Reviewerly/exHarmony huggingface dataset:
Multiculturalism as a pedagogical category of integrative-modular approach in education

The article is devoted to the study of problems of multicultural education, where not all aspects have received the necessary degree of scientific validity, which is reflected in the terminological and conceptual apparatus.The study of the history of the issue allows us to talk about the relevance and complexity of the stated problem.The authors of the article cited six main positions, revealing the complexity of the problem posed by scientists from different countries.And they unanimously agree that the main goal is to provide quality education at all levels to every member of the society.The results of the research and the questioning of the respondents determined the relevance of multicultural knowledge, which is necessary for conducting professional activities in a multicultural environment, taking into account the peculiarities of the socio-cultural situation of development.The students are obliged to know the modern problems of multicultural education, the history of the emergence and formation of the ideas of multicultural education.Methods of comparison and generalization allowed the authors of this article to determine the range of problems of modern multicultural education, which is considered as a means of development of ethnocultural and professional competence.In addition, the results of the respondents' questionnaire on the definition of the tasks and content of multicultural education are given.On the basis of these results, formed by conducting sociological surveys, the directions and conceptual provisions of the ideas of multicultural education in pedagogical science are proposed.The practical significance of the study is aimed at forming the sum of competencies for carrying out professional activities in a multicultural environment.
PEMBUATAN GAME ARCADE-STYLE RACING “CARSTIME” DENGAN TEMA ANTIGRAVITASI BERBASIS ANDROID

This game is made using Unity3D game engine with C# programming language. This game is developed for Android platform. In this game, player will race against bots on track that is not bound by gravity so each car can move even on upside down track. There are three cars that can be used by players, namely C.car, Sonic and Magnum. There are three stages, namely. Map Aspal which doesn’t have any additional difficulties, Map Pasir which makes player’s car move slower and Map Salju which makes player’s car slip. There are three items that player can collect or interact while racing, namely coin which player can collect for buying car, boost can which player can collect to fill boost meter which can be used when full to increase player’s speed for a certain time, and boost pad which will increase player’s and bots speed for certain time when touched. This game is tested by using three methods which are blackbox testing, alpha testing and beta testing. Based on the result, it can be concluded that “CarsTime” has an interesting racing gameplay with hard difficutly on Map Salju and easy difficulty on Map Aspal and Map Pasir
MMO-Net (Multi-Magnification Organ Network): A use case for Organ Identification using Multiple Magnifications in Preclinical Pathology Studies

Identifying organs within histology images is a fundamental and non-trivial step in toxicological digital pathology workflows as multiple organs often appear on the same whole slide image (WSI). Previous works in automated tissue classification have investigated the use of single magnifications, and demonstrated limitations when attempting to identify small and contiguous organs at low magnifications. In order to overcome these shortcomings, we present a multi-magnification convolutional neural network (CNN), called MMO-Net, which employs context and cellular detail from different magnifications to facilitate the recognition of complex organs. Across N=320 WSI from 3 contract research organization (CRO) laboratories, we demonstrate state-of-the-art organ detection and segmentation performance of 7 rat organs with and without lesions: liver, kidney, thyroid gland, parathyroid gland, urinary bladder, salivary gland, and mandibular lymph node (AUROC=0.99-1.0 for all organs, Dice≥0.9 except parathyroid (0.73)). Evaluation takes place at both inter- and intra CRO levels, suggesting strong generalizability performance. Results are qualitatively reviewed using visualization masks to ensure separation of organs in close proximity (e.g., thyroid vs parathyroid glands). MMO-Net thus offers organ localization that serves as a potential quality control tool to validate WSI metadata and as a preprocessing step for subsequent organ-specific artificial intelligence (AI) use cases. To facilitate research in this area, all associated WSI and metadata used for this study are being made freely available, forming a first of its kind dataset for public use.
Occluded-based Facial Recognition under mask using Deep learning method

At present, under the epidemic prevention and control, face recognition is facing the problem of masks. The traditional face recognition system dataset is composed of a face without mask pictures, and when the mask wearers need to do the face recognition, they have to take off the mask, which makes the epidemic situation environment increase the burden of prevention and control. Therefore, effectively improving the efficiency of face recognition under face masking is still a difficult point in the recognition system. In this paper, we aim to conduct facial mask detection to detect the person with or without the mask. Three steps are included in our method, including facial detection, adding mask and facial detection with mask. To achieve this goal, we aim to use MTCNN and FaceNet as the main components of our method. Our method can achieve 76.42% accuracy among all the data. The results on both real and generated dataset validate the effectiveness of out method.
CrawLabel

