Reviewerly/exHarmony huggingface dataset:
A Compact Hardware Architecture for Bilateral Filter With the Combination of Approximate Computing and Look-Up Table

Bilateral filter (BF) is a widely applied method for image denoising due to the characteristic of edge-preserving. This brief presents a super-compact hardware architecture for the BF by a piecewise approximate computing algorithm. The contributions are summarized as follows: 1) The architecture significantly reduces the storage and the arithmetic logic for the piecewise approximated filter weights and can attain comparable smoothy and edge-preserving performance to the standard BF. 2) The BF is accelerated by the parallel pixel-level pipeline architecture and the LUT-based divider for normalization. 3) The synthesized result shows that this architecture, even on a low-cost XINILX Zynq-7000 FPGA, can reach real-time denoising at more than 30 frames/second for 8M-pixel ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3268 \times 2448$ </tex-math></inline-formula> ) videos at the maximum working frequency of 278 MHz with the power dissipation of only 168 mW.
Predicting Relapse in Pediatric Patients with Acute Lymphoblastic Leukemia Undergoing Stem Cell Transplant Using Interpretable Machine Learning—Applying Machine Learning Beyond 'Big Data'

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The Application of Blockchain Technology in the Financial Field

The shortcomings of today's credit mechanism are exposed and crises are everywhere. Today, the blockchain has become the most popular technology in the Internet finance field. It has rushed into all walks of life with the power of breaking a bamboo, and is shining around the world. According to the basic functions of blockchain technology (BT), such as information tampering. Information transparency has certain security guarantees. This kind of security guarantee can ensure the reliability of the financial information system to a large extent and reduce financial fraud. The aim of this article is to study the application of regional chain technology in the financial field (FF). This article is based on the primary principles of blockchain, information asymmetry theory and moral theory. Through questionnaire surveys, corporate financial personnel, middle and high-level managers and other authorities, university researchers, scholars and other personnel with experience in the implementation and application of financial informationization in various industries to ensure the right and precise results of this survey with authority and credibility. It is helpful to improve the quality problems in the financial information processing process, and helps the company implement effective real-time and automatic control. Experimental investigations and studies have shown, Financial information quality issues have the highest score at 4.39 points, while management and control issues and security issues have scores of 4.26 and 4.22 points, respectively. That there is no doubt that the blockchain financial system(FS) has many advantages, but because the BT is still in its infancy, the research time is relatively short, and there are many unresolved and unidentified problems and deficiencies.
Semantic Information Extraction in Archaeology: Challenges in the Construction of a Portuguese Corpus of Megalithism

Archaeological records are a central part of this discipline research. It is essential that the records include the relationship between its parts to allow better understanding among man and machine. Here we present the challenges of applying NLP and Information Extraction to Archaeological data with a case study: Megalithism.
Formación del profesorado universitario en Competencia Digital: análisis con métodos de investigación correlacionales y comparativos

The global crisis caused by the current COVID19 pandemic has placed the distance teaching-learning process at the centre of educational practices at all educational stages, making clear the importance of training university teaching staff in digital competences. Given this approach, the purpose of this study is to analyse the use of ICT resources focused on teaching, learning and research tasks by Spanish Higher Education teaching staff according to gender, as well as the influence of certain academic variables on the use of these technological resources through correlational analysis. To this end, a non-experimental design with the UTIC-EEI instrument (use of ICT resources for teaching, assessment and research) was used with a sample of 867 university teachers. The results show a moderate level of mastery of digital tools and resources for teaching, assessment and research. These results require reflection on the part of educational organisations in order to establish training plans that meet these needs. For this reason, several lines of action related to teacher training with not only technical but also pedagogical content are proposed.
Implementation Strategies for Mutable Value Semantics.

Mutable value semantics is a programming discipline that upholds the independence of values to support local reasoning.In the discipline's strictest form, references become second-class citizens: they are only created implicitly, at function boundaries, and cannot be stored in variables or object fields.Hence, variables can never share mutable state.Unlike pure functional programming, however, mutable value semantics allows part-wise in-place mutation, thereby eliminating the memory traffic usually associated with functional updates of immutable data.This paper presents implementation strategies for compiling programs with mutable value semantics into efficient native code.We study Swift, a programming language based on that discipline, through the lens of a core language that strips some of Swift's features to focus on the semantics of its value types.The strategies that we introduce leverage the inherent properties of mutable value semantics to unlock aggressive optimizations.Fixed-size values are allocated on the stack, thereby enabling numerous off-the-shelf compiler optimizations, while dynamically sized containers use copy-on-write to mitigate copying costs.
In-situ 3D shape measurement system for a high-temperature object in a plasma wind tunnel via DMD-projection laser structured light and adaptive speckle filtering

