Reviewerly/exHarmony huggingface dataset:
The Design and Development of Virtual Simulation Experiment for Online learning

With the development of artificial intelligence, virtual reality, and multimedia, numerous digital interactive learning resources are produced for the purpose of e-learning in which the virtual experiment plays a vital role. It provides not only an advanced tool but also an open platform with high-quality studying resources for learners to conduct research-oriented learning, self-directed experiment, and innovative practice. This method increases the quality of personnel training and adds extra energy and motivation to the reformation of practice teaching and laboratory construction. In this paper, a complete solution of virtual experiments is proposed which includes both overall design and module design. The overall design of the virtual experiment can be divided into three modules i.e. hardware platform design, software platform design, and unified access portal design, which are all illustrated. In the end, the results of virtual experiments are shown. These steps, which should require the generation of the final output from the styled paper, are mentioned here in this paragraph. Firstly, users have to run "Reference Numbering" from the "Reference Elements" menu and this is the first step to start the bibliography marking (it should be clicked while keeping the cursor at the beginning of the reference list). After the marking is complete, the reference element runs all the options under the "Cross Linking" menu.
Identifying Video Forgery using Object Trajectory and Optical Flow

With the rapid growth in video editing software, digital video has become increasingly vulnerable to tampering which can be used to mislead the viewer with wrong information. This paper develops an algorithm to resolve the issue of most frequently practiced copy-move tampering or duplication of frames using optical flow. It extracts the foreground and estimates the object trajectory using its optical flow values. These motion paths are then refined to achieve a smooth trajectory of every motion in a video. Such motion data are analyzed to identify the forged area. Experimental evaluation on a public video data set is presented to effective and efficient in detecting and localizing duplication forgery.
Android-Based application system for monitoring baby’s growth and development

Abstract The use of Growth Chart ( Kartu Menuju Sehat ) all this time was conducted at Posyandu (Integrated Health Service Posts) for monitoring baby’s growth and development is not effective and efficient anymore since the cadres at Posyandu only record the baby’s height and weight that does not determine the nutritional status yet. Several studies on information technology to decrease the human error factors and optimum data storage has been widely developed via some applications, such as web server-based and android-based applications. This study aimed at developing an android-based application system of nutritional status assessment (NSA) for monitoring a baby’s growth and development using a weight-for-age index, weight-for-height index, height-for-age index. The method for developing this android-based application was using supporting devices, such as Android Studio and Visual Studio Code, as the Integrated Development Environment (IDE) and Flutter application as a unit of interface development (UI) and the development of database-driven applications, such as XAMPP software as a bundle for a web server, (Apache) and a database system (MySQL). The result of the study on designing an android-based application of nutritional status assessment at Posyandu could identify the menu of baby’s nutritional status assessment and the database for digital data storage. Furthermore, all in all, the use of this application can perform based on the instruction and be used for baby’s nutritional assessment at Posyandu. For further development, this application can be used for data synchronization in the programs of Posyandu at Public Health Centers.
Nonparametric Bayes subject to overidentified moment conditions

Nonparametric Bayesian estimation subject to overidentified moment equations is a challenge because the support of the posterior is a manifold of lower dimension than the number of model parameters. The manifold therefore has Lebesgue measure zero thus inhibiting the use of the most commonly used Bayesian estimation method: MCMC (Markov Chain Monte Carlo). This study proposes an effective MCMC algorithm and algorithms for estimating scale and the normalizing constant. The algorithms are illustrated with two illustrative applications.
Minimal Complexity Machines Under Weight Quantization

Implementing machine learning models on resource-constrained platforms such as hardware devices requires sparse models that can generalize well. This article analyzes the effect of parameter (or weight) quantization on the performance, number of support vectors, model size in bits, <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math> </inline-formula> norm and training time on various Support Vector Machines (SVM) and Minimal Complexity Machhines (MCM)-based kernel methods. We show that, Empirical Feature Space (EFS) and hinge loss-based MCM algorithms result in comparable accuracy, (8–190)x smaller model size in bits and (10k–16k)x smaller <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math> </inline-formula> norm at full precision compared with LIBSVM. The Least Squares (LS) variants of MCM based methods results in <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math> </inline-formula> 2% improvement in accuracy, upto 16x reduction in model size and upto 3x reduction in <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math> </inline-formula> norm at full precision compared with its state-of-the-art counterpart Sparse Fixed Size variant of LS-SVM (SFS-LS-SVM). We quantize the weights of the compared variants post-training and demonstrate that our methods can retain their accuracies even with 7 bits as opposed to 10 and 14 bits used by LIBSVM and SFS-LS-SVM, respectively. Our experiments illustrate that quantization further improves upon the model sizes used by our methods by upto 300x and 30x compared with the LIBSVM and SFS-LS-SVM. This has significant implications for implementation in Internet of Things (IoT) devices, which benefit from model sparsity and good generalization.
Key Techniques of Automatic Question-Answering Customer Service System in College Informatization Domain

