Reviewerly/exHarmony huggingface dataset:
Aplikasi Pengenalan Peta Indonesia untuk Anak Sekolah Dasar Menggunakan Augmented Reality Berbasis Android

Along with the development of the times, science and technology also experienced developments. The development of science has a considerable influence on several aspects of human life, especially aspects in the world of education. But it has not been utilized in the field of education that requires props in the delivery of teaching and learning processes, such as map materials in social studies subjects for elementary school children. Currently for the delivery of map material, teachers still use conventional methods and teaching is still focused on the media atlas, and globe. But using augmented reality technology is expected to make IPS lessons especially on map material more interesting and fun because augmented reality can be a prop to convey map material in 3D form. With the existence of augmented reality supported by markerless method, users are provided with 2D images presented in marker books by the author as a reference to display maps of Indonesia with 34 provinces in 3D form as well as information from each province.
On Fusion of Learned and Designed Features for Video Data Analytics

Video cameras have become widely used for indoor and outdoor surveillance. Covering more and more public space in cities, the cameras serve various purposes ranging from security to traffic monitoring, urban life, and marketing. However, with the increasing quantity of utilized cameras and recorded streams, manual video monitoring and analysis becomes too laborious. The goal is to obtain effective and efficient artificial intelligence models to process the video data automatically and produce the desired features for data analytics. To this end, we propose a framework for real-time video feature extraction that fuses both learned and hand-designed analytical models and is applicable in real-life situations. Nowadays, state-of-the-art models for various computer vision tasks are implemented by deep learning. However, the exhaustive gathering of labeled training data and the computational complexity of resulting models can often render them impractical. We need to consider the benefits and limitations of each technique and find the synergy between both deep learning and analytical models. Deep learning methods are more suited for simpler tasks on large volumes of dense data while analytical modeling can be sufficient for processing of sparse data with complex structures. Our framework follows those principles by taking advantage of multiple levels of abstraction. In a use case, we show how the framework can be set for an advanced video analysis of urban life.
ON NEURAL NETWORK GENERALIZATION VIA PROMOTING WITHIN-LAYER ACTIVATION DIVERSITY

During the last decade, neural networks have been intensively used to tackle various problems and they have often led to state-of-the-art results. These networks are composed of multiple jointly optimized layers arranged in a hierarchical structure. At each layer, the aim is to learn to extract hidden patterns needed to solve the problem at hand and forward it to the next layers. In the standard form, a neural network is trained with gradient-based optimization, where the errors are back-propagated from the last layer back to the first one. Thus at each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy. In this paper, we propose to complement this traditional 'between-layer' feedback with additional 'within-layer' feedback to encourage diversity of the activations within the same layer. To this end, we measure the pairwise similarity between the outputs of the neurons and use it to model the layer's overall diversity. By penalizing similarities and promoting diversity, we encourage each neuron to learn a distinctive representation and, thus, to enrich the data representation learned within the layer and to increase the total capacity of the model. We theoretically study how the within-layer activation diversity affects the generalization performance of a neural network in a supervised context and we prove that increasing the diversity of hidden activations reduces the estimation error. In addition to the theoretical guarantees, we present an empirical study confirming that the proposed approach enhances the performance of neural networks.
Compressive Learning of Generative Networks

Generative networks implicitly approximate complex densities from their sampling with impressive accuracy. However, because of the enormous scale of modern datasets, this training process is often computationally expensive. We cast generative network training into the recent framework of compressive learning: we reduce the computational burden of large-scale datasets by first harshly compressing them in a single pass as a single sketch vector. We then propose a cost function, which approximates the Maximum Mean Discrepancy metric, but requires only this sketch, which makes it time- and memory-efficient to optimize.
Dynamic Monitoring System of Big Data Leakage in Mobile Network Based on Internet of Things

Aiming at the problem of large acquisition time synchronization error caused by data explosion in traditional monitoring systems, a dynamic monitoring system for mobile network big data leakage based on Internet of Things is designed. The wireless sensor network is arranged in the system, the multi-channel base station node is designed, and the sensors are arranged in different channels to achieve the purpose of data diversion. Ep3c16q240 chip is selected as the core control chip of the multi-channel base station node, Based on the above hardware design, cluster monitoring, node performance monitoring and job operation monitoring functions are designed to upload the big data status information of the mobile network for job operation monitoring step by step to meet the needs of dynamic monitoring of data leakage. So far the overall design of the system is completed. The experimental results show that: compared with the traditional monitoring system, the designed monitoring system based on the Internet of things has smaller acquisition time synchronization error, better data acquisition synchronization performance, and improves the dynamic monitoring accuracy of mobile network big data leakage.
HISTORIAN ON SOCIAL NETWORKS: SOME METHODOLOGICAL CONSIDERATIONS

