Reviewerly/exHarmony huggingface dataset:
Algorithms for Selecting the Optimum Dataset While Providing Personalized Privacy and Compensation to its Participants

The privacy preserving microdata sharing literature has proposed several techniques that allow a database administrator to share a dataset in a privacy preserving manner. This paper considers the implications of adding a market layer to that setting. In this setting, individuals (data providers) can receive a market-determined compensation in exchange for their information while they also receive a personalized privacy protection. The computational burdens of satisfying a variety of privacy requirements of individuals (sellers) and dataset requirements of the data receiver (buyer) are analyzed in this paper. The author presents a polynomial time reformulation procedure that proves that the “optimum information product” creation problem reduces to multiple-choice knapsack problem, which is a weakly NP hard problem. The problem of various instance sizes is solved using FICO Xpress 7.0 optimization software. The insights presented in the paper can be utilized for creating a market of individual information in different settings.
Constructing Interesting Wavelets

Previously we have associated several MRA’s with filter bank transforms. Since filter bank transforms also can be constructed outside the setting of wavelets, it would be interesting to see when we can make the association the opposite way: Which filter bank transforms appear in the context of a multiresolution analysis? An answer to this question certainly could transfer much theory between wavelets and filters. Also, it may be easier to construct good filter bank transforms than good wavelet bases.
Blockchain for Universal Health Coverage

In this chapter the authors provide an overview of where Blockchain is being used in high resource settings and explore its potential use in emerging health systems for universal health coverage. There is opportunity to address issues in emerging health systems through adaptation and testing of Blockchain, especially in the management of patient records and data, financing, supply chain management, health workforce management, and surveillance processes. It also has complementary relevance for identity and financial inclusion, which are vital for improving the health of the poor in emerging economies. Reference is also made to the use of Blockchain for displaced people and humanitarian settings, which is the subject of Chapter 7 of this book. There remains, however, a need for more research and evaluation as these technologies are implemented and increased user participation in design to ensure that privacy and security issues are addressed. Furthermore, greater attention to local implementation and health sector applications in low resource settings is required.
Cybersecurity Legislation

This entry discusses the role of legislation governing cybersecurity and how this legislation seeks to control criminal activity on the dark web. As explained throughout this encyclopedia, the dark web consists of internet services that are accessible anonymously using “the onion router” (TOR). There has been a concentrated effort in recent years for federal legislation in the United States to be enacted to control the widespread criminal behavior that occurs on the dark web, which includes the sale of drugs, illegal weapons, and child pornography. Legislation, as the term is used in this work, refers to law enacted by a legislative body such as the US Congress. The article considers both existing relevant statutory law and proposed legislation on the local, state/provincial, and federal/national level. The focus will be on US federal legislation; however, the article will also consider US state and local legislation, the legislation from other nations, and international legislation.
Pashtu Numerals Recognition through Convolutional Neural Networks

In the proposed paper we introduce a new Pashtu numerals dataset having handwritten scanned images. We make the dataset publically available for scientific and research use. Pashtu language is used by more than fifty million people both for oral and written communication, but still no efforts are devoted to the Optical Character Recognition (OCR) system for Pashtu language. We introduce a new method for handwritten numerals recognition of Pashtu language through the deep learning based models. We use convolutional neural networks (CNNs) both for features extraction and classification tasks. We assess the performance of the proposed CNNs based model and obtained recognition accuracy of 91.45%.
Technologically Driven Legal Framework of Blockchain and Cryptocurrencies

Blockchain technology refers to a digital, immutable, distributed ledger that registers completed transactions in a well-ordered manner and near real time. Blockchain security creates a decentralized environment that bars any third-party organization from controlling the cryptographically validated transactions and data. Blockchain technology fosters business innovation by creating a peer-to-peer networking that prevents one central server from accessing as well as processing data belonging to all companies in the network. Cryptocurrency can be defined as a digital asset built to facilitate completed transactions using cryptography. It helps in providing protection to the completed transactions and controlling the creation of additional units of the currency. In the recent years, the application of blockchain technology has been associated with governance. Blockchain governance has been applied in different fields; for example, it can be used to create permanent laws that cannot be violated by any third party.
Multi-scale channel-attention model for speaker recognition with comparable loss functions

