Reviewerly/exHarmony huggingface dataset:
Searching String in Big-Data: A Better Approach by Applied Machine Learning

There are hot patterns of huge data sets in numerous regions in the course of the most recent 5 years. Looking through string or pattern from a gigantic record is intense generally in the event of randomized situation. These require the advancement of new algorithmic strategies to investigate such enormous data sets and solve optimization errands utilizing sorting on indexing levels and Applied Machine Learning Models. One of the essential strides in text processing is string searching and pattern matching. A word search algorithm works by finding the first or all the occurrences of a word in a textual data or ASCII files. The pre-processing phase is used to determine the formula for number of positions by which the pattern needs to be shifted in case of a mismatch in the matching phase. The fundamental objective of string search or pattern matching algorithms is to increase efficiency by reducing the number of comparisons and increase the length of shifts in event of a mismatch. The issue of efficiency of string search algorithms has probably never been considered so seriously and genuinely until the virtual content explosion caused by the web and the task of mining valuable data and information from it. In this paper, a better search algorithm “Tara–Paras String Search” is introduced that is faster than conventional Binary Search and Interpolation Search. Indexing levels are introduced by length of the word, sequence total of alphabets and starting letter of the word to reduce the size of input. For analysis, 2 data sets are considered. The dictionary of English words having more than 109,000 words and a list of more than 2.5 Lac sorted numbers and uniformly distributed (multiples of 7) are taken for data sets. Analysis and Implementations Models have been implemented, compared and executed in Python with time complexity and obviously Applied Machine Learning will select the faster one.
Two modifications for Loidreau’s code-based cryptosystem

This paper presents two modifications for Loidreau’s cryptosystem, a rank metric-based cryptosystem constructed by using Gabidulin codes in the McEliece setting. Recently a polynomial-time key recovery attack was proposed to break this cryptosystem in some cases. To prevent this attack, we propose the use of subcodes to disguise the secret codes in Modification I. In Modification II, we choose a random matrix of low column rank to mix with the secret matrix. Our analysis shows that these two modifications can both resist the existing structural attacks. Furthermore, these modifications have a much more compact representation of public keys compared to Classic McEliece, which has been selected into the fourth round of the NIST-PQC project.
Philosophy of Music in the Mirror of the Contemporary Age. Article 1

The article examines the situation that has developed in the contemporary age and being named differently: postmodernism, post-postmodernism, digital modernism, metamodernism, etc. It is noted that, despite the difference in naming, all the terms indicate a global crisis of culture and man. The three most important signs of this crisis are identified: (1) degradation of a man – the predominance of his animal nature; (2) total technicism; (3) oblivion of national traditions, sacred things. These features are briefly explained. It is concluded that the basis of these signs is the loss of human spirituality. It is stated that music has extraordinary opportunities to elevate the spiritual needs of a person. It is emphasized that these opportunities contributed to the strengthening of the impact of religious practices within various worships – shamanism, Zoroastrianism, Sufism, and others, which ensured the maintenance of the humanity of the world. It is believed that the use of spiritual-creative possibilities of music will help overcome the existing global crisis and prepare the formation of the era of culture and man, the era called a New Humanism by M. Eliade. It is argued that there are composers in Russia and in other countries today who purposefully use the spiritual-creative potential of music in their work to overcome the crisis that has arisen. We offer an interview with one of them – Russian composer Dmitry Stefanovich. In conclusion, it is noted that the following articles will continue to analyze the processes taking place in contemporary culture, and, in this regard, there will be presented stories about music and interviews with composers from Russia and other countries.
Ontology-supported rule-based reasoning for emergency management

Emergency situations (disasters) occur regularly, and they present a vast risk for human lives and infrastructures. Reactions to disasters can vary depending upon the availability of resources and the location of the emergency. Planning is essential for quick and efficient responses to emergencies in every location. In this chapter, a rule-based reasoning approach to emergency management has been formulated, on the basis of which we will make recommendations for recovery from emergencies. All the required facts should be collected first and then represented in the appropriate form. Both of these tasks take time, and time can be a determinant between death and life for the affected persons. We propose ontology-supported rule-based reasoning to automate this process of decision support in order to recommend actions faster than a human beings are capable of doing and under any circumstances. An extensible knowledge model (Resource Description Framework) has been framed using semantic technology, in which the data are described in triplet (subject, predicate, object) form. Ontology has been used to build a knowledge base that can store procedures and facts about emergencies, and experts' advice is stored in the form of rules.
An Efficient Internet Map Tiles Rendering Approach on High Resolution Devices

