Reviewerly/exHarmony huggingface dataset:
On Mathematical and Statistical Aspects of Linear Models

This research article presentssome mathematical and statistical aspects of linear models and their characteristic properties. In Statistics, the term linear model is used in different ways according to the context. The most common occurrence is in connection with regression models and the term is often taken as synonymous with linear regression model. However, the term is also used in time series analysis with a different meaning. In each case, the designation “linear” is used to identify a subclass of models for which substantial reduction in the complexity of the related statistical theory is possible. In this research article an attempt is made to presentsome specific forms of simple and multiple linear regression models. Here mathematical aspects of linear models are depicted in a lucid manner. A large number of mathematical models are discussed here and the methods of fitting transformed models are presented. Moreover specific form of linear statistical model is proposed and the crucial assumptions of general linear model are extensively discussed. Finally the method of ordinary least squares estimation of parameters of a linear model has been proposed
Blockchain-Based Supply Chain System Using Intelligent Chatbot with IoT-RFID

The blockchain is a peer-to-peer distributed-ledger technology that provides trust and immutability. The data on blockchain is cryptographically secured, and it ensures transparency, traceability, and quick updating. The artificial intelligence is the simulation of human process in intelligence and a smarter way. The chatbot helps us to provide customer services and customer satisfaction. The IOT (internet of things) means different devices are connected together to exchange information. The RFID tag is used to track and verify the product. The RFID tags contain the digitally stored information about the product. The main problem is traceability: how can we use these technologies to ensure product originality and trace the journey of the product. To ensure all these things, we are using all these technologies, the blockchain, artificial intelligence, and IOT, to ensure the security, transparency, and traceability of the product in supply chain. We will use RFID tags to verify the products and AI chatbot to provide the customer services. The data will be encrypted and secured on a blockchain network. All of this concludes that the blockchain with supply chain can give us good results. The system will be more efficient to ensure all the products' success and keep that data confidential.
Research on motion control of mobile robot based on S-type acceleration and deceleration

Under the control of ultrasonic admittance, the robot needs real-time speed control in its working process. The quality of speed control will directly affect the motion accuracy and stability of the robot. In this paper, a speed regulation method of robot based on S-type acceleration and deceleration is proposed for obstacle avoidance planning. First of all, using the method of ultrasonic real-time detection and admittance control control the speed of the robot after entering the obstacle avoidance range. At a certain distance from the obstacle, using the improved bug algorithm walk around the detected object contour to bypass him. And at the distance X from the turning point, the robot smooth control algorithm based on S-type acceleration and deceleration is used to achieve a smooth transition of the speed at the turning point. Through experimental simulation, the speed stability and obstacle avoidance trajectory of the robot based on S-type acceleration and deceleration under the control of admittance have been significantly improved.
Unstable Structure: The improvising modular synthesiser

The desire to work in real time and improvise with the possibilities of tape-based studio practice was a key design intention of early modular synthesisers. The resulting instruments, in particular the Buchla, emphasised possibilities for the creation of musical structure in combination with components designed to enable real-time, expressive interaction. As smaller, keyboard-based synthesiser instruments achieved broad commercial success, many of the possibilities for the real-time creation and manipulation of musical structures, developed in the larger modular synthesiser designs, were not carried forward into the new age of commercial electronic music instruments. The resurgence of the modular synthesiser has seen these possibilities for improvisation of musical structures revisited, opening up processes and approaches that were almost lost to a new generation. Following an analysis of the genesis of modular synthesisers, with a focus on the Buchla, this article describes three examples of modular synthesiser-based compositional processes that enable the performer to undertake improvised actions in real time that construct, influence and distort musical structure. These examples are then analysed to better understand the processes, motivations and experiences of using a modular synthesiser to construct musical structure in a real-time and improvised fashion.
Simulation-based Anomaly Detection in Nuclear Reactors

Anomaly Detection in the nuclear reactor is a crucial technology to prevent malfunctions or unplanned shutdowns and to enhance efficient operations. Despite its importance, it remains at the level of relying on rule-based diagnostic or expert judgment due to the lack of training data. It is challenging to obtain real data from the nuclear reactor because of safety and security issues. To overcome those challenges, this paper proposes a new simulation-based anomaly detection methodology in nuclear reactors. We investigate assignable causes of abnormal behaviors in the nuclear reactor, generate simulation data using nuclear core analysis code, RAST-K, and apply the classification models for detecting abnormal behaviors. The proposed method is validated by the control rod positional anomalies simulation data generated by RAST-K.
A Higher-Level Security Scheme for Key Access ON Cloud Computing

