Reviewerly/exHarmony huggingface dataset:
Unmanned Approach for Real Time Monitoring of People Wearing Mask in Pandemic Scenario

The most intelligent species of world, human being, is feared and being affected by the rapid spread of coronavirus (COVID-19). In this time of world level danger, World Health Organization (WHO) came up with few guidelines for protection against the speedy dispersion of coronavirus in human habitat. According to WHO, the most important step to be taken by everyone against COVID-19 is to wear a mask in their routine, whether it would be their home or office. But it is very difficult task to keep an eye people manually. Keeping this in mind, in this paper the emerging technologies in market like deep learning, machine learning and python is used for helping in this stage of country and make a automatic face mask detection that can be used in various public places to avoid spread of the virus. Also this can be integrated with CCTV camera companies for getting report of audience. Our model can detect mask in images as well as in real time video stream. Along with this it is also able to detect multiple facial masks in a single frame. It has wide number of applications in real world, this model can be used in public transport systems, malls, markets and other public areas for detecting face masks and giving alert over a certain limit. Even after everything comes back to normal, society will still use the mask and it became a basic need for us even to fight pollution and other harmful airborne diseases. Therefore, using the technologies and science advancement would be the best for unmanned approach of human health preventions.
Chinese Named Entity Recognition Based on Embedded Pinyin Information

Recently, word fusion techniques have become increasingly popular in Chinese Named Entity Recognition (CNER), which can make full use of displaying word information and word sequence information. However, the information of Chinese pinyin are often ignored in these methods. Pinyin characterizes the pronunciation of Chinese words, which can well deal with the phenomenon of “same words with different pronunciation” (the same words with different pronunciation and different meaning) in Chinese. Therefore, this paper proposes the Py-CNER (Pinyin Chinese Named Entity Recognition) model, which specifically embeds word information and pinyin information into a dual-stream converter to enhance Chinese named entity recognition in terms of metrics. The experimental demonstrate the advantages and superiority of the fused pinyin information, which finished on Weibo, Resume, and OntoNotes4 dataset.
Research on the Health Evaluation and Prediction of Switch Machine

The switch point machine is one of the key infrastructures of the railway signal system, which has an important influence on the safe operation of rail transit. This paper proposes a point machine health evaluation algorithm from both macro and micro perspectives and a health prediction algorithm based on Transfer Entropy (TE) and Graph Neural Network (GNN), which can not only fully and accurately reflect the operating status of the point machine equipment, but also the health score (HS) of the equipment can be predicted to prevent failures in advance, which are of great significance to the formulation of maintenance strategies and the improvement of maintenance efficiency.
Bit-serial convolution with prediction threshold for convolutional neural networks

To reduce the implementation complexity and power consumption of the convolution operation in a convolutional neural network (CNN), this work proposes a new convolution method using the serial input and prediction threshold. To confirm the benefits of the proposed method, we use the original parameters, i.e. kernel weights and biases, of the popular AlexNet to verify the proposed algorithm and then implement its digital circuit. According to implementation data and comparison with traditional convolution using bit-parallel input, the implementation gain in terms of the throughput/power/area of the serial convolution is 7.57 times that of parallel convolution.
Multiple Machine Learning Algorithms and Pedoenvironmental Attributes Applied in Geophysical Surveys

Geophysical techniques applied to soils and machine learning algorithms have been used to understand the dynamics of the pedosphere and model soil attributes, but studies exploring the spatial prediction of geophysical data are still scarce. In this research, we aimed to model and map geophysical and soil attributes using parent material and terrain attributes with different machine learning algorithms. In addition, we tested nested leave-one-out cross validation (nested-LOOCV) methodology to deal with datasets with a small number of samples. We collected soil physico-chemical and geophysical data (gamma-ray emission from uranium, thorium and potassium; magnetic susceptibility and, apparent electric conductivity) using three sensors: a gamma-ray spectrometer, a susceptibilimeter and conductivimeter. The performances of the best algorithms based on the root mean square error (RMSE) and mean absolute error (MAE) for each predictor were higher compared to the use of a mean value for the entire area (NULL Model). The most important variables were the parent material and the digital elevation model for most predictions. The nested-LOOCV methodology proved to be adequate for small samples. Machine learning techniques are potential tools for the model and mapping geophysical data when only field data with proximal sensors are available.
New Algorithms for Generation of Combinations in Lexicographical Order

