate was lower than 2.31%;At the same time,the number of requests was reduced and the detection time was saved.
Performance Evaluation of Permissioned-based Personal Data Vault Implemented Using Hyperledger Fabric v2.x

Blockchain is a fundamental technology that can decentralize how we organize, share, and preserve data and information. This paper evaluates and improves the performance of our Personal Data Vault (our ongoing framework) by focusing on Hyperledger Fabric (HLF) version 2.x (v2.x), one of the most popular open source and highly scalable permissioned blockchains, particularly taking advantage of their new chaincode lifecycle. We conducted several experiments using the Hyperledger Caliper Benchmark version 0.4.2 (v0.4.2), a performance measuring tool. First, we observed changes in performance by varying network parameters (e.g., block size, endorsement policy (EP), number of clients). Then, for further evaluation, we selected sets of network parameters that showed the best performance for a given number of clients. A first selected set of network parameters showed significant improvements in throughput and average latency compared to the parameters that were not selected. And, a second selected set of network parameters out-performed the first in almost every way. These improvements were obtained by using a faster smart contracts lifecycle.