Reviewerly/exHarmony huggingface dataset:
Structural analysis of stakeholder conflicts related to the utilization of private-led education data for edtech development

Objectives In the distance education field, education data are easing as the education paradigm shifts to personalized education courses, while the government is still focusing on data collection, and fast-changing edtech companies are experiencing difficulties.&#x0D; Methods This study examines the main issues through a focus group on the use of data in the field of distance education, where conflicts between various interests are occurring at the time of institutional transition in the field of distance education, and attempts to analyze it using the Q methodology to find conflict points and cooperative alternatives was derived.&#x0D; Results The analysis results are as follows. First, the government should prepare legal and institutional alternatives so that various unstructured data of students can be used as educational data. Second, it is necessary to set clear usage standards for the scope of data related to education data. Third, institutional supplementation is needed so that data can be collected from the stage of data collection to meet the purpose of use by stage so that Edutech companies can use it for their business models.&#x0D; Conclusions This study is meaningful in that it provides relevant implications by analyzing the conflict structure between stakeholders that occurs in the process of using educational data in distance education in preparation for the transition to the digital age.&#x0D; &#x0D;
Improving the Performance of Zero-Resource Children’s ASR System through Formant and Duration Modification based Data Augmentation

Developing an automatic speech recognition (ASR) system for children’s speech is extremely challenging due to the unavailability of data from the child domain for the majority of the languages. Consequently, in such zero-resource scenarios, we are forced to develop an ASR system using adults’ speech for transcribing data from child speakers. However, differences in formant frequencies and speaking-rate between the two groups of speakers degrade recognition performance. To reduce the said mismatch, out-of-domain data augmentation approaches based on formant and duration modification are proposed in this work. For that purpose, formant frequencies of adults’ speech training data are up-scaled using warping of linear predictive coding coefficients. Next, the speaking-rate of adults’ data is also increased through time-scale modification. Due to simultaneous altering of formant frequencies and duration of adults’ speech and then pooling the modified data into training, the acoustic mismatch due to the aforementioned factors gets reduced. This, in turn, enhances the recognition performance significantly. Additional improvement is obtained by combining the recently reported voice-conversion-based data augmentation technique with the proposed ones. On combining the proposed and voice-conversion-based data augmentation techniques, a relative reduction of nearly 32.3% in word error rate over the baseline is obtained.
POSYANDU INFORMATION SYSTEM USING BORLAND DELPHI 7 AT POSYANDU SETIA WARGI MARGAHURIP

Posyandu Setia Wargi Margahurip is a non-governmental activity that aims to maintain public health, especially for toddlers. Until now, data recording at the Setia Wargi Margahurip Posyandu is still manual, that data and information processing is still experiencing many obstacles, especially in terms of effectiveness. The development of technology and information that is growing rapidly in this era of globalization causes all fields in human life to involve technology to facilitate human work in various fields. Therefore, the idea emerged to create a desktop-based Posyandu information system design to facilitate data processing services at the Posyandu. This Posyandu information system was build using Borland Delphi 7 with Microsoft Access as the database. The Posyandu information system development method uses the SDLC (Software Development Life Cycle) method with the Waterfall Model and problem analysis using the PIECES method. The result of this research is a desktop-based Posyandu information system application that can help Posyandu cadres and Posyandu health workers Setia Wargi Margahurip in facilitating the process of data processing, searching, reporting, data storage
Synthesis, antibacterial activity and adsorption studies of Bentonite-iron nanoparticles in the removal of methyl blue dye