Generating r-combinations of the set N={1, 2, …, n} in lexicographical order has been an interest for researchers in the field of computer science and combinatorial optimization. This study provides three new algorithms for generating these r-combinations in lexicographical order for the given n and r. The first two of these algorithms generate all the combinations by using a backtracking process. The third algorithm directly generates the k-th sequence in the lexicographical order by using a hash function. All these algorithms perform an efficient running time compared to the well-known methods used in literature.
Location Based Privacy Protection Data Interference Method

In recent years, with the rise of the Internet of things industry, a variety of user location-based applications came into being. While users enjoy these convenient services, their location information privacy is also facing a great threat. Therefore, the research on location privacy protection in the Internet of things has become a hot spot for scholars. Privacy protection microdata publishing is a hot spot in data privacy protection research. Data interference is an effective solution for privacy protection microdata publishing. Aiming at privacy protection clustering problem, a privacy protection data interference method is proposed. In this paper, the location privacy protection algorithm is studied, with the purpose of providing location services and protecting the data interference of users&#x0027; location privacy. In this paper, the source location privacy protection protocol (PR _ CECRP) algorithm with controllable energy consumption is proposed to control the energy consumption of phantom routing strategy. In the routing process from the source node to the phantom node, the source data packet forwarding mechanism based on sector area division is adopted, so that the random routing path is generated and the routing energy consumption and transmission delay are effectively controlled.
Debris

This paper describes the artwork Debris (2021) and examines the impact of consumer electronics and digital media as electronic waste (e-waste) in environmental pollution. E-waste is in part motivated by planned obsolescence, a model for designing consumer electronics and digital media technologies to shorten their functional time period. The materiality of digital media as e-waste is explored in the installation Debris by repurposing found e-waste in an assemblage of broken hardware together with sound and a browser-based game. The assemblage becomes an autonomous device that reacts to the environment's light intensity to generate noise. As part of the assemblage, the game visualises the exponential growth of e-waste where the player has to survive planned obsolescence. Debris aims to raise awareness of the aggravating effects of e-waste on the environment.
Robust adversarial examples against scale transformation via generative network

Nowadays, adversarial examples of images which are generated by adding special perturbations on their hosts make great impact on many deep neural network (DNN)-based computer vision tasks and bring security risks. With the deep insight into this field, researchers realize that many simple image processing methods can easily reduce the attack success rate. Therefore, studies on robust adversarial examples that are able to defend destruction become the emphasis. However, most existing methods focus more on the distortion in user transmitting procedures and physical deformation such as JPEG compression and brightness. Given that scale transformation is one of the commonest measures in data transformation and enhancement, a two-step algorithm is proposed to eliminate the effect caused by resizing during the model inferring process. The first step is to select pixels that can affect the classification result through a DNN. As a binary classification task, the network predicts whether the pixel deserves to be modified or not. The second one is to strategically compute the amplitude of noise and add it to the host image. Experiments verify that this method resists the scale transformation and guarantees the invisibility of humans as well.
Optimizing Margin in Weighted Support Vector Machines for Flood Problem

Nowadays, the problem of floods has occurred frequently and it is a natural disaster that has been faced by people of the world. Thus to obtain the perfect information to be analyzed and classified accurately is quite impossible. Noise data decreases the quality of the data and can give poor result on the performance of learning algorithms especially on the classification accuracy. All of these limitations had cause the use of classification model for the inaccurate extraction of distribution pattern of floods in the broad areas. In this study, we investigate the drawbacks of classical weighted support vector machines (WSVM) and improve the WSVM algorithm using multiple hyper-plane and Instance-weighted (MHI) for flood classification. We compare our proposed method with existing WSVM to gain better decision margin for higher classification accuracy. The proposed method is evaluated using three KEEL datasets and real flood datasets. The results indicate that the proposed approach can be applied to existing approaches for computation with significantly in adapting and showed the improvement of the classification accuracy.
The Application Of LSB Steganography For Secure Text and Hiding Confidential Information Using AES Cryptography

