Reviewerly/exHarmony huggingface dataset:
A Framework for Decentralized Energy Trading Based on Blockchain Technology

The blockchain is a distributed database whose data are stored and managed in every node in the network. By design, the information of trade records is difficult to tamper. Therefore, it can deal with the trust problem without a central operator. Blockchain technology has brought about major reform in the financial field and has been paid more and more attention in many fields. The combination of blockchain technology and energy trade seems to have good prospects due to their common features of distribution, diversity and marketization. In this paper, a new decentralized energy trading framework based on blockchain technology is presented and analyzed. In this framework, four basic participators in the market are set, which are the consumer, supplier, speculator (with energy storage capacity) and regulator. In addition, the order format and price clearing mechanisms are designed accordingly. The new framework can accommodate a large number of entities and enable them to achieve better economic benefits. Small examples are used to analyze the framework. It is concluded that this technology has significant under-researched potential to support and enhance the efficiency gains of the energy revolution, and areas for future research are identified.
Investigation of Ethereum Price Trends using Machine learning and Deep Learning Algorithms

Over the previous decade, Cryptocurrency has maintained a steady increase in popularity. The very nature of cryptocurrencies is such that its imperceptible and ungovernable. These qualities intrigue a large number of people to forecast the future value of distinct cryptocurrencies based on their historical price inflation. This research paper assesses and estimates the price projections and volatility of the cryptocurrency named Ethereum (ETH). We accomplish this objective by the use of 4 machine learning algorithms and 3 deep learning techniques to time series analysis of Ethereum (ETH) prices from August 2015 to December 2021 (2315 days). In terms of RMSE, MAE, MSE, and R2 score, deep learning technique LSTM demonstrated superior prediction accuracy when compared to other learning methods.
Leveraging the Power of Blockchain in the Health Insurance Industry

The healthcare industry is an inseparable part of people's lives and contributes directly to their well-being. However, it is often seen that a rise in the costs of healthcare services leads to financial instability. In times of economic hardships, health insurance plays a vital role in providing financial support to patients. One of the functions of these health insurance providers is to monitor and maintain patients' health records while upholding their privacy. However, in most cases, claim processing is a multistage process. Delays caused by these multiple stages can affect the quality of treatment in patients. The need of the hour for claim processing systems is speed while maintaining integrity. We propose a blockchain-based solution to store health insurance records digitally on decentralized storage that is secure and free from tampering while being efficient. The claim processing system can then be included as a part of a smart contract that accesses these health insurance records. These smart contracts automate the process of claim approval removing any intervention from third parties or preventing any breach of pre-committed agreements.
A multi-stage multi-objective GWO based feature selection approach for multi-label text classification

In Information Retrieval (IR), Text Mining (TM), and web search, Multi-label Text Classification (MTC) plays an essential role. A document can fall into more than one category in MTC. Text documents frequently include High Dimensional (HD) non-discriminative (noisy and irrelevant) phrases, resulting in high computing costs and impoverish learning performance of Text Classification (TC). The Feature Selection (FS) procedure is complicated by three issues caused by small samples and HD datasets. First, given limited samples and HD, FS is unstable. Second, with HD, FS takes longer. Third, a particular FS approach may not provide enough Classification Accuracy (CA). In this paper, we have developed a two-stage FS approach based Meta-heuristics Algorithm (MA) for MTC. The first stage work on the filter-based FS approach, while the second stage is based on the multi-objective Grey Wolf Optimization (GWO) algorithm. The first objective is to diminish the Hamming Loss (HL), and the second objective is to decrease the Selected Features (SF). We have used the Multi-Layer Perceptron (MLP) model for the classification task. The experimental findings show that the suggested FS scheme achieves superior HL with a less number of features.
Designing In-Air Hand Gesture-based User Authentication System via Convex Hull

With the rapid development of personal computers and mobile devices, it is very important to properly authenticate a user’s identity to protect the information and data stored on these devices. Due to various privacy and security concerns, contactless authentication has received much attention, among which in-air gesture based authentication is one promising solution. Motivated by this observation, in this work, we develop and implement a real-time in-air hand gesture-based user authentication system, where users can define or select various gestures and generate their credentials. Our system can verify a user using a deep learning-enabled inference framework without the need of being trained by a powerful device. Different from the state-of-the-art, our system uses a method of convex hull to recognize the hand gesture. In our user study, we involve 20 participants to examine the system performance, and find that our system is viable and usable with a success rate of 95%.
MRIM: Enabling Mixed-Resolution Imaging for Low-Power Pervasive Vision Tasks

