Reviewerly/exHarmony huggingface dataset:
Using Visualization to Build Transparency in a Healthcare Blockchain Application

With patients demanding services to control their own health conditions, hospitals are looking to build agility in delivering care by extending their reach into patient and partner ecosystems and sharing relevant patient data to support care continuity. However, sharing patient data with several external stakeholders outside a hospital network calls for the development of a digital platform that is trusted by both hospitals and stakeholders, given that there is often no single entity supporting such coordination. In this paper, we propose a methodology that uses a blockchain architecture to address the technical challenge of linking disparate systems used by multiple stakeholders and the social challenge of engendering trust by using visualization to bring about transparency in the way in which data are shared. We illustrate this methodology using a pilot implementation. The paper concludes with a discussion and directions for future research and makes some concluding comments.
Digital Forensics Investigation Jurisprudence: Issues Of Admissibility Of Digital Evidence

Digital Forensics investigations represent the science and legal process of investigating cybercrimes and digital media or objects to gather evidence. The digital evidence must prove that it has been used to commit a crime or used to gain unauthorized access. Digital Forensics investigations jurisprudence is the theory and philosophy of the study of law and the principles upon which a law is based. For digital evidence to appear at court and be legally admissible, the evidence must be authentic, accurate, complete, and convincing to the jury. Presenting digital forensic evidence at court has proved to be challenging, due to factors such as inadequate chain of custody, not maintaining legal procedures and inadequate evidential integrity. Following legal procedures in evidence gathering at a digital crime scene is critical for admissibility and prosecution. However, inadequate evidence gathering and maintaining accuracy, authenticity, completeness has prevented many cases to be inadmissible at court. This paper aims to discuss digital forensics investigations jurisprudence and the issues of authentic, accurate, complete, and convincing evidence leading to inadmissibility at court. To achieve the applicability of the study, we highlight the legal and technical factors required to harmonize these issues and how it could be addressed.
Memes as the Phenomenon of Modern Digital Culture

The article analyzes an Internet meme as the newest information product of the society and a result of its intellectual and artistic practices. The analysis of the role of the Internet memes, created by means of the popular artistic images modification in the modern digital culture is made. Such methods as semiotic and hermeneutic analysis of the Internet memes are used in the research work. The authors seek to explore the reasons for the popularity of memes in the processes of symbolic production and exchange in contemporary society and the modern digital culture, which is the purpose of this study. We consider that Internet meme created by using and modifying artistic images is a new phenomenon in human public life and new type of communication. As a hypothesis, a distinctive feature of the Internet meme is the surprise and laughter it causes in the “man of the Internet”. The main result of this article is the analysis the role of Internet memes in the newest information space and their specific features as a special information product of the modern digital culture. The authors draw attention to the popularity of Internet memes caused by an increase in information chaos in the modern symbolic production and exchange and, therefore, a person’s confusion when meeting a large number of information.
The Correlation of Digital Literation and STEM Integration to Improve Indonesian Students’ Skills in 21st Century

Dynamic human development in the 21st Century demands students to make innovations in the world of education. Students are required to have 21st-century skills, namely creativity, critical thinking, collaboration, and communication. In contrast, teachers are required to have skills that must be mastered. The six capabilities are mastery of knowledge/content, mastery of 21st-century pedagogy, the ability of expertise in the development and achievement of students and give support, mastery of learning psychology skills; having counseling skills; and competence in using information technology and media. This article aims to discuss issues related to how to improve the ability of teachers and students to master 21st-century skills? Also, what extent is the role of digital literacy and STEM integration in improving 21st-century skills? This article using literature literacy methods sourced from international and national journals, books, and other relevant sources. Based on the study, it concluded that digital competence is not an absolute determinant of the development of 21st-century skills in students. Digital ability is a supporting factor for mastering 21st-century skills that should have been introduced and taught through schools. STEM learning with multidisciplinary mastery of science needs to be optimized in schools in Indonesia so that the expected learning objectives are in line with the demands of the 21st-century.
Automated Fraudulent Phone Call Recognition through Deep Learning