Green synthesis of bentonite-iron nanoparticles (Be-FeNPs) has been carried out using the leaf extract of Helicteres isora and its antibacterial and adsorption capacity for the removal of Methyl Blue (MB) dye have been also studied.T he prepared Be-FeNPs and iron nanoparticles (FeNPs) are characterized by UV-visible, FTIR spectroscopic techniques and SEM analysis. The antibacterial activity of Be-FeNPs show good zone of inhibition against E.coli and S.aureus. Further the prepared composite (Be-FeNPs) and bentonite are used as adsorbents for the removal of Methyl Blue (MB) dye in aqueous solution under various experiment parameters like initial concentration, contact time and pH. The optimum initial pH, adsorbent concentration and time were determined to be 8.1, 45°C, and 100 ppm, 80 min for MB adsorption on to Be-FeNPs. The experimental data for MB adsorption fits well to the Freundlich and Langmuir isotherm models. The kinetic equations like Natarajan-Khalaf, Bhattacharya-Venkobachar and Lagergren equations were found to be applicable. The surface morphology of the Be-FeNPs before and after adsorption of dye revealed that it can be used as an excellent adsorbent for dye removal from dilute industrial effluents.
Enabling secure mutual authentication and storage checking in cloud-assisted IoT

Internet of things (IoT) is a technology that can collect the data sensed by the devices for the further real-time services. Using the technique of cloud computing to assist IoT devices in data storing can eliminate the disadvantage of the constrained local storage and computing capability. However, the complex network environment makes cloud servers vulnerable to attacks, and adversaries pretend to be legal IoT clients trying to access the cloud server. Hence, it is necessary to provide a mechanism of mutual authentication for the cloud system to enhance the storage security. In this paper, a secure mutual authentication is proposed for cloud-assisted IoT. Note that the technique of chameleon hash signature is used to construct the authentication. Moreover, the proposed scheme can provide storage checking with the assist of a fully-trusted entity, which highly improves the checking fairness and efficiency. Security analysis proves that the proposed scheme in this paper is correct. Performance analysis demonstrates that the proposed scheme can be performed with high efficiency.
Calculating brand protection impact

Among the first things that executives want to know about a brand protection program is the impact it is having. Calculating the brand protection impact (BPI) can help demonstrate the impact a program is having and its change over time. The BPI should consider units seized through ground enforcement work, units seized through factory raids, marketplace and auction site enforcement, website and domain enforcement, and restitution. This chapter illustrates how brand protection professionals can calculate values in demonstrating the conservative impact of their efforts. It also discusses non-quantitative programs, such as trainings, that can have a positive impact on brand protection programs. Finally, it provides an example of technology (smart tags) that can have an impact across a company, engaging other functions in brand protection efforts while supporting the work of those functions as well.
An Improved and Customized Hybrid of Deep and Machine Learning Technique Model for Handwritten Digit Recognition

In modern research, pattern recognition is a vast field for the academicians and researchers to contribute their work. Various kinds of patterns like images, character, handwritten digit, etc can be recognized and classify with the help of the intelligent techniques. This research work is concentrated on the classification of handwritten digit recognition. In the world, peoples’ handwriting is different from each other’s and uses different languages, so it is necessary to develop a model that is able to recognize handwritten digits with high accuracy. Various deep and machine learning methods have been implemented by various authors and achieved satisfactory results. This research work has proposed a hybrid technique that combines deep learning and machine learning algorithms for classification of handwritten digit characters. The proposed model has two steps, first feature extraction, and second is classification of handwritten digits. The Convolutional neural network (CNN) has been used as feature extractor, and support vector machine (SVM), K-nearest neighbors (KNN), and Random Forest Classifier (RFC) algorithms have been used to recognize and classify handwritten digits. We have used very famous handwritten digit dataset as MNIST that contains 70000 samples of 0-9 digits, and EMNIST is an extended version of the MNIST dataset, that contains 280000 handwritten digit samples. The contribution of this research work is to enhance the recognition rate by our proposed hybrid models as CNN with SVM named as CNN-SVM, CNN with RFC named as CNN-RFC, and CNN with KNN named as CNN-KNN model, and achieved excellent recognition rate with both MNIST and EMNIST data samples. We have achieved the testing accuracy as 99.45% by CNN-RFC, 99.48% by CNN-KNN, and 99.55% by CNN-SVM with MNIST dataset while 99.54% by CNN-RFC, 99.46% by CNN-KNN, and 99.66% by CNN-SVM with EMNIST dataset. The testing accuracy achieved by our proposed model is tremendous and higher than previous research work.
Research on Korean Course Teaching Mode Based on Computer Internet Technology