While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce MRIM, a simple but effective framework to tackle this tradeoff. Under MRIM, the vision sensor platform first executes a lightweight preprocessing step to determine the saliency of different sub-regions within a single captured image frame, and then performs a saliency-aware non-uniform downscaling of individual sub-regions to produce a &#x201C;mixed-resolution&#x201D; image. We describe two novel low-complexity algorithms that the sensor platform can use to quickly compute suitable resolution choices for different regions under different energy/accuracy constraints. Experimental studies, involving object detection tasks evaluated traces from two benchmark urban monitoring datasets as well as a prototype Raspberry Pi-based MRIM implementation, demonstrate MRIM&#x2019;s efficacy: even with unoptimized embedded platform, MRIM can provide system energy savings of 35&#x002B;% or increase task accuracy by 8&#x002B;%, over conventional baselines of uniform resolution downscaling or image encoding, while supporting high throughput.
Robust Watermarking Algorithm against the Geometric Attacks based on Non-Subsampled Shearlet Transform and Harris-Laplace Detector

With the rapid spread of network information, the information maintenance has become the focus of information security on networks. Digital watermarking is one of the effective methods to protect information security, achieve anticounterfeiting traceability, and protect copyright, and it is an important branch of information hiding technology. However, one of the most challenging questions of digital watermarking is how to present strong robustness in geometric attacks. Nowadays, most watermarking algorithms are relatively weak robustness against geometric attacks. A robust watermarking algorithm against geometric attacks based on the non-subsampled shearlet transform and the Harris-Laplace detector is proposed. The host image is decomposed into subbands with different directions by the shearlet transform, and the Harris-Laplace detector is utilized to obtain the feature regions. Then, the nonoverlapping regions with strong robustness are selected to embed watermark by the fuzzy c-means cluster algorithm. The experimental results indicate that the proposed watermarking scheme can well resist geometric attacks.
Random Forest Algorithm Based on Linear Privacy Budget Allocation

In the era of big data with exponential growth in data volume, how to reduce data security issues such as data leakage caused by machine learning is a hot area of recent research. The existing privacy budget allocation strategies are usually only suitable for data applications in specific spaces and cannot meet users' personalized needs for privacy budget allocation. Therefore, a linear privacy budget allocation strategy is proposed. The strategy assigns each layer a linearly increasing privacy budget from the root of the decision tree to the bottom by adjusting the coefficient or constant term. Combining this strategy with the random forest algorithm, a random forest algorithm based on linear privacy budget allocation (DiffPRF_linear) is formed. Experimental results show that the proposed algorithm can realize uniform, arithmetic, and geometric privacy budget allocation policy effects and can also achieve better classification effects than the former, which not only meets the needs of users to protect private data personalized but also maintains high classification accuracy.
Privacy preserving data mining - past and present

Data mining is the process of discovering patterns and correlations within the huge volume of data to forecast the outcomes. There are serious challenges occurring in data mining techniques due to privacy violation and sensitive information disclosure while providing the dataset to third parties. It is necessary to protect user's private and sensitive data from exposure without the authorisation of data holders or providers when extracting useful information and revealing patterns from the dataset. Also, internet phishing gives more threat over the web on extensive spread of private information. Privacy preserving data mining (PPDM) is an essential for exchanging confidential information in terms of data analysis, validation, and publishing. To achieve data privacy, a number of algorithms have been designed in the data mining sector. This article delivers a broad survey on privacy preserving data mining algorithms, different datasets used in the research and analyses the techniques based on certain parameters. The survey is highlighted by identifying the outcome of each research along with its advantages and disadvantages. This survey will guide the future research in PPDM to choose the appropriate techniques for their research.
Understanding Severe Asthma Through Small and Big Data in Spanish Hospitals: The PAGE Study

Data on the prevalence of severe asthma (SA) are limited. Electronic health records (EHRs) offer a unique research opportunity to test machine learning (ML) tools in epidemiological studies. Our aim was to estimate the prevalence of SA among asthma patients seen in hospital asthma units, using both ML-based and traditional research methodologies. Our secondary objective was to describe patients with nonsevere asthma (NSA) and SA over a follow-up of 12 months.PAGE is a multicenter, controlled, observational study conducted in 36 Spanish hospitals and split into 2 phases: a cross-sectional phase for estimation of the prevalence of SA and a prospective phase (3 visits in 12 months) for the follow-up and characterization of SA and NSA patients. A substudy with ML was performed in 6 hospitals. Our ML tool uses EHRead technology, which extracts clinical concepts from EHRs and standardizes them to SNOMED CT.The prevalence of SA among asthma patients in Spanish hospitals was 20.1%, compared with 9.7% using the ML tool. The proportion of SA phenotypes and the features of patients followed up were consistent with previous studies. The clinical predictions of patients' clinical course were unreliable, and ML found only 2 predictive models with discriminatory power to predict outcomes.This study is the first to estimate the prevalence of SA in hospitalized asthma patients and to predict patient outcomes using both standard and ML-based research techniques. Our findings offer relevant insights for further epidemiological and clinical research in SA.
Object Recognition as Classification via Visual Properties

