Reviewerly/exHarmony huggingface dataset:
Multi-strategy Sparrow Search Algorithm based on Differential Evolution

For the purpose of improving the sparrow search algorithm, there are some disadvantages of low convergence accuracy, slow convergence speed and prone to get bogged down local optimization, a multi-strategy sparrow search algorithm is proposed in this paper. Firstly, Initializing the sparrow population using the chaotic opposition-based learning strategy, which raise the randomness of sparrow individuals and make the initial solution more uniformly distributed in the solution space. Secondly, the location updating formula of discoverer is improved, in which nonlinear weight factor and Cauchy operator are adopted to balance the global exploration and local exploitation ability of this algorithm. Finally, according to the idea of grey wolf optimizer and differential evolution algorithm, the individuals of the population are disturbed in stages to further improve the diversity of the population and reduce the possibility of the algorithm getting caught in the local extremum. Through experiments on 13 selected test functions, the simulation results prove that the proposed algorithm has better convergence speed and optimization accuracy than the original algorithm.
An Improved Block-matching and Three-dimensional Algorithm for Phase Interferogram

Aiming at the poor detail of the interference fringes in the phase interferogram of the Block-matching and three-dimensional (BM3D) filtering algorithm, it is difficult to deal with the false edge problem caused by the ringing effect. This study proposed an improved BM3D algorithm for the phase interferogram. The algorithm performs similar block search on the phase image, introduces cosine dissimilarity as the similarity measure, then converts the phase image to complex domain for filtering, and finally introduces a Kaiser window in the aggregation process, which further maintains the denoising ability while improving the texture details and phase information of the interference fringes are obtained.
AI Modeling Methods Research for Detecting Production Facility Problems, Using Power Pattern, Control Characteristics, Operation Condition Data

Although it is necessary to maximize efficiency through systematic maintenance of production facilities, convergence of various professional technologies and knowledge such as materials, machinery, and electrical facilities is required. Systematic management is difficult due to the diversity of expensive measuring equipment and objects. In order to overcome such problems, various experiences (implicit knowledge) of field experts are converted into a form that computers can understand. AI modeling and inference results for production facility abnormalities and quality abnormalities based on translated data (labeling data) are provided on-site Visualization technology is needed for. ① Learning data are generated from raw data of production facilities (operating conditions, operating conditions, and power patterns). ② We present a Ranked-FBD-based visualization method that can compare expert experience and machine learning results. It is expected that the visualization method based on Ranked-FBD and generation of learning data through this study can predictively diagnose unit production facilities (injection molding, press, fusion, painting, etc.) of small and medium-sized parts manufacturers and provide a customized solution.
Risk Prediction Model of Financial Lending Big Data Leakage Based on Association Rules

Aiming at the problem of insufficient prediction accuracy of financial lending big data leakage risk, this paper puts forward the construction method of financial lending big data leakage risk prediction model based on association rules, collects the risk characteristic data of financial lending big data leakage in combination with association rules, and constructs the risk data management system of financial lending big data leakage. Standardize the data threat evaluation index of financial lending big data leakage risk, and simplify the steps of financial lending big data leakage risk prediction. Finally, experiments show that the financial lending big data leakage risk prediction model based on association rules has higher prediction accuracy in the process of practical application and fully meets the research requirements.
Research on Knowledge Graph-Based Business Travel Analysis and Evaluation Methodology

To improve the marketing management of state grid corporation and further enhance the user experience of corporate training business, this paper proposes a knowledge graph-based business travel analysis and evaluation method. The method combines user data from the merchant-side system (TMC) of the training center and takes the training business and hotel service in the business travel process as the knowledge core to build a business travel graph and realize the visualization and business-related analysis of the original training data. At the same time, the method combines graph neural network to propose the analysis and evaluation algorithm of business travel graph (GNTE), which uses the network topology of knowledge graph to calculate the influence factors of evaluation indexes and realize the evaluation and auxiliary suggestions on the training effect of enterprises. Experimental simulations show that the method can fully expand the scope of e-services for business execution and effectively optimize the business environment.
Improved Cuckoo Search Algorithm with Escape Mechanism

