Reviewerly/exHarmony huggingface dataset:
A Differential Privacy Collaborative Deep Learning Algorithm in Pervasive Edge Computing Environment

With the development of 5G technology and intelligent terminals, the future direction of the Industrial Internet of Things (IIoT) evolution is Pervasive Edge Computing (PEC). In the pervasive edge computing environment, intelligent terminals can perform calculations and data processing. By migrating part of the original cloud computing model's calculations to intelligent terminals, the intelligent terminal can complete model training without uploading local data to a remote server. Pervasive edge computing solves the problem of data islands and is also successfully applied in scenarios such as vehicle interconnection and video surveillance. However, pervasive edge computing is facing great security problems. Suppose the remote server is honest but curious. In that case, it can still design algorithms for the intelligent terminal to execute and infer sensitive content such as their identity data and private pictures through the information returned by the intelligent terminal. In this paper, we research the problem of honest but curious remote servers infringing intelligent terminal privacy and propose a differential privacy collaborative deep learning algorithm in the pervasive edge computing environment. We use a Gaussian mechanism that meets the differential privacy guarantee to add noise on the first layer of the neural network to protect the data of the intelligent terminal and use analytical moments accountant technology to track the cumulative privacy loss. Experiments show that with the Gaussian mechanism, the training data of intelligent terminals can be protected reduction inaccuracy.
Preface

On behalf of the committee, I am very excited to welcome all of you to the International Conference on Technology and Vocational Teachers (ICTVT) held right here in Yogyakarta, Indonesia. This annual event is organized by Engineering Faculty, Universitas Negeri Yogyakarta and held from 14 th to 15 th September, 2019. The main purpose of this conference is to bring a unified communication platform for professional practice in engineering fields, researchers, vocational teachers, academia, and government. Under the theme “Enhancing Technology and Vocational Competency for Smart Industry”, the conference this year features a strong program, including three inspiring keynote speeches delivered by Dr. Dr. h.c. Georg Spöttl, M.A from German, Prof. Dr. Ramlee Mustapha from Malaysia, and Prof. Dr. Bruri Triyono from Indonesia. On top of that, we attracted more than 114 submissions from eight different countries. After reviewed by relevant experts, about 90 valuable research papers have been accepted for presentation. I would like to express our utmost gratitude to the keynote speeches, presenters, authors, and co-authors for contributing excellent papers. List of Words from the Rector, Peer Review General Policies, List of Comitee, are available in the pdf.
Control-Based Segmentation

Image segmentation is a fundamental problem in image processing, computer vision, and medical imaging applications. The design of generic, automated methods that work for various objects and imaging modalities is a daunting task. Instead of proposing new specific segmentation algorithms, in this entry, we review a general design principle on how to integrate user interactions from the perspective of feedback control theory. In particular, impulsive control and Lyapunov stability analysis are employed to design and analyze an interactive segmentation system. Stabilization conditions are derived to guide algorithm design. The effectiveness and robustness of the proposed methodologies are demonstrated.
Classification of Numbers Using Neural Networks

Pattern recognition in images is a classic application of neural nets. This chapter builds upon the previous one by exploring multi-layer networks, which fall into the Machine Learning branch of our Autonomous Learning taxonomy. In this case, we will look at images of computer-generated digits, and the problem of identifying the digits correctly. These images will represent numbers from scanned documents. Attempting to capture the variation in digits with algorithmic rules, considering fonts and other factors, quickly becomes impossibly complex, but with a large number of examples, a neural net can readily perform the task. We allow the weights in the net to perform the job of inferring rules about how each digit may be shaped, rather than codifying them explicitly.
Human perception

forum Share on Human perception Author: RYBN.ORG View Profile Authors Info & Claims InteractionsVolume 28Issue 1January - February 2021 pp 16–20https://doi.org/10.1145/3442196Online:23 December 2020Publication History 0citation540DownloadsMetricsTotal Citations0Total Downloads540Last 12 Months241Last 6 weeks18 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Secure Health Monitoring in the Cloud Using Homomorphic Encryption