In order to adapt map tiles to high resolution display devices, some internet map providers pre-generate and store more than two sizes of map tiles. However, it is inefficient to download larger high-resolution tiles, and they will occupy a larger storage space on map servers. In this paper, a new efficient internet map tile rendering approach on high resolution devices based on bicubic and Laplacian convolution (BLC) is proposed. Based on BLC, a new efficient internet map tile rendering framework is presented. The experimental results demonstrate that this new approach is effective and efficient for providing a tile map service.
Introducing Swordsman Robot Defensing

Tankendo is performed by exchanges of strikes with a light and short bamboo sword at a comparatively short distance. Since quick movements and decision-making determine the winner and the loser, without heavily depending on the physical strength and fitness, tankendo can be enjoyed even by elderly persons and females. In this study, an autonomous mobile “tankendo swordsman robot” was developed as a human to robot combat and a training aid machine for health promotion or entertainment. This paper presented this robot.
Slicing Wi‐Fi links based on QoE video streaming fairness

Summary Dynamic Adaptive Streaming over HyperText Transfer Protocol (DASH) video streaming is one of the dominant sources of traffic on the Internet, and this traffic is often delivered to users via Wi‐Fi access points. This makes the quality of experience (QoE) of the service susceptible to degradation when the Wi‐Fi link is underperforming, and it can also inflict QoE unfairness, creating situations where users connected to the same Wi‐Fi network feel different levels of QoE. This article presents a system that improves the QoE fairness of DASH video transmission network slicing in the context of Wi‐Fi networks. The proposed system slices the wireless resources unevenly among flows, taking into account the characteristics of the video and the QoE demands for each user. Experimental results show that fairness improves by 56% when compared to an unmanaged network, while the mean QoE is reduced by only 5%.
Will We Adopt AI Like We Adopted Electricity?

Recent artificial intelligence (AI) advances and the consequent gains in convenience signify that AI adoption is imperative. The major questions are how AI may be used appropriately and what the contexts for such use are.
Image style disentangling for instance-level facial attribute transfer

Instance-level facial attribute transfer aims at transferring an attribute including its style from a source face to a target one. Existing studies have limitations on fidelity or correctness. To address this problem, we propose a weakly supervised style disentangling method embedded in Generative Adversarial Network (GAN) for accurate instance-level attribute transfer, using only binary attribute annotations. In our method, the whole attributes transfer process is designed as two steps for easier transfer, which first removes the original attribute or transfers it to a neutral state and then adds the attributes style disentangled from a source face. Moreover, a style disentangling module is proposed to extract the attribute style of an image used in the adding step. Our method aims for accurate attribute style transfer. However, it is also capable of semantic attribute editing as a special case, which is not achievable with existing instance-level attribute transfer methods. Comprehensive experiments on CelebA Dataset show that our method can transfer the style more precisely than existing methods, with an improvement of 39% in user study, 16.5% in accuracy, and about 3.3 in FID.
THE USE OF AUGMENTED REALITY IN THE STUDY OF GEOMETRY

The purpose of studying geometry is the development of spatial abilities. However, sometimes it is difficult for beginners to perceive two-dimensional images representing three-dimensional space in the learning process. Augmented reality is a technology that has demonstrated its effectiveness in learning and can be used to create didactic materials. Its&nbsp;feature is the placement of a virtual object in a real environment, in real-time and considering the location in space. Complementing the real environment with virtual information, it helps the user solve real-world problems. Despite the benefits, novelty, and interest in technology in the field of education, there are very few studies that demonstrate practical implementation and how to use it in the study of geometry. The main goal of the study is to review and systematize foreign studies related to the use of augmented reality in the process of learning geometry. All of them demonstrate the use of specially designed programs. The review consists of their analysis, the used equipment, purpose, capabilities, types of&nbsp;activities, types of markers, design and interface features, the content used and the degree of students' perception. The tasks and capabilities of&nbsp;the&nbsp;programs vary in difficulty levels and demonstrate the suitability of&nbsp;the&nbsp;technology for use in both primary and higher education. The above programs differ in design and ways of interacting with the interface. In&nbsp;particular, the introduction of the interface into the physical world, as&nbsp;noted by the authors of the relevant studies, creates a favorable impression, and gives the process a sensation that is different from ordinary work with a&nbsp;computer. During the analysis, it was determined that all applications were positively received by students, aroused interest, and increased willingness to learn. The use of augmented reality is possible for both individual and&nbsp;group work. There are successful examples of the use of technology in&nbsp;classroom and distance learning. Programs with augmented reality can serve as a successful didactic tool, making the training varied and interesting. Keywords: augmented reality, AR, education, geometry training, application design, mobile application, didactic material.
Use of Virtual Reality as Learning Object of Electrical Substations

