Reviewerly/exHarmony huggingface dataset:
Biological Data Classification via Faster MAXimum Feasible Subsystem Algorithm

Machine Learning (ML) techniques are the foundation of many next-generation technologies and have been swiftly adopted in a variety of applications in healthcare from prognosis and medical diagnosis to personalized treatment and drug manufacturing. A central model in ML is classification, an important tool for intelligent decision making in medicine such as distinguishing ill patients from a normal population. In this paper, an algorithm using improved solution methods for the MAXimum Feasible Subsystem (MAX FS) problem is applied to biological classification problems in the UCI database. The proposed method is compared with four widely used classification models using 10-fold cross-validation with and without hyperparameter tuning. The proposed method provides higher accuracy in more cases than the comparators and shows promising results for recall-oriented ML tasks since it improves the average recall by 17% when compared to other well-known classifiers such as K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR).
Performance Comparison between S3, HDFS and RDS storage technologies for real-time big-data applications

Low latency and fast delivery became crucial challenges in real-time applications of Big Data and remained a hot research topic. This research sheds light on the performance differences between three types of storage technologies, which are: the storage rational database system (MySQL), object storage (S3), which is powered by Amazon Cloud Services, and the Hadoop distributed file system (HDFS). Four system architectures with three different storage technologies were tested on AWS cloud environment with many complex queries to determine the most appropriate technology for real-time applications (i.e., web application, air traffic control systems, networked multimedia systems, command control systems) in terms of speed (computation time), network overload, and cost (price).
Generating Correction Candidates for OCR Errors using BERT Language Model and FastText SubWord Embeddings

In this paper, we present a method to identify candidates for Optical Character Recognition (OCR) generated errors during the post processing stage using the BERT language model and FastText subword embeddings. Our results show that given an identified error, the model is able to consistently generate the correct candidate for 70.9% of the errors.
Weighted multiwindow discrete Gabor transform

According to the Heisenberg uncertainty principle, an analysis window with a high time resolution in time domain will result in a low frequency resolution in frequency domain, and vice versa. To obtain the Gabor spectrum with high time-frequency resolution and concentration, weighted multiwindow discrete Gabor transform (M-DGT) using weights and the biorthogonal analysis method for analyzing long (or infinite) sequences is proposed in this paper, in which the combined Gabor coefficients constructed by a combination of M-DGT coefficients can be adaptively changed according to the time-frequency distributions of an analyzed signal containing multiple time-varying frequencies. To obtain the weights of the weighted M-DGT, the M-DGT is converted into a sparse problem with ℓ1-ℓ2 regularization, then an efficient iterative algorithm for solving the weights in terms of real-valued matrix and real-valued vector is derived. The convergence of the iterative algorithm is proved by optimization theory. The experimental results demonstrate that the proposed method is an effective and efficient tool for nonstationary time-frequency analysis of signals.
Development of a solar energy meter for an experimental array

The relevance of the presented development is due to the need to create a device to read and process information from an experimental array of solar panels. The purpose of the array is to obtain reliable data for the verification of models for estimating photovoltaic power potential indicators used in justifying the feasibility of the adoption of solar power plants in the eastern regions of Russia. We present a description of the main and auxiliary equipment of the experimental array. The array’s solar panels are arranged in different ways so as to determine the most efficient tilt angle and justify the need for building a sun-tracking system. The proprietary device based on the Arduino microcontroller was designed to read and write the value of instantaneous power of solar panels. The sensor that will monitor the amperage is ACS712, which is connected to the gap of the power circuit. The study provides a diagram of the current meter and describe its operation. We outline the main stages of subsequent data processing.
Privacy-Preserving Password Strength Meters with FHE

Password strength meter service enables users to assess strength of the passwords and assist users in setting stronger passwords for their accounts. However, passwords are private to the users and may contain sensitive information about them. Hence, it is important to query password strength meter service in a privacy preserving manner. To address this, we propose fully homomorphic encryption (FHE) based privacy preserving password strength meters constructed using widely studied Markov model and Probabilistic Context Free Grammar (PCFG) model. These privacy preserving strength meters allow clients to securely evaluate strength of password by providing end-to-end query privacy to the users. The primitive operation in these constructions comprises of search operation. However, search over large datasets in FHE domain is expensive and induces worst case complexity. Therefore, our constructions focus on optimizing search space to suit FHE domain that improves the efficiency of privacy preserving password strength meter. Our construction achieves practical performance with accurate guessing probabilities.
Literature Review on Answer Processing in Community Question Answering System