End-to-end test cases that exercise the application under test via its user interface (UI) are known to be hard for developers to read and understand; consequently, diagnosing failures in these tests and maintaining them can be tedious. Techniques for computing natural-language descriptions of test cases can help increase test readability. However, so far, such techniques have been developed for unit test cases; they are not applicable to end-to-end test cases.
Bridging Signals and Human Intelligence

Large-scale Internet-of-Things (IoT) environments such as Intelligent Transportation Systems are facing tremendous challenges wrt. monitoring their operational technology (OT) not least due to its inherent heterogeneous and evolutionary nature. This situation is often aggravated by the lack of machine-interpretable information about the interdependencies between OT objects in terms of “semantic relationships”, thus considerably impeding the detection of root causes of cross-system errors or interrelated impacts. Therefore, we propose a novel hybrid approach for identifying semantic relationships based on both, mined functional correlations between OT objects based on log files and domain knowledge in terms of an IoT meta model. For this, we firstly contribute a systematic discussion of associated challenges faced in large-scale IoT environments, secondly, we put forward an IoT meta model based on both, industry standards and academic proposals, and finally, we employ this meta model as guidance and target template for the automatic population of semantic relationships into an OT ontology.
Reflecting on Design Methods and Democratic Technology Development: The Case of Dutch Covid-19 Digital Contact-Tracing Application

This article explores the idea that democratic technology development in public governance can be enhanced by adding an experiential dimension to it. Our work is situated in the context of an appathon organized by the Dutch government to initiate the development of a Covid-19 contact-tracing application. The appathon stimulated a multifaceted debate on technology design and societal values, and raised a crucial question: how can design methods enhance democratic technology development? To answer this question, we first identify three main democratic values (i.e., citizen participation, collective decision making, and critical engagement) that underpin three influential design methods: participatory design, adversarial design, and speculative-critical design. Next, based on design theory, we argue that these methods can bring three experiential qualities to democratic technology development: ownership, contestation, and imagination. We then situate this theoretical reflection in a reflexive thematic analysis of publicly available discussions that took place during the appathon. This makes it possible to identify opportunities to deploy participatory design, adversarial design, and speculative-critical design to engage citizens in political decision making directly and experientially. Based on our analysis, we highlight how abductive design reasoning may help iteratively deliberate sociotechnical challenges when using participatory design, adversarial design, and speculative-critical design. Ultimately, this paper explicates the role of design methods and practices in political participation.
Penggunaan Learning Management System di Pendidikan Tinggi Pada Masa Pandemi Covid-19: Model UTAUT

Technological advances in the last 10 (ten) years have increased the use of mobile devices, and the availability of the internet has made online learning a trend, especially during the Coronavirus Disease (COVID-19) pandemic where learning activities are centered in each other's homes, so it is necessary Learning Management System (LMS) supporting online learning. The data analysis method used in this study is Structural Equation Modeling-Partial Least Square (SEM-PLS) with a total research sample of 165 respondents consisting of students from the S1 Office Administration Education study program who have used the Virtual Learning platform at the Universitas Negeri Surabaya (VINESA) during the COVID-19 pandemic. The results obtained in this study are Performance Expectancy has a significant positive effect on Behavioral Intention in accepting and using VINESA during the COVID-19 pandemic, Social Influence significantly positive effect on Behavioral Intention in accepting and using VINESA during the COVID-19 pandemic, Facilitating Condition does not have a significant positive effect on Behavioral Intention in accepting and using VINESA during the COVID-19 pandemic, Effort Expectancy has a significant positive effect on Behavioral Intention in accepting and using VINESA during the COVID-19 pandemic, and Behavioral Intention has a significant positive effect on Use Behavior in accepting and using VINESA during the COVID-19 pandemic.
Brain tumor detection from 3D MRI using Hyper‐Layer Convolutional Neural Networks and Hyper‐Heuristic Extreme Learning Machine