Aiming at the problem of weak exploitation ability of Cuckoo Search (CS) algorithm, this paper proposes a Cuckoo Search algorithm with escape mechanism (ECS). First, we introduce a search strategy that enables candidate solution move closer to the optimal. Then, we design an escape mechanism based on backward learning. Numerical tests were conducted with the results showing a great improvement in the convergence speed and accuracy of the proposed ECS compared with the original.
Evaluation of Teaching Quality of English Courses in Comprehensive Universities under Multiple Indicators

With the continuous development of internationalization and the task of cultivating graduates with high comprehensive quality for the society, the importance of English teaching in comprehensive universities has been increasing. In this paper, based on the analysis of the evaluation subjects of university English teaching quality, 16 evaluation indexes are constructed from five aspects: teaching content, teaching method, teaching process, teaching literacy, and teaching effect, and the Particle Swarm Optimization-Least Squares Support Vector Machine (PSO-LSSVM) algorithm is used to comprehensively evaluate the teaching quality, and finally English teaching in universities is selected as the object of empirical research. The research results show that PSO-LSSVM algorithm has high applicability in evaluating ECCU and can provide over reference for universities to improve teaching quality and develop reform programs. Due to the lack of training data, although the evaluation index system in this paper is scientific and feasible, it still needs to be further adjusted and optimized according to the specific practice situation.
Rapid Aspect-Oriented Assessment of Relational Database Design Assignments

It can be argued that assignments in a Databases course, where students are required to build a database instance based on specifications provided in the form of Entity-Relationship Diagrams and descriptions, are one of the best ways to evaluate students' practical knowledge because they simultaneously cover a variety of topics and cognitive levels. Organizing an exam with such assignments is a time-consuming process, even more so in a large course. This work proposes a rapid aspect-oriented assessment process and presents a software system that automates most of the tasks needed to administer and assess an exam that includes such assignments.
National Cybersecurity Curriculum Task Force

Funded by the NSA through the NCAE-C program, the mission of the National Cybersecurity Curriculum Task Force is to catalog and create high-quality and relevant curricula on emerging cybersecurity topics, mapping to curricular and workforce guidelines, and make them freely available. This lightning talk will share the progress of the project so far, point the audience to where they can find vetted cybersecurity curriculum materials, and offer opportunities to contribute to the project.
IT Baccalaureate Programs and Program Leaders in the U.S.: A Profile, Characteristics, and Perceptions

The number of information technology baccalaureate programs continues to grow to meet the demand for rapid growth in IT occupations. With the growth of a relatively new academic discipline, it is important to identify its players, namely IT faculty and administrators, and the programmatic landscape. To gather data about IT programs, faculty, and administrators, a survey was conducted in spring 2021. This paper is presents survey data related to the profiles and perceptions of IT program leaders and characteristics of IT baccalaureate programs in the U.S.
Machine Teaching

While machine learning is catching most researchers’ eyes and has been practiced in many different aspects of our daily life, humans are equally benefited from machine teaching for a variety of purposes ranging from basic practical training to further advanced knowledge conveying. I, thus, am reemphasizing the teaching capabilities that machines could be equipped with. This paper is not for the purpose of coining a new term called ”machine teaching” which is contrary to ”machine learning”, but for the sake of restating a contrasting capability of machines that not only machines can learn but also can teach.
Banyan

Graph query services (GQS) are widely used today to interactively answer graph traversal queries on large-scale graph data. Existing graph query engines focus largely on optimizing the latency of a single query. This ignores significant challenges posed by GQS, including fine-grained control and scheduling during query execution, as well as performance isolation and load balancing in various levels from across user to intra-query. To tackle these control and scheduling challenges, we propose a novel scoped dataflow for modeling graph traversal queries, which explicitly exposes concurrent execution and control of any subquery to the finest granularity. We implemented Banyan, an engine based on the scoped dataflow model for GQS. Banyan focuses on scaling up the performance on a single machine, and provides the ability to easily scale out. Extensive experiments on multiple benchmarks show that Banyan improves performance by up to three orders of magnitude over state-of-the-art graph query engines, while providing performance isolation and load balancing.
Factors Influencing the Results of COVID-19 Case Outreach—Results From a California Case Investigation/Contact Tracing Program

