Reviewerly/exHarmony huggingface dataset:
Density-based multi-weight vector support vector machine

Recently, proposed multi-weight vector support vector machine (MVSVM) considers all of points and views them as equally important points. In real cases, most of the points of a dataset are highly correlated, at least locally, or the dataset has an inherent geometrical property. These points generally lie in the high density regions and are crucial for data classification. This motivates the rush toward new classifiers that can sufficiently take advantage of the points in the high density regions. In this paper, a novel binary classifier called density-based multi-weight vector support vector machine (DMVSVM) is presented. With the introduction of underlying correlated information between points, DMVSVM not only inherits the merit of MVSVM, but also has its additional characteristics: 1) density weighting method is adopted to measure the importance of points in the same class; 2) having comparable or better classification ability compared to MVSVM. The experimental results on publicly available datasets confirm the effectiveness of our method.
Use of Cloud, Multimedia, and QR Codes to Enhance Print Maps

Quick response (QR) codes are two-dimensional codes. They are machine-readable optical labels which contain information of the object on which they are attached. Due to their large storage capacity and faster readability, they are becoming more popular than one-dimensional bar codes. They are widely used in creation of inventories, product tracking, document management, etc. With the widespread use of smart phones, QR codes are becoming more popular. QR code interpreting apps are freely available and all modern smartphones are equipped with a camera and internet to process and interpret these codes. The value of printed maps can also be enhanced by using QR codes. This chapter reviews how the value of printed or hard copy maps can be enhanced by adding QR codes that have multimedia content placed on the Cloud.
Rainfall Estimation Using Neuron-Adaptive Higher Order Neural Networks

Real-world data is often nonlinear, discontinuous, and may comprise high frequency, multi-polynomial components. Not surprisingly, it is hard to find the best models for modeling such data. Classical neural network models are unable to automatically determine the optimum model and appropriate order for data approximation. In order to solve this problem, neuron-adaptive higher order neural network (NAHONN) models have been introduced. Definitions of one-dimensional, two-dimensional, and n-dimensional NAHONN models are studied. Specialized NAHONN models are also described. NAHONN models are shown to be “open box.” These models are further shown to be capable of automatically finding not only the optimum model but also the appropriate order for high frequency, multi-polynomial, discontinuous data. Rainfall estimation experimental results confirm model convergence. The authors further demonstrate that NAHONN models are capable of modeling satellite data.
A Tale of Policies and Breaches

While the use of social media offers great opportunities to interact with customers and business partners, there are significant risks associated with this technology if a clear strategy has not been defined to address both the risks and the benefits that come along with it. The best approach for an organization to effectively utilize the benefits of this technology is to engage all relevant stakeholders and establish a strategy that addresses the pertinent issues. The organization needs to have in place relevant policies so as to be able to achieve it. To be able to identify the most frequent risks and their source, we captured breach data from various sources. In the chapter, we analyzed that the most important source of risk that can occur due to use of social media for a company is from its own workforce and an employee might find various ways of doing so.
Vachanamrut Visualization

The authors discuss their interaction with data using various data visualization techniques, which are a quick, easy way to convey concepts universally. Currently, data has become more and more important; it is also important how one can visualize that data in the mind. This chapter is based on extracting important information from one of the holy Swaminarayan scriptures. The authors explore the content of the Vachanamrut, a unique work of prose in the Gujarati language, which contains discourses of Lord Swaminarayan and his conversation with saints and devotees. They convert the data in graphical interface using some libraries of the R tool. So, one can get the main idea of that data quickly, without a need to explain more about that data. Summing it up, this chapter examines the techniques of describing data of any ancient scripture or ancient text in any language by visualizing that data.
Multiparameter quantum tracking of optical activity

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text V. Cimini, L. Ruggiero, I. Gianani, M. Sbroscia, T. Gasperi, E. Roccia, L. Mancino, D. Tofani, F. Bruni, M. A. Ricci, and M. Barbieri, "Multiparameter quantum tracking of optical activity," in Conference on Lasers and Electro-Optics, OSA Technical Digest (Optica Publishing Group, 2019), paper JW2A.116. Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
Cantando con la Corrente (singing with current)

From the intersection of computational science and technological speculation, with boundaries limited only by our ability to imagine what could be. An augmented singer gets some unexpected feedback from his audience.
Meeting Compliance Requirements While Using Cloud Services