Summary Automated techniques for brain tumor classification using deep learning approaches have gained significant research interest in recent years. Yet, the difficulties in extracting and classifying the tumor regions from the 3D Magnetic Resonance Imaging (MRI) do not have a definite solution. The major challenge in utilizing machine and deep learning algorithms for brain cancer classification from 3D images is the time complexity in analyzing the multiple frames of a brain MRI. This paper introduces Hyper‐Layer Convolutional Neural Networks (HL‐CNN) and Hyper‐Heuristic Extreme Learning Machine (HH‐ELM). The proposed method consists of three main phases are pre‐processing, deep feature mining and selection, and classification. The input MRI images are pre‐processed through denoising and image enhancement methods in the first phase. In the second phase, the HL‐CNN is introduced for feature extraction. The hyper‐layer technique is a masking technique that also inherent the features of the specified layers instead of only considering the features at the last layer. The best features are selected using a simple correlation‐based selection approach through HL‐CNN validation to minimize the irrelevant features in the system. In the last phase, the HH‐ELM is introduced to classify the tumor images to identify the different types of tumors. HH‐ELM is an enhanced version of ELM through optimal tuning of ELM parameters using a hyper‐heuristic optimization algorithm. Evaluations are performed over the BRATS 2020 database of MRI images and the proposed method of HL‐CNN and HH‐ELM achieved dice scores of 0.9020, 0.9393, and 0.9589 for ED, WT, and TC tumor classes with 95.89% accuracy, 98.46% precision, 96% recall, and 97.21% f ‐measure which are 2%–13% higher and processing time of 139.88 s which is 66%–78% lesser than the existing methods.
ARL Battlespace: a platform for developing novel AI for complex adversarial reasoning in MDO

A barrier to developing novel AI for complex reasoning is the lack of appropriate wargaming platforms for training and evaluating AIs in a multiplayer setting combining collaborative and adversarial reasoning under uncertainty with game theory and deception. An appropriate platform has several key requirements including flexible scenario design and exploration, extensibility across all five elements of Multi-Domain Operations (MDO), and capability for human-human and human-AI collaborative reasoning and data collection, to aid development of AI reasoning and the warrior-machinelike interface. Here, we describe the ARL Battlespace testbed which fulfills the above requirements for AI development, training and evaluation. ARL Battlespace is offered as an open source software platform (https://github.com/USArmyResearchLab/ARL_Battlespace). We present several example scenarios implemented in ARL Battlespace that illustrate different kinds of complex reasoning for AI development. We focus on ‘gap’ scenarios that simulate bridgehead and crossing tactics, and we highlight how they address key platform requirements including coordinated MDO actions, game theory and deception. We describe the process of reward shaping for these scenarios that will incentivize an agent to perform command and control (C2) tasks informed by human commanders’ courses of action, as well as the key challenges that arise. The intuition presented will enable AI researchers to develop agents that will provide optimal policies for complex scenarios.
Cloud Service Providers

Before we embark on the attack vectors that make the cloud akin to the Wild West, we need to establish that the cloud is not a mysterious entity that operates in the vacuum of deep space (insert your favorite episode of Firefly here). In fact, while there are some companies that run their own clouds exclusively for their own solution, the vast majority of companies, application vendors, and third-party service providers use one of a handful of leading cloud computing providers for all their services and hosted solutions.
Assessment of Desired Core Competencies and its Acquisition Barriers of Agricultural Extension Advisors in Nepal

Modern agriculture is the era of 21st century. Competency is an indispensable in every aspect of delivering services to their ultimate users. Competency is integration of skills, knowledge, attitude and behavior that trigger to perform the delegated services in precise and methodical manner. The objective of this study was to identify important core competency required and its acquisition barrier for extension advisors of Nepalese agriculture extension service. Data were obtained using survey questionnaire from officers working at federal, state and local government offices of the selected 18 districts. The questionnaire was composed of open and close-ended questions based on 56 indicators of the nine core competencies. The descriptive statistics were used to analyze the responses. The findings indicated respondent perceptions on its all nine core competencies- program planning, program implementation, communication skills, extension education and information technology, program evaluation, personal and professional development, diversity, subject matter expertise and emotional intelligence from important to very important ranges. Subject matter expertise was perceived most important and communication skills as least one. Subject matter expertise, extension education and information technology skills positively correlate with education. The limited training opportunities and high cost for acquiring training personally are major barriers to gain competencies.
Problems of Ensuring Data Security in Digital Management of Processes in Animal Husbandry

With the advancement of digitalization and digital transformation, more and more activities and services are being transferred to the digital space. Digital management of technological processes in animal husbandry is based on the use of data at all stages of animal husbandry. Food producers throughout the chain - farmers, processors, distributors, traders, regulators - need to guarantee data security in existing processes to ensure greater protection of data privacy and to have unaltered digital content on the whole chain. In this way, end-users can be informed about the path of food in today's digital global world. This article focuses on the problems of data security in digital management of livestock processes. An approach that combines TSA and Blockchain technologies is proposed for ensuring data traceability. Advantages of the sustainable uses of the blockchain technology combined with timestamp services for the animal husbandry chain are presented.
Application of QR code as a watermark in multi-modal person's identification

