Reviewerly/exHarmony huggingface dataset:
Data Security in Wired and Wireless Systems

The issues related to network data security were identified shortly after the inception of the first wired network. Initial protocols relied heavily on obscurity as the main tool for security provisions. Hacking into a wired network requires physically tapping into the wire link on which the data is being transferred. Both these factors seemed to work hand in hand and made secured communication somewhat possible using simple protocols. Then came the wireless network which radically changed the field and associated environment. How do you secure something that freely travels through the air as a medium? Furthermore, wireless technology empowered devices to be mobile, making it harder for security protocols to identify and locate a malicious device in the network while making it easier for hackers to access different parts of the network while moving around. Quite often, the discussion centered on the question: Is it even possible to provide complete security in a wireless network? It can be debated that wireless networks and perfect data security are mutually exclusive. Availability of latest wideband wireless technologies have diminished predominantly large gap between the network capacities of a wireless network versus a wired one. Regardless, the physical medium limitation still exists for a wired network. Hence, security is a way more complicated and harder goal to achieve for a wireless network (Imai, Rahman, &amp; Kobara, 2006). So, it can be safely assumed that a security protocol that is robust for a wireless network will provide at least equal if not better level of security in a similar wired network. Henceforth, we will talk about security essentially in a wireless network and readers should assume it to be equally applicable to a wired network.
An Enhanced Task Scheduling in Cloud Computing Based on Deadline-Aware Model

Cloud computing is the latest in distributed computing technology. The delivery mechanism between the service provider and users depends on Service Level Agreement (SLA). SLA contains Quality of Service (QoS), which has some constraints such as deadline to achieve user satisfaction. In this article, the authors propose a Deadline-Aware Priority Scheduling (DAPS) model to minimize the average makespan, and maximize resource utilization under deadline constraint. In the proposed model, the tasks are sorted based on length priority in ascending order and labeling the VM's state as successful which achieves the deadline constraint, and then mapping the tasks to the suitable VM that has minimum processing time. The authors compared their proposed model to the existing algorithms GA, Min-Min, SJF and Round Robin. The proposed model outperforms other algorithms by reducing the average of makespan, mean of total average response time, number of violations, violation ratio, and failure ratio, while increasing resource utilization, and guarantee ratio for tasks that meet deadline constraint.
Accountability and Responsibility of Online Intermediaries

This chapter of the Oxford Handbook of Online Intermediary Liability summarizes recent developments in intermediary liability theory with special emphasis on the emergence of voluntary measures and private ordering. Looking at the legal liability rules always tells only half of a story. Legal rules are often only basic expectations which are further developed through market transactions, business decisions, and political pressure. Therefore, the real responsibility landscape is equally determined by a mixture of voluntary agreements, self-regulation, corporate social responsibility, and ad-hoc deal-making. Accountability schemes can differ significantly, ranging from legal entitlements to request assistance in enforcement to entirely voluntary private-ordering schemes. This chapter provides a mapping of these basic approaches in order to illustrate the richness and trade-offs associated with such measures. Miscellaneous policy and enforcement tools, such as monitoring and filtering, graduated response, payment blockades and follow-the-money strategies, private DNS content regulation, online search manipulation, are discussed to complement the typical legal liability view of the regulation of intermediaries. The discussion of these enforcement strategies will be framed within the investigation of notions such as market and private ordering, corporate social responsibility, assistance in enforcement of innocent third-party made accountable although not liable and public deal making.
Numerical Study Process Distribution of Contaminated Substances in the Atmosphere Taking Into Account the Physical and Mechanical Properties of Particles

The article deals with the numerical modeling of the processes of transfer and diffusion of air pollutants in the boundary layer of the atmosphere. A mathematical model of the spread of industrial emissions in the atmosphere was developed, taking into account the motion velocity of finely dispersed substances and a number of other factors affecting the change in the concentration of harmful substances in the atmosphere. The model is described by multidimensional partial differential equations with corresponding initial and boundary conditions. For the numerical integration of the problem, the method of splitting into physical processes (of transfer, diffusion and absorption) and an implicit finite-difference scheme of the second order of approximation in spatial variables and in time were used. A software tool was developed to conduct a computational experiment on a computer and to perform a comprehensive study of the processes of transfer and diffusion of harmful substances in the atmosphere
Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.
A Fuzzy Logic Based Synonym Resolution Approach for Automated Information Retrieval

Precise semantic similarity measurement between words is vital from the viewpoint of many automated applications in the areas of word sense disambiguation, machine translation, information retrieval and data clustering, etc. Rapid growth of the automated resources and their diversified novel applications has further reinforced this requirement. However, accurate measurement of semantic similarity is a daunting task due to inherent ambiguities of the natural language, spread of web documents across various domains, localities and dialects. All these issues render to the inadequacy of the manually maintained semantic similarity resources (i.e. dictionaries). This article uses context sets of the words under consideration in multiple corpora to compute semantic similarity and provides credible and verifiable semantic similarity results directly usable for automated applications in the intelligent manner using fuzzy inference mechanism. It can also be used to strengthen the existing lexical resources by augmenting the context set and properly defined extent of semantic similarity.
Improvement of Firefly Algorithm using Particle Swarm Optimization and Gravitational Search Algorithm