This article focuses on the automatic teaching of Korean text, and proposes a new Korean teaching algorithm based on key word extraction. At the same time, we use the best codebook LBG algorithm in the vector quantization process to realize Korean speech recognition. At the same time, we use MATLAB tools to realize the simulation. The results of experimental comparison of simulation data found that the Korean speech recognition rate of our proposed LBG algorithm is as high as 95%. This contributes to the intelligent realization of the teaching of Korean courses.
Research on adaptive conversion of AI language based on rough set

In order to solve the problems of high complexity and low computational efficiency in traditional artificial intelligence (AI) language conversion methods, an adaptive AI language conversion method based on rough set is proposed. AI language preprocessing is realised by pre-emphasising, adding window, frame processing and endpoint detection. The attribute reduction algorithm based on rough set theory is used to select the features of AI language. The dimension of input feature vector is reduced. The experimental results show that after feature extraction, the computational efficiency is obviously improved, and the efficiency of the proposed method is the highest, averaging close to 100%. Compared with the traditional method, the complexity of the proposed method is lower, and the average complexity is 1.68% during the ten experimental iterations. This method simplifies the adaptive conversion process of AI language and has high computational efficiency.
ML Based Authentication Scheme for Data Storage in Cloud Based IoT

Today, organizations are using IoT devices to accurately collect real data and make better business decisions to increase customer satisfaction. The data collected should be stored and stored in a well-designed storage system, which encourages companies to review their data storage infrastructure. The company needs to store data created by the Internet of Things, and that data grows exponentially, forcing IoT to think about cloud storage for data storage. Security issues are a major concern when handling and processing data in DI and cloud environments. Secure integration of IoT and cloud computing, and introduced a model to ensure this integration. The secure database of any IoT operating system was suffers from poorly protected read and write functions, which limits data storage on any IoT platform. In addition, clouds can provide space to store a wide variety of data that plays an important role in the world of cyber security. However, large centralized systems operating in the cloud are also very vulnerable due to their power, so they can be transformed into a kind of double-edged sword. In this paper, we propose a novel secure lightweight authentication scheme for data storage (SLA-DS) in IoT and cloud server. The SLA-DS integrates IoT and cloud technology combination which mainly focuses on security issues.
Generating Image Adversarial Examples by Embedding Digital Watermarks

With the increasing attention to deep neural network (DNN) models, attacks are also upcoming for such models. For example, an attacker may carefully construct images in specific ways (also referred to as adversarial examples) aiming to mislead the DNN models to output incorrect classification results. Similarly, many efforts are proposed to detect and mitigate adversarial examples, usually for certain dedicated attacks. In this paper, we propose a novel digital watermark-based method to generate image adversarial examples to fool DNN models. Specifically, partial main features of the watermark image are embedded into the host image almost invisibly, aiming to tamper with and damage the recognition capabilities of the DNN models. We devise an efficient mechanism to select host images and watermark images and utilize the improved discrete wavelet transform (DWT) based Patchwork watermarking algorithm with a set of valid hyperparameters to embed digital watermarks from the watermark image dataset into original images for generating image adversarial examples. The experimental results illustrate that the attack success rate on common DNN models can reach an average of 95.47% on the CIFAR-10 dataset and the highest at 98.71%. Besides, our scheme is able to generate a large number of adversarial examples efficiently, concretely, an average of 1.17 seconds for completing the attacks on each image on the CIFAR-10 dataset. In addition, we design a baseline experiment using the watermark images generated by Gaussian noise as the watermark image dataset that also displays the effectiveness of our scheme. Similarly, we also propose the modified discrete cosine transform (DCT) based Patchwork watermarking algorithm. To ensure repeatability and reproducibility, the source code is available on GitHub.
New Quantum-Safe Versions of Decisional Diffie-Hellman Assumption in the General Linear Group and Their Applications: Two New Key-agreements