Several studies have shown that the phone number and call behavior generated by a phone call reveal the type of phone call. By analyzing the phone number rules and call behavior patterns, we can recognize the fraudulent phone call. The success of this recognition heavily depends on the particular set of features that are used to construct the classifier. Since these features are human-labor engineered, any change introduced to the telephone fraud can render these carefully constructed features ineffective. In this paper, we show that we can automate the feature engineering process and, thus, automatically recognize the fraudulent phone call by applying our proposed novel approach based on deep learning. We design and construct a new classifier based on Call Detail Records (CDR) for fraudulent phone call recognition and find that the performance achieved by our deep learning-based approach outperforms competing methods. Experimental results demonstrate the effectiveness of the proposed approach. Specifically, in our accuracy evaluation, the obtained accuracy exceeds 99%, and the most performant deep learning model is 4.7% more accurate than the state-of-the-art recognition model on average. Furthermore, we show that our deep learning approach is very stable in real-world environments, and the implicit features automatically learned by our approach are far more resilient to dynamic changes of a fraudulent phone number and its call behavior over time. We conclude that the ability to automatically construct the most relevant phone number features and call behavior features and perform accurate fraudulent phone call recognition makes our deep learning-based approach a precise, efficient, and robust technique for fraudulent phone call recognition.
Heuristic Global Optimization of an Adaptive Fuzzy Controller for the Inverted Pendulum System: Experimental Comparison

In this paper an adaptive fuzzy controller is proposed to solve the trajectory tracking problem of the inverted pendulum on a cart system. The designed algorithm is featured by not using any knowledge of the dynamic model and incorporating a full-state feedback. The stability of the closed-loop system is proven via the Lyapunov theory, and boundedness of the solutions is guaranteed. The proposed controller is heuristically tuned and its performance is tested via simulation and real-time experimentation. For this reason, a tuning method is investigated via evolutionary algorithms: particle swarm optimization, firefly algorithm and differential evolution in order to optimize the performance and verify which technique produces better results. First, a model-based simulation is carried out to improve the parameter tuning of the fuzzy systems, and then the results are transferred to real-time experiments. The optimization procedure is presented as well as the experimental results, which are also discussed.
Fostering Supply Chain Integration through Blockchain Technology: A Study of Malaysian Manufacturing Sector

The study aims to identify the role of the blockchain-based supply chain in supply chain integration. The study also aims to investigate the role of various factors that possibly mediate the relationship between blockchain-based supply chain management and supply chain integration. The study adopted a twofold mixed-method approach—sequential explanatory— to attain the objectives. In the first phase, data were collected from Malaysian electrical and electronic firms, and by applying PLS-SEM, hypothesized relationships were examined. In the second phase, through a qualitative study, the results of the first phase are discussed with the industry experts to seek their expert opinion. In doing so, the semi-structured interview from seven experts, selected through purposive sampling, were conducted. The findings of both inquiries reveal that blockchain technology has significant potential to enhance the integration between numerous actors in multi-level supply chains while ensuring the transparency and traceability in the transactions. Moreover, the findings reveal that certain pre-requisites are missing at the moment in the Malaysian manufacturing sector, which are inevitable to be met, before implementing the block-chain based supply chain. Sophisticated infrastructure which could suffice the blockchain-based technologies implementation in supply chains and the absorptive capacity is prominent amongst them. Consequently, it is recommended that appropriate training should be given to employees in order to cater to the requirement of the technical skills needed to handle such advanced technology. Further, continuous infrastructural investments should be made to implement blockchain-based technology in the business ecosystem of Malaysia at the government level.
Solving longest common subsequence problems via a transformation to the maximum clique problem

Longest common subsequence problems find various applications in bioinformatics, data compression and text editing, just to name a few. Even though numerous heuristic approaches were published in the related literature for many of the considered problem variants during the last decades, solving these problems to optimality remains an important challenge. This is particularly the case when the number and the length of the input strings grows. In this work we define a new way to transform instances of the classical longest common subsequence problem and of some of its variants into instances of the maximum clique problem. Moreover, we propose a technique to reduce the size of the resulting graphs. Finally, a comprehensive experimental evaluation using recent exact and heuristic maximum clique solvers is presented. Numerous, so-far unsolved problem instances from benchmark sets taken from the literature were solved to optimality in this way.
AlphaGo Policy Network: A DCNN Accelerator on FPGA

