Reviewerly/exHarmony huggingface dataset:
The Concept of Information. Part 2. The Functions of a Scientific Theory

Any scientific theory should effectively perform a variety of information processing functions, regardless of the specifics of knowledge and the field of formalization. To achieve the global aim − maximum assistance in solving each of the infinite number of problems of the evolution of society − the information computer should be able to simulate all the functions of scientific theories, which are combined into a conceptually single system of knowledge processing, and due to this the information computer should provide the possibility to use knowledge directly in an explicit form for solving current and new problems. The meaning of communicative, descriptive-prescriptive, and argumentative (deduction, induction, explanation, prediction, understanding) functions of scientific theories is clarified. The models of synthesis of various components of the structure of scientific theories are described. The objects of real-world and the communicative functions of analysis and generation of language messages are enough for the organization of arbitrarily complex dialogue between a subject and the real world. The descriptive-prescriptive functions are the cornerstone for cognition and transformation of reality. All facts of reality are descriptive, whereas the standards, samples, ideals and norms are estimates. The argumentation is a process of reasoning during which the arguments, opinions, and conclusions are generated, that justify the assessment of truth or falsity of a statement or a theory. The self-description of the metalanguage of normal forms of knowledge is presented. The structure of the core of the information computer, which is based on this metalanguage, is presented, comprised of the following: the metaprogram compiler, two interpreters (the metaprogram interpreter and the terminal program interpreter), a glossary of terms and two arrays of data (input array and output array). The process of functioning of a knowledge processing system based on an information computer is described. The information on the implementation of the layout of the information computer in the form of a personal computer supplemented with the knowledge base interpreter made on a prototyping board of Actel corporation using minimum required hardware and software support is given. Giving users the metalanguage for describing application problems and the improved technology of formulating and solving problems, which realize all the features of a scientific theory, will provide extremely high growth of "soft" product reliability and programming efficiency.
Author Correction: Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Applications of Blockchain in Digital Forensics and Forensics Readiness

The advancement in information technology has changed our life significantly. No doubt, the Internet, smartphones, and social media have connected people and society. It has contributed to various aspects of our daily life. However, there is also a dark side of cyberspace, that is, cybercrime. It is becoming a global threat and needs technological solutions to combat. Whenever there is a crime, there is a law to curb the crime. The role of digital forensics is to understand the ‘Who, What, Where and Why’ of the incident. Digital forensics is a scientific process to collect, analyze, and present the evidence in a court of law. As it is used to link the person with criminal activities, it is crucial that the entire process of investigation be trustworthy. Managing the digital evidence and maintaining the chain of custody and integrity of digital evidence are utmost important. This chapter discusses the applications of blockchain technology to address the challenging issues faced by the digital investigation process and forensic readiness. The chapter also discusses the legal issues and admissibility of blockchain-based evidence in a court of law.
ANFIS based system identification of underactuated systems

Abstract In this work, the effectiveness of the adaptive neural based fuzzy inference system (ANFIS) in identifying underactuated systems is illustrated. Two case studies of underactuated systems are used to validate the system identification i. e., linear inverted pendulum (LIP) and rotary inverted pendulum (RIP). Both the systems are treated as benchmark systems in modeling and control theory for their inherit nonlinear, unstable, and underactuated behavior. The systems are modeled with ANFIS using the input-output data acquired from the dynamic response of the nonlinear analytical model of the systems. The dynamic response of the ANFIS model is simulated and compared to the nonlinear mathematical model of the inverted pendulum systems. In order to check the effectiveness of the ANFIS model, mean square error is used as the performance index. From the obtained simulation results, it has been perceived that the ANFIS model performed satisfactorily within the trained operating range while a minor deviation is seen outside the trained operating region for both the case studies. Furthermore, the experimental validation of the of the proposed ANFIS model is done by comparing it with the experimental model of the rotary inverted pendulum. The obtained results show that the response of ANFIS model is in close agreement to the experimental model of the rotary inverted pendulum.
Towards Continuous Quality Control for Spoken Language Corpora

&#x0D; This paper describes the development of a systematic approach to the creation, management and curation of linguistic resources, particularly spoken language corpora. It also presents first steps towards a framework for continuous quality control to be used within external research projects by non-technical users, and discuss various domain and discipline specific problems and individual solutions. The creation of spoken language corpora is not only a time-consuming and costly process, but the created resources often represent intangible cultural heritage, containing recordings of, for example, extinct languages or historical events. Since high quality resources are needed to enable re-use in as many future contexts as possible, researchers need to be provided with the necessary means for quality control. We believe that this includes methods and tools adapted to Humanities researchers as non-technical users, and that these methods and tools need to be developed to support existing tasks and goals of research projects.&#x0D;
MSE-based analysis of circular grating self-images for testing beam collimation

