Reviewerly/exHarmony huggingface dataset:
Multi-factor fusion index assessment of digital speckle interferometry images quality under dynamic environment

When digital speckle pattern interferometry (DSPI) is applied to dynamic environmental, it is often accompanied by severe speckle de-correlation, which severely limits the application and detection accuracy of DSPI. Currently, the existing speckle image quality evaluation methods only consider the contrast of the speckle image or the morphology of speckle particles, which results in these methods unapplicable to dynamic environments. For this reason, this paper proposes a new evaluation method for laser speckle image, namely dynamic multi-factor fusion index (DMFFI), which combines evaluation of four aspects: speckle stability, speckle resolution, uniformity of gray distribution, and contrast. And then, the DMFFI method is compared with the traditional evaluation methods including mean gray gradient and second order derivative of mean gray gradient. The experimental results show that DMFFI has a better ability to evaluate the quality of laser speckle images under dynamic environment compared with the traditional evaluation methods. Finally, a comparison test under different environments is conducted, and the effects of environments on the quality of speckle images are analyzed and discussed, and a guideline for speckle image acquisition in DSPI technique is proposed, which have great significance and value for the application of DSPI technique to dynamic environment detection.
Motion estimation of the common carotid artery wall in ultrasound images using an improved sub-pixel block matching method

To improve the accuracy of the two-dimensional motion estimation of the common carotid artery (CCA) wall, a modified sub-pixel block matching method is proposed in this study. First, the directional asymmetric search (DAS) method is used for the block matching, and the sum of absolute error criterion combined with fuzzy set theory is used to determine the similar blocks. After obtaining the best block matching position, a combination of grid slope and parabolic interpolation (GS15PI) method is used for sub-pixel estimation. Then, the best matching position (point) obtained in the previous step and all points in its 3×3 neighborhood need to be determined. The blocks with the same size as the reference block centered on these points are used for block matching with the reference block from the search frame − 2. After obtaining the best block matching position, the GS15PI method is used for sub-pixel estimation. Finally, the average of sub-pixel positions obtained in the two previous steps is calculated as the final sub-pixel estimation. In simulation experiments, the two-dimensional motion errors of proposed method for simulated ultrasound sequences with the center frequencies of 5 MHz, 7.5 MHz, and 10 MHz are decreased by 8.41%,4.65%, and 11.25%, respectively, compared with the state-of-the-art method. In clinical experiments, as compared with the state-of-the-art method, the radial and longitudinal motion estimation results of proposed method are more consistent with the actual movement trend of the human CCA. In conclusion, the proposed method has good performance and can be used for motion estimation of the CCA wall in ultrasound images.
Penggajian Berbasis Mobile di Universitas Ma’soem

Design a Mobile Based Lecturer Payroll Information System at Ma’soem University to provide solutions to several lecturers who have limited time so they do not have time to fill in lecturer attendance after completing lectures so that it affects the work of the finance and curriculum sections,The approach method used is an object-oriented approach (object oriented) which consists of object-oriented analysis (OOA), and object-oriented design (OOD). The system development method used is prototype. While the author's modeling system uses Unified Modeling Language (UML).With the design of the lecturer payroll information system, it is expected to facilitate lecturers in completing attendance and seeing details of their income, the finance department does not have to work overtime to make lecturer salary reports, and the curriculum section is more facilitated in controlling lecturer performance.
An economy in the making: Negotiating capitalist and beyond-capitalist ontologies and relations in makerspaces

This paper critically examines how capitalist, alternative capitalist and non-capitalist ontologies and relations are negotiated in a hybrid makerspace that hosts both for-profit and non-profit entities and integrates community and commercial aspects. Despite a growing body of knowledge on the distinct characteristics of non-commercial makerspaces, few scholars have analysed them in relation to capitalism. This applies even more to commercial or hybrid makerspaces that remain so far under-researched in diverse economies literature. These spaces, however, can be of increasing interest given what we know about makerspaces as hubs of budding entrepreneurship and that some makers avidly pursue entrepreneurial objectives while others are reluctant to even consider commercialising their projects. In this paper, I employ an extended framework of diverse economies that understands capitalism as not only a form of socio-economic organisation but also a cultural and political architecture. Followed by an overview of existing literature that sheds light on beyond-capitalist dimensions of makerspaces, I explore a case study of the Keilewerf, a hybrid makerspace situated in Makers District in Rotterdam, the Netherlands, disentangling how capitalist, alternative capitalist and non-capitalist ontologies of sustainability-oriented makers co-exist and conflict with economic relations, knowledge production and relations with the state.
Hierarchical Pointing on Distant Displays with Smart Devices