This book explores the latest advances in how knowledge organisation can both draw upon and inform diverse disciplines and technological developments. It explores how best to combine theory and practice, with its contents considering practical solutions as well as the theory behind the design, development and implementation of knowledge organising systems, ranging from controlled vocabularies, classification systems and metadata schemas to ontologies and taxonomies. With contributions by Carmen Agustín-Lacruz, Mayukh Bagchi, Mario Barité, Maria Teresa Biagetti, Briony Birdi, Lynne Bowker, Thiago Henrique Bragato Barros, Ana Inés Brozia, Gema Bueno-de-la-Fuente, Julia Bullard, Rafael Cacciolari Dalessandro, José Augusto Chaves Guimarães, Christian Cole, Stephanie Colombo, Luciana Corts Mendes, Luis Corujo, Niamh Nic Daeid, Patrícia de Almeida, Hadassa De Zen Itepan, Anthony Dunbar, Jonathan Furner, Francisco-Javier García-Marco, Fausto Giunchiglia, Claudio Gnoli, Míriam Gontijo de Moraes, Hazlem Granados-Amaya, Hugo Alberto Guadarrama-Sánchez, Carlos Guardado da Silva, David Haynes, Ania Hernández Quintana, Philip Hider, Chris Holstrom, Eva Hourihan Jansen, Fidelia Ibekwe, Mat Kelly, Michael Kleineberg, Nair Yumiko Kobashi, Deborah Lee, Maël Le Gall, Joaquín López del Ramo, Luís Miguel Oliveira Machado, Hugo Moquet, Catalina Naumis-Peña, Mirtha Rauch, Mariana Ramos Crivelente, Raissa Resende de Moraes, Jorge Revez, Riccardo Ridi, Somayeh Roshan, Clarissa Schmidt, Negin Shokrzadeh Hashtroudi, Richard P. Smiraglia, Mariângela Spotti Lopes Fujita, Adriana Suárez-Sánchez, Erik Stubkjwær, Rick Szostak, Seyed Mahdi Taheri, Marc Tanti, Ana Lúcia Terra, Amanda Terrero Trinquete, Natalia Bolfarini Tognoli, Jéssica Beatriz Tolare, Raquel Torrão Valentim, Hyung Wook Choi and Mohsen Haji Zeinolabedini.
Mapping Bit to Symbol Unpredictability with Application to Galileo Open Service Navigation Message Authentication

<h3>Abstract</h3> This paper investigates the distribution of unpredictable symbols in the open service navigation message authentication (OSNMA) scheme, which introduces cryptographic elements into the Galileo I/NAV message. Prior work has described the forward estimation attack (FEA; Curran &amp; O’Driscoll, 2016), that takes advantage of the forward error correction (FEC) employed by the Galileo E1 OS to ensure that a spoofed receiver correctly decodes the I/NAV message, even if it has been generated with errors in some symbols. In order to defend against such an attack, the receiver can re-encode the navigation message into symbols and compare the symbol error rates for those symbols that are predictable and those that are not. In order to perform this, it is first necessary to know which symbols are unpredictable. This paper presents in detail how this can be achieved, including the impact of the cyclic redundancy check (CRC) on symbol unpredictability.
Central bank digital currency: Advising the financial services industry

The teaching case focuses on central bank digital currency, or CBDC, which would be a new kind of government-issued digital currency. Currently, money already flows around the world through electronic circuits. Private/non-government digital currencies such as cryptocurrencies are decentralised, unregulated and highly volatile. Unlike the private digital money, CBDC would be centralised and controlled digital money. CBDC would provide a stable means of exchange amongst the citizens and businesses as it would be controlled by the central bank and backed by the government. CBDC could be programmed, transferred and traced more easily and at a lower cost. Through this case, students will get the opportunity to understand the advantages, risks and challenges of CBDC and how CBDC is different from the existing digital currencies such as Bitcoin, stable-coins and Diem. The efficient integration of CBDC with existing banking and payment systems to ensure flawless operations is a vital success factor for a country to embrace CBDC. The digital system’s simplified administrative and regulatory requirements will also assist governments in significantly lowering operational and technology maintenance expenses. At the same time, a CBDC could threaten the commercial banks, allowing the government to communicate directly with CBDC holders. Through this case, students will learn about different models that can be used to implement and integrate the CBDC system.
Themis

We propose an on-site voting system Themis, that aims at improving security when local authorities are not fully trusted. Voters vote thanks to voting sheets as well as smart cards that produce encrypted ballots. Electronic ballots are systematically audited, without compromising privacy. Moreover, the system includes a precise dispute resolution procedure identifying misbehaving parties in most cases.
Evaluating the development of reflexive personality skills in the conditions of distance educational technologies