Mean square error (MSE) is used to detect variations in the period between a pair of self-images formed at two different Talbot planes of a circular grating (CG) using a beam splitter in a conventional collimation testing setup. By varying the position of the collimator with respect to the point source, the collimation state of the input beam is varied and the computed MSEs are analyzed to deduce the collimation state. The minimum value of the MSE indicates beam collimation. For equal sized images, the MSE relates to the sum of the squared difference between spatially correspondent pixel values of the images. Since comparison of the spatial information takes place at the pixels’ level, any small spatial shift between patterns of the two self-images due to collimation error is detected with precision. The CG, comprising concentric circular structures, offers added advantage in terms of error-free alignment, which otherwise is error prone and cumbersome with widely used linear gratings. It is well known that self-images formed with circular grating have good fidelity with less optical distortions and irregularities, especially at distant Talbot planes. Also, the self-images formed with circular gratings are less affected by lens aberrations, tilts, misalignments, etc. Higher sensitivity in beam collimation is achievable, as self-images of a CG can be recorded at widely separated Talbot planes, and analyzed using an algorithm which is more responsive toward any minute difference between them. The suggested method is promising for a quick collimation setting with good accuracy and enhanced sensitivity.
Facilitating student behavioral engagement in ESP classroom through teachers’ scaffolding talk

This study aims to describe the language delivered by the teachers in the term of teachers’ scaffolding talk and the behavior engagement responded by the students in ESP agriculture Classroom. The investigation was conducted at a University in Sumbawa Besar, West Nusa Tenggara. Field note and video recordings were used to collect the data. During the observation, the data were then transcribed, simplified, displayed, and interpreted. The result shows there are four kinds of scaffolding talks namely prompting, dialoging, extension, and modeling from six kinds of scaffolding talk. Those scaffolding talks can facilitate the behavioral engagement of the students in ESP Classroom namely paying attention or focusing on the lesson, attempting to answer the question from the teacher, and exerting themselves actively participating in classroom interaction. These result suggest that lecturers and teachers may consider to apply the scaffolding talk before teaching the students in the classroom because methodology used by the teacher in teaching is as important as a talk in teaching. DOI: 10.26905/enjourme.v4i2.4264
Analysis on the Advantages and Disadvantages of Using Multimedia Courseware in Chinese Folk Dance Classes

The development of technology will affect the development of traditional dance classes. The teaching method is no longer a singular course of teaching and learning. Multimedia courseware is a commonly used course technology, based on computer and projection technology, to convey rich knowledge to students. This paper discusses the use of multimedia courseware in dance classes, then, combined with the majors the author have studied, creatively discusses the influence of multimedia courseware on Chinese folk dance courses.
CLOUDINESS IMAGES MULTILEVEL SEGMENTATION BY PIECEWISE LINEAR APPROXIMATION OF CUMULATIVE HISTOGRAM

The Ramer-Douglas-Peucker algorithm for piecewise approximation is used for image multilevel segmentation. The cumulative histogram is selected as a function for approximation. The algorithm allows you to determine threshold values of continuous and discrete images. The algorithm is used to separate cloudiness from background and to calculate cloudiness intensity. The found points of the approximated function have been accepted to change pixel intensity by proposed formulas. The algorithm efficiency is compared with those based on ordinary and cumulative histograms. By controlling the number of points for piecewise linear approximation function, the necessary segmentation accuracy can be achieved. The algorithm complexity is linear to the number of image pixels and to the number of intensity steps. The developed algorithm is applied to the satellite map images to separate clouds of different intensity. The extracted clouds of different intensity are used to classify regions by cloudiness with a developed clustering algorithm. Testing and experimental results are presented.
Recognition of Activities of Daily Living Based on a Mobile Data Source Framework