To improve the efficiency of customer service for college information, an automatic question-answering customer service system was designed and implemented. It could collect the domain-specific corpus was collected in combination with the general corpus, train word vectors, represent the semantics of words, and implement the question similarity calculation algorithm based on word co-occurrence and syntactic analysis to achieve question matching. To minimize the range of question matching and thus make the matching more efficient, a question classification module based on support vector machine (SVM) was implemented. Finally, the validity and usability of the proposed method were verified by experiments. The proposed method shows a certain universality. Given that some colleges have not established their automatic question-answering customer service system, this paper is of great research significance.
Blockchain-based Lightweight Transaction Process Modeling and Development

Recently, blockchain systems are being applied in various application fields by combining blockchain with existing legacy systems. In particular, the cryptocurrency payment transaction system to support digital financial transactions is emerging as an important issue. Nevertheless, the development and valuation of blockchain-based cryptocurrency transactions and application services are fluctuating. With the advent of the Untact era due to Covid-19 recently, the commercialization of cryptocurrency is becoming more focused. In addition, as technical constraints for the spread of commercialization, there are problems of reaching a fast consensus in a large-scale blockchain network, consuming excessive energy for calculation, and storing the entire blockchain for verification. We propose a lightweight blockchain transaction process modeling to overcome these problems and to enhance blockchain applicability in an application environment where computing resources are weak. In addition, we propose a lightweight transaction-based blockchain application model optimized for areas with weak computing and network resources such as vending machines and ATMs.
Secured Key Generation for Biometric Encryption using Hyper-chaotic Map and DNA Sequences

Security is the major concern in this digital era communication. A novel key generation algorithm is proposed which can be used for image encryption. The suggested work uses hyper chaotic map and DNA sequences for implementation. Beta chaotic function is used for hyper chaotic functionality and scrambling is also performed to increase chaos which will enhance key security. DNA addition and complement functions are applied to the scrambled image that increases the complexity of the system. Finally, the cryptographic key is generated using SHA256 which can be used for image encryption. Multimodal biometrics is used to enhance security and accuracy. The simulation results prove that the key generated using hyper chaotic function and DNA addition is highly sensitive and protected. Several methods are performed to check the strength of key generated and hence proved the algorithm is resistant to key sensitivity analysis, information entropy analysis and correlation co-efficient analysis.
Actualization of Environmental Education as an Innovative and Digital Resource for Sustainable Regional Development

The scientific and expert community does not fully explore such a factor of sustainable regional development as actualization of environmental education. The goal of this research is to develop a list of prospective majors in the higher education system for solving current ecological challenges ensuring stable regional development. We have made a predictive assessment of the demand for prospective majors in environmental science, which is not limited to the study of the need for such personnel strictly in the labor market. A number of factors that influence people's preferences when choosing promising areas of environmental education was taken into account, such as: the industry development in the field of renewable energy; the worldwide search for a waste-free model of plastic recycling; the global scientific, technological and innovative character of regional economy development, etc. The results of this study are based on expert opinions from the environmentally-oriented scientific community and employers in the Tula region, who took part in the online survey through questionnaires. The opinion of rectors of leading universities, as well as their staff and students, on the functioning of the higher education system in the area of environmental science considering its actualization was of no less importance. The results obtained include a list of recommended majors in the field of environmental science, capable of utilizing their digital competencies and skills in solving current environmental problems; trending activities in the field of environmental development and protection, which will be in demand in the economy of the future for the sustainable development of Russian regions.
Automating the BGE Attack on White-Box Implementations of AES with External Encodings