Abstract The evaluation of an object’s 3D shape change during ablation in wind tunnel testing is critical for the optimization of the thermal protection system. However, strong thermal radiation and a harsh environment cause challenges to the traditional measurement methods. A single camera digital micro-mirror device (DMD) projector-structured laser light system is proposed for in-situ 3D shape measurement in a wind tunnel test. A customized DMD combined with a laser source is devised for projecting concentrated full-field high-power structured light stripes. To filter laser speckle noise in projected chessboard images for model calibration, an auto-window Kuwahara adaptive filter is put forward. The 3D shape of a blunt-head cylinder was in-situ measured in a plasma wind tunnel which reached a maximum temperature of 1950 °C. The diameter reconstruction error was within 0.85% (0.17/20.03 mm). With projected laser light and multi-step filters, the ablation process of the front face of a dome-top cylinder at 1950 °C was clearly observed at a long working distance over 1.5 m.
Multi-interest Sequence Modeling for Recommendation with Causal Embedding

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Multi-interest Sequence Modeling for Recommendation with Causal EmbeddingCaiqi Sun, Penghao Lu, Lei Cheng, Zhenfu Cao, Xiaolei Dong, Yili Tang, Jun Zhou, and Linjian MoCaiqi Sun, Penghao Lu, Lei Cheng, Zhenfu Cao, Xiaolei Dong, Yili Tang, Jun Zhou, and Linjian Mopp.406 - 414Chapter DOI:https://doi.org/10.1137/1.9781611977172.46PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Recent methods in sequential recommendation focus on learning multi-interest embedding vectors from a user's behavior sequence for the next-item recommendation. However, behavior sequential data may result from users' conformity towards popular items, which entangles users' real interests and tends to recommend popular items by using interest embeddings. In this paper, we propose a novel multi-interest framework with causal embedding for sequential recommendation, called MiceRec. Specifically, we first obtain two embedding layers from behavior sequence by assigning items with separate embeddings for interest and conformity, then extract multiple pure interests from one embedding layer, while the other for users' conformity extraction. According to the colliding effect of causal inference, we mine cause-specific data for training causal embeddings. Our framework significantly outperforms state-of-the-art solutions on two real-world datasets1. We further demonstrate that the learned multi-interest embeddings successfully separate from each other, and show that conformity information is almost squeezed out from interest embeddings. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737Key words:recommender system, sequential recommendation, causal embedding, multi-interest framework
Decidable Problems in Substitution Shifts

In this paper, we investigate the structure of the most general kind of substitution shifts, including non-minimal ones and allowing erasing morphisms. We prove the decidability of all the properties of these morphisms with respect to the shift space generated by iteration, such as aperiodicity, recognizability or minimality.
Multilingual Dissemination of Corporate Websites from the Healthcare Sector

Companies have nowadays numerous opportunities to open new markets and expand their business thanks to globalization and the development of new technologies. Among the resources and tools available, multilingual corporate websites can be great allies for enterprises to reach clients globally at a considerably reduced cost. However, companies, especially small and medium-sized ones, have to face challenges such as language and cultural barriers when starting their internationalization processes. This paper aims to analyze the web presence of Spanish companies from the healthcare sector along with the multilingual dissemination of their corporate information. To this aim, a sample of 385 Spanish companies from the healthcare sector has been analyzed. Results show that there is still a long way to go, as less than a half of the analyzed companies had a website and only 27% of them offered their contents translated into other languages. In addition, correlations are found between the size of the companies and having a corporate website as well as offering its content translated.
Applications of Identity-Based Cryptography in Smart Home and Healthcare: A Recent Review

Advancement in technology has led to an exponential growth of Internet of Things (IoT) devices. IoT devices make our lives easier by providing all the advantages in relation to automation, convenience, and efficiency. The only caveats of IoT nodes are that they are resource constrained in terms of computation, energy, storage, and bandwidth. Also, their interaction with humans and Internet connectivity make them vulnerable to various security threats. Hence, security is a fundamental requirement of the IoT design but it should also be efficient in terms of resource usage. Transport layer security (TLS) is a protocol that has substantiated secure communication from one end to the other. TLS uses public key infrastructure or PKI to secure information over the Internet. But it is inefficient to use PKI in resource-constrained devices like IoT due to the need of storing certificates. Identity-based cryptography was introduced to relieve this domain from the overhead of public key certificate storage and management. This paper presents a comprehensive survey of identity-based cryptography in IoT technologies and security issues with a focus on the smart home and healthcare environments.
FORMATION OF VALUES OF DIGITAL EDUCATION AND DIGITAL PERSONS IN DIGITALIZED SOCIETY

