Reviewerly/exHarmony huggingface dataset:
Granular data representation under privacy protection: Tradeoff between data utility and privacy via information granularity

Information granules describe available experimental evidence at a more abstract level and facilitate the concise characterization of the structure of the numeric data. The utilization of unreliable data collectors poses a key challenge with respect to the privacy of the participants. In order to guarantee data confidentiality, we propose a granular data publishing method under differential privacy. In this study, a two-stage design of information granules considering the requirement of data privacy is proposed. The main idea is to protect individual sensitive pieces of data information against inference attacks and optimize the representation capabilities of information granules in a multi-dimensional data space. In the first stage, a clustering algorithm based on differential privacy is proposed to find some numeric prototypes in a numeric space, which considers sensitive data privacy protection. Second, this paper constructs spherical information granules involving the requirement of differential privacy. Their multi-dimensional and fuzzy granularity characteristics are considered. An improvement of the principle of justifiable granularity is utilized to optimize the bounds of information granules, and a new objective function is established to optimize the information granules. Our work is devoted to tradeoff data privacy and utility by constructing reasonable information granules. Unlike numerical data publishing, the publishing of interval information granules improves data utility in data protection. Experimental results demonstrate the superiority of the proposed granular description method based on differential privacy.
Application of streaming analytics for Artificial Lift systems: a human-in-the-loop approach for analysing clustered time-series data from progressive cavity pumps

Abstract Assessing real-time performance of Artificial Lift Pumps is a prevalent time-series problem to tackle for natural gas operators in Eastern Australia. Multiple physics, data-driven, and hybrid approaches have been investigated to analyse or predict pump performance. However, these methods present a challenge in running compute-heavy algorithms on streaming time-series data. As there is limited research on novel approaches to tackle multivariate time-series analytics for Artificial Lift systems, this paper introduces a human-in-the-loop approach, where petroleum engineers label clustered time-series data to aid in streaming analytics. We rely on our recently developed novel approach of converting streaming time-series data into heatmap images to assist with real-time pump performance analytics. During this study, we were able to automate the labelling of streaming time-series data, which helped petroleum and well surveillance engineers better manage Artificial Lift Pumps through machine learning supported exception-based surveillance. The streaming analytics system developed as part of this research used historical time-series data from three hundred and fifty-nine (359) coal seam gas wells. The developed method is currently used by two natural gas operators, where the operators can accurately detect ten (10) performance-related events and five (5) anomalous events. This paper serves a two-fold purpose; first, we describe a step-by-step methodology that readers can use to reproduce the clustering method for multivariate time-series data. Second, we demonstrate how a human-in-the-loop approach adds value to the proposed method and achieves real-world results.
Рsychological and pedagogical conditions for the development of STEM education

The article presents the relevance of the study and organization of psychological and pedagogical conditions for the development of STEM education. The authors assume that the study of this problem will contribute to the improvement of the effectiveness of educational and educational tasks of educational institutions.&#x0D; The authors stated that the psychological and pedagogical conditions for the development of STEM education in educational institutions are the purposeful organization of the educational STEM environment in the educational institution; educational and methodical support of educational institutions; improving the professional skills of scientific and pedagogical workers; psychological readiness of teachers for innovative activities; motivation of students for research, design, invention activities etc.&#x0D; STEM education is a tool for implementing the key provisions of the Concept of the New Ukrainian School regarding the renewal of didactic methods, means, forms and principles of education. Strengthening the role of STEM education will increase the motivation of students to study natural and mathematical subjects, as well as engineering, technology, and informatics. STEM education unites the efforts of the participants of the educational process in the formation of the competencies of the students, which will give the opportunity to propose innovative solutions for the economic development of the state and other spheres of society.
Cybersecurity for FinTech on Renewable Energy from ACD Countries

The function of financial technology (or “FinTech” or “FT”) in influencing the intake of Renewable Energy (REY) across the Asian Cooperation Dialogue (ACD) locations is underlined in this study. Reducing carbon dioxide emissions, increasing power safety concerns, and the volatility of oil filling are all driving forces behind REY growth. FT influences cybersecurity regarding consumer decisions, savings, and funding. Therefore in the digital 4.0 era, the renewable energy program carries cyber security is needed to protect systems and networks. Cryptocurrencies like NRGcoin, blockchain-based REY certifications like Origin from the Energy Web Foundation, and crowdsourcing for REY assignments are just a few examples. The purpose of this study was to measure the effect of FT on the use of REY. He did so by reading a balanced panel of 7 ACD countries from 2007 to 2020. The fixed yield estimator using the Driscoll-Kraay fashionable error was used to estimate the empirical model. Records show that increased use of FT and REY has a very favorable relationship. These findings provide a platform for governments and policymakers to nurture and inspire you to use FT to improve your green energy sector.
Blockchain Application in Educational Certificates and Verification Compliant with General Data Protection Regulations