The recent SENet has shown good performance in machine vision. However, it can not capture temporal feature between channels when it is applied in speaker recognition. To capture spatio-temporal features simultaneously, this paper presents a multi-scale Channel Attention Network (MSCAN). First, a channel attention (CA) module is designed with 1D convolutional layer and 2D convolutional layer combined to capture short-term and long-term dependencies between channels. A multi-scale (MS) module is composed of four dilated convolution with different expansion rates to obtain more receptive fields. Second, the hybrid loss function is applied to enhance the recognition performance by combining the Circle loss with the Angular Margin Softmax loss. Experiments show that the indexes on the public Librispeech dataset in terms of the Error and F-Measure reach 3.2% and 87.7%, respectively. In the meanwhile, the proposed model only contains 2.1M parameters.
Retrieval

This chapter discusses retrieval or search. It begins with an overview of the search process. It then turns attention to the general principles of searching, including a description of exact-match and partial-match approaches. This is followed by a discussion of specific searching interfaces. The chapter closes with a discussion of document delivery and a specific type of retrieval known as information filtering.
The Use of Non-Standard Problems with Matches When Teaching Younger Schoolchildren

The article deals with the actual problem of the intellectual development of primary schoolchildren by including in the work of non-standard tasks of a search nature, the use of sign-symbolic means of presenting information, which allow you to create models of the objects and processes under study. Among the non-standard are the problems with matches described by the authors in the form of visual calculating material, the search for a solution to which makes it possible to intensify the mental activity of students of this age category, to develop their fine motor skills of hands and mathematical observation. The article describes a methodology for the step-by-step familiarization of junior schoolchildren with simple and more complex transformations and manipulations with matches as geometric objects, the consistent formation of an independent creative approach in children to the choice of schemes for solving non-standard educational and practical problems.
Traffic Analysis Based on Short Texts From Social Media

Social networks provide information about activities of humans and social events. Thus, with the help of social networks, we can extract the traffic events that occur in a city. In the context of an urban area, this kind of data allows to obtaining contextual real-time information shared among citizens that will be useful to address social, environmental and economic issues. In this paper, the authors describe a methodology to obtain information related to traffic events such as accidents or congestion, from Twitter messages and RSS services. A text mining process is applied on the messages to acquire the relevant data, then data are classified by using a machine learning algorithm. The events are geocoded and transformed into geometric points to be represented on a map. The final repository lets data to be available for further works related to the traffic events on the study area. As a case of study we consider Mexico City.
Eye Image Segmentation Method Based on the Modified U-Net CNN Architecture

Abstract The paper presents a new eye image segmentation method used to extract the pupil contour based on the modified U-Net CNN architecture. The analysis was performed using two databases which contain IR images with a spatial resolution of 640x480 pixels. The first database was acquired in our laboratory and contains 400 eye images and the second database is a selection of 400 images from the publicly available CASIA Iris Lamp database. The results obtained by applying the segmentation based on the CNN architecture were compared to manually-annotated ground truth data. The results obtained are comparable to the state of the art. The purpose of the paper is to present the implementation of a robust segmentation algorithm based on the U-Net convolutional neural network that can be used in eye tracking applications such as human computer interface, communication devices for people with disabilities, marketing research or clinical studies. The proposed method improves uppon existing U-Net CNN architectures in terms of efficiency, by reducing the total number of parameters used from 31 millions to 38k. The advantages of using a number of parameters approximatly 815 times lower than the original U-Net CNN architecture are reduced computing resources consumption and a lower inference time.
M-LEARNING

This paper aims to provide quantitative data on students’ of Gujarat University Library for the Mobile learning to regard as the benefit to academic of mobile library services to library users. The M-learning research included an online survey through Google form that involved 80 participants. In addition, the paper provides data for libraries to establish the value of developing their own mobile learning and services. It also demonstrates the propagation of mobile device usage within the library and shows which services students would find most useful in Mlearning. The paper provides coming into a rapidly moving area of technology as confirmed through the research. The increase use of mobile devices among students is vital to acknowledge. The role of the academic library is to hug changing student behaviour by provide services optimised for mobile devices.
Physical Implementations of Quantum Information Processing

This chapter is devoted to several promising physical implementations of quantum information processing. The chapter starts with a description of physical implementation basics, di Vincenzo criteria, and an overview of physical implementation concepts (Section 13.1). The next section (Section 13.2) is related to nuclear magnetic resonance implementation, whose basic concepts are used in various implementations. In Section 13.3, the use of ion traps in quantum computing is described. Next, various photonic implementations (Section 13.4) are described, followed by quantum relay implementation (Section 13.5) and the implementation of quantum encoders and decoders (Section 13.6). Implementation based on optical cavity electrodynamics is further described in Section 13.7. Finally, the use of quantum dots in quantum computing is discussed in Section 13.8. After a short summary (Section 13.9), a set of problems (Section 13.10) is provided for self-study.
Cryptocurrency