Extending cloud computing to medical software, where the hospitals rent the software from the provider sounds like a natural evolution for cloud computing. One problem with cloud computing, though, is ensuring the medical data privacy in applications such as long term health monitoring. Previously proposed solutions based on Fully Homomorphic Encryption (FHE) completely eliminate privacy concerns, but are extremely slow to be practical. Our key proposition in this paper is a new approach to applying FHE into the data that is stored in the cloud. Instead of using the existing circuit-based programming models, we propose a solution based on Branching Programs. While this restricts the type of data elements that FHE can be applied to, it achieves dramatic speed-up as compared to traditional circuit-based methods. Our claims are proven with simulations applied to real ECG data.
Appropriation of the Maker methodology to stimulate Invisible Learning in high school students in times of Covid-19

The current educational context is facing new educational challenges due to the worldwide proliferation of Covid-19. For this reason, it was necessary to generate a digital literacy oriented from a creative and innovative perspective, in this regard it was decided to apply the Maker methodological style, to high school students from the Arequipa region in Peru. Likewise, considering that education is being developed in homes under the E-learning modality, it was necessary to stimulate invisible learning, because its application offers many possibilities to develop skills, abilities, capacities, competencies that are not formally established in the curriculum and that are necessary for life; starting from learning by doing, promoting creativity and taking the context into consideration. The main objective of this study was to analyze the appropriation of the Maker methodology in the educational field to determine the impact it may have. The applied research methodology was mixed, and a survey was applied to 350 students, of both sexes, at the secondary level, whose ages are between 14 and 17 years old. This research study allowed to conclude that the Maker methodology, applied in times of pandemic, is effective to improve the learning and motivational level of students starting from learning by doing, experimenting, playing, creating, researching, and innovating, where the initiative and strengths of the students they are the driving forces for this development.
A Hierarchical Hadoop Framework to Handle Big Data in Geo-Distributed Computing Environments

Advances in the communication technologies, along with the birth of new communication paradigms leveraging on the power of the social, has fostered the production of huge amounts of data. Old-fashioned computing paradigms are unfit to handle the dimensions of the data daily produced by the countless, worldwide distributed sources of information. So far, the MapReduce has been able to keep the promise of speeding up the computation over Big Data within a cluster. This article focuses on scenarios of worldwide distributed Big Data. While stigmatizing the poor performance of the Hadoop framework when deployed in such scenarios, it proposes the definition of a Hierarchical Hadoop Framework (H2F) to cope with the issues arising when Big Data are scattered over geographically distant data centers. The article highlights the novelty introduced by the H2F with respect to other hierarchical approaches. Tests run on a software prototype are also reported to show the increase of performance that H2F is able to achieve in geographical scenarios over a plain Hadoop approach.
Marine Target Detection Algorithm Based on Deep Learning

Aiming at the problems of low detection accuracy and slow training speed of YOLOv3 for marine target, a ship target detection method based on the improved YOLOv3 algorithm is proposed. In this method, the BN layer is merged into the convolutional layer to improve the forward inference speed of the model. GloU is selected to replace the original mean square error loss function, which can not only focus on overlapping areas, but also other non-overlapping areas to improve the detection accuracy. In order to solve the problem of image distortion, blur and low contrast, gaussian filter brightness enhancement and histogram equalization were used to preprocess the image to improve the image quality. The preprocessed ship image was input into the improved YOLOv3 network for training. The experimental results show that, compared with the original YOLOv3 algorithm, the training speed is improved by 7.69%, the reasoning speed is improved by 27.47%, and the AP is improved by 3.12%, which verifies the effectiveness of the proposed algorithm in marine ship target detection.
Integrating iPads in Middle School Science Instruction

With the appropriate support and experience, iPads and other mobile devices can be used for collaborative scientific inquiry moving beyond individual skill practice and assessment appropriations. The mobility and access the iPad provides opens up the classroom for innovative instructional practices, allowing students to physically explore their world, though the devices themselves are not enough to guarantee student engagement and learning. The Gradual Increase of Responsibility (GIR) model for teacher coaching (Collet, 2008) is used as the instructional coaching model of professional development. Through GIR, coaches model, make recommendations, ask probing questions, and affirm teachers' decisions over the course of several months to increase the teacher's independence in using technology to transform instruction for students. This study aims to examine how middle grades teachers' integration of one-to-one technology moves beyond drill and practice and using apps as extension activities.
Police raids take down cybercrime servers

Police in the Netherlands, working with the UK's National Cyber Security Centre (NCSC), claim to have taken down a ‘bulletproof’ hosting operation and, in the process, to have disrupted a Mirai-based botnet.
Characteristics of the Experimental Study on Assessing the Levels of Proficiency of Student Social Activity in Conditions of Higher Education Institution

