Reviewerly/exHarmony huggingface dataset:
Using facial landmarks to assist the stereo matching in fringe projection based 3D face profilometry

A three-dimensional (3D) face profilometry based on facial landmarks and a multi-view system is proposed. Theoretically, at least three fringe patterns are required in phase-shifting profilometry, but the stereo relative phase pair obtained by a spatial unwrapping algorithm cannot be used for stereo matching directly because the starting points of phase unwrapping are not on the same fringe period order, so it will result in incorrect disparity. We proposed an innovative method that utilizes facial landmarks as prior knowledge to adjust the relative phase pair to the same phase reference for accurate stereo matching through a phase adjustment algorithm to determine the accurate fringe order difference. The three-step phase-shifting patterns are projected in a looping sequence, and each three-adjacent fringe stereo pair is treated as a group to obtain one frame of a 3D face, which means that the 3D face output of our proposed method can achieve the same frame rate as the camera. The accuracy evaluation and dynamic 3D facial expressions reconstruction verify the success of the proposed method.
Mobile Augmented Reality and Outdoor Education

This paper describes a research project that explored the use of mobile augmented reality combined with outdoor education in a Swedish primary school. Special attention was paid to mobile augmented reality (AR). Project documentation and communication were reviewed, three iterations of design and usability evaluations of the Minnesmark mobile AR platform was made. Observations and interviews were held with teachers and students. The results describe early experiments, and both the opportunities and challenges that faced the participating teachers. The opportunities included connecting the outdoor learning environment and the indoor learning environment, directing the students' focus, posing questions and affording actions, rendering symbols and timelines visible, providing a point of entry to the context and the narrative, and facilitating conceptualization. Challenges concerned how to make pedagogical use of the landscape, producing or choosing content, structuring processes, setting up student groups, and aligning activities and content with learning objectives. It is concluded that the teachers and the students used the mobile augmented reality to make places in the local environment, outside the classroom, the starting point to increase the authenticity of the teaching.
Virtual reality applications in education: a developing country perspective

Virtual reality applications in education have emerged as rapidly developing technologies that have the potential to change education. However, the adoption of these applications has been relatively slow comparing to other applications. This study investigated the factors that affect students' intention to adopt virtual reality applications in education. Utilising the innovation of diffusion theory, the study investigated the factors that affect VR applications' adoption. These factors include social influence, image, and self-efficacy. The moderating influence of gender and age was also investigated. Results show that relative advantage, observability, compatibility, and complexity have a significant positive impact on VR apps adoption, complexity has a negative impact, and trialability has no significant impact. The results also supported the moderating impact of gender and age. Implications, recommendations, and suggestions for future work are presented and discussed.
Multi-channel support and ticketing interface for online support management system platforms

This paper discussed the development of multi-channel support and ticketing interface for online support management system (OSMS) platforms that streamline the entire support requests and convert the stakeholder's queries coming from multiple channels to provide a holistic view of all ticket-related information in one place. The system increases organization efficiency, allowing the technical support agents to track, prioritize, and resolve support queries within a single interface. The researcher anchors the study on research and development (R&D) which aims to understand a subject matter more completely and build on the body of knowledge relating to it. The data collection technique used was unstructured interviews, document analysis, and modified survey-questionnaire adapted from ISO/IEC 25010 software product quality models had been used for system evaluation. The results showed that the respondents were satisfied with the features and capabilities of the developed system and complied with the specifications based on ISO/IEC 25010 software product quality standards.
IDENTIFYING BEHAVIORAL CONSTRUCTS IN RELATION TO USER CYBERSECURITY BEHAVIOR

A behavior such as enabling two factor authentication has a positive impact on a users’ information security. It is assumed that given the benefits, users will want to perform this cybersecurity related behavior. However, some users choose not to perform the beneficial security behavior. Varied explanations have been provided as to why users choose to perform or not perform cybersecurity behaviors. The factors that influence users in the decision making of whether to perform or not perform a cybersecurity related behavior are referred to as constructs. This study seeks to combine the results of selected studied, with the aim of identifying prominent user cybersecurity behavior constructs, as well as the relationships between the constructs. The contributions made by the study is the consolidated visualization of behavior constructs that have an influence on user cybersecurity behavior. Furthermore, the study also provides practical applications of the cybersecurity behavior constructs. To achieve the goals of the study, a literature review is used as the study methodology. Data from previous studies is systematically collected, and analyzed. The study makes use of the Theoretical Domains Theory as a tool, which aids in consolidating the different behavior constructs found in cybersecurity literature. The constructs Beliefs about Capabilities, Beliefs about Consequences, Reinforcements, Social Influences, Intentions, Emotions, Social/Professional Role and Identity, Knowledge and Skills are found to have influence on cybersecurity behavior.
A New Feature Hashing Approach Based on Term Weight for Dimensional Reduction