Diffie-Hellman key-agreement and RSA cryptosystem are widely used to provide security in internet protocols. But both of the two algorithms are totally breakable using Shor's algorithms. This paper proposes two connected matrix-based key-agreements: (a) Diffie-Hellman Key-Agreement with Errors and (b) RSA-Resemble Key-agreement, which, respectively, bear resemblance to Diffie-Hellman key-agreement and RSA cryptosystem and thereby they gain some of the well-known security characteristics of these two algorithms, but without being subject to Shor's algorithms attacks. That is, the new schemes avoid the direct reliance on the hardness of Discrete Logarithm and Integer Factoring problems which are solvable by Shor's algorithms. The paper introduces a new family of quantum-safe hardness assumptions which consist of taking noisy powers of binary matrices. The new assumptions are derived from Decisional Diffie-Hellman (DDH) assumption in the general linear group GL(n,2) by introducing random noise into a quadruple similar to that which define the DDH assumption in GL(n,2(. Thereby we make certain that the resulting quadruple is secure against Shor's algorithm attack and any other DLP-based attack. Thence, the resulting assumptions, are used as basis for the two key-agreement schemes. We prove that these key-agreements are secure -- in key indistinguishability notion -- under the new assumptions.
C2

Smartphones have emerged as ubiquitous platforms for people to consume content in a wide range of consumption contexts (C2), e.g., over cellular or WiFi, playing back audio and video directly on phone or through peripheral devices such as external screens or speakers, etc. In this paper, we argue that a user's specific C2 is an important factor to consider in Adaptive Bitrate (ABR) streaming. We examine the current practice of using C2 in four popular ABR players, and identify various limitations in existing treatments that have a detrimental impact on network resource usage and user experience. We then develop practical best-practice guidelines for C2-cognizant ABR streaming. Instantiating these guidelines, we develop a proof-of-concept implementation in the widely used state-of-the-art ExoPlayer platform and demonstrate that it leads to significantly better tradeoffs in terms of user experience and resource usage.
INFLUENCING FACTORS AND SOLUTIONS TO THE MANAGEMENT OF EXPERIENTIAL AND CAREER-ORIENTED ACTIVITIES FOR STUDENTS FOLLOWING 2018 GENERAL EDUCATION CURRICULUM

Experiential and career-oriented activities are mandatory in the 2018 General Education Curriculum (GEC); it plays an essential role in education in the new era, contributing to developing qualities and competencies for high school students. In reality, experiential and career-oriented activities and the management of these activities have been implemented in schools according to the 2018 General Educational Curriculum, but there remain many problems to overcome which necessitates a fast improvement, especially in the management of the organization of experiential and career-oriented activities (in order to ensure the improvement of comprehensive education quality and to develop the quality and capacity of learners). With a conceptual system that contributes to clarifying the perspective of experiential teaching and career orientation, in this article, the author outlines the opportunities and challenges to the management of the organization of experiential and career-oriented activities and proposes five specific groups of solutions: (1) The group of solutions to raise general awareness of the role and meaning of experiential and career-oriented activities; (2) The group of solutions to develop a plan for organizing experiential and career-oriented activities; (3) The group of solutions to organize experiential and career-oriented activities effectively; (4) The group of solutions to strengthen the direction of experiential and career-oriented activities; (5) The group of the solution to contribute effectively to improving the quality of experiential and careeroriented activities under the general education program 2018.
AN OVERVIEW TO MANAGEMENT OF PROFESSIONAL DEVELOPMENT FOR PRIMARY SCHOOL TEACHERS BASED ON COMPETENCY APPROACH