Cloud-based payments, virtual car keys, and digital rights management are examples of consumer electronics applications that use secure software. White-box implementations of the Advanced Encryption Standard (AES) are important building blocks of secure software systems, and the attack of Billet, Gilbert, and Ech-Chatbi (BGE) is a well-known attack on such implementations. A drawback from the adversary's or security tester's perspective is that manual reverse engineering of the implementation is required before the BGE attack can be applied. This paper presents a method to automate the BGE attack on a class of white-box AES implementations with a specific type of external encoding. The new method was implemented and applied successfully to a CHES 2016 capture the flag challenge.
Enhancing Relational Topic Models with Named Entity Induced Links

Relational topic modeling as an extension to classical topic modeling assumes that documents with some form of link between the documents share topics. The links between documents are given from hyperlinks in web documents, citations in articles, or friendships in social networks. In this work, we consider links between documents induced from named entities: Two documents are linked to each other if both documents have a named entity in common. We present a case study on the performance of relational topic modeling using named-entity induced links between documents. Comparing the prediction accuracy with different sets of named-entity induced links, the results show that additional links between documents can increase the performance of topic models.
Rapid quantitative phase imaging using deep learning for phase object with refractive index variation

In quantitative phase imaging (QPI), it is greatly important to extract the phase from the phase-shifting interferograms. Despite extensive research efforts for decades, how to retrieve the actual phase using the minimum number of interferograms, continues to be an important problem. To cope with this problem, a deep-learning-based method of phase extraction is proposed in QPI. After the fringe pattern features of interferograms associated with phase retrieval are extracted, the proposed approach can establish the pixel-level mapping relation between the interferograms and ground-truth phases so that it can rapidly recover the true phase, without phase unwrapping, from one-frame interferogram. The feasibility and applicability of this method are demonstrated, respectively, by the datasets of the microsphere, neuronal cell with refractive index variation and red blood cell. The results show that this method has obvious advantages in terms of phase extraction, compared with the traditional phase retrieval algorithms.
SIC<sup>2</sup>: Securing Microcontroller Based IoT Devices with Low-cost Crypto Coprocessors

In this paper, we explore the use of microcontrollers (MCUs) and crypto coprocessors to secure IoT applications, and show how developers may implement a low-cost platform that provides protects private keys against software attacks. We first demonstrate the plausibility of format string attacks on the ESP32, a popular MCU from Espressif that uses the Harvard architecture. The format string attacks can be used to remotely steal private keys hard-coded in the firmware. We then present a framework termed SIC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (Securing IoT with Crypto Coprocessors), for secure key provisioning that protects end users' private keys from both software attacks and untrustworthy manufacturers. As a proof of concept, we pair the ESP32 with the low-cost ATECC608A cryptographic coprocessor by Microchip and connect to Amazon Web Services (AWS) and Amazon Elastic Container Service (EC2) using a hardware-protected private key, which provides the security features of TLS communication including authentication, encryption and integrity. We have developed a prototype and performed extensive experiments to show that the ATECC608A crypto chip may significantly reduce the TLS handshake time by as much as 82% with the remote server, and it may lower the total energy consumption of the system by up to 70%. Our results indicate that securing IoT with crypto coprocessors is a practicable solution for low-cost MCU based IoT devices.
Visual-Based Motorcycle Detection using You Only Look Once (YOLO) Deep Network

Abstract In Malaysia and various Asian countries, use of motorcycle is growing in numbers, and the motorcycle is the dominating transport mode. In fact, the number of motorcycles per thousand people averaged over several major Asian cities is significantly higher than the average of the rest of the world. With growing use of motorcycle, blind spots have become one of major cause to road injuries and fatalities of motorcyclists in Malaysia. In this work, a visual-based detector is proposed to reduce the risk of blind spots in causing road injuries to motorcyclists. The objective of this detector is to provide alerts to the drivers of cars and other vehicles when there are motorcyclists in proximity to the vehicles especially around blind spot areas. In developing this solution, a variant of deep network called You Only Look Once (YOLO) is chosen as visual-based detector. This YOLO deep network is trained and tested using the 5811 collected images of motorcyclists. A benchmark with regards to its accuracy and speed is conducted by comparing this visual detector against several methods such as Aggregate Channel Features (ACF) and Faster Region Convolutional Neural Network (FRCNN). Results showed that YOLO detector is the most superior detector since it has the best average precision out of all detectors, and its inference time at 22.55 ms (44 fps) is able to provide real-time implementation. Besides, YOLO inference on machines without GPU still manage to achieve a commanding performance, which is on average, at 17 fps.
Blockchain Technology