Community question answering (CQA) websites like Quora, Yahoo!Answers, Reddit enables users to ask questions as well as to answer questions. These sites are online communities that are popular now a days on the internet due to the increase of Question Answering (QA) websites and covers a wide variety of topics. Answer Processing task is classified as the ranking of answers, selection of answer through voting correlation, predicting the answer, selecting an appropriate answer from the candidate answers by classifying answer in good, bad, and potential category and then performing Yes/No task on selected answers or through best answer prediction or best answer selection. The shortcomings in the current approaches are the lexical gap between text pairs, dependency on external sources, and manual features which leads to a lack of generalization ability and to learn the associate patterns among answers. These shortcomings are resolved by already proposed work but they lack generalization ability and their performance is not satisfying. Feature extraction based methods mostly involve manual featurization which are not generalized form, therefore it can be avoided by deep learned feature. Whereas to focus on rich quality answers attention mechanism can be integrated with the neural network.
Adversarial Defense Networks via Gaussian Noise and RBF

Convolutional Neural Networks (CNNs) have excellent representative power and are state-of-the-art classifiers on many tasks. However, CNNs are vulnerable to adversarial examples, which are samples with imperceptible perturbations while dramatically misleading the CNNs. It has been found that, in past studies, Radial Basis Function (RBF) network can effectively reduce the linearization of the neural networks model, and Gaussian noise injection can prevent the network from overfitting, all of which are conducive for defending against adversarial examples. In this paper, we propose an incorporated defense method with Gaussian noise injection and RBF network, and analytically investigate the robustness mechanism of incorporated defense method. For our proposed method, it has the following two advantages: (1) it has significant classification accuracy, and (2) it can resist to various adversarial attacks effectively. The experimental results show the proposed method achieves the performance of about 79.25% accuracy on MNIST dataset and 43.87% accuracy on Fashion-MNIST dataset, even in the full white-box attack where attackers can craft malicious adversarial examples from defense models.
Privacy Vulnerability of NeNDS Collaborative Filtering

Many of the data we collect today can easily be linked to an individual, household or entity. Unfortunately, using data without protecting the identity of the data owner can lead to data leaks and potential lawsuits. To maintain user privacy when a publication of data occurs many databases employ anonymization techniques, either on the query results or the data itself. In this paper we examine variant of such technique, “data perturbation” and discuss its vulnerability. The data perturbation method deals with changing the values of records in the dataset while maintaining a level of accuracy over the resulting queries. We focus on a relatively new data perturbation method called NeNDS [1] and show a possible partial knowledge privacy attack on this method.
Parallel Enhanced Chaotic Model-Based Integrity to Improve Security and Privacy on HDFS

This article presents how to provide security to large chunk of data. Data be the crucial in all areas, is to be protected during storage and retrieval. The large volumes of data called Big Data will be stored in HADOOP in HDFS file systems. This research helps in understanding the Big Data and provides a model which improves security and efficient storage of data onto HDFS. This paper uses an enhanced Quadratic Chaotic Map in the process of generating keys to improve integrity. The model is implemented in parallel manner to reduces time in the process of verifying integrity. The model is implemented and tested with HADOOP Cluster which improved security and minimized time.
Long Short-Term Memory Model Appliance for Generating Music Compositions

This paper represents an approach to the music generation based on a recurrent neural network. The key goal is to create a model, that can learn different musical styles, and then generate new compositions based on previously explored content. The model declares a function with the ability to remember the temporal state and short pieces of data to use them for future predictions. The deep-learned neural network uses a one-to-many bi-axial LSTM model trained with the convolutional kernel to generate a polymorphic music piece. A deep reinforcement learning (DRL) approach was used to encourage research and increase the global consistency of music compositions being created. For quantitative and qualitative analysis, this approach works well when generating polyphonic music.
University Extension Project as a Health Promotion Instrument during the Covid-19 Pandemic

The COVID-19 pandemic has transformed the reality, made social isolation urgent aiming at reducing the contagion of the disease and, as a consequence, had to interrupt the classes and actions of extension projects in person. In this aspect, the Internet has become a possibility of interaction between teachers, nursing students and the community in an extension project university entitled: "Aromatherapy as a tool for coping in times of pandemic. Thus, the present study is a report of the authors' experience in the project of aromatherapy as an activity for health promotion in times of pandemic by COVID-19. A descriptive qualitative report was carried out on the experiences of the authors with the use of a virtual platform as an alternative informative content on the subject, during this period. The project was carried out in five weeks, with three synchronous meetings, the programmatic content was guided by scientific evidence, with guidelines of great importance to the public assisted with the aim of promoting health and be a tool for coping in times of pandemic. Thus, access to health promotion and exchange of knowledge of the target audience of the extension project was expanded.
The lower bounds on the runtime of the (1 + (λ, λ)) GA on the minimum spanning tree problem

