Reviewerly/exHarmony huggingface dataset:
Neural Network for 3-D Prediction of Breast Cancer Using Lossless Compression of Medical Images

Neural network (NN) technology plays a vital role to predict the breast cancer for women. All existing system has to find the breast cancer with tissue in the breast of the women. In our proposed system, it is mainly focused on magnetic resonance imaging (MRI) scanning image of breast and finds the tissue size using 3-D image lossless compression in NN. The main feature of this paper is the use of 3-D predictor to classify the images and using NN technique to find the accuracy level of breast cancer.
Modeling Information Flow in Social Networks: Metrics and Evaluation

In modern world of advancement, cloud technology is taking its grip. COVID-19 also enhance the use of the cloud services. The cloud concept comes with the new advancement of technology, but with that data breaches and data hacks are also increasing. In the cloud-based environment, we suggested the model of security enhancement which, make use pf role-based security model where the files are shared according to the role and the access of such file is controlled using TPA validation and apart for that the concept of authentication of user is also suggested using the Pattern Lock based graphical system for pattern formation. The pattern obtained are validated using the various of online password or pattern strength validation tools, results which are obtained proves that pattern obtained through the proposed concept performs better against the attack.
Crystal Growth

Building upon the award-winning second edition, this comprehensive textbook provides a fundamental understanding of the formative processes of igneous and metamorphic rocks. Encouraging a deeper comprehension of the subject by explaining the petrologic principles, and assuming knowledge of only introductory college-level courses in physics, chemistry, and calculus, it lucidly outlines mathematical derivations fully and at an elementary level, making this the ideal resource for intermediate and advanced courses in igneous and metamorphic petrology. With over 500 illustrations, many in color, this revised edition contains valuable new material and strengthened pedagogy, including boxed mathematical derivations allowing for a more accessible explanation of concepts, and more qualitative end-of-chapter questions to encourage discussion. With a new introductory chapter outlining the “bigger picture,” this fully updated resource will guide students to an even greater mastery of petrology.
A Vision-Based System Facilitating Communication for Paralytic Patients

Locked-in syndrome is a critical and a rare neurological disorder characterized by the complete paralysis of all voluntary muscles except the extraocular muscles that control the eye. It is predominantly caused by brainstem or lower cerebral lesions. Cognitive abilities are often intact. The paralysis induces aphonia, but communication is possible via eye movements. This paper proposes a system that helps the sick individual communicate their needs to care providers using the preserved ability of lateral eye movements and blinking. The aim is to provide an economical and efficient medium for conversation. While there is no specific treatment for locked-in syndrome, palliative care and a systemized medium of communication can significantly improve the quality of life. A Raspberry pi integrated with a Raspberry pi v2 camera module enables detection and continuous monitoring of the patient's eyes. The point of gaze and intentional blinks produced are determined employing three novel algorithms. A simple code based on the above deductions is used to interpret the needs of the patient. A detailed analysis of all the proposed algorithms is presented.
Natural Selection Based Recommendation System for Personalization of Point of Interests Using Linguistic Techniques

The success of a personalization system depends on how well the system can understand the user persona and recommend the most appropriate content for that user context. User generated content such as reviews, comments or blogs can be considered as a form of an “explicit feedback” to build the users persona. It also serves as a source of information about the content. These content when subject to natural language process (NLP) results in many attributes both about the content and the user preference. Such attributes are to be considered in totality to derive any meaningful trait about the user or the content. In short, NLP mechanism is a NP-Hard Problem and using the results thereof does not reduce the complexity of tasks that follow. This paper attempts to solve this hard task by fusing Natural selection based algorithm specifically Genetic Algorithm (GA) to ultimately provide a highly accurate personalized recommendation for any point of interest. In this paper we focus on using NLP and GA (natural selection) in unison to provide personalized recommendation of restaurants (point of interest).
Approaches to Representativeness in Previous Corpus Linguistic Research

We demonstrate that there is little consensus on what representativeness is, either in statistics or in corpus linguistics. Representative is a general term that must be made specific within a particular context in order to evaluate a sample. We introduce ten attested conceptualizations of corpus representativeness: (1) representativeness as “general acclaim for data”; (2) a representative corpus has been collected with the “absence of selective focus”; (3) a representative corpus contains texts that are “typical or ideal cases” of the target domain; (4) a representative corpus is a “miniature of the population”; (5) a representative corpus achieves “coverage of the population’s heterogeneity”; (6) a representative corpus “permits good estimation”; (7) a representative corpus is a corpus that is “good enough for a particular purpose”; (8) a large corpus is more important than a representative corpus; (9) a representative corpus is a “balanced” corpus; (10) a representative corpus is never possible. The term “balance” does not have a single agreed-upon definition in CL, and in fact, is often defined in contradictory ways. A unified and operational definition of corpus representativeness is needed.
Augmented Reality: Removing the Background of the Users