Blockchain technology is probably the most attractive innovation since the emergence of the internet. Blockchain refers to an open distributed ledger spread across multiple computers that hold digitally recorded transactions in a much more efficient, transparent, and secure manner. A blockchain consists of a number of blocks, each containing data relating to digital assets and a hash header that links it to the previous block in the chain. The blocks are linked together, and new blocks can be added and removed, following a process of consensus. Also, those involved in the transactions can share the distributed digital ledger without needing a centralized intermediary. This chapter aims to introduce blockchain technology and discuss the use cases and initiatives in various sectors of the society, in particular within the blockchain product vendors and service providers. Characteristics, benefits, types, underlying technologies, and platforms are also discussed for the sake of completeness.
Detection on a distributed network using information geometry

A passive detection scheme based on information geometry is tested on underwater acoustic field data acquired through multiple sensors from the Distributed Network Consensus experiment. These detectors use a signal discovery method wherein a target is detected when the statistics of that signal are sufficiently different from those in a broadband background. The signal excess is viewed here as the non-Euclidean distance between two probability distributions interpreted as points in a Riemannian manifold. The SNR is artificially lowered to create a low SNR scenario and it is shown that in spite of this, the geometric detectors perform quite well.
Blended Learning Practices in Russian Higher Education: Benefits, Challenges, Perspectives

The COVID-19 pandemic and a forced lockdown in spring 2020 made us reconsider the way we deliver higher education. Today we are all adopting new teaching tools and methods and redesigning our materials to be meant for diverse educational contexts. The present paper deals with the phenomenon of blended learning, which is a relatively new approach in Russian higher education. We introduce and critically discuss the concept of blended learning and given our own experience and the data of the survey we have conducted we analyze the strengths and weaknesses of blended learning approach and assess the prospects of its implementation in Russian higher education.
A big Data Analytics Framework for the Integration of Heterogeneous Federated Data Centers

Big Data is a collection of data sets that are enormous and complex to store and process using conventional data storing and processing techniques. The emergence of data in different domains causes significant challenges in data manipulation and decision making. In recent years, the requirement for the analysis of heterogeneous data on distributed data storages has been increased and has gained a lot of researchers' attention. With the rapid growth of data, a single cluster environment becomes inadequate to manage this data. At the same time, there are heterogeneous data sources on different platforms, which need to inter-connect to derive meaningful analysis. The MapReduce software paradigm has surfaced to fill the gap, and it has been successfully operating on systems. However, only single cluster environments are supported by the current implementation of MapReduce and this framework cannot be applied to federated heterogeneous data centers. Hence, it does not have enough capabilities to process heterogeneous data sources. This research presents a big data analytic framework that supports the integration of heterogeneous data sources on distributed computing models across multiple data centers. Besides, the performance of the proposed framework is measured under different cluster configurations, and experimental evaluations had shown promising results for the proposed framework compared to a single cluster environment.
A dimension reduction method of situation knowledge based on Sparse Autoencoder

Under the background of great changes in military science and technology theory, in order to solve the problem of massive high-dimensional situation knowledge processing in the process of battlefield situation assessment.The current dimensionality reduction methods often ignore the influence of algorithm complexity and model representation ability on dimensionality reduction when solving the massive dimensionality reduction problem of high-dimensional situation knowledge. In order to balance this problem, this paper proposes a situation knowledge dimension reduction method based on Sparse Autoencoder, which has a good performance in achieving dimension reduction of high-dimensional situation information and obtaining its abstract feature representation.
Smart Glass Using IoT and Machine Learning Technologies to aid the blind, dumb and deaf

Abstract One of the earliest written records of a sign language is from the fifth century BC, in Plato’s Cratylus, where Socrates says: “If we hadn’t a voice or a tongue, and wanted to express things to one another, wouldn’t we try to make signs by moving our hands, head, and the rest of our body, just as dumb people do at present?”[Source: Wikipedia]. Technology is far more developed(advanced) than time then. Now, we can detect what the deaf and mute person is saying just by recording the same and comparing it with predefined datasets, revealing what the individual is trying to accomplish. In this paper, we have proposed a solution by which we can help disabled people, people who are Deaf, Dumb, or Blind by providing them a new technological eye, ear, and brain. Machine learning algorithms are used for object detection to provide the eye for the blind. Speech to text conversion to aid the deaf and text to speech conversion to aid the dumb while communicating. The amalgamation of all these technologies with IOT technologies will help resolve the issues related to these differently abled people.