Reviewerly/exHarmony huggingface dataset:
Image registration

Image registration is the process of spatially aligning a set of images. It plays an essential role in common image analysis tasks, such as the quantification of differences in imaging biomarkers across populations, or the estimation of changes in these markers over time. It also underpins many of the MRI techniques reviewed in this book, such as to allow the correction of imaging artifacts due to subject motion. This chapter begins with relevant applications of image registration in the context of MRI. The common structure and building blocks of image registration algorithms are then presented. This is followed by a high-level overview of different classes of algorithms available today. The chapter ends with a highlight of how deep learning is shaping the future of image registration.
Semantic Teaching Process as a Basis for the Development of Education in Polyfunctional Environment

This chapter discusses the conceptual basis of the studied structure and content of the semantic interaction of a teacher and students on the basis of the relationship between the pedagogy of formation and the pedagogy of self-development. The purpose of the chapter is to detect the opportunities of further implementation of the verbal-illustrative education model based on the re-establishment of the participants' interaction design technology of pedagogical process as the semantic technology of the educational process, and the basis of educational development in the polyfunctional environment. The justification of the relationship of the pedagogical process and the semantic control, the designation of the essence and the basic conditions for the effectiveness of meaning reveal the relevance of the question concerning the factors which contribute to the enhancing students' research competence in modern educational contexts.
Big Data and Its Visualization With Fog Computing

Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This article discusses what is Big Data, and its characteristics, and how this information revolution of Big Data is transforming our lives and the new technology and methodologies that have been developed to process data of these huge dimensionalities. Big Data can be discrete or a continuous stream of data, and can be accessed using many types and kinds of computing devices ranging from supercomputers, personal work stations, to mobile devices and tablets. Discussion is presented of how fog computing can be performed with cloud computing as a mechanism for visualization of Big Data. An example of visualization techniques for Big Data transmitted by devices connected by Internet of Things (IoT) is presented for real data from fatality analysis reporting system (FARS) managed by the National Highway Traffic Safety Administration (NHTSA) of the United States Department of Transportation (USDoT). Big Data web-based visualization software are discussed that are both JavaScript-based and user interface-based. Challenges and opportunities of using Big Data with fog computing are also discussed.
Background: Introduction

In this background chapter, we present a brief history of the roots of HCI and UX and how the related paradigms have shifted over time. Other background topics include the role of fun interaction at work, phenomenology in UX, who introduced the waterfall model, and silos, walls, and boundaries in development teams and their impact on UX.
ExperienceDNA

It is often challenging to measure participants' reactions during user tests where a Wizard-of-Oz (WoZ) method is applied. This method is applied by the observers to validate functionalities of concepts that are difficult to build. In this study, a new technique is developed using virtual reality (VR) to improve the measurement of the participant's reactions during such a test. In this system, called ExperienceDNA, VR user tests can be monitored and controlled through a desktop interface. In addition, physiological trackers (eye tracking and heart rate monitoring) are used to measure what the participant is looking at and to gauge their preferences. Moreover, the use of VR allows for quick adaptations to the virtual environment the participant is confronted with. In this way, highly versatile tests can be conducted while minimising the initial setup effort. Our approach has been validated by performing a pilot test on a predefined use case. The qualitative feedback collected from analysing batches of data from the pilot test is presented in the results section. In conclusion, this paper covers the development, description and evaluation of the ExperienceDNA framework, as well as some ideas for future improvements to this framework.
Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’21)

Recommender systems were originally developed as interactive intelligent systems that can proactively guide users to items that match their preferences. Despite its origin on the crossroads of HCI and AI, the majority of research on recommender systems gradually focused on objective accuracy criteria paying less and less attention to how users interact with the system as well as the efficacy of interface designs from users’ perspectives. This trend is reversing with the increased volume of research that looks beyond algorithms, into users’ interactions, decision making processes, and overall experience. The series of workshops on Interfaces and Human Decision Making for Recommender Systems focuses on the ”human side” of recommender systems. The goal of the research stream featured at the workshop is to improve users’ overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems. In this summary, we introduce the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’21, review its history, and discuss most important topics considered at the workshop.
Child development and interaction design