Considerable research has examined impacts of case investigation and contact tracing (CI/CT) programs on the spread of infectious diseases such as COVID-19, but there are few reports on factors affecting the ability of these programs to obtain interviews and acquire key information.To investigate programmatic and case-specific factors associated with CI outcomes using data from the Public Health Institute's Tracing Health CI/CT program. Analyses were designed to detect variability in predictors of whether interviews and key information were obtained rather than quantify specific relationships.Logistic regression models examined variability in the predictive value of interview timeliness and respondent characteristics on outreach outcomes and interview results.Participants were members of a large California health care network with a positive laboratory test for COVID-19 and outreach from January 1 to July 31, 2021.The primary outcome was the result of outreach attempts: completed interview, refused interview, or failure to reach the infected person. Secondary outcomes considered whether respondents provided information on symptom onset, employment, and contact information or a reason for declining to provide information, and whether resource support was requested or accepted.Of 9391 eligible records, 65.6% were for completed interviews, 6.0% were refusals, and 28.3% were failed outreach. One-third of respondents (36.7%) provided information on contacts (mean = 0.97 contacts per respondent, 2.6 for those naming at least 1). Privacy concerns were the most common reasons for not providing contact information. Among respondent characteristics and interview timeliness, only race and number of symptoms showed statistically significant effects in all adjusted analyses.Significant variation existed in outreach outcomes by subject characteristics and interview timeliness. CI/CT programs carefully focused to characteristics and needs of specific communities will likely have the greatest impact on the spread of COVID-19 and other communicable diseases.
Application of Blockchain Technology Based on Privacy Data Protection in RMB Internationalization Path

With the integration of industrial Internet of Things technology and traditional industries, industrial Internet of Things has profoundly changed the production mode, organization mode, and business model of traditional industries. While people are enjoying the convenience brought by the Internet, their concerns about personal privacy are gradually increasing. Maintaining the value stability of RMB payment means and the security and efficiency of RMB settlement system are the key measures to promote the internationalization of RMB. Aiming at the problem of data insecurity and untrustworthiness in traditional information systems, this paper proposes a data protection technology for information systems based on blockchain. In the hierarchical authority information system, multichain structure is adopted to divide the authority of nodes in each layer, so that only the nodes with corresponding authority can access and operate data. Private digital currency, which is based on blockchain technology and represented by payment token and stable currency, helps to improve the defects of the agent banking model but increases additional market risks and trust risks. Blockchain technology adopts special networking technology and consensus mechanism. Therefore, in blockchain privacy protection, the focus of blockchain privacy protection is identity information and transaction information.
The Behaviour Change Wheel to Support the Design of Gameful Interventions

Gameful interventions (including serious games and gamification) are a popular tool to motivate and engage users towards improved behavioural outcomes. However, such interventions often fail due to poor design, specifically due to a fundamental lack of understanding of the audience and the required behavioural outcomes, and the consequent uninformed selection of potentially inappropriate game elements by designers. This chapter describes exploring the behaviour change wheel (BCW) method as a tool to augment gameful intervention design and selecting appropriate game elements to action gameful intervention strategies. This exploration is undertaken in the context of developing a gameful intervention targeted toward energy conservation. Within this context, the BCW is shown to assist the designers in understanding the audience and the intervention's behavioural outcomes, which has led to a theoretically informed and rigorous selection of game elements that better support the achievement of the targeted behavioural outcomes.
Artificial Intelligence and Auditing

Artificial intelligence (AI) technology has impacted businesses and industries as well as audit companies. With the emergence of AI-enhanced systems, many tasks performed by auditors can now be completed more efficiently by these technologies. Such systems are used in different audit tasks, such as risk assessment, audit planning, fraud detection, audit inquiry, transaction testing, inventory count, and document testing. AI platforms designed for auditing provide time-saving, higher efficiency and accuracy, minimized risks and biases, and improved audit quality. This chapter provides examples of AI platforms and tools developed by Big 4 audit firms and discusses the benefits and risks of implementing AI technology in auditing regarding the extant literature.
VR/AR Technology Adoption in Architecture Education

In recent years, many architecture practices are reinventing themselves to adapt to the aspects of technological needs, such as virtual and augmented reality (VR/AR). Keeping up with the industry, architecture schools are slowly moving towards the adoption of the technology. However, it is important to note that the students' acceptance and confidence in the technology are crucial for the successful implementation of the technology. The findings demonstrated that the students perceived the technology as useful and are willing to adopt it. Therefore, the architecture school demonstration on the innovation process is crucial to have successful adoption of the VR/AR technology.
Pedagogical Shift in Education