Evolutionary algorithms are among the most powerful algorithms for optimization, Firefly algorithm (FA) is one of them that inspired by nature. It is an easily implementable, robust, simple and flexible technique. On the other hand, Integration of this algorithm with other algorithms, can be improved the performance of FA. Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are suitable and effective for integration with FA. Some method and operation in GSA and PSO can help to FA for fast and smart searching. In one version of the Gravitational Search Algorithm (GSA), selecting the K-best particles with bigger mass, and examining its effect on other masses has a great help for achieving the faster and more accurate in optimal answer. As well as, in Particle Swarm Optimization (PSO), the candidate answers for solving optimization problem, are guided by local best position and global best position to achieving optimal answer. These operators and their combination with the firefly algorithm (FA) can improve the performance of the search algorithm. This paper intends to provide models for improvement firefly algorithm using GSA and PSO operation. For this purpose, 5 scenarios are defined and then, their models are simulated using MATLAB software. Finally, by reviewing the results, It is shown that the performance of introduced models are better than the standard firefly algorithm.
Support-Vector Machines and Kernel Methods

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.
Privacy-Preserving Public Auditing and Data Dynamics for Secure Cloud Storage Based on Exact Regenerated Code

Cloud storage allows users to store their data in the cloud to avoid local storage and management costs. Since the cloud is untrusted, the integrity of stored data in the cloud has become an issue. To address this problem, several public auditing schemes have been designed to verify integrity of the data in the cloud. However, these schemes have two drawbacks: public auditing may reveal sensitive data to verifier and does not address the data recovery problem efficiently. This article proposes a new privacy-preserving public auditing scheme with data dynamics to secure the data in the cloud based on an exact regenerated code. This scheme encodes the data for availability, then masks the encoded blocks with randomness for privacy of data and enables a public auditor to verify the integrity of the data. Further, this scheme also supports dynamic data updates. In addition, security and performance analysis proves that proposed scheme is provably secure and efficient.
Hardware Implementation of a Visual Image Watermarking Scheme Using Qubit/Quantum Computation Through Reversible Methodology

In this chapter, hardware implementation of an LSB replacement-based digital image watermarking algorithm is introduced. The proposed scheme is developed in spatial domain. In this watermarking process, data or watermark is implanted into the cover image pixels through an adaptive last significant bit (LSB) replacement technique. The real-time execution of the watermarking logic is developed here using reversible logic. Utilization of reversible logic reduces the power dissipation by means of no information loss. The lesser power dissipation enables a faster operation as well as holds up Moore's law. The experimental results confirm that the proposed scheme offers high imperceptibility with a justified robustness.
Gene Expression Programming

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.
JawSense

JawSense is a wearable device that enables interaction with a computing machine based on unvoiced jaw movement tracking. Speech and touch-typing are the two most common input modalities for smart devices [5]. However, they pose challenges in many scenarios where touch-typing restricts hands-free operation and speech commands are not desirable [1]. JawSense offers a novel sensing modality through unvoiced jaw movement tracking. It is hands-free, socially acceptable, privacy-preserving, and an intuitive input sensing modality. In addition to jaw movements, accelerometer measurements can be affected by body movements and mechanical waves (music) from external sources vibrating the system.
Shoehorning

In this article, we discuss the concept of shoehorning system requirements into (possibly) poor solutions.Some examples are provided that demonstrate that shoehorning may result in inadequate or even catastrophic system solutions.
Randomized Evaluation of Reading Skills: an Opportunity for Systematic Literature Review

In the context of public policies, randomized evaluation of the impact of public programs and policies is fundamental because it provides the most credible and reliable way to learn what works and what does not in education to reduce poverty and improve the well-being of individuals and society. There is an urgency in the literature emerging during the pandemic to develop each student's reading skills since there is no development without literacy. For this reason, research studies report randomized control trials (RCT) for comprehensive reading programs that are of great interest to the educational system. This article aims to analyze the characteristics of these studies and the trends of new contributions to education. To achieve this, we conducted a systematic literature review (SLR) of 63 articles published between January 2015 and January 2019 in the Web of Science (WoS) and Scopus databases. We explored three themes using seven questions. The themes were a) Impact evaluation and randomized control trial (RCT), its approach and criteria; b) innovation and technology in reading programs; and c) the use of technology in the social appropriation of knowledge. The findings showed an increase in randomized control trials in impact evaluation, the need to optimize the quality of these studies, and the challenge of integrating innovation and technology in reading programs. We concluded that increasing and optimizing the impact evaluation approach in these topic research contributes in a substantive way to researchers and government decision making and to advance in the path of achieving a fair, equitable and quality education for all.
Mathematical model of bank scoring in conditions of insufficient data