Most mobile devices include motion, magnetic, acoustic, and location sensors. These sensors can be used in the development of a framework for activities of daily living (ADL) and environment recognition. This framework is composed of the acquisition, processing, fusion, and data classification features. This study compares different implementations of artificial neural networks. The obtained results were 85.89% and 100% for the recognition of standard ADL and standing activities with Deep Neural Networks, respectively. Furthermore, the results present 86.50% for identification of the environments using Feedforward Neural Networks. Numerical results illustrate that the proposed framework can achieve robust performance from the incorporation of data fusion methods using mobile devices.
On the Emergence of Abstract Sciences and Breakthroughs in Machine Knowledge Learning

Recent basic studies have revealed an unprecedented phenomenon of the emergence of Abstract Sciences (AS) [Wang & Tunstel, 2019] as a counterpart of classic concrete sciences. AS encompasses contemporary disciplines of data, information, knowledge, intelligence, mathematics, and system sciences. AS leads to novel theories and technologies for AI in general, and Machine Knowledge Learning (MKL) in particular [Wang, 2016]. The latest discovery in AS reveals that the basic unit of knowledge is a binary relation (bir) [Wang, 2017] as that of bit for information and data. MKL powered by the breakthroughs in Cognitive Knowledge Bases (CKB) and denotational mathematics will enhance human learning capability. MKL leads to advanced form of machine learning, which enables cognitive machines as an indispensable assistant to humans with mutually sharable knowledge bases towards collective knowledge learning. A wide range of novel applications in AI and cognitive systems will be presented.
ECOM'20: The SIGIR 2020 Workshop on eCommerce

eCommerce Information Retrieval (IR) is receiving increasing attention in the academic literature and is an essential component of some of the largest web sites (e.g. Amazon, Alibaba, Taobao, eBay, Airbnb, Target, Facebook). eCommerce organisations consistently sponsor SIGIR, reflecting the importance of IR research to them. This workshop (1) brings together researchers and practitioners of eCommerce IR to discuss topics unique to it, (2) determines how to use eCommerce's unique combination of free text, structured data, and customer behavioral data to improve search relevance, and (3) examines how to build data sets and evaluate algorithms in this domain. Since eCommerce customers often do not know exactly what they want to buy, recommendations are valuable for inspiration, serendipitous discovery and basket building. The theme of this year's eCommerce IR workshop is integrating recommendations into search for eCommerce. In addition to the focus on recommender systems in eCommerce search, Rakuten France is sponsoring a data challenge on taxonomy classification using multi-modal (image, text and structured data) input. The data challenge reflects themes from the 2017--2019 SIGIR workshops.
Video-Based Automated Pedestrians Counting Algorithms for Smart Cities

Smart cities possess several technologies in collecting pedestrian activity data, which may be used to manage city planning.A growing body of research exists on video processing based pedestrian counting methods, due to the development of new computer vision techniques.This research reviews different, vision-based methods for counting pedestrians and applies a specific counting method which is formed by a combination of You Only Look Once Version 3 (YOLOv3) and Simple Online Real-time Tracking (SORT) with a deep association metric.The results suggest that although clustering, as well as the direction and intensity of pedestrian traffic, achieves a minimal effect on the count, occlusion constitutes the main source of errors.Adequate training may serve to increase accuracy.
Median Filtering Detection Based on Quaternion Convolutional Neural Network

Median filtering is a nonlinear signal processing technique and has an advantage in the field of image anti-forensics. Therefore, more attention has been paid to the forensics research of median filtering. In this paper, ... | Find, read and cite all the research you need on Tech Science Press
Fast Human Head and Shoulder Detection Using Convolutional Networks and RGBD Data

We introduce a new real-time approach for human head and shoulder detection from RGB-D data based on a combination of image processing and deep learning approaches. Candidate head-top locations (CHL) are generated from a fast and accurate image processing algorithm that operates on depth data. We propose enhancements to the CHL algorithm making it three times faster. Various deep learning models are then evaluated for the tasks of classification and detection on the candidate head-top locations to regress the head bounding boxes and detect shoulder keypoints. We propose three different models based on convolutional neural networks for this problem. Experimental results for different architectures of our model are discussed. We also compare the performance of our models to other state of the art methods in terms of accuracy of detections and computational cost and show that our proposed models are on par with the state of the art in terms of precision-recall of head detection and precision of shoulders detection, with the biggest advantage of our models being in terms of computation time. We also analyze the effect of adding the depth channel on the performance of the network.
Storage and Computation: A Tradeoff in Secure Distributed Computing