Plenty of inspiring runtime analysis results have been recently obtained for the (1 + (λ, λ)) genetic algorithm (GA) on different benchmark functions. These results showed the efficiency of this GA, but we still do not have much understanding of its behavior on the real-world problems. To shed some light on this problem, we analyze the (1 + (λ, λ)) GA on the minimum spanning tree problem. We prove a lower bound of Ω (m2/λ) fitness evaluations on its runtime, which shows that the considered GA with constant values of parameter λ does not significantly outperform the simple (1 + 1) EA on this problem.
Usability and Users Readability of Nuclear Evacuation Map Design

Taiwan’s nuclear accident evacuation map is an informative disaster prevention tool. It is important to understand the readability of current evacuation maps. This study is divided into legend, information, and elements to explore user satisfaction. In this study, participants have to draw the icons and identify the questionnaire’s existing map legends. Design suggestions are made based on the associative elements of icons drew by participants. Then, the task was carried out through three nuclear accident evacuation maps in Taiwan so that the subjects could judge the risk level and make route planning after using them. The study uses. The 5-point Likert Scale to understand the post-use evaluation of information and elements. We use the semi-structured interview and the System Usability Scale to understand the expectations on the evacuation map. If we provide “You-are-here” and “Evacuation Arrow,” which are more expected by users, the graph can improve the readability. Evacuation Arrow is an eye-catching element to guide the user to make a judgment, at the same time easy to mislead, so we need to be more careful labeling. Important information such as “evacuation principles” and “gather point” should be read to guide the user to absorb effectively. This study’s results help understand the user’s preference for the evacuation map and optimize the evacuation map.
The need for life cycle assessment in India for achieving the sustainable development goals (SDGs)

Systematic education policy is essential for offering school and college education in a country due to the reason that education leads to progress in society. Different countries use different education systems with different stages during its life cycle of school and College education levels. Recently, the Government of India received a draft Education policy suggested by an expert committee headed by Dr. K. Kasturirangan, former chairman of Indian Space Research Organization (ISRO). The committee suggested both incremental and drastically changes in existing Indian education policy and give guidelines to effective implementation in the country by 2030. This is also in line with the Objectives of fourth Sustainable Development Goals to provide quality education for all citizens through fourth Educational Industry Revolution (EIR 4.0) in the country. This paper reviews the related literature during last few years on Indian Higher Education Policies and their consequences, Salient features and their focuses on the present draft of National Education policy 2019 through content analysis. The paper highlights on various policies proposed in the draft with the special emphasis on higher education section and compare them with the previous policies. The analysis also compares the possible effects of NEP 2019 proposal on private and public HEIs in terms of facilities & restrictions. The strength and weakness of the new policy are identified with respect to various stakeholders and listed. Appropriate suggestions are made to realize the policy and make it defect free and effective from a public point of reference and for the prosperity of the country.
Graph Models in Information Hiding

Information hiding allows us to hide secret information into digital objects such as images without significantly distorting the objects. The object containing hidden information will be transmitted to a data receiver via a probably insecure channel. To securely transmit the object carrying hidden information, the distortion caused by data embedding should be as low as possible, which is referred to as the rate-distortion optimization problem. Many conventional methods optimize the data embedding procedure by a heuristic fashion, which may be not optimal in terms of the rate-distortion performance. In this chapter, we introduce novel approaches that use graph theory for information hiding. These graph models are general and can be used for improving the rate-distortion performance of information hiding systems. In addition to rate-distortion optimization, recent graph models used for system design of information hiding will be also reviewed. This chapter is intended as a tutorial introducing advanced graph models applied to information hiding.
Secure wireless communication via adversarial machine learning: A Priori vs. A Posteriori