Abstract: In this work, we employ a key access control management scheme which effortlessly transitions any organization-like security policy to state of the art cloud level security. Offering a very flexible, secure, and hierarchical key access mechanism for institutions that deal with mission-critical data. The scheme also minimizes concerns about moving critical data to the public cloud and ensures only the users with sufficient permission from equal or higher privileged members can access the key by the use of topological ordering of a directed graph which includes self-loop. The main overheads such as public and private storage needs are restricted to a level that is tolerable, and the derivation of key is computationally fast and efficient. From a security perspective, the proposed scheme would be resistant to collaboration attacks and would provide key in distinguishability security. Since the key isn’t stored anywhere so, the problem of a data breach is eliminated. Keywords: Cloud platform; plagiarism check; Shamir’s Secret Key, Cloud security, hierarchical, interpolation, key access, key assignment, secret sharing.
Voice Coding using ML

Abstract: In this digital era there are various digitization is happening like the AI is deploying so fast, various automations are happening and taking place of human and reducing the efforts of human. And all this things are happening because of programmers/developers they are doing a very hard work for the development of technology. As effect of this this work many developers/programmers are facing various physical / mental disorders like stress, carpel tunnel syndrome and more problems. So we are developing a system which will reduce their injuries or problems like this by creating solution i.e. they have to just speak they want code the system will take it and create proper code. In today’s world, many artificial intelligence applications developed using programming languages like Python, R and so on. Each language comes with its own programming structure and syntactical forms. Programmers are broadly classified into three categories namely, novice users, knowledge intermittent and expert one. For novice users, it is always a challenge to write a code without typographic errors though users know theoretical knowledge of Programming language, its structure and syntax as well as logic of program. Therefore, this paper explores use of voice recognition technique in the field of programming, specifically for writing program with Python programming language. In experimental analysis, it found helpful for new Python programmers and provide new learning curve for programmers wherein beginner can experience hassle free program writing. This paper adds new way of creating interest in beginners for judging their coding paradigm understanding and explore one of the area for user experience field for better programming Integrated Development Environment Development (IDE). Keywords: Artificial Intelligence, Learning, User Experience, Voice Recognition. Keywords: Artificial Intelligence, Learning, User Experience, Voice Recognition and Speech Recognition.
Memorial

Donald Macpherson was born in Edmonton, Alberta, Canada, on 6 October 1941. He passed on 20 August 2020. Though he was a proud Canadian till the end, he clung to his Scottish culture and became a fixture with his bagpipes at many events throughout the Dallas–Fort Worth area. He attended the University of Alberta, initially studying music and fine arts and earning a bachelor's degree in 1964 with a minor in math and chemistry. He graduated with a master's degree in isotope geochemistry and geophysics from the University of Alberta in 1965. Don walked into the “best job in the world” as a geophysicist at Mobil Oil Canada in 1965. There, he was responsible for seismic acquisition crews, processing, and interpretation of geophysical data.
Towards Black Box Forensic Cybercrime Investigation Model (BBFCIM)

Although internet has emerged to bridge digital divides and improve how things are done across diverse spheres of life, its explosion has also brought unexpected threats, risks and loss of valuables over a decade. Consequently, there seems to have been plethora of cybercrime investigation models but the proliferation of these models has not substantially reduced the frequency of cyber attacks globally. Given that the recent development in cyberspace seems to follow same trends of how survivable Black Box (Flight Data Recorder) emerged, this chapter proposes a Black Box Forensic Cybercrime Investigation Model (BBFCIM). BBFCIM sets a new agenda for cybercrime investigation process by focusing on survivability and reliability of existing and would-be models rather than evolving as a distinct model of itself. It adopts soft innovative skills in the development of Black Box components to shape proactive cybercrime investigation process through sequential tests on each networking layers.
Discussion and Conclusion

This chapter summarizes the key findings of the dissertation and illustrates its theoretical as well as methodological and managerial contributions. From the theoretical point of view, journey experiences are ultimately interpreted as performances on the move, mobility spaces as their stages. Interactions among infrastructures and individuals are deeply mediated by speed, in turn depending on individual attitude of riders, travel modes and contextual conditions. The strong linkage between individuals and modes enables the theorisation of the notion of human-vehicle hybrids, which needs further study in future research. Methodological contributions relate to the measurement of journey experiences in a real-life environment, as well as to the innovative visual representation of empirical results from multiple sources. Managerial implications in terms of tourism product development, road planning and sustainable mobility are valuable for future destination development. Finally, limitations related to the work are discussed and used as opportunities and leverages to improve future research.
Good Life/Good Existence [Belle Vie / Belle Existence] (Introduction to the Ukrainian Translation of the Interview “Foucault in the Valley of Death”)