Maintaining the confidentiality and security of a company&#x0027;s message are two fundamental aspects, primarily if the message is used for business purposes. It will be perilous for the company if the message file falls into the wrong hands. So that in sending messages, encryption should be done first and hidden into the media. The encryption technique we use in this application is Advanced Encryption Standard (AES) cryptography combined with Least Significant Bit (LSB) steganography techniques in mp3 extension files. The integration of the two techniques aims to provide protection or security systems for secret file messages in the form of images or images sent. This study indicates that the application of a combination of AES and LSB techniques can be used on document files so that the application can provide information security solutions for highly confidential company documents both when the document is sent. When the document is stored, then there are no errors in the functionality of the application. As evidenced by the results of comparing the encryption and decryption process of messages through the application with manual encryption and decryption of messages.
Angle Only Tracking for Infrared Search and Track (IRST) System Mounted on an Aircraft

This paper presents an interactive multiple model (IMM) based angle only tracking approach with global nearest neighborhood (GNN) data association algorithm for infrared search and track (IRST) system mounted on an aircraft. Two target motion models are considered in IMM namely, Constant Angular Velocity (CAV) model and Constant Angular Acceleration (CAA) model. Simulation scenarios are generated with targets maneuvering and detected by an IRST in track while scan (TWS) mode of operation. Performance of the proposed algorithm for tracking multiple maneuvering targets with IRST in TWS mode is presented in this paper.
Proposal and evaluation of didactic materials to support the teaching of Physics

The teaching of science has always faced multiple adversities for the construction of meaningful learning and the subject of Physics is no exception, however, if we consider that today the educational field has had to change due to the sanitary contingency caused by Covid-19, it has made it clear that the form of teaching must be modified to adapt to current conditions. Hence, the objective of this study was to design and evaluate the impact of a didactic material that contributes to improving the understanding of the learning that are most difficult for students in the subject of Physics III at the Escuela Nacional Colegio de Ciencias y Humanidades plantel Vallejo, through a pilot sample by convenience in which 22 students and 12 teachers participated. For this purpose, a hybrid methodology was used in which pedagogical, disciplinary, and technological aspects were aligned; finding that this is a viable option to improve the student performance. Therefore, it is required that teachers be trained in these topics, since a facilitator with greater variability is needed to adjust his teaching to current requirements.
A Combination of Resampling and Ensemble Method for Text Classification on Imbalanced Data

One of the major factor which can affect the accuracy of text classification is the imbalanced dataset. In order to find the suitable method to handle this issue, six different ensemble methods are used to train models on imbalanced dataset. The result shows that, without resampling the dataset, Stacking algorithm performs better than other ensemble method, it can increase the recall of the minority class by 19.3%. Meanwhile, ensemble algorithms combined with resampling methods are used to train the model. Results show that, ensemble algorithms combined with undersampling method (RUS) can improve the predictive ability of models on minority class, but it reduces the accuracy of models on other majority classes because of feature dropping; while voting algorithm combined with oversampling method (SmoteTomek) can improve the recall of the minority class by 40.4%, without decreasing the accuracy of models on other majority classes. Afterall, in training a text classification model with multi-class imbalanced datasets, Voting algorithm combined with SmoteTomek can be a preference.
Software Quality Prediction Using Machine Learning

Since twenty-first century, software quality is considered as a vital factor in the global competitive position for any software product in order to ensure quality and to ensure the dependency of software product. Software fault proneness has made tremendous progress in predicting the faults. On the other hand, the prediction model helps to create an accurate model and also helps the developers to deliver the software in time to their customers [4]. A software quality prediction model seeks to predict the quality factor that whether the software is prone to fault or not. In the early stage of software development, the users use the fault prediction model to detect the faults. So, our main aim should be to create reliable, portable, and robust software that minimizes the errors that occur when a program runs. Software quality prediction model helps us to know which components are at fault which can be corrected by detailed testing. To improve the quality, reliability, efficiency, and maintenance cost, the software fault should be predicted beforehand. It is difficult to develop fault prediction software. The cost of detecting and correcting the errors becomes extremely higher as we move from requirement analysis to maintenance phase, where defects might even lead to loss of life [3]. Fault detection in the beginning stages help the stakeholders [5] to converge their resources on modules that are likely to cause fault in the beginning phase.
Distribution Linux for Installation Software using Remastering Technique