The article discusses some of the research findings related to the topic of the place and function of histori-ans on social networks. The main attention is paid to identifying the role of web technologies in the con-ventional practices of creating historical knowledge creation of historical knowledge. It is emphasized that the thesis about blurring the boundaries between the professional community and users in general does not mean rejection of the recognition of the decisive role of the professional community in the production of knowledge. The ways and methods of historians' actions in social networks and their advantages over traditional methods of knowledge production are revealed.
COVID-19 pandemic and the changing paradigms of higher education: A gulf university perspective

With the outbreak of COVID-19 in the month of February 2020, Bahrain government had to enforce distance learning amidst the school and university students and the faculty were directed to work from home With no formal and intense training, it was completely a fire fighting scenario be it for students or for the faculty members Thanks to digital tools, supportive IT staff, tech-savvy students, and easy availability of high-speed internet - without which the coping with could have been impossible for the faculty With this backdrop, this paper intends to spell out the changing paradigms of Gulf University with respect to teaching and learning process during COVID-19 phase and how the faculty members and students have been managing to achieve the objectives of the teaching and learning © 2020 Asian E F L Journal Press All rights reserved
Research on D-GERT Model of Joint Operational Situation Assessment Based on Information Flow

To solve the problem of inaccurate description of battlefield information changes in the process of joint system operations, this paper constructed a joint operational situation assessment D-GERT network model based on information flow, on the basis of the GERT network. And the calculation method of the threat degree, the increment of battlefield information value and the importance of network nodes were given. Finally, MATLAB was used to simulate and verify the model. The results showed that the method in this paper conforms to objective reality and can provide good method support for the commander's combat decision-making.
Media Interaktif untuk Pembelajaran Matematika secara Daring

Online learning or distance learning forces teachers to innovate in carrying out activities for students. As a result, many learning activities are less interesting and monotonous, especially for elementary school students who should be more active in playing and exploring. The purpose of this community service is to provide training in the use of interactive media in  mathematics learning that can be used for online learning. The hope is that students will be active and enjoy learning mathematics even though they are online. The training participants were 54 SD / MI Muhammadiyah in the DIKDASMEN PDM Bantul area. The method of implementation is seminars using Google Meet and workshops using Google Classroom. The training was held for 2 days on 19-20 November 2020. The feedback from the participants was very good, they were happy with this training. The output of this activity is activity sheets designed by the teacher for learning activities using interactive media.
Defending against black-box adversarial attacks with gradient-free trained sign activation neural networks

While machine learning models today can achieve high accuracies on classification tasks, they can be deceived by minor imperceptible distortions to the data. These are known as adversarial attacks and can be lethal in the black-box setting which does not require knowledge of the target model type or its parameters. Binary neural networks that have sign activation and are trained with gradient descent have been shown to be harder to attack than conventional sigmoid activation networks but their improvements are marginal. We instead train sign activation networks with a novel gradient-free stochastic coordinate descent algorithm and propose an ensemble of such networks as a defense model. We evaluate the robustness of our model (a hard problem in itself) on image, text, and medical ECG data and find it to be more robust than ensembles of binary, full precision, and convolutional neural networks, and than random forests while attaining comparable clean test accuracy. In order to explain our model's robustness we show that an adversary targeting a single network in our ensemble fails to attack (and thus non-transferable to) other networks in the ensemble. Thus a datapoint requires a large distortion to fool the majority of networks in our ensemble and is likely to be detected in advance. This property of non-transferability arises naturally from the non-convexity of sign activation networks and randomization in our gradient-free training algorithm without any adversarial defense effort.
Improving Data Security Using Modified LSB-Based Image Steganography Technique

