Reviewerly/exHarmony huggingface dataset:
The Promises and Pitfalls of a Blockchain Driven Tax System

Policymakers and international organizations such as the OECD defend new tax measures to increase tax compliance and decrease tax competition and tax evasion. The proposed new policies often change the nature or distribution of tax liabilities, for instance by changing the rate structure, introducing new taxable events, or redistributing taxing rights. In a divided political landscape, this article suggests another approach to simplify taxes and raise compliance. This article doesn’t intend to touch upon what we owe the government, but only regards how we execute the existing set of tax rules. Moreover, I will explore enhancing tax efficiency by altering the technological design that undergirds the tax system – by relying on blockchain technology. The main characteristics that currently undermine the functioning of the system are that tax payments are taxpayer driven, that payments occur after the fact, and that the nature of one’s tax liability is highly complex. Because of these three problems, governments miss huge amounts of tax revenue, they are paid much later than the taxable event, and taxpayers spend huge amounts of money and energy to be (non)compliant. Blockchain technology can hit not two but three birds with one stone – and solve these problems altogether. This article will show that, under a blockchain driven consumption tax, taxes are paid in an automated and automatic way – one that doesn’t rely on taxpayer initiative, in real time, i.e., simultaneously with the taxable event. Compliance costs can be radically reduced both for taxpayers. This article also reveals the costs of transforming to a blockchain-driven tax, and deals with the energy-consumption problem, the last-mile problem, the issue of false entry and the related concern of taxpayer privacy. The current tax system was designed in a pre-digital era, where financial institutions were hand-operated and essentially paper driven. Amid consensus that the current tax system is outdated, this article investigates a radically new way of levying, paying, and auditing taxes – whereby tax payments become a by-product of each transaction and are integrated within the business operations. If specific costs are borne and the proper technological choices are made, the blockchain driven tax would slash the costs of taxation, increase compliance, and enable lower overall tax rates, creating both stronger governments and better functioning economies.
Web search of software developers—Characteristics and tips

There is more and more software in the world and this software has to be developed. All the people who develop software can be regarded as software developers, not just the professionals. Naturally, they often perform web searches to support their development activity. The article, based on the pertinent literature and also on the author’s own experiences as a longtime software developer, discusses characteristics of software developers’ web searches and gives some recommendations and tips to increase the efficiency of their searches, especially complex, exploratory searches. To the author’s knowledge, no such summary combined with tips, aimed at software developers, has been published before. It has been written in the hope that software developers, such students and their teachers would find it useful.
Influence and implementation of Industry 4.0 in health care

With the advancement of technology, the world is changing and automating at a rapid pace. Digitization plays a major role in the automation of technology. In this context, Industry 4.0 shows how industrial production is developing along with the latest technology. In Industry 4.0, much manual work has been replaced by automated machines that can be controlled with developing technologies such as artificial intelligence (AI), big data, cloud computing, and so on. Various technologies have been introduced in the healthcare sector due to Industry 4.0. These include AI, three-dimensional printing, machine learning, cognitive systems, autonomous robots, autonomous vehicles, augmented reality, big data, Internet of Things (IoT), blockchain technology, and more. This chapter discusses the transformation of the healthcare industry in the context of Industry 4.0. It presents a detailed study on big data, IoT, and blockchain technology with different applications that can enhance what is known as Healthcare 4.0. The chapter includes three case studies that illustrate the use of innovation in Healthcare 4.0 to detect and diagnose disease using portable medical devices connected to the IoT.
A reconfigurable high-speed and low-complexity residue number system-based multiply-accumulate channel filter for software radio receivers