This is an important interview for understanding Michel Foucault's creative evolution. It was conducted by American scholar Heather Dandes in 2017. It is translated into Ukrainian for the first time. Clermont High School professor Simeon Wade and his friend, musician Michael Stoneman, invited Foucault to take part in a journey to Death Valley in June 1975. The philosopher accepted the proposal. They spent two days and one night in the desert. Foucault described this experience as one of the most important in his life.
Adaptasi Busana Muslimah Era Millenial: Antara Trend dan Syariat

Muslimah clothing is faced with the challenges of developing fashion trends that are developing in this millennial era. This paper intends to understand and explain how the findings of adaptation of Muslim clothing occurred in the millennial era. The method used is a survey method as well as library research where this survey method is carried out randomly on Muslim women aged 15-30 years to answer about understanding the adaptation of Muslim clothing that occurs in the millennial era and understanding clothing that is in accordance with Islamic law. The results of this study indicate that Muslim women in the millennial era already understand how to dress in accordance with Islamic rules and recommendations but are still unable to practice or carry out these rules and recommendations. Millennial-era Muslim women still dress according to trends in the environment and have not used clothing in accordance with Islamic rules and recommendations. This research is able to describe the development of Muslim clothing in the millennial era. So that it can measure how well Muslim women understand trends and the Shari'a in dress.
Anomaly detection based on time series data from industrial automatic sewing machines

Industrial automatic sewing machines are used for the production of technical textiles and safety critical work pieces such as parachutes or safety belts. Hence, the production does not tolerate sewing errors within the final product. Unfortunately, errors like skip stitches or illusive sewing may occur even on correctly adjusted production machines due to their complex kinematics. A recently developed sensor for such production systems is able to measure the lower thread rotation, a key parameter of the sewing process. This opens up the possibility to analyze the sewing quality on the basis of time series data whilst the work piece is still in production, thus being able to instantly detect anomalous sewing operations which leads to reduced cost for quality control and improves safety. In this research we analyze the suitability of existing Neural Network based Machine Learning algorithms to detect anomalies within the available process data. We give an overview regarding the Machine Learning pipeline, the tested Neural Network types and the experimental results. We show that several Neural Network architectures are able to detect anomalies within the sewing process.
THERA APPLICATION DEVELOPMENT TO IMPROVE READING SKILLS AND THE CHARACTER OF LOVE LOCAL CULTURE

The purpose of this research and development THERA application is to improve English reading skills and introducing local culture for character building THERA application is designed for smartphones with android specifications. Specifications THERA application requires 32.62 MB of storage space and 340 KB of data. THERA application can be used both online and offline. This research was conducted at SMA Negeri 1 Blora class X MIPA with 108 students. This study measures the validity, practicality, and effectiveness of THERA educational game learning media to improve reading skills and enhance the character of loving local culture. The Research and Development method used is the Sugiyono and Dick &amp; Carey development model. The flow of the research development model is: potential and problems, collection, product development, validation, revision, product testing, product revision and usage testing. The application has been validated by material experts and media experts and shows excellent results. Likewise, the results of using applications for learning English through tests also increased significantly. Students' attitudes towards local culture are measured using indicators of interest, loyalty, concern and appreciation. The results have increased significantly. It can be concluded that the THERA application in learning English improves reading skills and character of love for local culture. This is in accordance with the characteristics of students who like games.
Bayesian causal inference in probit graphical models

We consider a binary response which is potentially affected by a set of continuous variables. Of special interest is the causal effect on the response due to an intervention on a specific variable. The latter can be meaningfully determined on the basis of observational data through suitable assumptions on the data generating mechanism. In particular we assume that the joint distribution obeys the conditional independencies (Markov properties) inherent in a Directed Acyclic Graph (DAG), and the DAG is given a causal interpretation through the notion of interventional distribution. We propose a DAG-probit model where the response is generated by discretization through a random threshold of a continuous latent variable and the latter, jointly with the remaining continuous variables, has a distribution belonging to a zero-mean Gaussian model whose covariance matrix is constrained to satisfy the Markov properties of the DAG. Our model leads to a natural definition of causal effect conditionally on a given DAG. Since the DAG which generates the observations is unknown, we present an efficient MCMC algorithm whose target is the posterior distribution on the space of DAGs, the Cholesky parameters of the concentration matrix, and the threshold linking the response to the latent. Our end result is a Bayesian Model Averaging estimate of the causal effect which incorporates parameter, as well as model, uncertainty. The methodology is assessed using simulation experiments and applied to a gene expression data set originating from breast cancer stem cells.
A Machine Learning Approach for Predicting the Death Time and Mortality