This course will cover child development frameworks from the "classics" that have had a significant impact on interaction design, such as Piaget, Vygotsky, and Papert, to more recent ideas, such as dynamic systems approaches. The materials presented will include concepts such as embodiment, emergence, and plasticity. Hourcade will examine how these frameworks can inform the design, implementation, and evaluation of technologies for children with the goal of promoting healthy development.
Phishing Web Page Detection with HTML-Level Graph Neural Network

Phishing web page is one of the most serious threats to the users of the Internet. Traditional phishing web page detection methods rely on manually designed features. Recently, deep learning-based methods using HTML as input have achieved significant detection performance improvement. They usually treat HTML codes as sequences of characters and utilize Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) for classification. However, CNN and RNN typically can only extract local features in the HTML code sequences while failing to model the long-range semantics that is crucial for phishing detection. In this paper, we propose a novel Graph Neural Network (GNN) based phishing web page detection method that can effectively utilize the inherent structural information of HTML to capture the long-range semantics. We first naturally represent an HTML as a graph according to its Document Object Model (DOM) and utilize RNN to extract the local features of node attributes. Then we adopt GNN to model the long-range relations between nodes based on these local features and the graph structure. Our proposed model combines the advantage of RNN and GNN to better understand the intention of HTML codes. Extensive experiments on a real-world dataset demonstrate that the accuracy of our method outperforms other state-of-the-art methods by a large margin.
Classification methods, Deep Learning Architecture, Data source and Challenges in Detection of Breast Cancer

Different types of cancer can be prevented, screened for and/or detected and treated at an early stage. According to recent statistics breast cancer has a mortality rate of 12.7 per one lakh women. Mutation of genes at an abnormal rate leads to cancer. Changes in the size, color, texture and constant pain are the initial symptoms of breast cancer. A person presented with these symptoms requires breast cancer screening which would help in the diagnosis. Early detection can help health care professionals to start with the treatment, thereby reducing the mortality rate. Recent advances in breast cancer detection have proven to aid both medical professional and patients in making health care decisions. In this paper image acquisition technique, classification techniques, deep learning models and data sets available are highlighted.
The Effect of Remittances on Financial Development: Evidence from a Meta-Analysis

Research into how remittances affect financial development of the remittance recipient countries has generated a great deal of interest. However, the outcome of these studies is far from being conclusive as there are increasing ambiguities and heterogeneity in terms of the remittances-financial development nexus. In this study, we conduct a meta-analysis of 79 empirical studies to determine the factors that explain the heterogeneity endemic in remittances-financial development linkage. Our results find that although there is a general positive effect of remittances on financial development, the economic effect is modest. Also, we find that effect is more pronounced for studies that estimate long-run effects compared to short-run effects. In addition, we notice regional differences in the effect, however, these differences disappear when we control for publication bias and heterogeneity. Our results demonstrate the likelihood of publication bias in this literature using both linear and nonlinear techniques.
Low-light image enhancement and acceleration processing based on ZYNQ

Most embedded vision systems are used to capture an image in low-light condition which results in an image with low visibility and quality. However, high-performance image enhancement algorithms take up lots of chip resources and are time consuming in embedded vision application scenarios with high real-time requirements. In this paper, a multi-core heterogeneous embedded vision system is designed based on ZYNQ, and it uses the optimized screen blending algorithm to complete the enhancement of low-light images. First, the inverse color coefficient is optimized to enhance the texture details of the image. Secondly, adaptive gamma correction is performed on the inverted image to maintain the trueness of the enhanced image color. Finally, through reasonable hardware and software co-design, the inverse color coefficient is calculated by hardware logic circuit to speed up the algorithm. Experimental results show that the low-light images collected and processed by this system have a certain improvement in the image quality than the original algorithm. The processing speed is nearly 5 times faster than without hardware acceleration, which meets the real-time requirements of embedded vision systems.
Face Recognition Algorithm Based on Broad Learning System

Face recognition is a well-known issue in the realm of image processing, which has made tremendous strides in recent years, owing to the rapid development of artificial intelligence technology, and has become one of the most prominent research areas in a variety of fields. However, when uncontrollable variables such as light, face occlusion, and expression change are present, the recognition accuracy suffers as a result of the change in facial features. Face recognition in a complex environment is challenging since the accuracy of the algorithm is insufficient. This paper proposes a k-means clustering face recognition method based on the Broad Learning System (BLS),and discusses the principle and performance of the algorithm. The experimental results demonstrate that the proposed strategy improves identification accuracy and is more resistant to noise interference without requiring any changes to the model structure.
Improvement Optical Character Recognition for Structured Documents using Generative Adversarial Networks