Introduction. Nowadays, student self-government is one of the effective forms of student self-organization; it appears to be the mechanism of representing and protecting students' rights. It is the factor of self-realization and development of social activity in conditions of higher education institution.Problem Statement.The formation of student social activity is a problem of modern pedagogy, since it requires the development of technologies for participating in socially significant activities within the social environment of higher education institutions.Purpose.To characterize and to analyze the stages of the pedagogical experiment regarding the state of the students' proficiency in terms of social activity. Materials and Methods.A series of diagnostic methods has been used: overt observation, surveys (polls), testing, ranking, moderation method, method of independent expert assessments, a modified version of E. Shakirova's methodology for diagnosing student social activity, and V. Yadov's methods.Results.The stages of the assessment of the levels of the formation of student social activity as well as their indicators and criteria have been characterized and analyzed; the socio-pedagogical conditions for the formation of student social activity within the self-government bodies of a higher education institution have been determined: the creation of the structure and bodies of student self-government alongside with the ensuring of the representation of each student groups; the organization of student self-government activities in various fields (organizational, public, research-oriented, physical culture and health-targeted, artistic, creative, educational, informational, and volunteering); the self-organization of students' life and their involvement into various forms of work performed by self-government bodies; the involvement of teachers into the work of student self-government and promotion of step-by-step formation of student social activity.Conclusions.Currently, students are considered to be not only an intellectual and demographic, but also a spiritual resource of our country.Therefore, social and pedagogical support for the formation and development of indicators of student social activity is a priority task of social and educational work of higher education institutions.K e y w o r d s : social activity, formation of student social activity, and student self-government.
Using Cognitive Psychology to Understand Anticipated User Experience in Computing Products

User Experience assessment is an evaluation of user's experience with the product, system or service during ‘use' (i.e., actual interaction experience) as well as ‘anticipated or before use' (i.e., pre-interaction experience).Whereas many user experience researchers may be conversant with explaining a person's experience during use of a product, system or service, they find it difficult to explain experience before a product or service is used (Anticipated Use), which in this chapter is referred to as Anticipated User Experience (AUX). This chapter applies the theory of cognitive psychology and its principles to best explain how Anticipated User Experience occurs and how this experience can be achieved. This chapter goes a long way in informing user experience researchers and practitioners on the relevance of attaining AUX in a computing product and how it can be achieved.
Renewable Energy Sources Development in Rural Areas of African Countries

The prospects of increasing access to electricity for the population of rural areas of Africa are considered. The main international funds and organizations aimed at sustainable energy development in Africa are described. An analysis of the state and possible options for using renewable energy sources for this purpose in decentralized energy supply through the creation of mini-grids or stand-alone systems is given. The risks by developing renewable energy sources in rural areas and modern mechanisms for financing in solar energy are presented.
Superconducting Qubits

Thus far, we have examined quantum computing based on single particle states in atoms, ions and semiconductor structures. In this chapter, we will examine quantum states in superconductors and their application as qubits. This chapter is particularly extensive due to the large variety of possible superconducting quantum circuits. We introduce superconductivity and examine the three main types of superconducting qubit: the flux qubit, the charge qubit and the phase qubit. We will also examine the transmon qubit and circuit quantum electrodynamics.
Note-Taking Evaluation Using Network Illustrations Based on Term Co-occurrence in a Blended Learning Environment

Note contents taken by students during a blended learning course were evaluated to improve the quality of university instruction. To conduct a quantitative comparison of the contents of all notes for effective instruction from lecturer to students to occur, the contents were mathematically compared and evaluated using two ways of summarizing the frequency of term co-occurrences. In order to evaluate visually the differences between the contents of notes taken, an adjacency matrix and Levenshtein distance metrics were employed to represent noun co-occurrence in the notes. In comparing notes between the lecturer and students, insufficient distance and additional distance are defined and measured for all participants during the course. In the results, students recorded additional nouns to replace the nouns given by the lecturer, and introduced new nouns of their own as substitutes as well. There are significant correlational relationships between the above mentioned distances and the ratios, such as the word ratio and coverage, which have been defined in previous studies. Also, summarizing ways of co-occurrence frequency did not affect any of the relationships. Finally, possible applications for visualizing learning activities of the lecturer and students are discussed.
Neural network and applications