The urgency of the formation and development of education in a digital society is determined by information and computer technology, software and computer programs that are beginning to penetrate into education – in school classrooms, universities, educational process aimed at forming digital competencies in the digital society. The aim of the article is to conceptualize the future development of education in the digital society. Objectives of the study: 1) to analyze the model of digital education, which changes the rules of the game in the educational process; 2) to find out the process of formation of a digital person who becomes open, mobile, meaningful, adapted; 3) to explore the role of values - big data (BIG DATA) in the reformatting of digital education and the digital person; 4) to reveal the digitalization of education as a powerful trend in the formation and development of a digital society. The object of research is the formation and development of education in a digital society. The subject of education is the influence of digitalized society on the formation and development of education. Research methodology – general philosophical methods – analysis and synthesis, generalization, systemic and structural-functional analysis, anthropological, axiological, phenomenological, to show the formation and development of education in a digitalized society. It is concluded that digital (convergent) technologies offer completely different ways to solve problems of quality and quantity. Digital technologies need to do more for modern education, its transformation and modernization. Modern intelligence will help high school to create a unique educational digital space. The impact of digitalization on education is and will be even stronger in the future, driven by algorithms, artificial intelligence, digital technologies that change this world. Education changes the physical and mental landscape of the individual and the ways he works in digital society. The values of digital education and the digital person in a digital society are linked to the formation of a digital culture determined by big data, which gives educational transformations such an acceleration that change the fundamentals of business, trade, public services, affecting every aspect of our lives.
Leveraging Management of Customers’ Consent Exploiting the Benefits of Blockchain Technology Towards Secure Data Sharing

Abstract Open banking holds the potential of expanding traditional banking data flows, placing the customer at its core and in control of their banking data, including their personal information. Consent management enables the tracking, monitoring and managing the personal data lifecycle in a GDPR compliant manner, and improves customers’ control over their data, empowering them to manage their consent throughout its lifecycle. However, traditional technologies have failed to become a key enabler of trust, due to multiple security/data tampering incidents. This chapter introduces a blockchain-empowered Consent Management System (CMS). It aims at presenting the design and implementation of a robust CMS, enabling the sharing of customers’ consent, thus facilitating the exchange and the utilization of customer data, across different banking institutions. The proposed CMS implementation will enable the financial institutions to effectively manage and share their customers’ consents in a transparent and unambiguous manner, ensuring compliance to PSD2 and GDRP, while lowering the barriers of secure data sharing.
Entanglement Distribution in Installed Fiber with Coexisting Classical Light for Quantum Network Applications

We show polarization entangled photons coexisting with milliwatt power classical light over 45.6 km of installed optical fiber. The entanglement source has a built-in alignment signal for quantum transmitter-receiver polarization basis alignment.
High Accuracy Predictive Model on Breast Cancer Using Ensemble Approach of Supervised Machine Learning Algorithms

This research article is based on the ensemble approach of different supervised machine learning algorithms to identify the early stages of breast cancer problems. The World Health Organization (WHO) approved that existence of the breast tumor is high for the women in developing countries and it is one of the significant research issues in current scenario in the real world. In this research article researcher used the 30 features to extract and predict accurate prediction on breast cancer using ensemble approach of supervised machine learning algorithms. It is a great challenge in designing a machine learning model to evaluate the performance of the classification of breast tumor. Implementing an efficient classification methodology will support in resolving the complications in analyzing breast cancer. This proposed model employs four machine learning (ML) algorithms Decision tree classifiers, Random Forest KNN, and support vector machine (SVM) and found support vector machine (SVM) which given the high accuracy of 0.976688 among them for the categorization of breast tumor in women. This classification includes the two levels of disease as benign or malignant. The researcher also used the other parameters and evaluated this predictive model using Precision, Recall and F1-Score. The data analysis report is proved that this predictive model is having 98% accuracy level to predict the cancer at early stages in women.
Private and Online Learnability Are Equivalent

Let H be a binary-labeled concept class. We prove that H can be PAC learned by an (approximate) differentially private algorithm if and only if it has a finite Littlestone dimension. This implies a qualitative equivalence between online learnability and private PAC learnability.
Application of Risk Assessment Method to Local Government Security Models