The forensic analysis of cryptocurrencies presents their own unique challenges to the examiner. In recent years the understanding of these payment systems has improved for consumers and for examiners. Cryptocurrencies appeal to criminals to facilitate their nefarious conduct due to three key features: (1) ensuring limited anonymity in which users might only reveal their identity either negligently or might be revealed by other parties who use external data to match transactional data that is visible in the public ledger, (2) independence from a central authority in which rules are made by consensus, rather than imposed by the central authority, that cannot be abolished or regulated by force, and (3) double spending attack protection where the owner of cryptocurrency cannot use the same units to pay two different recipients. As of January 2016 there were over 600 cryptocurrencies that have since grown to 1596 as of 1 April 2018 and 9914 markets available in which to trade the currencies. The proliferation of both cryptocurrencies and their markets represents an impossible task for law enforcement and regulating authorities to monitor proactively.
Secure Speaker Recognition using BGN Cryptosystem with Prime Order Bilinear Group

Speech being a unique characteristic of an individual is widely used in speaker verification and speaker identification tasks in applications such as authentication and surveillance respectively. In this paper, framework for secure speaker recognition system using BGN Cryptosystem, where the system is able to perform the necessary operations without being able to observe the speech input provided by the user during speaker recognition process. Secure speaker recognition makes use of Secure Multiparty Computation (SMC) based on the homomorphic properties of cryptosystem. Among the cryptosytem with homomorphic properties BGN is preferable, because it is partially doubly homomorphic, which can perform arbitrary number of addition and only one multiplication. But the main disadvantage of using BGN cryptosystem is its execution time. In proposed system, the execution time is reduced by a factor of 12 by replacing conventional composite order group by prime order group. This leads to an efficient secure speaker recognition.
Athlete Health Prediction Using Machine Learning Methods

&lt;p&gt;Due to an increase in sports activities, the prediction of athletes’ health (AH) has recently become an important research topic. However, it is a challenging task to predict AH because of the nature of the data and the limitations of predictive models. The main objective of this work is to develop appropriate models that can forecast AH using historical data. This work will enable sport organizations to monitor the well-being of their athletes. In this thesis, we explore the applicability of various machine learning (ML) methods for predicting AH. Traditional ML methods do not perform well for class-imbalanced data as these methods are biased towards the majority class. In this work, we propose to use ensemble-based methods which utilize downsampling, bootstrap sampling, and boosting techniques to improve the classification performance. Various metrics are used to evaluate and to compare the model performance. Our results show the superiority of ensemble-based methods over traditional approaches. The random forest and the RUSBoost classier models are in particular found to produce the best performance in handling imbalanced classes.&lt;/p&gt;
Using Tensor Products to Apply Wavelets to Images

Previously we have used wavelets to analyze sound. We would also like to use them in a similar way to analyze images. In Chap. 8 we used the tensor product to construct two dimensional objects (i.e. matrices) from one-dimensional objects (i.e. vectors). Since the spaces in wavelet contexts are function spaces, we need to extend the strategy from Chap. 8 to such spaces. In this chapter we will start with this extension, then specialize to the resolution spaces V m, and extend the DWT to images. Finally we will look at several examples.
Putting the learner at the heart of student experience: The role of the University Library in a seven-year journey of superconvergence at Northumbria University, UK

Higher Education in the UK is experiencing a level of change unprecedented in the last twenty-five years. Coupled with global drivers and disruptors, from technology to generational differences, the new higher education landscape that is emerging is presenting academic libraries with a series of new and extreme strategic challenges and opportunities. Putting students at the heart of learning, by aligning with and influencing university strategy, creating new and more integrated learning environments, and designing support with student outcomes in mind, are rapidly becoming business critical. In this article, we describe the experience of the directorate of Student and Library Services at Northumbria University, in which the University Library has played a pivotal role driving a broad, deep, and sustainable superconvergence while transforming in its own right to deliver innovative new services and successful performance. In so doing, we discuss the evolution of the superconvergence portfolio, how structure and roles were arrived at, what phases of change and stages of convergence we passed through, and how we have adopted a new support framework to integrate distinct services, and physical and virtual spaces, while retaining the quality of our broad professional expertise. We provide several examples of how the new service is operating and specifically how the University Library works in this wider context. We conclude with a reflective discussion on the outcomes and impact our approach has afforded, and what may follow.
Research on Early Warning Algorithm of Active Distribution Network Operational Risk Based on Improved Apriori Association Rules