This paper presents the original concept of inserting a person's identity data into a photo. Prior to insertion, the identification data is encoded with a two-dimensional QR code. The resulting QR code is a binary watermark that is inserted into a person's color photo. Specialized algorithms based on the YCbCr color model and block SVD transformation have been developed for inserting and extracting QR code from photo. The presented concept can be classified into multi-modal identification because it allows instant link between the appearance and identity of a person. The advantage of the presented concept is reflected in the fact that the creation of an identification document can be done in the field without the need for a network connection. The results of the experiment confirm that the presented concept can be successfully used in the identification of persons in the field without the need to have a network connection.
A Learning-Based Iterated Local Search Algorithm for Solving the Traveling Salesman Problem

In this paper, we study the use of reinforcement learning in adaptive operator selection within the Iterated Local Search metaheuristic for solving the well-known NP-Hard Traveling Salesman Problem. This metaheuristic basically employs single local search and perturbation operators for finding the (near-) optimal solution. In this paper, by incorporating multiple local search and perturbation operators, we explore the use of reinforcement learning, and more specifically Q-learning as a machine learning technique, to intelligently select the most appropriate search operator(s) at each stage of the search process. The Q-learning is separately used for both local search operator selection and perturbation operator selection. The performance of the proposed algorithms is tested through a comparative analysis against a set of benchmark algorithms. Finally, we show that intelligently selecting the search operators not only provides better solutions with lower optimality gaps but also accelerates the convergence of the algorithms toward promising solutions.
Teaching Histology During Covid-19 Restrictions

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Reorganization of nursing work in an intensive care unit during the COVID-19 pandemic

To describe the reorganization of nursing work in an intensive care unit of a public hospital due to the COVID-19 pandemic.Report of the experience from February to April 2020, about the reorganization of a unit.The description of the experience was divided into four moments: Definition of the cohort isolation; Reorganization of the intensive care units as General and Respiratory; Health care teams and work shifts; and Wearing and removing protective clothing by the teams.The COVID -19 pandemic brought numerous challenges to the management of intensive care units. The socialization of management experiences can contribute to the definition of new strategies, including in the post-pandemic period.
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive  Learning

Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN). However, most HGNNs follow a semi-supervised learning manner, which notably limits their wide use in reality since labels are usually scarce in real applications. Recently, contrastive learning, a self-supervised method, becomes one of the most exciting learning paradigms and shows great potential when there are no labels. In this paper, we study the problem of self-supervised HGNNs and propose a novel co-contrastive learning mechanism for HGNNs, named HeCo. Different from traditional contrastive learning which only focuses on contrasting positive and negative samples, HeCo employs cross-viewcontrastive mechanism. Specifically, two views of a HIN (network schema and meta-path views) are proposed to learn node embeddings, so as to capture both of local and high-order structures simultaneously. Then the cross-view contrastive learning, as well as a view mask mechanism, is proposed, which is able to extract the positive and negative embeddings from two views. This enables the two views to collaboratively supervise each other and finally learn high-level node embeddings. Moreover, two extensions of HeCo are designed to generate harder negative samples with high quality, which further boosts the performance of HeCo. Extensive experiments conducted on a variety of real-world networks show the superior performance of the proposed methods over the state-of-the-arts.
One Shot Face Swapping on Megapixels

Face swapping has both positive applications such as entertainment, human-computer interaction, etc., and negative applications such as DeepFake threats to politics, economics, etc. Nevertheless, it is necessary to understand the scheme of advanced methods for high-quality face swapping and generate enough and representative face swapping images to train DeepFake detection algorithms. This paper proposes the first Megapixel level method for one shot Face Swapping (or MegaFS for short). Firstly, MegaFS organizes face representation hierarchically by the proposed Hierarchical Representation Face Encoder (HieRFE) in an extended latent space to maintain more facial details, rather than compressed representation in previous face swapping methods. Secondly, a carefully designed Face Transfer Module (FTM) is proposed to transfer the identity from a source image to the target by a non-linear trajectory without explicit feature disentanglement. Finally, the swapped faces can be synthesized by StyleGAN2 with the benefits of its training stability and powerful generative capability. Each part of MegaFS can be trained separately so the requirement of our model for GPU memory can be satisfied for megapixel face swapping. In summary, complete face representation, stable training, and limited memory usage are the three novel contributions to the success of our method. Extensive experiments demonstrate the superiority of MegaFS and the first megapixel level face swapping database is released for research on DeepFake detection and face image editing in the public domain. The dataset is at this link.