Blockchain technology is a relative innovation in computer science that is expected to fuel global economic growth. In recent years, various sectors and one of the fields of education have implemented Blockchain technology to improve information integrity in ensuring the security, privacy, and authenticity of educational data. There is a problem in education, namely the existence of data that is not following the General Data Protection Regulation. So the purpose of this research is to provide a descriptive analysis of the education sector, the role of big data, and applying Blockchain technology to verify education data and develop solutions to overcome it. The research method used is descriptive qualitative with a literature study approach to track event record data, which supports publishing, storing, and leveraging various types of academic information. The results and conclusions of this study are increasing awareness of the world of education, of the importance of protecting educational data in the form of the General Data Protection Regulation, which can further provide a sense of security and comfort in processing data in universities.
Security Vulnerability Analysis of the Sharia Crowdfunding Website Using OWASP-ZAP

Indonesia is a country with fairly high market development in Financial Technology (FinTech) Services in the Asia Pacific region. The innovative benefit of FinTech is sharia crowdfunding. Data and information security are important for a company or organization. The problem faced by the use of websites in various fields, especially on the sharia crowdfunding website, is the security of information concerning data from an organization. This study aims to analyze the security vulnerabilities of the sharia crowdfunding website with the Open Web Application Security Project (OWASP) approach using the Zed Attack Proxy (ZAP) tool. OWASP is an open-source framework for improving the security of application software on websites. The results of this study determine the level of vulnerability in the Sharia Crowdfunding Website. This test is carried out to find vulnerabilities and risks on a crowdfunding website and provide recommendations for improving security on the website. The top 10 Security Vulnerabilities based on OWASP consist of 4 high levels, 5 medium levels, 14 low levels, and 9 information levels including Broken Access Control, Injection, Insecure Design, Security Misconfiguration, Vulnerable and Outdated Components, and Software and Data Integrity Failures.
ARCritique: Supporting Remote Design Critique of Physical Artifacts through Collaborative Augmented Reality

Critique sessions are an essential educational activity at the center of many design disciplines, especially those involving the creation of physical mockups. Conventional approaches often require the students and the instructor to be in the same space to jointly view and discuss physical artifacts. However, in remote learning contexts, available tools (such as videoconferencing) are insufficient due to ineffective, inefficient spatial referencing. This paper presents ARCritique, a mobile Augmented Reality application that allows users to 1) scan physical artifacts, generate corresponding 3D models, and share them with distant instructors; 2) view the model simultaneously in a synchronized virtual environment with remote collaborators; and 3) point to and draw on the model synchronously to aid communication. We evaluated ARCritique with seven Industrial Design students and three faculty to use the app in a remote critique setting. The results suggest that direct support for spatial communication improves collaborative experiences.
A survey of recent advances in image steganography

Abstract Over the course of technological development, data security while transmission and storing has always been a field of continuous advancements and key importance. In the recent times, its importance has increased even further and the main reason behind this is the increase in the amount of data that is being transferred over internet and various networks. Although there are cryptographic approaches to handle these situations, steganography provides a different aspect to the solution. While cryptography encrypts the data such that the cipher text is visible to human eye, but the meaning of that encrypted text is never known. Steganography is the process of hiding data in plain sight inside some images, audio, video and so on, such that it is very difficult for the viewer to directly differentiate if there is some data hidden in the host or not. This paper consists a study of various methodologies to implement Image steganography like spatial domain manipulation, encryption based and deep learning approaches. The paper focuses upon recent advances in all these approaches for image steganography and aims to help other researches in this domain to understand recent developments as well future advancements possible in the same field. Furthermore, this paper illustrates about how different image formats effect various image steganography methods and how the various image color models interact with image steganography. Later this article sheds some light on the domain of steganalysis and reports about the fundamental principles as well as various implementations of steganalysis systems in recent times.
BotFinder: a novel framework for social bots detection in online social networks based on graph embedding and community detection