Augmented Reality has become a familiar term mostly due to mobile devices. Major smartphone manufacturers such as Apple and Google are making huge breakthroughs in the ways our everyday devices understand the environment. Augmented Reality is the result of enhancing the physical world with digital content, such as 3D objects, interactable elements, and so on. Usually, people see such enhanced versions of reality through headsets like Microsoft’s HoloLens or through the screens of their phones. Few people attribute the wide use of AI to Kinect, which has been the device that made AR mainstream.
On-Chain Metrics and Technical Analysis in Cryptocurrency Markets

This study attempts to analyze comprehensive data for the cryptocurrency market. In detail, this paper gathers all pertinent market currency data, on-chain data, and technical analysis to predict investor sentiment. In this research, we analyze the most effective tools to predict the market price of cryptocurrencies, from 2016 to 2021 and to determine the market price trend and its forecasts, through complete historical data in on-chain and crypto-currency market price. The methodology and tools used to analyze this crypto assets, provide a more effective understanding, through on-chain metrics indicators, with increase results in the accuracy on forecasting the crypto-currency prices in the future. Back tests are examined as evidence of the reliability of these indicators such as cryptographic portfolio sizes, sales, purchases and increase of relevant economic activity of the blockchain network to anticipate the crypto market prices rises.
Extraction of news topics in quality papers using topic models based on word embeddings

In this paper, we analyze newspaper articles using the topic model, a method for extracting potential topics, to obtain relevant information on the discipline of food systems. As an example, we analyzed obesity-related articles published in the New York Times and The Guardian. Moreover, the topic model used in this study is based on the word embeddings acquired from distributed representation; time transition and topic similarity analyses were performed by this model. In this manner, the effectiveness of the topic model for finding newspaper articles was demonstrated.
Knowledge Gain Relativization (R)

Differential privacy ensures that not more is revealed than a fixed amount of probabilistic information. Instead, one can explicitly take into account other ways data can leak. This chapter of the Brief gives an overview of the corresponding notions.
Bionic Banking Life Cycle

The chapter analyzes in detail bionic banking across the banking life cycle. It starts with the bionic business model’s support to customer acquisition, credit decisioning, monitoring and collection. The chapter deepens the relationships with wealth management and intelligent servicing. The chapter also analyzes the characteristics of the bionic insurer and its future developments.
English Camp as Learning and Teaching Atmosphere of Speaking Ability Development

Teaching and learning English method recently have become very important issue to discuss as it is seen to not be effective anymore. This is proven by the majority of students who cannot achieve the learning target even when they learn English for many years. English Camp serves as a method that gives the teacher and the student a new learning atmosphere in which it can be done either inside or outside the classroom. This study employed a descriptive qualitative approach where the data were collected through observation, interview, and documentation. The date then was analyzed by using triangulation consisting the process of; data condensation, the display of the data, conclusion verification. The result of this research suggested that English Camp is perceived as an effective atmosphere for English learners and teachers in achieving the learning objective. It can improve students speaking performance for speaking is all about forming habits when this is the focus of the entire learning activities.
Mechanisms for improving the professional andcreative activity of a computer science teacher

Reforms In Society Today Allow To Intensify The Professional And Creative Activity Of Teachers. Recommendations For Taking Into Account The Requirements For The Qualification Of Specialists In The Labor Market In The Training Of Future Teachers Highlight The Urgency Of Forming A Graduate.
Quest as a modern form of teaching physics and vocational guidance

The most important component of improving the educational system of a higher educational institution in the way of training students is the activity of the teaching staff associated with individual work with applicants and students by involving them in research work, involving students in projects and participation in career guidance work. Currently, there is an overestimated expectation of adolescents from the choice of their future activities after finishing secondary educational institutions and their vague idea of the profession when choosing a particular higher educational institution. This paper examines a game in the form of a quest as a means of increasing schoolchildrens motivation to study Physics and as an example of demonstrating vocational guidance work of a higher educational institution with students in order to increase their interest in the conscious choice of further education in natural science. The forms of carrying out such quests at the Institute of Physics are given and possible ways for the development of such forms are discussed. Additionally, data is provided on the increased interest in their future profession among students participating in the quests. The approbation of this form of work with applicants at Kazan (Volga Region) Federal University showed a significant degree of involvement of applicants and contributed to the development of interest in Physics.
Self-configuring programmable photonics for processing, communications and sensing