The game of GO has long been regarded as the most challenging game for artificial intelligence because of its enormous search space and the difficulty of evaluating its board positions. In early 2016, the defeat of Lee Sedol by AlphaGo became the milestone of artificial intelligence. AlphaGo's success lies in that it efficiently combines policy and value networks with Monte Carlo tree search (MCTS). And these deep convolutional neural networks (DCNNs) are trained by the combination of supervised learning and reinforcement learning. However, large convolution operations are computationally-intensive and typically require a powerful computing platform, for example, a graphics processing unit (GPU). Therefore, it is challenging to apply DCCNs in resource-limited embedded systems. Field programmable gate array (FPGA) is proposed to be an appropriate solution to implement real-time DCCNs models. However, the limited bandwidth and on-chip memory storage are the bottlenecks for DCCNs acceleration. In this article, an AlphaGo Policy Network is designed, and efficient hardware architectures are proposed to accelerate the DCCN model. The accelerator can be fit into different FPGAs, providing the balancing between processing speed and hardware resources. As an example, the AlphaGo Policy Network is implemented on Xilinx design suite VCU118, and the results show that our implementation achieved a performance of 3036.32 GOPS and achieved up to 56x speedup compared to CPU and 22.4x speedup compared to GPU.
Into the Unknown: Active Monitoring of Neural Networks

Neural-network classifiers achieve high accuracy when predicting the class of an input that they were trained to identify. Maintaining this accuracy in dynamic environments, where inputs frequently fall outside the fixed set of initially known classes, remains a challenge. The typical approach is to detect inputs from novel classes and retrain the classifier on an augmented dataset. However, not only the classifier but also the detection mechanism needs to adapt in order to distinguish between newly learned and yet unknown input classes. To address this challenge, we introduce an algorithmic framework for active monitoring of a neural network. A monitor wrapped in our framework operates in parallel with the neural network and interacts with a human user via a series of interpretable labeling queries for incremental adaptation. In addition, we propose an adaptive quantitative monitor to improve precision. An experimental evaluation on a diverse set of benchmarks with varying numbers of classes confirms the benefits of our active monitoring framework in dynamic scenarios.
Pose-Guided Person Image Synthesis in the Non-Iconic Views

Generating realistic images with the guidance of reference images and human poses is challenging. Despite the success of previous works on synthesizing person images in the iconic views, no efforts are made towards the task of poseguided image synthesis in the non-iconic views. Particularly, we find that previous models cannot handle such a complex task, where the person images are captured in the non-iconic views by commercially-available digital cameras. To this end, we propose a new framework - Multi-branch Refinement Network (MR-Net), which utilizes several visual cues, including target person poses, foreground person body and scene images parsed. Furthermore, a novel Region of Interest (RoI) perceptual loss is proposed to optimize the MR-Net. Extensive experiments on two non-iconic datasets, Penn Action and BBC-Pose, as well as an iconic dataset - Market-1501, show the efficacy of the proposed model that can tackle the problem of pose-guided person image generation from the non-iconic views. The data, models, and codes are downloadable from https://github.com/loadder/MR-Net.
ReLU Networks Are Universal Approximators via Piecewise Linear or Constant Functions

This letter proves that a ReLU network can approximate any continuous function with arbitrary precision by means of piecewise linear or constant approximations. For univariate function [Formula: see text], we use the composite of ReLUs to produce a line segment; all of the subnetworks of line segments comprise a ReLU network, which is a piecewise linear approximation to [Formula: see text]. For multivariate function [Formula: see text], ReLU networks are constructed to approximate a piecewise linear function derived from triangulation methods approximating [Formula: see text]. A neural unit called TRLU is designed by a ReLU network; the piecewise constant approximation, such as Haar wavelets, is implemented by rectifying the linear output of a ReLU network via TRLUs. New interpretations of deep layers, as well as some other results, are also presented.
A novel classification approach based on integrated connection cloud model and game theory

The classification of problems under uncertainty environments involves the precise description and weighting of fuzzy and random indicators. Although the one-dimensional normal cloud model can effectively handle the fuzziness and randomness of the indicator in an infinite interval, it may ignore the changing tendency of the rating boundary, and become complex with the increment of indicator and sample numbers. To overcome above shortcomings, a novel classification based on the integrated connection cloud model and game theory was proposed here to depict fuzzy indicators randomly distributing in finite intervals and simultaneously embody the importance and inherent information of indicators in harmony. Namely, the connection numbers theory and the game theory based combination weighting method were first utilized to aggregate the connection cloud model of the individual indicator. Next, integrated connection clouds were simulated to express the interval-valued classification standards, and the connection degrees were calculated to identify the rank from the identity, discrepancy, and contrary relationships. Finally, the validity and feasibility of the approach proposed here were further verified by example application to the classification of surrounding rock stability and comparisons with other methods. Results indicate that the integrated cloud model based classification approach presents a precise description of fuzzy indicators distributing randomly in the asymmetrical intervals and better computational efficiency relative to the classification method using the one-dimensional cloud model. It also minimizes errors caused by the multiplication singularity point, human subjectivity and neglect of the intrinsic information of indicators.
A Socio-Seismology Experiment in Haiti