Machine learning models usually face a problem when encountered with large scale text dataset. Such kind of dataset produces sparse features of a high-dimensional, which makes it complex or infeasible to process by the learning models. Feature hashing is a dimensional reduction technique commonly used in the pre-processing phase to overcome the aforementioned problem. However, models performance are negatively affected due to the inherited so-called collisions that occur during the hashing process. In this study, we proposed a new Feature hashing approach that hashes similar features to the same bin based on their weight known as "weight term" while minimizing certain collisions. The approach effectively reduces the collisions between dissimilar features, thus improving model performance. The experiment results conducted on binary and multi-class classification datasets with a very high number of sparse features show that the proposed approach achieved competitive performance compared with the conventional FH.
Recognition and Classification of Glomerular Pathological Images Based on Deep Learning

<p indent=0mm>The identification and classification of glomeruli in pathological sections is the key to diagnosing the degree and type of renal lesions. In order to solve the problem of glomerular recognition and classification, a complete glomerular detection and classification framework based on deep learning is designed. Glomeruli are detected and classified in the entire slice image. The framework includes four stages of glomerular recognition. In the first stage of scanning window generation, a new network framework, RGNet, is designed to initially determine the possible location of glomeruli. In the second stage of detection and coarse classification, Faster R-CNN is improved for glomerular data. In the third stage, the NMS-Lite algorithm is designed based on the NMS algorithm to merge the detected glomeruli. In the fourth stage of fine classification, two neural networks are trained using data augmentation to classify the degree of glomerular lesions. The experimental results has show that the glomerulus detection method proposed in this paper has achieved comparable accuracy on the test set with similar methods, and to a certain extent solves the problem that similar types of glomeruli are difficult to distinguish.
Special issue: Informatics &amp;amp; data-driven medicine

&lt;abstract&gt; &lt;p&gt;The current state of the development of Medicine today is changing dramatically. Previously, data of the patient's health were collected only during a visit to the clinic. These were small chunks of information obtained from observations or experimental studies by clinicians, and were recorded on paper or in small electronic files. The advances in computer power development, hardware and software tools and consequently design an emergence of miniature smart devices for various purposes (flexible electronic devices, medical tattoos, stick-on sensors, biochips etc.) can monitor various vital signs of patients in real time and collect such data comprehensively. There is a steady growth of such technologies in various fields of medicine for disease prevention, diagnosis, and therapy. Due to this, clinicians began to face similar problems as data scientists. They need to perform many different tasks, which are based on a huge amount of data, in some cases with incompleteness and uncertainty and in most others with complex, non-obvious connections between them and different for each individual patient (observation) as well as a lack of time to solve them effectively. These factors significantly decrease the quality of decision making, which usually affects the effectiveness of diagnosis or therapy. That is why the new concept in Medicine, widely known as Data-Driven Medicine, arises nowadays. This approach, which based on IoT and Artificial Intelligence, provide possibilities for efficiently process of the huge amounts of data of various types, stimulates new discoveries and provides the necessary integration and management of such information for enabling precision medical care. Such approach could create a new wave in health care. It will provide effective management of a huge amount of comprehensive information about the patient's condition; will increase the speed of clinician's expertise, and will maintain high accuracy analysis based on digital tools and machine learning. The combined use of different digital devices and artificial intelligence tools will provide an opportunity to deeply understand the disease, boost the accuracy and speed of its detection at early stages and improve the modes of diagnosis. Such invaluable information stimulates new ways to choose patient-oriented preventions and interventions for each individual case.&lt;/p&gt; &lt;/abstract&gt;
Data-Efficient Information Extraction from Documents with Pre-trained Language Models

Like for many text understanding and generation tasks, pre-trained languages models have emerged as a powerful approach for extracting information from business documents. However, their performance has not been properly studied in data-constrained settings which are often encountered in industrial applications. In this paper, we show that LayoutLM, a pre-trained model recently proposed for encoding 2D documents, reveals a high sample-efficiency when fine-tuned on public and real-world Information Extraction (IE) datasets. Indeed, LayoutLM reaches more than 80% of its full performance with as few as 32 documents for fine-tuning. When compared with a strong baseline learning IE from scratch, the pre-trained model needs between 4 to 30 times fewer annotated documents in the toughest data conditions. Finally, LayoutLM performs better on the real-world dataset when having been beforehand fine-tuned on the full public dataset, thus indicating valuable knowledge transfer abilities. We therefore advocate the use of pre-trained language models for tackling practical extraction problems.
Sharing of Network Flow Data across Organizations using Searchable Encryption