Large display interaction has undergone considerable growth, whereas distant pointing poses challenges due to distance limitations and input devices. Smart devices with superior computing power and rich input capabilities aim to surmount these restrictions. However, pointing interactions in single-layer have limited accuracy in large display scenarios. We investigate a hierarchical pointing approach that couples multiple control modes of rotation attitude, touch operation, and pressure simulation with different cursor strategies. Specifically, it covers the dual-layer cursor with the hierarchical structure, the pressure-dependent multistage gain cursor, and the cursor projection technique based on the target acquisition mechanism. We contrast seven distant pointing techniques in two groups of experiments. Our results indicate that the hierarchical interaction strategy improves user performance, and cursor projection achieves an optimal trade-off between velocity and accuracy for high Fitts’ index of difficulty (ID) contexts.
Milton in the Bishop's “Kitchin”: Domestic Narratives in <i>Animadversions</i>

Milton QuarterlyVolume 55, Issue 3-4 p. 130-143 ORIGINAL ARTICLE Milton in the Bishop's “Kitchin”: Domestic Narratives in Animadversions Zoe Hawkins, Corresponding Author Zoe Hawkins zoevhawkins@gmail.com University of SouthamptonEmail: zoevhawkins@gmail.comSearch for more papers by this author Zoe Hawkins, Corresponding Author Zoe Hawkins zoevhawkins@gmail.com University of SouthamptonEmail: zoevhawkins@gmail.comSearch for more papers by this author First published: 07 September 2022 https://doi.org/10.1111/milt.12387Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume55, Issue3-4October-December 2021Pages 130-143 RelatedInformation
Accurate Performance Predictions with Component-Based Models of Data Streaming Applications

Data streaming applications are an important class of data-intensive systems and performance is an essential quality of such systems. Current component-based performance prediction approaches are not sufficient for modeling and predicting the performance of those systems, because the models require elaborate manual engineering to approximate the behavior of data streaming applications that include stateful asynchronous operations, such as windowing operations, and because the simulations for these models do not support the metrics that are specific to data streaming applications. In this paper, we present a modeling language, a simulation and a case-study-based evaluation of the prediction accuracy of an approach for modeling systems that contain stateful asynchronous operations. Our approach directly represents these operations and simulates their behavior. We compare measurements of relevant performance metrics to performance simulation results for a system that processes smart meter readings. To assess the prediction accuracy of our model, we vary both the configuration of the streaming application, such as window sizes, as well as the characteristics of the input data, i.e., the number of smart meters. Our evaluation shows that our model yields prediction results that are competitive with a state-of-the-art baseline model without incurring the additional manual engineering overhead.
The Design of Learning Results According to International CDIO Standards as a Context for Improving Engineering Education at Agrarian University

The need for a meaningful transformation of the professional education of specialists in agroengineering specialization is due to both the increasing demands on the part of employers for the level of training of agricultural engineers, and the intensive development of agricultural technologies and equipment. The authors reveal the features of designing the planned learning outcomes for a qualitative update of the content of modern educational programs of engineering training in an agricultural university based on CDIO standards. The scientific novelty of the research is the development and methodological justification for designing learning outcomes according to international CDIO standards on the example of a separate area of professional training in the agricultural university (35.03.06 “Agroengineering”, specialization “Technical systems in agribusiness”). The article describes in detail the design of the results of the training according to international CDIO standards, based on the principles of “result-based” and “practice-oriented” by considering the interaction of the three factors determining the effectiveness of modern agricultural engineering training. These factors include: 1. Federal State Educational Standard of Higher Education (FSES HE); 2. CDIO Syllabus (a content system or a list of employers’ requirements for engineering education in terms of attributes (competencies) and 3) the results of a stakeholder survey on the list of significant competencies of an engineering university graduate and their priority. Considering the CDIO approach as one of the leading advanced methods for the development, continuous improvement and evaluation of higher education programs in the field of engineering and technology, the authors propose a further algorithm for creating professional programs by defining the intended outcomes of the training. In the Stavropol State Agrarian University, 12 pilot programs have been developed and are being implemented in a similar way, based on the implementation of the approaches of the Worldwide CDIO Initiative. The result of the design and qualitative updating of educational programs based on CDIO standards will be the formation of a set of professional competencies in demand for graduates of an agricultural university of engineering specialization, allowing them to effectively perform professional tasks and function constructively in the conditions of innovative technological and technical development of agricultural production.
Persistence Factories Architectural Design Pattern