This paper considers wireless communication system consisted of one transmitter, one legitimate receiver, and one eavesdropper. The transmitter transmits perturbation-added signal (i.e. adversarial example) with a certain modulation type, while the legitimate receiver and the eavesdropper adopt deep neural networks (DNN)-based classifier to recognize the modulation type of the received signal. Compared to the fact that the general goal of adversarial examples being a misclassification of all available classifiers, our objective is to design an adversarial example that lets the legitimate receiver classify accurately while the eavesdropper misclassifies. To this end, we propose two design approaches of the adversarial examples: (i) A priori; (ii) A posteriori, i.e. before and after learning steps of the receiver, respectively. Numerical results show that both approaches are effective for securing the communication link.
Analysis of Improved User Experience When Using AR in Public Spaces

The new immersive technologies have been offering several possibilities and creating new markets around the world. Projects using Augmented Reality offer different experiences in the most diverse segments, including culture. If well designed in public spaces, AR can become a powerful tool in situations that simulate, facilitate, and favour cultural learning, making it more interesting and interactive. This article aims to investigate the improvement of relationship of users with sculptures in public spaces through the use of an AR tool. This article aims to investigate the improvement of relationship of users with sculptures in public spaces through the use of an AR tool. We build on this analysis to identify improvements promoted by the use of AR tool in the learning and engagement process of the users about relevant statues of remarkable personalities of local culture. The idea was to integrate technology, culture, and design so that public spaces, which contain these monuments, could be more visited and have their values re-meant through information.
Exploring the Interactive Mode and User Experience of Dining Space Alone During the Post-Covid-19 Period from the Perspective of Cultural Probes

In 2019, the entire world was under the panic and threat of rapid spread of "COVID-19" virus. Especially, the global social structure pattern was changed. At the same time, traditional Eastern culture attaches great importance to table etiquette and eating together around the table. With the transformation of social lifestyle and structure, more and more people must start to face the situation of being alone or the mentality of dining alone in a restaurant. This research uses the theory of cultural probes to study the post-COVID-19 period, during which consumers stay alone in the restaurant and generate daily context and behavior patterns. Its research method adopts the role of "first-person perspective", combined with the context of "modeling feature track". Based on the cultural probe toolkit, this research obtains information for integrated research and simulation test verification. In the experimental testing phase, participants designed the user experience for the product's real-time "design interactive mode" and "personal space experience" through interviews, taking photos for record and writing mood logs, and created a new interactive design framework. The theme of this research is "The Interactive Mode and User Experience of Alone Dining Space during the Post-Covid-19 Period from the Perspective of Cultural Probes". It conducts research and draws conclusions based on the cultural probe theory and provides an innovative model that focuses on the interactive design relationship between people and products, supplemented by product appearance molding design. The name of the design work is "Intelligent Partition Plate System Product". It aims to establish a new type of human-computer interaction communication framework mode, so as to meet the future interactive behavior of alone dining space in the post-COVID-19 era and enhance the overall user experience.
Using Neuromodels for Evaluating and Determining Productivity Technical Processes

The paper is devoted to solving the actual problem of creating mathematical support for constructing models for evaluating and monitoring the performance of technical and production processes. Since the known methods have a number of disadvantages, it is proposed to use artificial neural networks as a basis for models. During the synthesis of neuromodels, two methods will be used to compare their performance characteristics. The obtained models are planned to be used for further research and design of special control systems for technical processes.
Prediction of COVID-19 Infection Based on Symptoms and Social Life Using Machine Learning Techniques

COVID-19 pandemic has affected nearly every aspect of life. Observing online the spread of the virus can offer a complementary view to the cases that are daily officially recorded and reported. In this article, we present an approach that exploits information available on social media to predict whether a patient has been infected with COVID-19. Our approach is based on a Bayesian model that is trained using data collected online. Then the trained model can be used for evaluating the possibility that new patients are infected with COVID-19. The experimental evaluation presented shows the high quality of our approach. In addition, our model can be incrementally retrained, so that it becomes more robust in an efficient way.
Recent Trends in Deepfake Detection

Almost 59% of the world's population is on the internet, and in 2020, globally, there were more than 3.81 billion individual social network users. Eighty-six percent of the internet users were fooled to spread fake news. The advanced artificial intelligence (AI) algorithms can generate fake digital content that appears to be realistic. The generated content can deceive the users into believing it is real. These fabricated contents are termed deepfakes. The common category of deepfakes is video deepfakes. The deep learning techniques, such as auto-encoders and generative adversarial network (GAN), generate near realistic digital content. The content generated poses a serious threat to the multiple dimensions of human life and civil society. This chapter provides a comprehensive discussion on deepfake generation, detection techniques, deepfake generation tools, datasets, applications, and research trends.