Power substations play a key role in electrical systems. They aim to ensure system reliability, electrical operation without interruptions and safety and protection for the users and system. In this sense, the knowledge of the main concepts associated with substations is essential for the training of students and engineers who work in this area. However, the development of substation courses and training by conventional methodologies does not allow an optimal integration with users. In general, technical visits are used as an auxiliary activity. However, it takes time, since it is usually far from urban centers, where educational institutions are located, and additionally, for safety reasons, it is not allowed being close to the equipment, especially in high voltage substations. In this context, the use of virtual reality may offer educational mechanisms for users to have improved knowledge retention and motivation. Thus, this paper presents a virtual substation as a teaching tool to complement substation courses and trainings. The present system mitigates the impact on the courses and training quality, when it is not possible or it’s difficult the access on site. For example, the scenarios caused by COVID-19, which imposes remote trainings and classes at companies and universities.
Research on an Interpretable Real-Time Information Recommendation Model based on BAS-ICF algrithm

Most of the existing studies on recommendation system are designed for the accuracy and novelty, but few are designed from the perspective of the user experience, which will cause users to feel confused about the recommendation results, especially in unfamiliar fields. So, this paper will introduce bias into the object-based collaborative filtering (ICF) algorithm. The bias can simultaneously quantify both the quality of the movie and the users' requirements, which not only helps to improve the accuracy of the recommendation, but also enhances the interpretability and real-time performance. It's shown graphically and experimentally. Even though the base-ICF algorithm is not as accurate as the deep-learning algorithm, it performs better in real-time and interpretability, which can achieve a good balance among these three factors.
Implementation of a selective mean filter algorithm to reduce noise in computed radiography images

This study implements a selective mean filter (SMF) and determines its effectiveness on computed radiography (CR) images. A phantom of the Kualitas Citra Imajing (KUCING) was used. The effectiveness of the SMF was compared to the adaptive mean filter (AMF). The assessment of image quality was based on several parameters, such as the peak signal to noise ratio (PSNR), mean squared error (MSE), structural similarity index (SSIM), contrast consistency (CV), contrast linearity (CL), and modulation transfer function (MTF). The results show that the PSNR value generated by the SMF is higher than with the AMF. The MSE value generated by the SMF algorithm is lower than AMF. The SSIM values showed that SMF algorithm produces a better image similarity between the original image and the filtered image compared to the AMF algorithm. In consistency and linearity, the AMF leads to significant changes from the original image, while the SMF image is closer to the original image. The 10% MTF generated by the SMF is the same as the original image, while AMF produces a significant decrease in the 10% MTF. The SMF took 17.9 ± 0.7 seconds for denoising one CR image, while AMF took 519 ± 5 seconds. SMF decreases image noise without a significant effect on the spatial resolution of the image. SMF filtering is superior to AMF filtering in terms of spatial resolution and a shorter computational time.
IR Image Disruptor With Embedded Code Tracker

Recent Indian entertainment world loses more than Rs.18,000 Cr ($2.7 billion) and 60,000 positions because of robbery on a yearly scale. The principle wellspring of this issue is use of portable in theater corridors to catch the film. To beat this threat, this research work proposes an infrared picture disrupter behind performance center screens to obscure the picture caught. Added to this each film job will be implanted with a code so the beginning of robbery can be pinpointed to the specific theater where the theft has occurred and appropriate safety efforts can be taken to forestall future accidents. It is sure that this idea will save enormous misfortunes to the entertainment world and the individuals who work in it.
Code integrity verification using cache memory monitoring

This paper addresses the challenges of building a secure software system to prevent Man-at-the-End attacks. Ensuring the security of systems is challenging due to unfavorable constraints faced by the end-point host system. Constraints such as hostile environments leave the host system at the peril of would-be attackers. In this paper, we verify program integrity through L3 cache by monitoring the security-sensitive code points and verify them in memory. This paper uses a cache-based monitoring program to verify code integrity. In particular, we show that side-channel information can be used to encode the invariant of the program execution state. These invariants can be periodically and externally monitored as a proxy for application integrity. This monitoring system uses a sliding window scheme that can detect the violation of these invariant with high reliability. The proposed solution is transparent to the attacker and utilizes a side-channel technique (Flush + Reload) along with a sliding window scheme to monitor security-sensitive code and detect MATE attacks to prevent malicious manipulation of software.
Research on Asset Information Integration Meta-Model of Power Project