In order to improve the accuracy and rapidity of risk early warning of active distribution network, this paper adopts the idea of data mining and proposes an improved Apriori algorithm for early warning of risks in the distribution network. This method takes weather, risk equipment and risk reasons as the research objects, and through mathematical analysis, proposes an improved Apriori algorithm based on seasonal classification, which greatly improves the efficiency of the algorithm, thus improving the response speed of the risk early warning system. At the same time, a failure mode library of typical environmental scenarios has been established, and enumerate the algorithm flow. The display interface of the system is designed and developed, so that the matching information is displayed in a separate display interface. Through the example analysis, not only the feasibility of the algorithm is verified, but also the readability of the risk warning data is improved, which has a guiding significance for the actual engineering construction.
Security Mechanisms in Cloud Computing-Based Big Data

In the existent system, data is encrypted and stored when passed to the cloud. During any operations on the data, it is decrypted and then the computation is done. This decrypted data is vulnerable and prone to be misused. After the computations are done, the data and the result are encrypted and stored back in the cloud. This creates an overhead to the system as well as increases time complexity. With this chapter, the authors aim to reduce the overhead of the systems to perform repeated encryptions and decryptions. This can be done by allowing the computations to happen directly on the encrypted text. The result obtained by performing computations on encrypted data will be the same as the ones done on the original plain text. This new security solution is fully fit for processing and retrieval of encrypted data, effectively leading to the broad applicable project, the security of data transmission, and the storage of data. The work is secured further with additional concepts like probabilistic and time stamp-based encryption processes.
Integrating Machine Learning Based Operators in Visual Effects Toolsets

Post-production workflows rely heavily on image processing and computer vision algorithms for the implementation of their visual effect (VFX) tools. In these fields, machine learning has been shown to be disruptive. By integrating the domain statistics from a given training dataset, machine learning based operators may be more efficient at solving existing problems and may enable new problems to be solved, expanding the VFX toolset. In this paper we are sharing our experience on developing and integrating several machine-learning based operators into software for the Post-production industry. We will present a sky segmentation operator, a depth map estimation operator and an operator to compute face geometric maps (UVs, depth and normals) from sequences of images. More specifically, these operators consist in trained deep convolutional neural networks (DCNN) taking as input an RGB color image and outputting the associated maps, i.e. a matte, depth, normals, and/or UVs maps. Such maps permit to apply many different effects to the input image during color grading, including beautification or even relighting of faces. These works will serve as a case study to review and discuss the multiple challenges posed by the implementation, integration and deployment of machine learning based operators into a VFX toolsets.
Word File Parsing Based On Python

With people’s fast-paced lifestyle, more and more people use files to store information. Unstructured data contains many different forms, and the data is sparse. In order to facilitate users to quickly obtain a large amount of required information, this paper designs an information extraction software based on Python platform, which can efficiently store and search a large number of digital content.
VeTo-web: A Recommendation Tool for the Expansion of Sets of Scholars

Expanding a set of known experts with new ones that share similar expertise is a problem that emerges in various real-life applications. We demonstrate VeTo-web, an open source, publicly available tool that deals with this problem in the context of searching for academic experts. VeTo-web exploits analysis techniques for scholarly knowledge graphs to identify scholars that share similar research activities with a given expert group and offers a Web-based user interface to assist its users in expanding a set of academic experts with additional scholars with similar expertise.
Handling Critical Issues of Big Data on Cloud

Big Data is driving radical changes in traditional data analysis platforms. To perform any kind of analysis on such voluminous and complex data, scaling up the hardware platforms becomes impending. With the entire buzz surrounding Big Data; it is being collected at an unprecedented scale. Big Data has potential to revolutionize much more than just research. Loading large data-sets is often a challenge. Another shift of this Big Data processing is the move towards cloud computing. As many communities begin to rely on cloud based data management, large shared data goes up extensively. Analysis of such large data on distributed processing system or cloud is a bit difficult task to handle. The aim of this chapter is to provide a better understanding of the design challenges of cloud computing and analytics of big data on it. The challenge is related to how a large extent of data is being harnessed, and the opportunity is related to how effectively it is used for analyzing the information from it.