Introduction. The article examines the formation of cognitive foundations of individual’s intellectual potential and their ability to reflect and introspect. The purpose of the study is to determine and evaluate the characteristic features of the development of reflexive personality skills in the conditions of distance educational technologies. Materials and Methods. The methodology of the research includes a conceptual analysis and reviewing research papers focusing on cognitive development of the individual, the assessment of this development in a distance educational environment, and the specifics of the manifestation of reflexive cognitive processes. The philosophy of education as a methodological basis of the research involves the identification, evaluation and generalization of the main features of the development of reflexive individual skills in the conditions of distance educational technologies. Results. The authors consider reflection in the education system, on the one hand, as a process of understanding knowledge and information through study and comparison, on the other hand, as a principle of human thinking aimed at self-understanding and self-examination. The main content of a person's reflexive ability as a cognitive ability for critical self-examination is presented: developed human abilities to recognize and solve complex mental problems, developed imagination; an aptitude to introspection and critical thinking. The authors have identified the key problems in the development of reflexive personality skills in the conditions of distance educational technologies. They include a decrease in the quality of assimilation and understanding of information and knowledge; difficulties in retaining attention and implementing reflection in general; problems in the formation and implementation of personality self-organization, the development of self-control skills, the development of uncertainty in the positive result of cognitive activity and in their own capabilities. It is emphasized that the dissonance in the development of reflexive personality skills in the conditions of distance learning technologies contributes to a restrained attitude to online learning. Conclusions. The authors summarize the main features of the development of reflexive personality skills in the conditions of distance educational technologies.
Security Issues in Situational Awareness: Adversarial Threats and Mitigation Techniques

Situational awareness (SAW) is of paramount significance for successful operations in many domains, such as surveillance, humanitarian, and search and rescue missions. SAW, however, is susceptible to adversarial attacks. This article contemplates adversarial threats and attacks on SAW systems and discusses various mitigation approaches.
Blocked or Broken? Automatically Detecting When Privacy Interventions Break Websites

A core problem in the development and maintenance of crowdsourced filter lists is that their maintainers cannot confidently predict whether (and where) a new filter list rule will break websites. The enormity of the Web prevents filter list authors from broadly understanding the compatibility impact of a new blocking rule before shipping it to millions of users. This severely limits the benefits of filter-list-based content blocking: filter lists are both overly conservative (i.e. rules are tailored narrowly to reduce the risk of breaking things) and error-prone (i.e. blocking tools still break large numbers of sites). To scale to the size and scope of the Web, filter list authors need something better than the current status quo of user reports and manual review, to stop breakage before it has a chance to make it to end users. In this work, we design and implement the first automated system for predicting when a filter list rule breaks a website. We build a classifier, trained on a dataset generated by a combination of compatibility data extracted from the EasyList filter project and novel browser instrumentation, and find that our classifier is accurate to practical levels (AUC 0.88). Our open-source system requires no human interaction when assessing the compatibility risk of a proposed privacy intervention. We also present the 40 page behaviors that most predict breakage in observed websites.
Advancing Nursing Research Through Interactive Data Visualization With R Shiny

Scientific data visualization is a critical aspect of fully understanding data patterns and trends. To date, the majority of data visualizations in nursing research – as with other biomedical fields – have been static. The availability of electronic scientific journal articles (which are quickly becoming the norm) has created new opportunities for dynamic and interactive data visualization which carry added cognitive benefits and support the ability to understand data more fully. Therefore, here we highlight the benefits of R, an open-source programming language, for scientific data visualization, with a specific focus on creating dynamic, interactive figures using the R shiny package. For R users, we have included a tutorial with example code to create three increasingly complex shiny applications. For individuals more interested in understanding the potential of R shiny as an innovative tool to interact with research data, we have included links to online versions of the examples that do not require any programming or R experience. We believe that widespread adoption of dynamic and interactive scientific data visualization will further support nurse scientists’ higher-level mission of advancing our understanding of health and wellness of individuals and communities.
N-Cubic Sets Applied to Linear Spaces

The concept of N-fuzzy sets is a good mathematical tool to deal with uncertainties that use the co-domain [−1, 0] for the membership function. The notion of N-cubic sets is defined by combining interval-valued N-fuzzy sets and N-fuzzy sets. Using this N-cubic sets, we initiate a new theory called N-cubic linear spaces. Motivated by the notion of cubic linear spaces we define P-union (resp. R-union), P-intersection (resp. R-intersection) of N-cubic linear spaces. The notion of internal and external N-cubic linear spaces and their properties are investigated.