Given that an increasingly larger part of an organization's activity is taking place online, especially in the current situation caused by the COVID-19 pandemic, network log data collected by organizations contain an accurate image of daily activity patterns. In some scenarios, it may be useful to share such data with other parties in order to improve collaboration, or to address situations such as cyber-security incidents that may affect multiple organizations. However, in doing so, serious privacy concerns emerge. One can uncover a lot of sensitive information when analyzing an organization's network logs, ranging from confidential business interests to personal details of individual employees (e.g., medical conditions, political orientation, etc). Our objective is to enable organizations to share information about their network logs, while at the same time preserving data privacy. Specifically, we focus on enabling encrypted search at network flow granularity. We consider several state-of-the-art searchable encryption flavors for this purpose (including hidden vector encryption and inner product encryption), and we propose several customized encoding techniques for network flow information in order to reduce the overhead of applying state-of-the-art searchable encryption techniques, which are notoriously expensive.
Gender Detection with Smartphone Motion Sensors Using Convolutional Neural Networks

In the literature, there are many studies showing that user characteristics such as age and gender can be obtained by using sensor data from smart devices. Behavioral differences in users' interactions with smart devices make these studies effective. In this study, it is shown that the gender of the user can be recognized using data from the accelerometer sensor on smartphones. In this context, we develop an Android application and collect data from 120 users interacting with their devices while walking. Then, we extract features in order to analyze the effects of differences in users' touching and gait behaviors on sensor readings. For gender detection, machine learning methods are firstly investigated and several classification algorithms are applied. In addition, we develop a Convolutional Neural Network (CNN) model and test it on our dataset. Consequently, we obtain an %88.3 success rate with CNN and also show that the CNN model outperforms other machine learning methods.
Blockchain and citizenship: Uneasy bedfellows

Distributed Ledger Technology can be an effective tool for resource distribution. As individuals and organisations explore innovations which allow to redefine the rules of access, possession and sharing these developments also become important for the future of self-determination. Demonstrated through credit scoring and ‘social credit systems’, the identity of an individual is intertwined with resource access, possession and transferability. A key pre-requisite for participation is formal legal status, which translates to citizenship. However, many proponents of Distributed Ledger Technology focus predominantly on technological features and capabilities, which might enable the implementation of concepts such as decentralised governance, ‘self-sovereign identity’ management, and trust-less transactions based on ‘zero-knowledge proof’. Nevertheless, such narrow consideration overlooks existing legal and political realities. Considering the lessons learned from citizenship, it becomes questionable whether Blockchain as player in the area of identity management will ultimately increase human dignity, or further manifest traditional patterns of discrimination and inequality.
the Characterization of high school students in the department of Risaralda using the Chi-Square metric

The education is a fundamental law that promotes the social and economic development of a nation. Also, it is how a country can achieve its sustainable development objectives. Because of this, various international organizations (Unicef-UNESCO- Banco Mundial -OECD) are promoting the coverage of education in underdeveloped countries. On the other hand, projects such as PISA that aim to carry out annual evaluations in more than 30 countries that serve as a reference of the educational level and encourages nations to improve the quality of education. Therefore, the objective of this work is to implement a methodology to predict the results of ICFES tests SABER 11 °. that can correct the educational problems that Colombia has presented in secondary education. For the development of this methodology, the ICFES repository database was used. Data were pre-processed using MATLAB software. Also, combined tests were carried out with the new Chi-Square metric developed by the researchers of the automation group of the Technological University of Pereira, obtaining results that were 20% higher compared with conventional classification techniques. In this project, the most influential characteristics were found in the students, who are responsible for the loss of the ICFES SABER 11° exam in Risaralda.
Quantum Relational Hoare Logic with Expectations

We present a variant of the quantum relational Hoare logic from (Unruh, POPL 2019) that allows us to use expectations in pre- and postconditions. That is, when reasoning about pairs of programs, our logic allows us to quantitatively reason about how much certain pre-/postconditions are satisfied that refer to the relationship between the programs inputs/outputs.
Exploring a Board Game to Improve Cloud Security Training in Industry (Short Paper).