Silicon photonics allows remarkably complex interferometric optical circuits. Novel algorithms and architectures, including self-configuring and self-stabilizing approaches, can control these, enabling new functions and applications, and a new class of programmable optical components and systems.
To Trust or Not to Trust My AI Based Voice Assistant: Dealing with Consumer Uncertainties: An Abstract

Despite the increasing adoption rate of AI-based technologies, as well as their estimated future growth, very few research has explored the factors that influence the usage of Voice Activated assistants (VAs) in daily life (McLean and Osei-Frimpong 2019; Moriuchi 2019), and the factors affecting users’ trust with VAs interactions remain underexplored (Foehr and Germelmann 2020). To this aim, this study integrates Human-Computer Interaction literature on the functional and hedonic attributes of the system and individual’s perceived privacy risks, by adopting a Para-Social Relationship Theory perspective (Turner 1993) and it investigates the drivers of users’ trust toward VAs. According to Wirtz et al (2018), when interacting with an AI-based personal assistants, functional elements, such as usefulness and perceived ease of use, will appear to be given in most cases, but would be a barrier if not provided at a level expected by consumers. However, the peculiarities of AI agents, and their ability to engage in conversational-based communication with users, go beyond a functional approach and require a more social-relationship perspective (Foehr and Germelmann 2020). A large body of research has outlined how individuals apply social roles and treat computes like a social entity (Nass and Brave 2005; Nass and Moon 2000). This is especially true when technology mimics human-like attributes (Li 2015). As VAs use natural language, interact with users in real-time, and are characterised by human- like attributes such as voice, it is possible to expect that interactions with VAs may elicit social responses, such as a sense of social presence (Chattaramn et al. 2018). Moreover, being an AI-based technology, VAs are more likely to be perceived as intelligent and skilful (van Doorn et al 2016). While these social elements can foster trust building, the process may be affected by the perceived risks surrounding privacy and security (Lei et al. 2018). Building on the above, the research develops and tests a comprehensive model of theory-drivers of users’ trust, attitude and intentions to use VAs on a sample of 547 VA-users. In addition, a qualitative study, involving in-depth interviews, is conducted to further investigate trust developing mechanisms with smart technology. The study identifies social presence and social cognition to be the main drivers of user trust towards VAs. Further, it shows the existence of different sources of trustworthiness (Foher and Germelmann 2020) that allow individuals to direct their privacy concerns toward VA producers rather than toward the AI agent (Belanche et al. 2020). Finally, the research confirms the role of functional and hedonic elements in the acceptance of advanced smart technology (Wirtz et al. 2018, 2019), while highlighting the role of emotional reactions in driving users’ attitudes towards human-AI agents’ interactions (van Pinxteren et al. 2019).
Deep Learning Techniques for Side-Channel Analysis on AES Datasets Collected from Hardware and Software Platforms

Side-channel analysis (SCA) is launched by exploiting the information leaking from the implementation a cryptographic algorithm, e.g., power consumption information. Recently, deep learning-based SCA techniques have also facilitated SCA against software and hardware implementations of various cryptographic algorithms. In this work, we perform SCA using various deep learning (DL) techniques such as Multi-layered Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) on the datasets collected from hardware and software platforms. The objective of this work is to identify the performance of DL techniques in performing SCA for secret key recovery and finding out the best settings for the model to optimize the attack performance in terms of on computation time and SCA efficiency. In our study, we have focused on two open-source AES-128 encryption algorithm databases, ASCAD and DPA contest v2 (DPAv2), where ASCAD database consists of the power traces captured from a software implementation of the AES and DPAv2 database consists of the power traces captured a hardware implementation of the AES. For the first time, we applied hyperparameter tuning with Bayesian Optimization and distributed computing on ASCAD database and we investigated the impact of MLP and RNN along with the distributed computing and hyperparameter tuning with Bayesian optimization on DPAv2 database. Our results show that the CNNs are the best models for performing the attack on software implementation while MLPs are the best for attacking hardware implementation of cryptographic algorithms.
An Intelligent Fault Location Algorithm of High Voltage Lines Using Cascading Deep Network