Each screen of the user interface of a mobile or desktop application coordinates one or more use cases. Each one of those use cases may need to access different entities of the persistent data. It is a common practice to inject a provider for each of those entities of the model. Thus, the more use cases the view deals with, the more entity providers need to be injected. If we extend that to all the views of the application, the complexity increases and the impact in maintainability of the application is huge. In this paper we present an architectural design pattern to address this problem, as well as an analysis of its merits. The main idea behind the architectural pattern is that use cases take care of requesting the required entity providers from a unique element that is the persistence factory. Therefore use cases are created with the required entity provider(s) injected, allowing the other components of the architecture to be completely decoupled from the implementation of the persistence and simplifying the dependency injection process. The included code snippets compare our solution with most used industrial alternatives.
Ethics of Face Recognition in Smart Cities Toward Trustworthy AI

In the past few decades, thanks to the continuous development of machine learning and deep learning algorithms, as well as the continuous improvement of computing power and databases, facial recognition technology (FRT) has developed rapidly. Widespread use of this technology can be seen in all fields of life, such as facepay, individual identification, smart-city surveillance, e-passport or even face to DNA identification. However, some experts believe that certain errors that commonly creep into its functionality and a few ethical considerations need to be addressed before its most elaborate applications can be realized. This article explores the ethical issues of FRT used in different scenarios, tries to examine some legal and regulatory issues that may be encountered during the use of FRT, and technically analyze how to build a trustworthy intelligent system.
Blockchain Based Health Information Exchange Ecosystem: Usecase on Travellers

Various international initiatives have been launched in 2021 to generate digital vaccination certificates to address challenges faced by travellers exposed during COVID19. While COVID19 has impacted multiple industries where alternate solutions have emerged at competing rates, airline industry in particular have been hit hard due to highly regulated protocol. Owing to its unique characteristics of transparency and decentralization, block chain technology provides an opportunity to create smart solutions for travel industry. We present a solution that describes a scaled, blockchain-based infrastructure for exchanging COVID-19 pandemic travellers’ history and vaccination status in a secure manner. The proposed approach ensures the travellers are coronavirus-free and check their COVID 19 history and vaccination status across the borders and immigration in particular. The framework employs a permission blockchain and Proof of Authority to check vaccination status on airports using the smart contract. It provides a distributed infrastructure for national and international healthcare systems, to check digital vaccination certificate and individual vaccination history and their verification by relevant stakeholders, such as airport securities, health authorities, governments, border control authorities and airlines.
Equalities design: Toward post-normative equity

This chapter discusses an “Equalities Design” approach that informs and guides my practice as a designer. Using the frame of post-normative equity, I have been developing Equalities Design tools, methods and approaches that interrogate and queer normative (including neuronormative) assumptions in order to create alternative bodymind explorations and experimental outputs. These explorations and outputs can be contrasted with design solutions that have been presented as inclusive but are in fact simplistic design responses that fail to take into account intersectionality and thus the profound challenges encountered by multiply-marginalized groups. The chapter argues for the necessity of such an approach, and describes the “Design Exosystem” Framework that guides my work. The chapter also briefly introduces my involvement with the Wellcome Trust's Design and the Mind initiative, focused on co-creation and engagement with neurodivergent, disabled and D/deaf people and groups, and which produced outputs such as toolkits and a public-facing 5-day Program in the Wellcome Collection Hub.
Quantum Bitcoin: The Intersection of Bitcoin, Quantum Computing and Blockchain

This survey briefly describes the background and development history of quantum bitcoin, introduces the relationship between blockchain and cryptocurrency, introduces in detail the structure of two quantum bitcoin systems and the advantages of quantum bitcoin compared with classical bitcoin, and discusses the advantages and possible challenges of the development of quantum bitcoin in the future.
Incident Detection System for Industrial Networks