The Optical Character Recognition (OCR) models have improved a lot in the last years thanks to the advance of machine learning techniques. However, there is still a great challenge when images are too distorted, such as old scanned documents, where the characters are blurred or/and with irregular backgrounds. Document digitization is a crucial task in many enterprises to get all their information available. For industries with long life cycles, such as energy and mining, old reports are relevant. This paper proposes a methodology to improve the performance of OCR algorithms. It uses conditional generative adversarial networks (GANs) for improving the quality of rendered text in images, which maximizes the performance of the OCR algorithms. We created a synthetic dataset, which emulates most of the problems presents on scanned documents, to train the network. Besides synthetic data, we tested our methodology on real images collected from USA journals and Brasilian thesis. The results showed relevant improvements, in particular when evaluated images were too distorted. Besides, we performed experiments with text in English and Portuguese, demonstrating the efficiency of our work in different languages. The proposed methodology overcomes most of the problems that impact the quality of Tesseract, the OCR algorithm used in this work. Our results demonstrated the capability of the proposed OCR system over other algorithms in low-quality documents. Metrics of errors showed a decrease of up to 50% regarding the best results without using our methodology.
Academic Libraries in Partnership With the Government Publishing Office

The implementation of the Government Printing Office Electronic Information Access Enhancement Act of 1993 (P.L. 103-40) brought the Federal Depository Library Program (FDLP) fully into the digital age. The transition has created expected and unexpected changes to the way the Government Publishing Office (GPO) administers the FDLP and, in particular, to the relationships between the GPO and academic depository libraries. Innovative partnerships, use of emerging technologies to manage and share collections, and greater flexibility on the part of the GPO have given academic depository libraries a prominent and proactive role within the depository program. Newly announced initiatives from the GPO, the National Plan for Access to U.S. Government Information and the Federal Information Preservation Network (FIPNet) potentially could either increase academic depository libraries' collaboration with the FDLP and the likelihood that they will remain in the program, or accelerate the rate at which academic depositories are dropping depository status.
Applying Digital Pedagogy at HCMC Industry and Trade College

In EMVITET Erasmus+ capacity building project the digital pedagogy has been a very central theme in developing Education 4.0. Digital pedagogy plays an essential role in modern education and its role in ensuring and improving the quality of training is even further emphasised when COVID-19 pandemic is still unpredictable around the world. However, thorough application of digital pedagogy at HCMC Industry and Trade College (HITC) has not received proper attention from the majority of lecturers. The authors have applied digital pedagogy to teach theory with the main goal of assessing student satisfaction and the results show that the digital pedagogy is feasible to ensure and improve the quality of training in present and in the future.
Development of Language Accuracy Using Synchronous and Asynchronous Learning Activities

Language teaching is a field in constant evolution and the use of technology has boosted its development. This article explores the design and the implementation of synchronous and asynchronous learning activities to develop oral skills in an online English course. The article uses a mix-methods approach that combines qualitative and quantitative research methods. The participants in the study were 6 graduate students. The main findings of this investigation show that the synchronous learning activities prompt errors that translate into opportunities for learning. These results show that the use of asynchronous learning activities produce errors despite the fact that the participants have time to prepare and anticipate language inaccuracies. The results suggest that the use of asynchronous learning activities promotes the development of language awareness as participants can identify general categories of errors.
TransDeeplabv3: Multi-Prior Segmentor for Medical Image Segmentation

At present, medical image segmentation is a challenging and developing task in medical image field, in which semantic segmentation algorithms play an important role in many clinical applications. The atrous spatial pyramid pooling (ASPP) module of Deeplab-V3 can well balance the diversity, robustness, and connectivity of global feature map extraction under different scenarios. However, there is still a large improving prospect for medical image segmentation. Therefore, we introduce mechanisms of multi-group parallel processing, mass guided loss focusing on irregular shape, and k-nearest attention architecture, respectively, to achieve higher robustness and stronger global connectivity with more sufficient receptive field compared to original methods. The proposed model effectively alleviates the deficiencies of local semantic receptive fields, improves the ability of generalization of our model, and overcomes the agnosticism of feature spatial distribution. Under the premise of maintaining feature diversity and enhancing heterogeneous information correlation, we obtain better segmentation performance in our experiments by enriching global feature representations. We conduct a series of segmentation experiments using the proposed model and on medical image datasets: STARE, CHASE, DRIVE and HRF. Compared with Deeplab-V3 baseline, the performance is significantly improved in terms of several metrics, which verifies the effectiveness of our method.
Select-Storage: A New Oracle Design Pattern on Blockchain