The importance of risk assessment of information systems is increasing. It is also necessary to request the security model of Japanese local governments to determine a combination of countermeasures with a good balance between security risk, countermeasure cost, and work burden. In order to obtain the optimum combination of information system countermeasures for local governments, we have developed an extended event tree and defense tree combined (extended EDC) method, which is an improvement of the previously developed EDC method, and developed a support program for the extended EDC. By applying these countermeasures to model cases of information systems of local governments in Japan, we were able to determine the optimal combination of countermeasures in various cases. As a result, the EDC method was demonstrated to be highly expandable, and the extended EDC method was shown to be an appropriate method for assessment of information systems for local governments.
Youtube Data Analysis using Linear Regression and Neural Network

The popularity of YouTube provides an effective way to propagate epidemic prevention knowledge by analyzing the video preferences of viewers from different locations. However, it is challenging to analyze video preferences due to the dispersed geographical locations of the YouTube viewers and the indistinguishable video categories and subcategories. This paper combines linear regression and neural networks to unravel both geographical and categorical difficulties and improve the accuracy of task-solving models. First, the YouTube dataset and extract variables are preprocessed, including categories, subcategories, countries, number of subscribers, and view counts of each YouTubers. Then, linear regression and neural networks are trained to classify and find the correlation between these variables. Finally, Matplotlib, Google chart, and Tableau are utilized to visualize the result based on video categories and geographical locations. The accuracies of linear regression and neural network models are verified through the R-squared estimation. Both linear regression and neural network models show the trending types of videos and a positive correlation between the number of viewers and subscribers. The experimental results show a remarkable user’s tendency of watching films and listening to music, a concentration of YouTube users from India and the U.S., and propose targeted COVID-19 prevention propaganda based on the above two characteristics.
On the use of hardware accelerators in QC-MDPC code-based cryptography

Public-key cryptography (PKC) allows exchanging keys over an insecure channel without sharing a secret key. However, quantum computers threaten to break traditional PKC, thus, to mitigate such risk, post-quantum cryptography (PQC) aims to develop cryptosystems that are secure against attacks from quantum and classical computers. BIKE [1] is a key encapsulation mechanism (KEM) based on quasi-cyclic moderate-density parity-check (QC-MDPC) codes that is a candidate within the NIST standardization process to identify a set of PQC algorithms [4]. Figure 1 depicts the key exchange between two client and server nodes, which requires the sequential execution of the key generation, encapsulation, and decapsulation KEM primitives. Key generation and decapsulation are performed on the client side, while encapsulation is carried out by the server. Despite the vast literature targeting efficient hardware support for BIKE, each proposal delivered computing platforms meant either to maximize performance or minimize resource utilization.
Network representation learning method embedding linear and nonlinear network structures

With the rapid development of neural networks, much attention has been focused on network embedding for complex network data, which aims to learn low-dimensional embedding of nodes in the network and how to effectively apply learned network representations to various graph-based analytical tasks. Two typical models exist namely the shallow random walk network representation method and deep learning models such as graph convolution networks (GCNs). The former one can be used to capture the linear structure of the network using depth-first search (DFS) and width-first search (BFS), whereas Hierarchical GCN (HGCN) is an unsupervised graph embedding that can be used to describe the global nonlinear structure of the network via aggregating node information. However, the two existing kinds of models cannot simultaneously capture the nonlinear and linear structure information of nodes. Thus, the nodal characteristics of nonlinear and linear structures are explored in this paper, and an unsupervised representation method based on HGCN that joins learning of shallow and deep models is proposed. Experiments on node classification and dimension reduction visualization are carried out on citation, language, and traffic networks. The results show that, compared with the existing shallow network representation model and deep network model, the proposed model achieves better performances in terms of micro-F1, macro-F1 and accuracy scores.
Cost-aware hybrid cloud scheduling of parameter sweep calculations using predictive algorithms

This paper investigates various techniques for scheduling the costs of parameter sweep calculations efficiently in a hybrid cloud environment. The combination of both a private and a public cloud environment integrates the advantages of being cost effective and having virtually unlimited scaling capabilities at the same time. To make an accurate estimate for the required resources, multiple prediction techniques are discussed. The estimation can be used to create an efficient scheduler which respects both deadline and cost. These findings have been implemented and tested in a Java-based cloud framework which operates on Amazon EC2 and OpenNebula. Also, we present a theoretical model to further optimise the cost by leveraging the Amazon Spot Market.