Modbus/TCP is one of the most used industrial protocol, but this protocol is unsecured and does not implement encryption of communication or authentication of the clients. Therefore, this paper is focused on the techniques of incident detection in Modbus/TCP communication, but it is possible to implement the proposed solution on different protocols. For this purpose, a Modbus Security Module was created. This module can sniff specific network traffic, parse particular information from the communication packets, and store this data into the database. The databases use PostgreSQL and are placed on each master and slave stations. The data stored in each database is used for incident detection. This method represents a new way of detecting incidents and cyber-attacks in the network. Using a neural network (with an accuracy of 99.52%), machine learning (with an accuracy of 100%), and database comparison, it is possible to detect all attacks targeting the slave station and detect simulated attacks originating from master or non-master station. For additional database security of each station, an SSH connection between the databases is used. For the evaluation of the proposed method, the IEEE dataset was used. This paper also presents a comparison of machine learning classifiers, where each classifier has adjusted parameters. A mutual comparison of machine learning classifiers (with or without memory parameter) was done.
Comparison of New Approaches of Semi-syllable Units for Speech Recognition of Any Arabic Word

Abstract This study uses the same semi-syllable units already defined in our previous work; a semi-syllable is a simple Arabic letter followed by its diacritic information. This work experiment with three configurations. In the first configuration, we redefine the “السكون” (Soukoun is an Arabic diacritical sign representing the absence of a vowel) as a new independent phonemes that is different from the consonant itself. In the second configuration, we also redefine the geminated consonant with a new independent phoneme. In the last configuration, we combined the first two experiments. The results proved that the three configurations gave better results compared to regular semi-syllables, and the highest recognition rate was achieved on the third configuration.
Innovative Legitimate Non-Traditional Doctorate Programs in Cybersecurity, Engineering, and Technology

According to the US Federal Bureau of Investigations (FBI) the number of complaints about cyberattacks to their cyber division is up to as many as 4,000 a day. Every year in the U.S., 40,000 jobs for information security analysts go unfilled, and employers are struggling to fill 200,000 other cybersecurity-related roles. Colleges and universities have created certificate, undergraduate, and graduate programs to train professionals in these job roles. The challenge to meeting the cybersecurity workforce shortage through degree programs is intensified by the reality of the limited number of cybersecurity and engineering faculty at colleges and universities. This chapter explores the essential need to develop more doctorate faculty in technology-related areas and explains some unique and non-traditional paths to doctoral completion that allow professionals with significant real-world work experience to complete a doctorate without career interruption and relocation from highly respected and established universities in the US and the UK.
Application of Machine Learning to User Behavior-Based Authentication in Smartphone and Web

Authentication is the preliminary security mechanism employed in the information system to identify the legitimacy of the user. With technological advancements, hackers with sophisticated techniques easily crack single-factor authentication (username and password). Therefore, organizations started to deploy multi-factor authentication (MFA) to increase the complexity of the access to the system. Despite the MFA increasing the security of the digital service, the usable security should be given equal importance. The user behavior-based authentication provides a means to analyze the user interaction with the system in a non-intrusive way to identify the user legitimacy. This chapter presents a review of user behavior-based authentication in smartphones and websites. Moreover, the review highlights some of the common features, techniques, and evaluation criteria usually considered in the development of user behavior profiling.
Detecting Bank Financial Fraud in South Africa Using a Logistic Model Tree

Artificial intelligence is gradually becoming the standard mechanism underpinning online banking. Users' profiles can be confirmed using a variety of methods, including passcodes, fingerprints, acoustics, and images through this technology. On the other hand, traditional cybersecurity measures are unable to prevent internet-based fraud after the visualisation process has been infiltrated. In light of this, the aim of this chapter is to examine the efficiency of the logistic model tree (LMT) in detecting financial fraudulent transactions in South African banks and, ultimately, to develop a financial fraud early warning system. Web-scraping credit and debit card fraud data from SA are used to acquire daily data. The LMT is constructed utilizing a training set from the LogitBoost algorithm and obtained 17 financial conditioning elements. Overall, an early warning system model has shown to be a good performer with a prediction rate of 99.9%. This appears to be a promising approach for detecting online fraud vulnerabilities.
Privacy Preservation of Image Data With Machine Learning

The methods used to predict, categorize, and recognize complex data like pictures, audio, and texts have been popular in machine learning. These methods are the basis for future AI-driven internet providers because of unparalleled precision in deep learning methodologies. Commercial firms gather large-scale user data and perform machine learning technique. The massive information necessary for machine learning raises privacy problems. The user's personal and extremely sensitive data such as photographs and voice records are gathered and retained forever by these commercial firms and users can not limit the intents of these sensitive information. In addition, centrally stored data is susceptible to legal and extrajudicial monitoring. Many data owners use profound extensive learning by security and confidentiality. This chapter contains a practical approach that allows several parties to learn a precise model of complex systems for a specific purpose without disclosing their data sets. It provides an interesting element in utility and privacy.