Compliance with government and industry regulations is an essential part of conducting business in several sectors. Many of the requirements revolve around financial, privacy, or security aspects. Most of the requirements are due to federal regulations in USA while some are industry requirements that are applicable globally. Even some of the federal regulations in USA apply to service providers abroad when they are providing service to entities in USA. In that sense, all of the compliance requirements discussed here apply to a global audience. In this chapter, the authors discuss in detail the scope of the Health Insurance Portability and Accountability Act, Sarbanes-Oxley Act, Federal Information Security Management Act, Gramm-Leach-Bliley Act, Payment Card Industry Requirements, and the Statement on Auditing Standards 70. These compliance requirements concern protecting the customer data stored in the cloud with respect to confidentiality and integrity. Several of these requirements have significant enforcement powers associated with them, and businesses need to take these requirements seriously and comply. The compliance aspect involves gathering and reporting appropriate information on a regular basis. The authors present details on all these aspects in this chapter.
Cyber Crimes

The financial cost of cyber crime now has an annual cost estimated in the UK in eleven figures. In this chapter an ethic based definition of cyber crime is introduced and cyber crimes are classified. The impact of each class of cyber crime on society, individual, government and international security is highlighted. The cost of cyber crime is evaluated and a technique to prevent and mitigate the effect of these crimes on individual, government and international security and world peace is indicated. The forensic techniques and tools used in cyber crime evidence gathering and prosecuting procedure is also indicated. Finally, recommendations and suggestion are given to mitigate the impact of cyber crime on individuals, societies, world finance and international security.
E-Resources Marketing in African Academic Libraries

Significant changes are taking place in the digital information environment that necessitate a marketing-oriented paradigm shift in the delivery of e-resources in most academic libraries in Africa. These changes present different challenges and prospects in terms of newer skills and programming that require a high degree of adaptability to well-designed marketing ethos in the delivery of e-resources offerings to the increasingly diverse and sophisticated clientele in the academic environment in Africa. This chapter examines the challenges and prospects of marketing e-resources in the digital environment in academic libraries in Africa. It explores the holistic marketing readiness of academic libraries in terms of product orientation, promotion, pricing, delivery channels, skills-set and atmospherics that would ensure that the ultimate exchange of value to their clientele is sustained over time. This will accentuate the survival and relevance of African academic libraries in the current dynamic, competitive, and technology-driven environment in the world.
Processing and Recognition of Images Based on Asynchronous Cellular Automata

This chapter discusses the use of asynchronous cellular automata with controlled movement of active cells for image processing and recognition. A time-pulsed image description method is described. Various models and structures of cellular automata for transmitting active signals are presented. The image of the figure is binarized and an active signal moves along its edges. At every moment in time, the active cell of an asynchronous cellular automaton generates a pulse signal. The shape of the generated pulse sequence describes the geometric shape of a flat figure. Methods for describing images of individual plane figures, as well as a method for describing images consisting of many separate geometric objects, are proposed. Cellular automaton is considered as an analogue of the retina of the human visual canal. The circuitry structures of cells of such asynchronous cellular automata are presented, and the software implementation of the proposed methods is also performed. Methods allow one to classify individual geometric image objects.
Introduction

Abstract Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and statistically sound inferences about the population as a whole. The Introduction orients the reader to the essential content and structure of the text, including (a) an overview of the nature and objectives of design-based sampling theory as well an explanation for why we emphasize the design-based approach to sampling, (b) organization of content by chapters, (c) summary of notational conventions adopted throughout, (d) suggestions for instructors using the text for a class, (e) assumed level of mathematics and statistics background expected from readers, and (f) discussion of how this text differs from other published sampling texts. The chapter concludes with a brief review of the history and development of sampling theory, including both the design-based theory (that is stressed in the text), and model-based prediction theory (that is motivated and illustrated in Chapter 7). The Introduction notes that the text presents small sample space numerical illustrations of the performance of many sampling strategies, and that the text adopts a logical and consistent notation which clarifies presentation of sampling theory concepts. The ecological/environmental/natural resources orientation of the text is most evident in problem exercises associated with most chapters, many of which have been motivated by practical resource applications.
Impact of variable positioning of text prediction in gaze-based text entry