The blockchain system allows various trans-actions and information storage to be executed in a decentralized manner, while smart contracts require multiple nodes to be executed in the local sandbox environment according to preset settings to ensure the consistency of each node, which makes smart contracts unable to proactively obtain data from the outside world. Decentralized oracle can realize the acquisition of off-chain data with a low speed under the premise of ensuring the decentralization of the blockchain. Some oracles use on-chain data storage and maintenance to speed up data acquisition, but this will face higher costs of data storage and maintenance, so current oracles cannot simultaneously ensure privacy and security while taking into account execution cost and processing speed. In this article, we propose Select-Storage, a new oracle design pattern to achieve low operating cost and high processing speed without compromising security. Through experimental analysis, and comparison with other design patterns in processing time and on-chain and off-chain call costs, we have proved the superiority of the Select-Storage design pattern.
A Cloud Based Solution for Collaborative and Secure Sharing of Medical Data

Healthcare sector is under pressure to reduce costs while delivering high quality of care services. This situation requires that clinical staff, equipment and IT tools to be used more equitably, judiciously and efficiently. In this sense, collaborative systems have the ability to provide opportunities for healthcare organizations to share resources and create a collaborative working environment. The lack of interoperability between dissimilar systems and operating costs are the major obstacle to the implementation of this concept. Fortunately, cloud computing has great potential for addressing interoperability issues and significantly reducing operating costs. Since the laws and regulations prohibit the disclosure of health information, it is necessary to carry out a comprehensive study on security and privacy issues in cloud computing. Based on their analysis of these constraints, the authors propose a simple and efficient method that enables secure collaboration between healthcare institutions. For this reason, they propose Secure Multi-party Computation (SMC) protocols to ensure compliance with data protection legislation. Specifically, the authors use Paillier scheme to protect medical data against unauthorized usage when outsourcing computations to a public cloud. Another useful feature of this algorithm is the possibility to perform arithmetic operations over encrypted data without access to the original data. In fact, the Paillier algorithm is an efficient homomorphic encryption that supports addition operations on ciphertexts. Based on the simulation results, the proposed framework helps healthcare organizations to successfully evaluate a public function directly on encrypted data without revealing their private inputs. Consequently, the proposed collaborative application ensures privacy of medical data while completing a task.
Optimizing Homomorphic Encryption based Secure Image Analytics

Data privacy is a growing concern as more cloud-based solutions for extracting insights from images are available. Fully Homomorphic Encryption (FHE) is one of the state-of-the-art techniques to enable privacy preserving machine learning. However, encrypted deep neural network-based inference methods face the fundamental challenge of optimizing computational depth and complexity to achieve an acceptable trade-off between accuracy and practical feasibility. Existing works only report high level implementations without providing rigorous analysis of the intricacies involved in the FHE implementation of generic primitive operators of a convolutional neural network (CNN). In this paper, we use the CKKS encryption scheme available in the open-source HElib library to run encrypted inference experiments on the MNIST dataset. The experiments indicate that efficient ciphertext packing schemes, model optimization and multi-threading strategies play a critical role in determining the throughput and latency of the inference process. We also show that operational parameters of the chosen FHE scheme such as the degree of the cyclotomic polynomial, depth limitations of the underlying leveled HE scheme, and the computational precision parameters result in significant trade-offs between accuracy, security level and computational time of the machine learning model. The key contribution of the paper is the analysis and recommendation of optimization techniques for efficient encrypted CNN inference.
Information Seeking Models in the Digital Age

A model may be defined as a structure for thinking about a problem and may evolve into a statement of the relationships among theoretical propositions. Most models of information behavior are generally the statements, often in the form of diagrams that attempt to explicate an information-seeking activity, the causes and consequences of that activity, or the relations among stages in information-seeking behavior. This chapter explores, introduces, and discusses select information-seeking models and explains various elements of each model. Various online resources like database, research articles, and other web tools will be accessed to retrieve relevant information related to the select models. Further, the chapter also focuses on the diagrammatic or pictorial representation of each model.