Purpose Filter design plays an essential role in most communication standards. The essential element of the software-defined radio is a channelizer that comprises several channel filters. Designing filters with lower complexity, minimized area and enhanced speed is a demanding task in currently prevailing communication standards. This study aims to propose an efficient reconfigurable residue number system (RNS)-based multiply-accumulate (MAC) channel filter for software radio receivers. Design/methodology/approach RNS-based pipelined MAC module for the realization of channel finite impulse response (FIR) filter architecture is considered in this work. Further, the use of a single adder and single multiplier for realizing the filter architecture regardless of the number of taps offers effective resource sharing. This design provides significant improvement in speed of operation as well as a reduction in area complexity. Findings In this paper, two major tasks have been considered: first, the RNS number conversion is performed in which the integer is converted into several residues. These residues are processed in parallel and are applied to the MAC-FIR filter architecture. Second, the MAC filter architecture involves pipelining, which enhances the speed of operation to a significant extent. Also, the time-sharing-based design incorporates a single partial product-based shift and add multiplier and single adder, which provide a low complex design. The results show that the proposed 16-tap RNS-based pipelined MAC sub-filter achieves significant improvement in speed as well as 89.87% area optimization when examined with the conventional RNS-based FIR filter structure. Originality/value The proposed MAC-FIR filter architecture provides good performance in terms of complexity and speed of operation because of the use of the RNS scheme with pipelining and partial product-based shift and adds multiplier and single adder when examining with the conventional designs. The reported architecture can be used in software radios.
Audience Design and Egocentrism in Reference Production During Human-Computer Dialogue

Current work supposes that people tend to adapt their language allocentrically, taking into account the perceived limitations of their partners, when talking to computers. Yet, debates in human-human dialogue (HHD) research suggest that people may also act egocentrically when producing language in dialogue. Our research aims to identify whether, similar to HHD, users also produce egocentric language within speech-based human-computer dialogue (HCD) interactions and how this behaviour compares to interaction with human dialogue partners. Through two controlled experiments using an adapted director-matcher task similar to those used in research on perspective-taking in psycholinguistics, we show that people do take the computer’s perspective into account less (i.e. behave more egocentrically) during HCD than in HHD (Experiment 1). However, this egocentric effect is eliminated when computers are framed as separate interlocutors rather than system tools and where differences in perspective are made salient, leading to similar levels of perspective-taking as with human partners (Experiment 2). We discuss the findings, emphasising potential explanations for this effect, focusing on how egocentric and allocentric production processes may interact, along with the impact of social roles and the division of labour in HCD as an underlying explanation for the effects seen.
Patterns on Logistic Map: Back to Front

The logistic map is normally calculated with a determinednumber of decimal places, which makes truncation androunding, common in your calculation. Thus, the dynamics of the logistic map observed computationally isgenerated only by the most significant digits, excludingthe least significant digits. In this article, the oppositeprocess was done: the last digits of the logistic map orbitsin chaotic regime were analyzed. With this approach,patterns with interesting behaviors were found. Thesepatterns are formed by fixed and periodic digits, whichhave a behavior similar to the fixed and periodic points ofdiscrete dynamical systems. Since fixed digits have stability and attraction phenomenon as well as fixed points.While periodic digits and periodic points present periodicdynamics, in periodic digits, the numbers presented in the period and the period size are well-defined.Thus, the logistic map has a sub-dynamic generated bythe digits in the orbit values.
Trustworthiness Models to Categorize and Prioritize Code for Security Improvement

The exploitation of software security vulnerabilities can have severe consequences. Thus, it is crucial to devise new processes, techniques, and tools to support teams in the development of secure code from the early stages of the software development process, while potentially reducing costs and shortening the time to market. In this paper, we propose an approach that uses security evidences (e.g., software metrics) to feed a set of trustworthiness models, which allow characterizing code from a security perspective. In practice, the goal is to identify the code units that are more prone to be vulnerable, thus helping developers to improve their code. A clustering-based approach is used to categorize the code units based on the combination of the scores provided by several trustworthiness models and taking into account the criticality of the code. To instantiate our proposal, we use a dataset of software metrics (e.g., CountLine, Cyclomatic Complexity) for files and functions of the Linux Kernel and Mozilla Firefox projects, and a set of machine learning algorithms to build the trustworthiness models. Results show that code that is more prone to be vulnerable can be effectively distinguished, thus demonstrating the applicability and usefulness of the proposed approach in diverse scenarios.
Privileged Information Learning with Weak Labels