Cloud computing provides a flexible and cost-effective solution to big data applications. Data privacy, however, is a main concern. To avoid information leakage to a cloud service provider, a user may store portions of encoded data in multiple clouds and perform computing tasks in a distributed way. In this work, nested MDS codes and the criterion of perfect secrecy are adopted. The allocation of encoded data to be stored in heterogeneous clouds with different computing capability is formulated as an optimization problem, subject to data reliability and security constraints. The problem is shown to be feasible if and only if the storage budget is above a certain level. The tradeoff between minimum computation time and storage budget is analytically characterized, and the former is proved to be a piecewise-linear decreasing function of the latter. When the storage budget increases beyond a certain threshold, the optimal computation time levels off. Closed-form expressions of minimum computation time and optimal storage allocation are obtained. Numerical results show that the optimized allocation outperforms equal allocation significantly if the computing rates of different clouds have large variation.
On the Practical Detection of Hierarchical Heavy Hitters

Finding the network’s heaviest flows is an important and challenging network monitoring task and a critical building block for many other applications. In the hierarchical heavy hitters (HHH) problem, one needs to identify the most frequent network IP-prefixes hierarchically. This is a challenging task since the number of relevant IP-prefixes of flows in a busy router is much higher than the number of counters. To address this point, many streaming algorithms were recently developed, but they use complex data-structures and usually have non-constant per-packet update-time, preventing them from being deployed in line-speed. A randomized constant-time algorithm was proposed recently; however, it is only applicable to extremely large streams. In this paper, we propose a constant-time algorithm for detecting the HHH that does not have any convergence requirements and achieves comparable results to state of the art. Furthermore, our algorithm uses only efficient built-in counters available in current network devices, making it deployable on commercially off-the-shelf network gear. We provide an analytical study of the problem and show, using emulation over real traffic, that our algorithm performs at least as well as the best-known streaming algorithms without performing expensive per-packet operations or requiring convergence periods.
Random permutations without macroscopic cycles

We consider uniform random permutations of length $n$ conditioned to have no cycle longer than $n^{\beta }$ with $0<\beta <1$, in the limit of large $n$. Since in unconstrained uniform random permutations most of the indices are in cycles of macroscopic length, this is a singular conditioning in the limit. Nevertheless, we obtain a fairly complete picture about the cycle number distribution at various lengths. Depending on the scale at which cycle numbers are studied, our results include Poisson convergence, a central limit theorem, a shape theorem and two different functional central limit theorems.
Relevance of Authenticity in the World of Automated Service Encounters

Research has shown that the authenticity of positive emotional displays during traditional service encounters (i.e., human-human interactions) is particularly important. Due to digitization, traditional interpersonal interactions are being replaced in favor of human-machine interactions. This study answers the question regarding the relevance of the authenticity of automated service encounters. By presenting current research and the results of a moderated moderation analysis, the authors show that the relevance of authenticity depends on the generation of the customer.
The Anthropocene, Affect, and Autoethnography?

Research Article| July 27 2020 The Anthropocene, Affect, and Autoethnography? Ken Gale Ken Gale Ken Gale works at the Institute of Education in the Faculty of Arts and Humanities at the University of Plymouth in the United Kingdom and has published widely and presented at a number of international conferences on the philosophy of education, research methodologies, and collaborative approaches to education practices. His most recent book, Madness as Methodology: Bringing Concepts to Life in Contemporary Theorising and Inquiry, was published by Routledge in 2018. Email: K.J.Gale@plymouth.ac.uk Search for other works by this author on: This Site PubMed Google Scholar Journal of Autoethnography (2020) 1 (3): 304–308. https://doi.org/10.1525/joae.2020.1.3.304 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Ken Gale; The Anthropocene, Affect, and Autoethnography?. Journal of Autoethnography 27 July 2020; 1 (3): 304–308. doi: https://doi.org/10.1525/joae.2020.1.3.304 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentJournal of Autoethnography Search Freedom is not simply something that can be stood outside of and decided upon in terms of freedom to or freedom from. Freedom is fluid, dynamic, and ecological, existing where, in the volatility and unpredictability of the act, difference can be made. In the practice of experience making that Stewart1 refers to as “worlding,” there exists the processually differentiating capacity to bring to life encounters and events in which the energy of the future lies in the speculative force and living potential of the always not yet known. Autoethnographies are not to be considered epistemological groundings that assert what they mean, or to state what they are or might be in some metaphysics of the future. Working instead with “futurity,” autoethnographic doing is at the forefront, present in the possibilities of the more-than and the always new possibilities that might be just around the corner. The future is... You do not currently have access to this content.