Text predictions play an important role in improving the performance of gaze-based text entry systems. However, visual search, scanning, and selection of text predictions require a shift in the user's attention from the keyboard layout. Hence the spatial positioning of predictions becomes an imperative aspect of the end-user experience. In this work, we investigate the role of spatial positioning by comparing the performance of three different keyboards entailing variable positions for text predictions. The experiment result shows no significant differences in the text entry performance, i.e., displaying suggestions closer to visual fovea did not enhance the text entry rate of participants, however they used more keystrokes and backspace. This implies to the inessential usage of suggestions when it is in the constant visual attention of users, resulting in increased cost of correction. Furthermore, we argue that the fast saccadic eye movements undermines the spatial distance optimization in prediction positioning.
Application of VR technology in architectural decoration engineering technology

With the rapid development of social economy and the increasing complexity of architectural decoration projects, there is an urgent need to introduce new technologies to improve work efficiency and solve practical problems. Nowadays, the emerging VR technology, a computer-aided technology, has been widely used in actual engineering projects and has proven to greatly improve work efficiency. This article will discuss the specific application and operation of VR technology in architectural decoration engineering technology, analyze the application advantages of VR technology and look forward to its application prospects in architectural decoration engineering technology.
Introduction

Recent years have witnessed an exponential growth of online platforms such as online social networks (OSNs) and microblogging websites. These platforms play a major role in online communication and information sharing as they have become large-scale and real-time communication tools. This leads to massive user-generated data produced on a daily basis and via different forms that are rich sources of information and can be used in different tasks from marketing to research. On the negative side, online platforms have become widespread tools exploited by various malicious actors who orchestrate societal-significant threats leading to numerous security and privacy issues.
MakerSpace technology and digital environment for speech development of bilingual preschoolers: the experience of Integration

The goal of this article consists in examination of the effective ways of implementation of digital educational technologies into the system of preschool educational institutions functioning in a bilingual environment. The subject of this research is the digital educational resources and innovative technologies in the area of speech development of preschoolers. The technology of implementation of innovative comprehensive approach towards creating a developing environment in digitalization era is addressed to the experts in preschool education. The novelty of this work lies in substantiation of the effectiveness of implementation into the practice of preschool educational institutions &amp;nbsp;of digital educational resources for the development of communication skills of bilingual children in using the MakerSpace technology. The article reveals the essence and advantages of introduction of the innovative concepts of teaching that are founded on digital technologies; underlines the need for a comprehensive approach towards implementation of digital resources into the practice of preschool educational institutions; substantiates the effectiveness of implementation of comprehensive approach to the organization of a developing environment for the development of communication skills of children 3-7 years of age by means of the Russian language. The results of the conducted experiment in preschool educational institutions of the Republic of Tatarstan prove the effectiveness of creating a single developmental environment. Such environment is being formed by the joint efforts of the pedagogues and preschool students using the digital educational resources during classes. The acquired rudimental knowledge is solidified with experience in the course of realization of creative activity using the MakerSpace technology.
Hardware implementation of Hopfield-like neural networks: Quantitative analysis of FPGA approach

Recent progress in AI is largely attributed to the development of machine learning, especially in the algorithm and neural network models. On the other hand, hardware is on the critical path for the future of AI in the big data era. The rapid development of embedded intelligence for machine learning applications is causing the systems to grow more and more complex. FPGA-based solutions are emerging as the right choice for the implementation of these applications. Obviously, it is very important to understand the impact of architectural parameters on the performance and hardware resources utilization. This paper provides a rigorous analysis of FPGA implementation of Hopfield-like neural networks. The relationship between the hardware resources used to synthesize the data path and those used to provide network connections is discussed, as well as the distribution of these resources and how it depends on the variation in the architectural parameters of the network. The analysis presented in this paper is based on Intel/Altera Cyclone FPGA devices.
It's Not My Fault

This article investigates the transfer of information security breach information between breached firms and their peers. Using a large data set of information security incidents from 2003 to 2013, the results suggest that 1) the effect of information security breach information transfer exists between breached firms and non-breached firms that offer similar products and 2) the effect of information transfer is weaker when the information security breach is due to internal faults or is related to the loss of personally identifiable information. Additional tests demonstrate that the effect of information transfer exhibits consistent patterns across time and with different types of information security breaches. Finally, the effect does not depend on whether the firms are IT intensive. Implications, limitations, and future research are discussed.
Data protection