This research improves the Savitha Bhallamudi technique by randomizing the hiding places of bits in the cover image. To enable the main quality characteristics of the image, the following steganography algorithms were used: hiding capacity, perceptual transparency, robustness, and resistance against attack. This method, using the Savitha technique, is based on the least significant bit (LSB) system. The LSB system involves sequentially hiding secret data in the LSB of the image. The main idea of the proposed method is to enhance security by embedding the bits in an image randomly, depending on the value of different bytes of the original (cover) image. This methodology was developed in stages, with the first process hiding data randomly in the images by checking the first byte of the original image only. The second process goes through some bytes in the cover image and hides data based on the value of these bytes. With this new method, results were tested on a sample of ten images. The evaluation of the effectiveness of this method’s experimental results was confirmed by using the calculations of two metrics: mean squared error (MSE) and peak signal-to-noise ratio (PSNR). The achievement of better outcomes from the proposed method could improve the results of MSE, provide better visual quality (PSNR), and be used for comparison to the original Savitha method. The new results reveal the improvement of the amount of randomness that can reduce the level of capacity and enhance security (MSE average is 27.8 using the Savitha technique; MSE is 21.1 using the first enhanced method). The new results further illustrate that steganography performed by random bits-hiding is less prone to attack compared to sequence embedding as it is difficult to recognize the pattern used in hiding the secret message . Keywords: security, steganography, least significant bit, image, randomness
Digital cash system of Invisibility of user Identity and Concealment

Bitcoin can be considered as one of the most prevailed applications of Blockchain technology developed so far. It is the most eminent, decentralized and distributed network-based platform. Without using real names, it provides a pseudo name interface through which management and verification of transactions are performed by the user being anonymous. Blockchain which itself works as a virtual ledger consists of data in the form of various blocks connected in a chain. Although it is providing the Invisibility of user Identity but extracting and combining the data and profile from the Blockchain, the identity can be revealed. Thus, in contrast to that, many researchers and data scientists have suggested the amendments and proposals to enhance the Invisibility of user Identity and Concealment of Bitcoin customer. We project out some theories, project analyses and methodologies that certainly supports the concern of the improvement of Bitcoin Customer anonymity.
Energy-based Out-of-distribution Detection for Multi-label Classification

Out-of-distribution (OOD) detection is essential to prevent anomalous inputs from causing a model to fail during deployment. Improved methods for OOD detection in multi-class classification have emerged, while OOD detection methods for multi-label classification remain underexplored and use rudimentary techniques. We propose SumEnergy, a simple and effective method, which estimates the OOD indicator scores by aggregating energy scores from multiple labels. We show that SumEnergy can be mathematically interpreted from a joint likelihood perspective. Our results show consistent improvement over previous methods that are based on the maximum-valued scores, which fail to capture joint information from multiple labels. We demonstrate the effectiveness of our method on three common multi-label classification benchmarks, including MS-COCO, PASCAL-VOC, and NUS-WIDE. We show that SumEnergy reduces the FPR95 by up to 10.05% compared to the previous best baseline, establishing state-of-the-art performance.
High Secured Data Access and Leakage Detection Using Attribute-Based Encryption

Brilliant innovations within reach have encouraged age and assortment of immense volumes of information, on regular schedule. It includes exceptionally touchy and differing information like individual, authoritative, condition, vitality, report, and personal information. Information analytics give answer for different issues being looked by brilliant hospital information like emergency reaction, debacle versatility, rise the executives, doctors, the executives framework, and so on. It requires conveyance of delicate information among different elements inside or outside the hospital. Sharing of touchy information makes a requirement for effective utilization of brilliant hospital information to give savvy applications and useful to the end users in which are liable and experimental method. This mutual delicate information if gets pilled as an outcome can make harm and extreme hazard the hospital assets. Strong hold of basic information from informal exposure is greatest issue for accomplishment of any task. Information leakage perception gives a lot of instruments and innovation that can productively settle the worries identified with patient basic information. The principle goal of this task is to identify the guilty operator/individual who are indented to hack the information. We additionally send blockchain idea over this undertaking for high security. We give fake/copy record to those liable individual.
Choice of HPC cluster performance indicators for the example of the "Informatika" Center for Collective Use of the FRC CSC RAS