With the widespread popularity of online social networks (OSNs), the number of users has also increased exponentially in recent years. At the same time, Social bots, i.e. accounts that controlled by program, are also on the rise. Service providers of OSNs often use them to keep social networks active. Meanwhile, some social bots are also registered for malicious purposes. It is necessary to detect these malicious social bots to present a real public opinion environment. We propose BotFinder, a framework to detect malicious social bots in OSNs. Specifically, it combines machine learning and graph methods so that the potential features of social bots can be effectively extracted. Regarding the feature engineering, we generate second order features and use coding methods to encode variables that have high cardinality. These features make full use of both labelled and unlabeled samples. With respect to the graphs, we firstly generate node vectors through embedding method, following which the similarity between vectors of humans and bots can be further calculated; Then, we use an unsupervised method to diffuse labels and thus the performance can be improved again. To valid the performance of the proposed method, we conduct extensive experiments on the dataset provided by an artificial intelligence contest which is composed of over eight million records of users. Results show that our approach reaches a F1-score of 0.8850, which is much better compared to the state of the art.
IoT-enabled smart healthcare data and health monitoring based machine learning algorithms

A smart healthcare network can use sensors and the Internet of Things (IoT) to enhance patient care while decreasing healthcare expenditures. It has become more difficult for healthcare providers to keep track and analyze the massive amounts of data it generates. Health care data created by IoT devices and e-health systems must be handled more efficiently. A wide range of healthcare industries can benefit from machine learning (ML) algorithms in the digital world. However, each of these algorithms has to be taught to anticipate or solve a certain problem. IoT-enabled healthcare data and health monitoring-based machine learning algorithms (IoT-HDHM-MLA) have been proposed to solve the difficulties faced by healthcare providers. Sensors and IoT devices are vital for monitoring an individual’s health. The proposed IoT-HDHM-MLA aims to deliver healthcare services via remote monitoring with experts and machine learning algorithms. In this system, patients are monitored in real-time for various key characteristics using a collection of small wireless wearable nodes. The health care business benefits from systematic data collection and efficient data mining. Thus, the experimental findings demonstrate that IoT-HDHM-MLA enhances efficiency in patient health surveillance.
Topic-sensitive expert finding based solely on heterogeneous academic networks

Identifying experts in a specific research field is an essential and practical task in academia and industry. Although some efforts have been attempted for expert finding, they rely on abundant text mining to reveal the target research topic accurately. Furthermore, it is time-consuming to collect and process these text materials for each candidate, and it is difficult to prepare a precise description for a topic that can cover all sub-topics. To this end, in this study, we develop a new expert finding model called TOpic-Sensitive representative Author identification (TOSA), to find representative authors in a specific research topic from a heterogeneous topic-insensitive academic network involving various topics, with a few well-known experts collected previously who are professional in the target topic. Specifically, we first learn the high-order relationships about authors in the heterogeneous academic network, which contain enough topic-related information. Next, an embedding space is found, where the collected experts are close to each other; in other words, the area they gather indicates the features about the collected authoritative experts. Finally, a prototype is calculated based on the collected experts in the embedding space, and we can rank other unlabeled authors by measuring the closeness between them and the prototype. Along this line, in addition to the collected experts in advance, more authoritative experts in the target topic will be identified. Extensive experiments on DBLP and ACM datasets demonstrate that the proposed TOSA model can identify experts who are professional in the query topic directly from a heterogeneous topic-insensitive academic network without text data processing, and its performance is significantly superior to the baseline models.
The bayesvl package: An R package for implementing and visualizing Bayesian statistics

The bayesvl R package implements Bayesian analysis using the Stan’s no-U-turn sampler (NUTS). Two distinguished functions that the bayesvl package complements to other developed packages and software performing Bayesian analysis are the “relationship tree” construction protocol and the ability to visualize estimated posteriors graphically. The “relationship tree” construction protocol is inspired by the Bayesian network approach that illustrates the model through directed acyclic graphs (DAGs), while the graphical visualization capability of the bayesvl package is built upon the graphical generation power of the ggplot2 package. After constructing a “relationship tree”, the posterior can be automatically simulated and graphically visualized with some simple codes. With two distinctive characteristics, the bayesvl package’s main aims are to improve user experience (chance of creativity and serendipity; productivity, flexibility and intuitiveness; and scientific communication power) and pedagogical effectiveness in statistics and other sciences (cognitive instruction strategy; analytical reasoning and argument understanding skills; and idea exchange between social sciences and mathematical education).
‘Go home to the second wave!’: Discourses of trans inclusion and exclusion in a queer women’s online community