Earthquake risk reduction approaches classically apply a top-down model where scientific information is processed to deliver risk mitigation measures and policies understandable by all, where end-users are shielded from the initial, possibly complex, information. Alternative community-based models exist but are rarely applied at a large scale and rely on valuable, but non-scientific, observations and experiences of local populations. In spite of risk reduction efforts based on both approaches, changes in behavior or policies are slow or even non-existent, in particular in developing countries. Here we report on the initial stage of a project that aims at testing, through a participatory seismology experiment in Haiti – a country struck by a devastating earthquake in January 2010 – whether public or community involvement through the production and usage of seismic information can improve earthquake awareness and, perhaps, induce grassroots protection initiatives. This experiment is made possible by the recent launch of very low-cost, plug-and-play, Raspberry Shake seismological stations, the relative ease of access to the internet even in developing countries such as Haiti, and the familiarity of all with social networks as a way to disseminate information. Our early findings indicate that (1) the seismic data collected is of sufficient quality for real-time detection and characterization of the regional seismicity, (2) citizens are in demand of earthquake information and trust scientists, even though they appear to see earthquakes through the double lens of tectonics and magic/religion, (3) the motivation of seismic station hosts has allowed data to flow without interruption for more than a year, including through a major political crisis in the Fall of 2019 and the current COVID19 situation. At this early stage of the project, our observations indicate that citizen-seismology in a development context has potential to engage the public while collecting scientifically-relevant seismological information.
Model of the On-Line Course for Training Master Students Majoring in Mathematics for Teaching at University

This article considers the issue of on-line training of Master students, majoring in Mathematics (Speciality code in Ukrainian educational system "014.04Secondary Education.Mathematics") and preparing them for teaching at University.The research papers and resources, focusing on the vocational bent of on-line training of undergraduate students majoring in Mathematics have been analyzed in this paper.The present paper describes designing the structure and components for the on-line course "Methods for Teaching Mathematics to Students at Technical Universities", which is in open access on the educational platform "Higher School Mathematics Teacher".This article presents the analysis of the survey results, in which 107 students in their first and second year of studying took part, which allowed defining the content of the course.The model is presented as a polyhedron, vertices, faces and edges of which show structural elements of the model and relations between them.The paper also introduces the analysis of the internship syllabi at Ukrainian universities and aims at defining the criteria for assessing learning performance of the on-line course users.It also considers the impact of the course on the efficiency of training 112 Master students in their second year of study, when they do their assistant internship at technical universities.The qualitative and quantitative analysis of the results of the experiment proved the efficiency of introducing the course into the process of practical training of Master students.The interns who joined the course, showed a better understanding of how to motivate students when teaching Mathematics to them.They also showed better results in applying active learning tools and techniques for teaching Mathematics, as well as in managing the audience through information technologies and communication strategies.
Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training

INTRODUCTION: Deep learning-based Image compression achieves a promising result in recent years as compared with the traditional transform coding methodology.Autoencoder, an unsupervised learning algorithm with the input value as same as that of the output value, is considered in this research work for effective medical image reconstruction.OBJECTIVES: Medical data needs to be reconstructed without distorting the details present over it.A deep neural network that accepts the data and processes it to the other several layers and reconstructs that data is achieved by autoencoder.METHODS: Deep Autoencoder is implemented in this methodology as it has been considered for high dimensionality reduction.Layer by layer pretraining is achieved using an approximate inference algorithm called Deep Boltzmann Machine.RESULTS: The proposed method proves to be efficient when compared with the performance of the other autoencoders such as Deep Autoencoder with multiple Backpropagation (DA-MBP), Deep Autoencoder with RBM (DA-RBM) and Deep Convolutional Autoencoder with RBM (DCA-RBM).CONCLUSION: Performance metrics are measured in terms of Mean Square Error, Structural similarity Index and PSNR.
Blockchain and Cryptocurrencies

Cryptocurrencies are essentially digital currencies that use blockchain technology and cryptography to facilitate secure and anonymous transactions. Many institutions and countries are starting to understand and implement the idea of cryptocurrencies in their business models. With this recent surge in interest, we believe that now is the time to start studying these areas as a key piece of financial technology. The aim of this Special Issue is to provide a collection of papers from leading experts in the area of blockchain and cryptocurrencies. The topics covered in this Special Issue includes the economics, financial analysis and risk management with cryptocurrencies.