This article discusses a methodology for assessing the effectiveness of a high-performance research platform. The assessment is carried out for the example of the "Informatika" Center for Collective Use (CCU) established at the Federal Research Center of the Institute of Management of the Russian Academy of Sciences, for solving new materials synthesis problems. The main objective of the "Informatika" Center for Collective Use is to conduct research using the software and hardware of the data center of the FRC IU RAS, including for the benefit of third-party organizations and research teams. The general characteristics of the "Informatika" Center for Collective Use are presented, including the main characteristics of its scientific equipment, work organization and capabilities. The hybrid high-performance computing cluster of the FRC CSC RAS (HHPCC) is part of the data center of the FRC IU RAS and also part of the “Informatika” Center for Collective Use. HHPCC provides computing resources in the form of cloud services as software (SaaS) and platform (PaaS) services. With the aid of special technologies, scientific services are delivered to researchers in the form of subject-oriented applications. Based on the analysis of the structure and operation principles of the Informatika Center, key performance indicators of the Center have been developed taking into account its specific tasks in order to characterize its various activity aspects (development, activities and performance). CCU efficiency evaluation implies calculation, on the basis of the developed indicators, of overall (generalized) indicators that characterize the CCU operation efficiency in various areas. An integral indicator is also calculated showing the overall CCU efficiency. To develop the overall performance indicators and the integral performance indicator, it is suggested to use the methods of weighted average and analysis of hierarchies. The procedure of determining partial performance indicators has been considered. Specific features of the choice of CCU performance indicators for solving new materials synthesis problems have been identified that characterize computing complex capabilities in the creation of a virtualization environment (peak performance of a computing system, real performance of a computing system on specialized tests, equipment loading with applied tasks and program code efficiency).
Ranking News Feed Updates on Social Media

Social media are used by hundreds of millions of users worldwide. On these platforms, any user can post and share updates with individuals from his social network. Due to the large amount of data, users are overwhelmed by updates displayed chronologically in their newsfeed. Moreover, most of them are irrelevant. Ranking newsfeed updates in order of relevance is proposed to help users quickly catch up with the relevant updates. In this work, the authors first study approaches proposed in this area according to four main criteria: features that may influence relevance, relevance prediction models, training and evaluation methods, and evaluation platforms. Then the authors propose an approach that leverages another type of feature which is the expertise of the update's author for the corresponding topics. Experimental results on Twitter highlight that judging expertise, which has not been considered in the academic and the industrial communities, is crucial for maximizing the relevance of updates in newsfeeds.
An Analysis of Scientific Publications on 'Decision Support Systems' and 'Business Intelligence' Regarding Related Concepts Using Natural Language Processing Tools

In this paper we have analyzed scientific data regarding Decision Support Systems and Business Intelligence available on an online platform, namely Web of Science, in the Core Collection database. Starting from individual blocks consisting in concatenations of abstracts, titles and keywords, we extracted related concepts and corresponding importance scores using IBM Watson and a custom API. The results highlight the common points of the two topics being analyzed. They also reveal a much longer history of Business Intelligence, whose beginnings are usually placed almost 15 years later (e.g. Howard Dresner, 1989). Next we have plans to find the publication frequency considering both sets of related concepts and using the same Web of Science Core Collection database, and further perform analyzes of the resulting multiple time series using Data Mining tools.
Bounding uncertainty in functional data: A case study

Functional data are fast becoming a preeminent source of information across a wide range of industries. A particularly challenging aspect of functional data is bounding uncertainty. In this unique case study, we present our attempts at creating bounding functions for selected applications at Sandia National Laboratories (SNL). The first attempt involved a simple extension of functional principal component analysis (fPCA) to incorporate covariates. Though this method was straightforward, the extension was plagued by poor coverage accuracy for the bounding curve. This led to a second attempt utilizing elastic methodology which yielded more accurate coverage at the cost of more complexity.
New forms of exploring the library space

The findings of the study of the spatial sign navigation quality fulfilled at Arkhangelsk Regional Scientific Library are discussed. Within the study, 4 groups of undergraduate students equipped with GoPro cameras, trackers and observation cards searched the library for rooms, exhibitions, and books in open access.
Instrumentalização dos enfermeiros de uma unidade de internação para o uso da escala de braden / Instrumentalization nurses of a unit for hospital use of braden scale

Objective: to analyze the knowledge of care nurses about the use of the Braden Scale as an instrument for preventing pressure injuries.Method: convergent care research conducted in a medical clinic hospitalization unit of a university hospital in southern Brazil, from May to August 2012, with the participation of eight nurses, through questionnaires with open questions and discussion groups.Content Analysis was used in the data processing.Results: It was observed that nurses already had knowledge about the Braden Scale, but through the research they had the opportunity to update their understanding and observe the application of this instrument as a predictive measure for pressure injuries.Conclusion: Nurses deepened their knowledge about the prevention of pressure injuries, facilitated by instrumentalization with the application of the Arco de Maguerez and direct repercussions for professional practice.