A combination of social constructivism and critical pedagogy is what is referred to as transformative pedagogy. One industry where pedagogy reform is strongly desired is education. In addition, the COVID-19 curse forced society to choose such options above the demand in order to shore up the aspirants' future. Researchers and industry professionals are looking for automated solution methods to make it more interactive and simple for children and young people to grasp. Artificial intelligence is a technology solution that can be used to equip and improve human relationships and bonding with learning. Along with the beneficial inclusion, there are a few drawbacks that could cause a catastrophe, much like anything in the current educational system. This chapter, therefore, covers both parts of artificial intelligence-based solutions and futuristics with the challenges. The research being done in this area and the solutions that have already been produced are also highlighted. Discussion of the findings and potential future applications in the context of security and privacy follows.
USE OF VISUALIZATION TOOLS FOR CREATION OF ELECTRONIC EDUCATIONAL RESOURCES IN THE PROCESS OF LEARNING INFORMATION DISCIPLINES

The article briefly analyzes approaches to the interpretation of the term "visualization", the problem of information visualization in the study of information disciplines. The latest information and communication technologies for expanding the visualization tools of information disciplines are considered. The use of educational online resources as a means of activating students' cognitive activity is proposed. A systematic review of educational online resources was conducted, which can be used both for learning new material and for consolidating acquired abilities and skills in various forms of educational organization.
A Case Study on the Development of an AI-Enabled Food Delivery System From Home to Home

Like other domains of life, the food processing industry is also not untouched by the use of artificial intelligence. Though this industry has not been amenable to technology in the past, AI will play the role of catalyst in this industry going forward. Many working people who live alone in far-away cities and students living in PG have resorted to either consuming ready-to-eat food or ordering take-aways from restaurants. Moreover, people opting for tiffin service are usually bound to a single vendor for an indefinite period, giving them fewer options for customization and portion size. This chapter presents a case study for creating an AI-based platform where users can decide which supplier they want their meals from. The system will present the user with nearest tiffin suppliers who can provide home-cooked meals on weekly or monthly subscription bases. The user will also be able to customize the meal quantity and frequency. It will also empower homemakers to start their small businesses and have financial independence from the comfort of their homes.
AI and Over-the-Top (OTT)

Artificial intelligence (AI) is a field of study that focuses on the development and theory of computer systems that are capable of doing tasks that would normally need the intelligence of humans. Language translation, decision-making, and speech recognition are only a few of the tasks that, in general, need the use of a human brain to be performed properly. In the context of content delivery, an OTT platform (also known as over the top) is a platform that does not provide video via traditional cable or receivers. Video and audio distribution via the internet without the involvement of a multiple system operator (MSO) is referred to as online video and audio distribution. When it comes to the administration and transmission of information, there are several options. Viewers are able to access it from any place at any time and save it for later viewing convenience.
Emerging Blockchain Technology vis-à-vis Limitations and Issues

Blockchain has now become blockchain technology, and this is the record of data that are encrypted, and here distributed database is required for the purpose of transaction, contract, and independent record. It is therefore a digital ledger and can be reachable in different platforms for different financial activities and services. It is supported by healthy digital currency systems like bitcoin transactions and does not depend on any third party systems. Blockchain supports the multiple numbers of shared copies, and it works in a same database. Very recently blockchain was considered as an important subfield of information technology. It is therefore treated as a tool, technique, as well as procedure for financial management. This chapter is based on existing literature on blockchain with basics and special focus on the issues and limitations. This chapter highlights the scenario of blockchain technology in India with opportunities, companies, and scenario of manpower availability and potentiality.
Blockchain in Gaming

Blockchain is a leading-edge innovation that gives the possibility to streamline numerous enterprises like gaming. There have been many investigations done utilizing blockchain innovation in money, wellbeing, and different areas, yet gaming is a region yet to be conquered. This chapter will explain how blockchain innovation can be utilized in games. Blockchain is known to make exceptional computerized resources that can be copied and make decentralized environments. There are two parts of blockchain innovation that can change the gaming industry. Blockchain has generally affected our ways of life somewhat recently. A word that frequently emerges while discussing blockchain is bitcoin. Current answers for planning and building decentralized blockchain applications need interoperability. Thus, blockchains and existing advancements don't incorporate well in a brought together system. The authors propose a design expecting to effortlessly connect existing decentralized advances and blockchains. Blockchain gaming is an arising diversion worldview.