The goals and requirements of the 2018 general education program have set out the contents, methods, forms, and conditions to ensure the management of professional development in general education and primary school teachers. Ultimately, the highest level of professional development for teachers aims to enhance teachers' professional and personal development, while benefiting students. The content of the article focuses on an overview international and national researches on managing professional development for primary school teachers to approach competencies with 03 researched issues: 1) Research on competency and competency approach; 2) Research on developing professionals for primary school teachers based on competency approach; and 3) Research on management of professional development for primary school teachers based on competency approach. On that basis, the article is generally evaluated in this review, as a basis for the identification and implementation of further studies.
BUILDING PROCEDURE FOR TRAINING COMPETENCE OF LEARNING-OUTCOMES ASSESSMENT FOR BIOLOGY STUDENT TEACHERS

Regarding the teaching process in general, including teaching Biology, assessment of learning outcomes (LOs) is always considered an important step, helping teachers and students record the outcomes of teaching activities as well as creating a basis for adjusting and continuously improving the quality of teaching and learning. Currently, the General Education Curriculum, in general, and the General Education Curriculum in Biology (2018), in particular, are constructed with the competence development orientation, which has set out a requirement to comprehensively and accurately assess the development of learners' competencies and consider the development level of competency groups as the most important LOs. This requires student teachers (STs) to be trained and fostered to develop their ability to assess LOs under the competency approach, in order to help them catch up with the requirements of professional activities in the context of educational innovation. In this article, we would like to present the study results on the procedure of developing STs' competence to assess LOs in teaching Biology at high schools. The study results are built on the basis of analyzing theoretical and practical bases on assessment activities in teaching and developing assessment competence for STs in the process of pedagogical training.
USING GRADED READERS AS INTENSIVE-EXTENSIVE READING MATERIALS TO TEACH MULTI-LEVEL ESL CLASSES

Teaching English using the same textbook, on the same curriculum to multi-level students is challenging for teachers and demotivating for students. Graded readers were used as intensive-extensive reading materials to help English lessons become more suitable for students' diverse levels. After applying graded readers to a 46-student class for one course, the results are very positive as students are motivated and their reading skills are improved.
APPLYING FORMATIVE ASSESSMENT IN PROJECT-BASED LEARNING WITH THE TOPIC OF “MATTER AND ITS CHANGES” OF NATURAL SCIENCES FOR GRADE 6 TO DEVELOP THE STUDENTS’ COMPETENCE IN STUDYING NATURE

The competence of studying nature is one of the three components of natural science competence - the specific competence in Natural Sciences (according to the 2018 curriculum). Formative assessment is a form of assessment for the development of learners, thus it is one of the measures to develop competence in studying nature. In this article, we have investigated the current situation of applying formative assessment to develop the competence of studying nature in teaching Natural Sciences, thereby proposing measures to apply formative assessment in project-based learning with the topic of “Matter and its changes” of Natural Sciences for grade 6 to develop the students’ competence of studying nature.
Scrambled Time-Bin Encoding for Efficient High-Dimensional Quantum Key Distribution

We propose analyze and demonstrate a novel high-dimensional quantum key distribution protocol. We implement the protocol using a software update in a standard two-dimensional system and observe a twofold enhancement in the secure key rate.
Versatile quantum enabled telecom receiver

We demonstrate a time-resolving quantum receiver at a telecom wavelength. We show state discrimination errors below the shot noise limit for legacy, novel telecommunication protocols with long alphabets.
All-optical Quantum State Engineering for Rotation-symmetric Bosonic Codes

We propose a resource-efficient scheme to generate various non-Gaussian states including binomial code states and truncated Schrödinger cat code states using coherent photon subtraction from a two-mode squeezed state followed by photon-number-resolving measurements.
Diffractive Networks All-Optically Reconstruct Holograms

We present a new framework for computer-free, all-optical reconstruction of inline holograms through a passive diffractive network that is trained with holographic image data using deep learning.