Neural networks (NNWs), the most generic form of AI, are discussed very comprehensively with example applications in this chapter. Artificial neuron structure with different activation functions is explained in the beginning, followed by a simple NNW structure for sine-wave fabrication. Feed-forward perceptrons and Adaline/Madaline structures are explained with their performance features. Then, the most commonly used back-propagation network is explained in detail along with its performance features, flowchart for training, and theory of supervised training. Then, principles of dynamic networks are discussed followed by a discussion of adaptive neuro-fuzzy networks. Example applications of feed-forward NNWs, such as selected harmonic elimination PWM; delayless harmonic filtering; feedback signal estimation of induction motor drives; estimation of distorted current waves; space vector PWM of two-level, three-level, and five-level NPC; and ANFIS-based DTC, are given. Dynamic networks for emulation of dynamic systems are explained followed by applications in PCLPF programmable filter, machine stator flux estimation, sensorless vector drive, neuro-fuzzy efficiency optimization control, and inverse dynamics-based MRAC system. The MATLAB Neural Network Toolbox is discussed with example applications and examples of back-propagation training. Finally, advances and trends in the technology are summarized. In the recent years, the terminology of machine learning (ML) and deep learning (DL) has evolved. ML is a general term in AI which may be based on supervised, unsupervised or reinforcement learning. DL (also known as structured learning) is part of broader family of ML. Both are generally implemented by NNWs.
Information Societies to Interactive Societies

Information is crucial for the development of any sector, including agriculture where information needs to be exchanged with farmers and other stakeholders quickly. Thus, efficient linkages for information sharing are essential. ICT innovations enable the shaping and reshaping of communication and interaction. Many of the technology driven information dissemination methods have been initiated by government, private, non-profit making bodies and independent research groups. This chapter explains the integration of ICT within Sri Lankan agriculture communities and how the focus is changing from information dissemination towards facilitating interactions among the stakeholders. The present status of agriculture information dissemination, including the ICT interventions is given. Prevailing issues and limitations in these ICT-based information dissemination approaches initiated by the different entities is explained, giving due recognition to various factors that have contributed to the adoption of ICT initiatives. The chapter ends outlining the possibilities for future focus on ICT activities in an agriculture information society.
The Development of search engine service for official academic documents

Abstract Document search engines are necessary in educational institutions, where search engines are expected to find the right document quickly. Academic official documents are generally private so that public search engines such as Google cannot index and search these documents. Therefore, we need a specific search engine developed for educational institutions’ needs, in this case, Universitas Pendidikan Ganesha. This article discussed how to develop an academic official document search engine, starting from data retrieval and extraction, query parser development, and search engine evaluation. The query parser developed utilized the field annotation mapping mechanism and n-gram keyword mapping. The evaluation results showed a significant increase in performance compared to only using Solr’s Standard Query Parser.
Introduction

Time series analysis is widely utilised in many areas, from business forecasting to the prediction of population sizes of species. Various types of time series models and techniques are studied, ranging from analysis and inference to classification and forecasting. It is important to determine the appropriateness and accuracy of these models and techniques before applying them. This book is devoted to time series data analysis using SAS® Enterprise Guide® (SAS EG). The base SAS® program is widely used at universities, research institutions, government departments, and business sectors in many different areas, both public and private. SAS EG is a point-and-click, menu-driven data analysis software which is driven by the power of SAS. In this first chapter, we will briefly cover:
A Comprehensive Report on Security and Privacy Challenges in Software as a Service

Researchers have done tremendous works for data security, but a robust security mechanism is not available yet. Researchers are doing continuous work to build robust SaaS mechanism. SaaS has several security vulnerabilities. Data security is still the most important challenge to researcher and they can constantly do research to protect the data over the network but they are facing numerous technical challenges to completely secure the cloud network and cloud storage. This work would be helpful to understand data security and privacy problems. Researchers can find the new way to understand SaaS security vulnerabilities and currently available solutions.
Descrizione semantico-funzionale delle particelle russe e corpora paralleli

This paper analysesthe use of the Russian functional marker ved’based on the Russian-Italian parallel corpus within the Russian National Corpus. Data show that in over 50% of examplesved’is used to enhance the illocutionary strength of a sentence, helping the inferential processof a relation of motive with the preceding phrase. Ved’ also occurs in bothinterrogative sentences in order to verify knowledge, with different pragmatic effects depending on the context, and complexsentences, where itacquires a factual meaning and functions as a text cohesion device. Theextensiverange of possibleItalian translations ofved' is a sign of its languagespecificity.