Nowadays, companies are increasingly using cloud-based platform for its convenience and flexibility. However, companies still need to protect their assets when deploying their infrastructure in the cloud. Over the last years, the number of cloud-specific vulnerabilities has been increasing. In this work, we introduce a serious game to help participants to understand the inherent risks, understand the different roles, and to encourage proactive defensive thinking. Our game includes an automated evaluator as a novel element. The players are invited to build defense plans and attack plans, which will be checked by the evaluator. We design the game and organize a trial-run in an industrial setting. Our preliminary results bring insight into the design of such a game, and constitute the first step in a research using design science.
POTENTIALS AND CHARACTERISTICS OF LANDSAT IMAGERY IN RELATION TO LAND USE /COVER IN OKITIPUPA METROPOLIS, ONDO STATE, NIGERIA

Landsat satellite imagery plays a crucial role in providing information on land use/cover modifications on local, regional, and global scales, especially where aerial photographs are missing. Monitoring land-use changes from past to present tends to be time-consuming especially when dealing with ground-truth information. Determining the past and current land-use change on Earth's surface using Landsat imagery tends to be effective and efficient when high-resolution imagery is unavailable. This study employed the use of Landsat satellite imagery to assess the past and present land use/cover using supervised classification and Normalized Difference Vegetation Index (NDVI). The result of the supervised classification land use/cover showed that forest cover and woodland undergo rapid loss, while farmland, wetland, built-up, and waterbodies tend to experience gradual loss. The NDVI demonstrated that farmland and forest cover was the most affected land use/cover. Hence, land use/cover of the study area is affected by human activities, such as intensive farming, population size, and deforestation.&#x0D;
Comparative Breast Cancer Detection with Artificial Neural Networks and Machine Learning Methods

The number of cancer patients is increasing all over the world due to reasons such as environmental pollution, excess of technological and biological wastes, climate change, and increased consumption of unnatural foods. According to the data of the World Health Organization in 2020, there are over 19 million cancer patients in the world. The most common type of cancer is breast cancer with 11.7%. Breast cancer is as high as 24.5% in women and 15.5% of these cases result in death. Therefore, it is important to detect cancer at an early stage. The purpose of this study is to detect cancer in the fastest way and with the highest accuracy rates. In the study, high performance results were obtained by comparing artificial neural networks and traditional machine learning methods. In the study; Logistic Regression (LR), k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Xgboost (XGB) and Artificial Neural Network (ANN) were used. The highest result obtained using traditional machine learning methods is 98.08% with LR. In addition, using ANN, the highest value was obtained with an accuracy rate of 99.36%.
Efficient Multi-Organ Multi-Center Cell Nuclei Segmentation Method Based on Deep Learnable Aggregation Network

Automated cell nuclei delineation in whole-slide imaging (WSI) is a fundamental step for many tasks like cancer cell recognition, cancer grading, and cancer subtype classification. Although numerous computational methods have been proposed for segmenting nuclei in WSI images based on image processing and deep learning, existing approaches face major challenges such as color variation due to the use of different stains, the various structures of cell nuclei, and the overlapping and clumped cell nuclei. To circumvent these challenges in this article, we propose an efficient and accurate cell nuclei segmentation method based on deep learning, in which a set of accurate individual cell nuclei segmentation models are developed to predict rough segmentation masks, and then a learnable aggregation network (LANet) is used to predict the final nuclei masks. Besides, we develop cell nuclei segmentation software (with a graphical user interface—GUI) that includes the proposed method and other deep-learning-based cell nuclei segmentation methods. A challenging WSI dataset collected from different centers and organs is used to demonstrate the efficiency of our method. The experimental results reveal that our method obtains a competitive performance compared to the existing approaches in terms of the aggregated Jaccard index (AJI=89.25%) and F1-score (F1=73.02%). The developed nuclei segmentation software can be downloaded from https://github.com/loaysh2010/Cell-Nuclei-Segmentation-GUI-Application.
Recent Advances in Edge Computing Paradigms

Edge computing is an exciting new approach to network architecture that helps organizations break beyond the limitations imposed by traditional cloud-based networks. It has emerged as a viable and important architecture that supports distributed computing to deploy compute and storage resources closer to the data source. Edge and fog computing addresses three principles of network limitations of bandwidth, latency, congestion, and reliability. The research community sees edge computing at manufacturing, farming, network optimization, workplace safety, improved healthcare, transportation, etc. The promise of this technology will be realized through addressing new research challenges in the IoT paradigm and the design of highly-efficient communication technology with minimum cost and effort.