Recently, different methods of object classification using training datasets is actually. One of these methods is naive Bayesian classifier. Class of objects can consist of low number of elements. Such class is called poor class. In this paper we consider classification problem in poor class. Logical classifier doesn’t work in this case. Metric classifier can give good results if and only if there are quite dense set of metrically nearby classified objects in neighborhood of the considering object. Bayesian classifier reevaluates all hypotheses about belonging of the object to certain class. Therefore, Bayesian classifier can solve this classification problem. For example, we considered classic problem of bank scoring. This scoring is based on two criteria. Classified object has two belonging hypotheses. We can apply such reasoning for more difficult cases.
Human factors

From the beginning of digital evidence’s formation as a discipline, it has been generally considered to be fact-based evidence. Although anecdotal, conversations with stakeholders within the justice system, including members of the judiciary revealed a continued widespread belief that digital evidence is still considered to be fact-based as that is the way that it has been presented in court, which has remained unchallenged by defence counsel. While the assertion that digital evidence is fact based has always been erroneous and without foundation, that assertion could now quite rightly be considered to be a fallacy. Research on miscarriages of justice in multiple jurisdictions has highlighted the prevalent issue of human error in forensic science, with a particular focus on cognitive bias in several forensic disciplines. In recent years, digital forensics has increasingly taken a more scientific approach to analysis and interpretation of evidence accompanied by an increased focus on quality management, error mitigation, tool testing and verification methodologies. A growing number of peak organisations and professional bodies recognise digital evidence as a discipline of forensic science and, therefore, are subject to uncertainties, vulnerabilities, limitations and the potential for error in a similar way to other forensic disciplines.
The High-Frequency Impact of Macroeconomic News on Jumps and Co-Jumps in the Cryptocurrency Markets

This study investigates the extent to which intra-day jumps and co-jumps in cryptocurrency markets stem from the release of macroeconomic news from the U.S., Germany and Japan. Using 5-min frequency prices for Bitcoin and Ethereum quoted against the U.S. dollar over the 2016-2019 period, we find that intra-day jumps are three times more frequent in Ethereum than in Bitcoin. More importantly, we show that jumps in Ethereum are more sensitive to macroeconomic news than jumps in Bitcoin, and that U.S. news releases exhibit higher influence on jumps in both cryptocurrencies than German and Japanese news announcements. We also find that co-jumps among Bitcoin and Ethereum are scarce and tend be associated only with a few U.S. news announcements, and in particular those related to the unemployment rate, new home sales, housing starts and Fed Beige Book.
Relevant Logics

Abstract Relevant logics aim to avoid the ‘paradoxes’ of the material and strict conditionals. Their most natural semantics, the Routley-Meyer semantics, is given in terms of impossible worlds. By placing certain further conditions on those worlds, we can obtain stronger relevant logics. One of the main philosophical issues surrounding the general approach concerns how to interpret the Routley-Meyer ternary relation on worlds and the Routley star. The information-theoretic interpretation has proved popular but, it is argued, it faces philosophical issues. An alternative interpretation takes its cue from ways of thinking about conditionality in general. The three options are considered, but issues are found with each of them. A final option is the truthmaker interpretation of relevant logics, which is promising but under-developed.
Semantic modeling

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.
A Survey on Supervised Convolutional Neural Network and Its Major Applications

With the advances in the computer science field, various new data science techniques have been emerged. Convolutional Neural Network (CNN) is one of the Deep Learning techniques which have captured lots of attention as far as real world applications are considered. It is nothing but the multilayer architecture with hidden computational power which detects features itself. It doesn't require any handcrafted features. The remarkable increase in the computational power of Convolutional Neural Network is due to the use of Graphics processor units, parallel computing, also the availability of large amount of data in various variety forms. This paper gives the broad view of various supervised Convolutional Neural Network applications with its salient features in the fields, mainly Computer vision for Pattern and Object Detection, Natural Language Processing, Speech Recognition, Medical image analysis.
Semantically Secure Classifiers for Privacy Preserving Data Mining

Essential predictions are to be made by the parties distributed at multiple locations. However, in the process of building a model, perceptive data is not to be revealed. Maintaining the privacy of such data is a foremost concern. Earlier approaches developed for classification and prediction are proven not to be secure enough and the performance is affected. This chapter focuses on the secure construction of commonly used classifiers. The computations performed during model building are proved to be semantically secure. The homomorphism and probabilistic property of Paillier is used to perform secure product, mean, and variance calculations. The secure computations are performed without any intermediate data or the sensitive data at multiple sites being revealed. It is observed that the accuracy of the classifiers modeled is almost equivalent to the non-privacy preserving classifiers. Secure protocols require reduced computation time and communication cost. It is also proved that proposed privacy preserving classifiers perform significantly better than the base classifiers.
Large-Scale Hypothesis Testing and FDRs

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.