The ICU mortality prediction models were built in the time frame of 24 h and 48 h after getting admitted to the hospital from the last few decades. To overcome this situation and to try to make things more retrospective, in this paper, authors are trying to build a machine learning framework that will predict the hospital mortality of the ICU patients within 6 h of ICU admission. This model will also provide the estimated death time for particular patients who are suffering from critical illness. Therefore, the doctors and the medical staff can take precautionary movement with this model's data to take necessary actions. The model is built on the MIMIC III dataset, from which authors have extracted the data based within the required time frame. Talking about the accuracy of the model, the mortality prediction model has an accuracy of 88%. The death time prediction model has an accuracy of 77% in terms of the “Area Under the Operational Receiver Curve.” Also, the authors ensured and verified that this newly developed 2-phase model would help the healthcare system a lot by providing the correct predictions so that the critical patients can be adequately treated within the stipulated period.
Multimedia Immersion System for Band Jumping Training

The military training activities are mostly carried out in real environments with the use of firing ranges, obstacle courses, combat zones, among others. In the field of military parachuting, the training is carried out in fixed infrastructure, in which it is instructed with techniques of lateral or posterior jump, driving, and landing, to later move on to the real jump; one of the main limitations in Ecuador is the high cost and logistics of this training, which is why a multimedia immersion system for band jumping is proposed, in this way to contribute to the improvement of the skills of paratroopers using the existing infrastructure in the military units and validating the system with a usability test.
Combining local and global approaches to ascertain semantic similarity

Interviewing potential candidates is both time consuming and resource intensive. This is particularly prominent in organizations which go for large scale recruitment processes. In the current study, a client based application has been proposed for interviewing candidates for data science profiles where interviewee’s answers are scored using machine learning. Different approaches were tried with pretrained models but as the application was very much domain specific, those models did not provide good results. Hence a custom embedding layer was built on open source data science textbooks. These embeddings were used with Gated Recurrent Units (GRU) to capture a local approach (subject specific) in the interview answers. However, this neglected the nuances of the English language involved in critical reasoning. Hence Bi-directional Encoder Representation from Transformers (BERT) was employed to capture the global approach (interaction between words in the English language) in the interview answers. The similarity scores from these two approaches were ensembled into a machine learning model which allotted the final score to the interviewee’s answer. The proposed method outperformed the pretrained models with significant margin when tested with the validation data.
2019: the most successful year for book publishing before the COVID-19 pandemic

The COVID-19 pandemic has made significant adjustments to the life of each sector of the creative industry. One of those that has been hit hard by today's crisis is the book publishing industry. The crisis continues today, and it is too early to summarize the losses and losses in this area.&#x0D; The article examines some trends in the development of world book publishing in 2019 before the pandemic COVID-19, summarizes the main indicators of the publishing industry in the leading countries. Based on the analysis of reports of the National Publishers' Associations, the peculiarities of publishing development, the ratio of printed and electronic books, the role and place of audiobooks in modern conditions are highlighted.&#x0D; It is proved that self-publishing today creates a strong competition to traditional book publishing and distribution, and in the conditions of technological progress it can become a threat to traditional models of publication.
Development kit for recording and processing of EOG signals for eye tracking

Eye tracking is the process of measuring the movement of the eye and determining the direction where the user is looking (point of gaze). Using eye movement and gaze tracking for communication and control is especially beneficial for disabled people with minimal ability to perform voluntary motion. Our research is directed to development of an eye tracker based on Electro-oculography (EOG) technique allowing physically impaired patients to control their wheelchairs or home robots. This paper presents the design of a prototype of the Development Kit for recording and processing of EOG signals. The structure and functional blocks of the Development Kit together with the electrical schemas of analogue front-end and digital part are presented. For visualization and on-line processing of recorded EOG signals a special Toolbox is designed. Finally, the examples of recorded EOG signals for different gaze directions are presented.