Due to ever-increasing power equipments and the distances of power transmission lines, insulator inspection presents a valuable but challenging issue. As a common insulator defect, missing-cap defects affect the structural strength of power insulators and cause irreparable harm to power supply security. Therefore, insulator defect detection is a basic and critical task for power line inspection. Most detection methods are mainly based on machine learning algorithms. Shallow learning methods rely on handcrafted image features and are always aimed at specific scenarios or prior knowledge. The unbalanced data sets of insulators affect the detection performance of deep learning algorithms. To address the above problems regarding insulator defect detection, a novel detection algorithm based on a cascading deep architecture is proposed for unmanned aerial vehicle (UAV) inspection. Combined with the strong detection performance of deep architecture, an insulator location algorithm based on the improved YOLOV3 model is proposed to remove complex backgrounds such as ”region of interest (ROI) extraction”. On this basis, a novel semantic segmentation algorithm is proposed to realize defect segmentation for small missing-cap defects. Experiments show that the proposed algorithm can satisfactorily meet the precision and robustness requirements of power line inspection compared with other related detection models.
Movie Recommendation System Using Hybrid Collaborative Filtering Model

Recommendation systems play an important role in e-commerce marketing and online streaming, such as Netflix, YouTube, and Amazon (Chin et al. in A fast parallel stochastic gradient method for matrix factorization in shared memory systems, 2015 [1]). Making the right recommendation for the next product, music, or movie increases user retention and satisfaction, leading to sales and revenue growth. The hybrid model is a combination of user-based collaborative filtering and item-based collaborative filtering and is using the cosine similarity and jacard index (Chin et al. in A Learning-rate Schedule for Stochastic Gradient Methods to Matrix Factorization, 2015 [2]) to find the best results possible from the MovieLens dataset that the model is using. The system is combination of random prediction, regularization, linear model, and matrix factorization to get the best plausible results (Chin et al. in LIBMF: a library for parallel matrix factorization in shared-memory systems, 2016 [3], Subramaniyaswamy in A personalized movie recommendation system based on collaborative filtering [4]).
Scrutinizing materials in organizing class for high school students

This article covers with information on analysis materials above designating effective classes for high school students. Sifting valuable information among the data storage and implementing into classes are the part of research aims. It will be presented that some steps to take into account in picking valid data, especially high school students, which appropriate their level.
How many trees in a random forest?

We propose diagnostic statistics which might assist in choosing the size of a random forest for classification. We use these statistics sequentially as we construct the forest. The statistics are computed from out-of-bag or test set votes and give an estimate of expected disagreement between the current and infinite forests. Simulation studies are provided to illustrate the performance of these statistics and to compare them with other methods for choosing the size of a random forest.
A modified chaotic image encryption scheme for color image using diagonal pixel confusion and diffusion method

The tremendous growth in wireless and mobile communication for the past few decades gives rise to data sharing through the Internet. These data that are transferred through the open channel are more prone to several types of attacks. The traditional and only way to protect these data from hackers is Encryption. So that the confidential data can be secured from the hackers. The system that performs the process of encryption is called as cryptosystem. Encryption of data in the fields of communication has become a significant task to secure the data in this modern communication world. In this paper, a new image encryption algorithm is proposed using chaotic encryption theory via Diagonal Shuffling. The proposed scheme incorporates two-dimensional Henon, Ikeda chaotic maps, and shuffling processes. Henon Map is used to generate a random S-Box and is selected and the image pixel is substituted randomly. This cryptosystem is investigated on various measuring parameters such as information entropy, histogram investigation, correlation analysis, key sensitivity analysis, NPCR, UACI and time complexity are performed. The simulation results shows that the proposed method of encryption outperforms the other encryption process proposed in this era.
Improved Grasshopper Optimization Algorithm Using Crazy Factor

This paper presents the new improved grasshopper optimization algorithm using crazy factor (crazy-GOA). The crazy factor technique helps to make a sudden direction change in order to enhance the diversity while exploring the entire search space. This method is a blessing in most of the modified version of the algorithms to achieve the optimal solution with smaller number of iterations. This paper explains the utilization of such technique to achieve the global optimum very fast, and this is a prime requirement of most of the applications. The experimental result analysis is performed using unimodal as well as multimodal standard benchmark functions. For extra verification as well as validation, the new improved algorithm is compared with other popular intelligent algorithms. The test result projected by this modified algorithm is superior to other intelligent algorithms in addition to classical GOA.
A First Approach to Image Transformation Sequence Retrieval

Detecting the corresponding editions from just a pair of input-output images represents an interesting task for artificial intelligence. If the possible image transformations are known, the task can be easily solved by enumeration with brute force, yet this becomes an unfeasible solution for long sequences. There are several state-of-the-art approaches, mostly in the field of image forensics, which aim to detect those transformations; however, all related research is focused on detecting single transformations instead of a sequence of them. In this work, we present the Image Transformation Sequence Retrieval (ITSR) problem and describe a first attempt to solve it by considering existing technology. Our results demonstrate the huge difficulty of obtaining a good performance—being even worse than a random guess in some cases—and the necessity of developing specific solutions for ITSR.