This chapter offers the legal basis for understanding the impact of data protection legislation on data sharing. Toward this direction, significant challenges are presented including legal and ethical barriers, patient privacy issues, technical limitations, and other factors that pose barriers during data sharing. Well-known data protection regulations that deal with such challenges, such as the General Data Protection Regulation (EU) 2016/679 that superseded the Directive 95/46/EC in Europe, as well as the Federal Trade Commission Act and Health Insurance Portability and Accountability Act in the United States, are extensively described in terms of their legal basis. Promising initiatives that envisage to overcome the legal and ethical obstacles that are present during cross-border data sharing are finally discussed toward the establishment of a global data protection framework for different areas of life, especially in healthcare.
Bracco Diagnostics Inc. v. Maia Pharm., Inc. United States Court of Appeals of the Federal Circuit, 2020 No. 2020-1387, 2020 WL 7393233 (Non-precedential)

Biotechnology Law ReportVol. 40, No. 2 Case in PointBracco Diagnostics Inc. v. Maia Pharm., Inc. United States Court of Appeals of the Federal Circuit, 2020 No. 2020-1387, 2020 WL 7393233 (Non-precedential)Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:30 Mar 2021https://doi.org/10.1089/blr.2021.29218.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 40Issue 2Apr 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Bracco Diagnostics Inc. v. Maia Pharm., Inc. United States Court of Appeals of the Federal Circuit, 2020 No. 2020-1387, 2020 WL 7393233 (Non-precedential).Biotechnology Law Report.Apr 2021.117-124.http://doi.org/10.1089/blr.2021.29218.cmhPublished in Volume: 40 Issue 2: March 30, 2021Online Ahead of Print:March 2, 2021PDF download
A self-learning fall detection system for elderly persons using depth camera

The machine learning revolution is redesigning modern healthcare, and with the growth of the elderly population, fall detection has become an important research topic in healthcare. This paper surveys advances in machine learning-based fall detection technologies and reviews sensor-based, image processing-based, and wearable sensor-based fall detection systems and applications. In addition, this paper proposes a self-learning posture analysis and eye status-based fall detection system to solve the issue of mis-detections in fall detection systems, which have not been addressed in past works. Furthermore, this work proposes an image-feature-separation system that can use image processing with a low risk of privacy disclosure. Moreover, this work establishes a dataset, which includes 36 non-fall/fall cases comprising 25,200 images that can be used not only for this research but also in related studies. Experimental results show that this system can detect a fall with high accuracy and solve mis-detections in machine learning-based fall detection systems.
P‐13.3: A Novel Sub Pixel Arrangement based on OLED

Several normal sub pixel arrangements were introduced in this paper, including RGB Stripe and PenTile RGBG. The paper compared the advantage and disadvantage of each arrangement. The pixel of PenTile RGBG includes only two color channels, which causing the color leakage and image distortion. To solve this problem, two novel arrangements after light interleaved and light overlapped based on lens refraction were proposed. The results of image test and contrast experiment showed that the improved sub pixel arrangement can weaken color artifacts effectively.
Background

The computers are able to perform complex calculus operations in a short amount of time. However computers cannot compete with humans in dealing with: common sense, ability to recognize people, objects, sounds, comprehension of natural language, ability to learn, categorize, generalize.Therefore, why does the human brain show to be superior w.r.t common computers for these kind of problems? Is there any chance to mimic the mechanisms characterizing the way of working of our brain in order to produce more efficient machines?In the field of signal analysis, the aim is the characterization of such real-world signals in terms of signal models, which can provide the basis for a theoretical description of a signal processing system. They are potentially capable of letting us learn a great deal about the signal source, without having to have the source available.Therefore, in this chapter two families of modelling technique are described, i.e., the hidden Markov models (HMM) and the Deep Neural Network (DNN). After a theoretical description, the algorithms used for their parameter estimation are described, with a focus on the most widely model structure used in the field of the NILM.
Digital Video Watermarking Using Diverse Watermarking Schemes

As the significance of the internet is increasing day by day so is the need of protecting the media over the internet. In order to protect the copyright information of the media over the internet, the authors use the technique of watermarking. Watermarking is the process of embedding a watermark in the media and then extracting it for ownership verification. Different types of watermarking schemes exist in the world, but we always look for techniques which are highly imperceptible and do not lead to loss of fidelity. Here the researchers have put forward a technique that instills different watermarking schemes to different sets of frames.