Power enterprises lack of a unified model for power project asset information integration. Base on meta object facility (MOF) theory, we analyse the whole process of power project information integration and model requirements, and propose asset information integration meta-model from three aspects including equipment, material and value. We realize a modeling tool according to the meta-model, and design a case of power transformer asset integration model. The validity of the proposed meta-model is proved by the case study and large-scale application.
If the bitcoin market grows, size matters

This paper studies the herding behaviour among different exchanges trading bitcoin. The analysis allows us to conclude that the size of the exchange is an influencing parameter. Since 2018, when the significant growth in the number of exchanges became a reality, smaller exchanges have shown strong herding behaviour, whereas large exchanges seem to respond to their own information and beliefs and lead the process of price definition. This result may originate some temporary profitable strategies in the process of evolution towards efficiency according to the Adaptive Markets Hypothesis.
The Heartfelt and Thoughtful Rulers of the World: AI Implementation in HR

The emerging technologies such as robotics, artificial intelligence, and machine learning are exponentially changing the work performance and showing transformation in various fields such as companies, shedding its light on medical, financial affairs, logistics, retail market, education, and human resource. The prevailing AI data-driven substantial IQ interactivity is moving towards the deeply challenged human traits, including emotional skills, establishing and maintaining interpersonal relationships, detecting the severity of anxiety as well as stress in mental health care, implanting empathy. The emerging bond between the physical world and digital technologies leads to a powerful creation of Emotion AI or affective computing agents that contribute to sensing, adapting, and interpreting the human emotions wellbeing at the workplace. The paper presents the proposed framework for the evolutionary stages of artificial intelligence in the business domain, specifically human resource management. The methodology attempts to examine the role of artificial Intelligence technologies in human resources practices. The statistical computation of proposed methodology is measured using T and Anova Test respectively.
Enhanced Security in Cloud Using Data Partitioning Technique

Cloud computing is now one of most popular technology in the computer science. Due to the invention of cloud, the perception of everyone has changes with infrastructure, deployment and development of software. The cloud provide various type of services like software as a service (SaaS),platform as a service (PaaS), and infrastructure as a service (IaaS).The sharing of data is the most important facility of the cloud. The emphasis of this paper is to provide a second layer of security to existing cloud storage solutions. The data is split into number of block and individual blocks are encrypt with the help of respective encryption keys within an image file and store the same on local hardware storage.
A Quality in Use Model for Ambient Assisted Living (AAL) Systems

Ambient Assisted Living (AAL) aims to improve the people’s quality of life, especially vulnerable groups, with the support of information technologies, increasing the autonomy, self-confidence and mobility of the users in their daily activities. Hence, it is essential to know the end-user's opinion about the perceived quality when using an AAL system in terms of the degree of satisfaction of their real needs and achieved objectives. However, since AAL is a relatively new domain, existing quality models are limited, with an incomplete set of attributes and metrics. Therefore, this research proposes a quality in use model for AAL systems focused on the followings characteristics: effectiveness, efficiency, satisfaction, freedom from risk, and context coverage; and their corresponding metrics adjusted to the AAL domain. Finally, the proposed model has been applied as a guideline during the evaluation of an intelligent pillbox. The results of this application have proven that this model is a valuable tool for evaluating the results and effects of using an AAL system from the perspectives of either a primary user (e.g., elderly people, people with special needs, patients) or a secondary user (e.g., family, caregivers). Consequently, it identifies essential aspects that contribute to improving the quality of those systems.
A Brief History of Warehouse-Scale Computing

Presents the author's response to receiving the 2020 Eckert-Mauchly Award.
Face Mask Recognition Based on MTCNN and MobileNet

The[aut]Cao, Jianzhao COVID-19[aut]Pang, Renning can[aut]Ma, Ruwei be[aut]Qi, Yuanwei transmitted by air droplets, aerosols, and other carriers, the spread of the virus can be effectively prevented by wearing masks in public. Therefore, it is meaningful to identify whether a mask is worn in particular places. In this paper, a method based on multi-task convolutional neural networks (MTCNN) and MobileNet algorithms is proposed to implement mask recognition on human face. Firstly, MTCNN is used to detect facial contours. Then the output image is used to train MobileNet model. By comparing the extracted facial feature data, the human with mask or not can be marked. The method has been tested in a 1.8 GHz Intel Core machine with 160 × 160 static images. Average accuracy rate of 94.73% and detection speed of 1.9 s are achieved.