Computer needs an operating system in order to work. The operating system still needs to be equipped with supporting applications in order to meet user needs. The process of completing a computer with an operating system and supporting applications is called installation. Installation takes time and effort, the more applications you will use the more time and interaction it will take. Installation is done when you buy a computer or a problematic operating system. There are several installation techniques used, first by installing the operating system first then followed by installing supporting applications. Another way is to customize the operating system, this is known as remastering. This research was conducted to measure and compare the installation time and the comfort level of the Windows and Linux remastering installation process. In the experiment, participants installed a remastering Windows and Linux operating system. Next, they were asked to record the time of installation and give a rating related to ease on a scale of 1 to 7. The data were then collected and analyzed. The results showed that participants required less time to install Linux operating system than Windows operating system. Furthermore, the participants were more preferred to install Linux operating system since the process was easy to perform. To sum up, the installation process of Linux operating system with the remastering technique is quick and easy to perform.
Last-Round and Joint First/Last-Round Power Analysis Attacks on PRESENT

This paper explores new power analysis attacks on cryptographic circuits based on the PRESENT cipher, and discloses an unreported vulnerability of this cipher. For the first time, an attack to the last round is introduced to identify the secret key based on the knowledge of ciphertext, as opposed to conventional attacks based on the first round (i.e., knowledge of plaintext). This attack is enabled by the analysis of the bit-permutation structure in the last round, and by introducing a suitable bit regrouping strategy. As further contribution, a more powerful attack combining first/last-round Correlation Power Analysis attacks with Top-N guess strategy is demonstrated. The analysis and the attack results show that the optimal choice of Top-N key guesses makes the proposed attack more powerful than a conventional single-round attack, reducing the number of power traces required by 34%.
Applications and Potential of Blockchain in Modern Technological Systems

Blockchain, a relatively recent technological breakthrough, has been spreading like wildfire with reports of adoption extending across industries. Although most popularly known in the context of cryptocurrencies, this technology's true strength lies in its ability to disrupt existing, traditional industries. It can be used to run any form of exchange, agreement (trust), or tracking processes. The capability to negotiate decentralized trust is key to its application in SCM. Holding the capacity to transform SC functionality, blockchain technology creates possibilities for security and redundancy mechanisms that are foundational for a revolutionary new approach to SCM. While aiming to explore the incumbent status, potential applications, and future directions of blockchain technology, this study iterates that its advent opens up new and exciting ways to implement a variety of different modern technological systems. The writers offer an overview of what an efficient SCM system should entail, with the aim to overcome any existent limitations via the application of blockchain technology.
Digital Leadership and its Development in Russia: Top managers and Companies

The objective of this article is to identify the features of digital leadership as a special form of manifestation of strategic human capital in Russia and directions for its further development. The phenomenon of leadership will be considered at the individual level – the level of managers and at the micro level – the level of companies, where leadership potential is realized. The study of company leadership is complemented by an analysis of 100 interviews with top leaders in the digital economy from the annual ranking of key persons on the Russian Internet market, presented by the Tagline agency in 2020. The narrative method is used to analyze their life stories. Based on the two-level analysis, it has been established that digital leadership is expressed by the presence of a critical number of high-resource individuals and groups of people that have a multiplier effect on the degree and pace of digitalization of the economy; general trends and features of the development of IT leaders in Russia, their key competencies have been identified; the necessity of changing the institutional conditions of digitalization and the transition to transformational leadership of the distributed type has been substantiated.
Basic Framework of Different Steganography Techniques for Security Applications

The main aim of this article is to develop a basic framework for different steganography techniques in real-time applications. Today, as the utilization of the Internet expands, security is gaining momentum. Steganography is a strategy for camouflage confidential data behind an innocent cover record, so that all subtitles are not known. The most effective method to ensure data information to utilize the idea of steganography. The aim of steganography is separated into a few types. In this study report, we will examine main sorts of steganography, by taking a gander at the cover record as initial step—basic to all classifications, we will talk about text, image, speech, and video steganography. Steganography text is an interaction that utilizes at text cover. We will go with line change, how to change names, syntactic strategy, and favored technique for covering. Coming to speech steganography, it contains LSB coding, encoding segment, spread spectrum, echo hiding techniques, and speech steganography utilizing fast Fourier transforms. The idea of video steganography is utilized to shroud data behind the scenes of video outlines. The concept of steganography and cryptography security principles is given in information. Strategy used to implant LSB information on paper. Video steganography can shroud a lot of information in a basic and compelling manner, and we will utilize a wide range of steganographic techniques. Along these lines, the test proof demonstrates the adequacy of the covering technique in speech steganography. In this way, we will actually want to recover classified data for somewhat quality corruption utilizing steganographic decoder concept.