As the visibility of trans movements has increased in recent years, so too has the antagonism between trans rights supporters and some sections of the feminist and lesbian communities (Phipps, 2016; Hines, 2017, Pearce et al., 2020). This antagonism is especially pronounced in digital spaces, where online discussions have fuelled an increasing polarisation of the debate (Hines, 2017). This paper examines the representation of trans identities on Autostraddle: a popular entertainment, news and lifestyle website for lesbian and bisexual women. It focuses on the longest and most controversial comment thread in the 2-million-word Queer Women’s Advice Corpus. The thread is a response to a guide to dating trans women for cis women. Using a combination of critical discourse analysis, sociocultural linguistics and corpus linguistics, I unpack the argumentation strategies (Fairclough and Fairclough, 2012) that commenters use to construct stances on the inclusion of trans women in the queer women’s online space. The major strategies include persuasive definitions of lesbian, imaginaries about trans women’s hypothetical bodies and the illegitimation of trans-exclusionary commenters as bad feminists and community outsiders. I find that that trans inclusion is successfully negotiated on a community level, but that trans women are still problematised on an intimate level due to their (imagined) genitalia. Trans women are ‘hyperembodied’ in the data, with the presence or absence of a penis acting as the focal point for inclusion and desirability.
Development of a multi-technology, template-based quantum circuits compilation toolchain

Abstract With noisy intermediate scale quantum computers (NISQ) becoming larger in scale and more reliable, quantum circuits are growing in size and complexity. In order to face the challenge of achieving optimal circuits, design automation approaches for improving and mapping quantum circuits on different architectures have been proposed, each one characterized by a specific optimization strategy. In this article, the use of a template-based approach for quantum circuits optimization purposes is explored, and the proposal of a modular compilation toolchain, which supports three quantum technologies (nuclear magnetic resonance, trapped ions and superconducting qubits), is presented. The toolchain tackles the task of implementing logic synthesis for single-qubit and multi-qubit gates in the compilation process and it is structured with multiple steps and modular libraries. The toolchain was tested through a benchmarking procedure, and the results for a subset of complex quantum circuits as inputs are here reported, alongside a comparison with those provided by the compilers of IBM’s Qiskit and Cambridge Quantum Computing’s $$ \mathrm{\left. {t|ket} \right\rangle } $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mfenced><mml:mrow><mml:mi>t</mml:mi><mml:mo>|</mml:mo><mml:mi>ket</mml:mi></mml:mrow></mml:mfenced></mml:math> . The current toolchain prototype was crafted to be an easily expandable and reliable core for future developments, which could lead it to support even more quantum technologies and a fully fledged layout synthesis. Nonetheless, the obtained results are quite encouraging, and they prove that in certain conditions the Toolchain can be competitive in quantum circuits optimization, especially when dealing with single-qubit gates.
Re-Identification for Multi-Target-Tracking Systems Using Multi-Camera, Homography Transformations and Trajectory Matching

This paper proposes an algorithm for multiple-target re-identification of targets viewed using images captured from multiple cameras. B-spline polynomials are used to approximate and define the target trajectories. This allows a smoother representation of irregular trajectories of rapidly moving targets. Homography transformations of the different camera frames into one common frame allow a comparison between target trajectories. Based on their similarity targets are re-identified, meaning that corresponding targets observed by multiple cameras are associated across cameras. Results show that the algorithm is able to re-identify targets in a simulated environment as well as on recorded data of football players from field tests.
Improved Design Based on IoU Loss Functions for Bounding Box Regression

In object detection, the bounding box regression loss calculation has a great influence on the positioning effect of object detection. At present, the common loss function is smooth L1 loss or Intersection over Union(IoU)-based loss function. For the problems of large training error and low training accuracy based on the IoU loss function, two improved versions of loss functions BaIoU and BhIoU are proposed. Among them, BaIoU combines the balanced L1 loss and the original IoU loss function; based on BaIoU, BhIoU increases the loss gradient of IoU by improving the form of IoU to improve the algorithm of IoU. The bounding box regression simulation experiment proves that BaIoU and BhIoU can effectively overcome the problems of slow convergence and large training error of the loss function based on IoU. Using the MS COCO dataset, training tests are conducted using a two-stage object detector and a single-stage object detector, and the test results prove that BaIoU and BhIoU can improve the performance of the object detector with better localization accuracy than the existing IoU-based loss functions.