Privileged information learning is proposed to construct a better classifier by incorporating some privileged knowledge. At present, most of the privileged information learning methods assume that the instance is accurately labeled. However, in real-world applications, an instance may be annotated by a number of labelers, and the labels obtained by these labelers are used to calculate the final label. Since the weight of each labeler is unknown, people always give an equal or random weight to each labeler. It leads to the ambiguous labels for instances, which are known as weak labels. In this paper, we consider the weak labels problem in privileged information learning problem, where the training data and privileged data is associated with ambiguous labels, rather than accurate labels. To tackle this problem, we propose a novel privilege information learning method with weak labels (PLWB). We assign each labeler a weight and these labeler weights are then incorporated into a privileged information learning process. An heuristic framework is proposed to train the privileged information learning model and update the labeler weights alternately. Numerous experiments have shown that PLWB can get better classification performance on weakly labeled data compared with state-of-the-art privileged information learning methods.
Joint Strong Edge and Multi-Stream Adaptive Fusion Network for Non-Uniform Image Deblurring

In this paper, we propose a new joint strong edge and multi-stream adaptive fusion network to achieve non-uniform motion deblurring. The edge map and the blurred map are jointly used as inputs of Edge Extraction Network (EEN) and Deblurred Network (DN) to complement the important edge information. The Multi-stream Adaptive Fusion Module (MAFM) adaptively fuses the edge information and features from the encoder and decoder to reduce feature redundancy to avoid image artifacts. Furthermore, the Dense Attention Feature Extraction Module (DAFEM) is designed to focus on the severely blurred regions of blurry images to obtain important recovery information. In addition, an edge loss function is added to measure the difference of edge features between the generated and clear images to further recover the edges of the deblurred images. Experiments show that our method outperforms currently public methods in PSNR and SSIM, and generates images with less blur and sharper edges.
Voice Enabled Application for the Visually-Impaired to Detect &amp; Recognize Indian Currency in Hindi and English Using Deep Learning

Life for the outwardly handicapped is regularly troublesome as they need to manage different issues that are simple for an ordinary individual to manage, one of them is cash related exchanges. Money acknowledgment has turned into a bulky assignment since the demonetization came to impact in India. As novel cash bank-notes are comparable in dimension, and a portion of the hoary money bank-notes are as yet being used, making two banknotes for one category available for use in India. Because of the present circumstance, it turns out to be truly challenging to recognize banknotes for the outwardly weakened. Along these lines, we propose a framework where the outwardly debilitated can recognize the money notes through a sound notice from a versatile application. In this work, we have fostered an informational index of almost 800 Indian cash banknotes and utilized two pre-prepared models, specifically Faster-RCNN and MobileNet SSDV2 to assess their presentation on our dataset to accomplish our essential objective. This paper represents the Hindi and English supported Indian Currency Detection Applications for the Visually Decreased using Deep-Learning.
Self-Admitted Technical Debt in Commit Messages: Comparing Java, Python, and R

Software systems, Open Source Systems (OSS) or commercial, are prone to (knowingly or unknowingly) incur Technical Debt (TD), a metaphor used to describe code that is ``not quite right." Although several studies approached Self-Admitted TD (SATD) in source code comments in Object-Oriented projects written predominantly in Java, identifying SATD in commit messages remains primarily unexplored, specifically in statistical/scientific projects written in R and Python. This study explores SATD in commit messages in OSS written in three languages: Java, Python, and R. It allows to contrast the findings (presence, types, and activities) between OO and statistical/scientific software. Our results indicate that SATD in R commit messages is more difficult to detect, given the ambiguous nature of the natural language used. Moreover, Code Debt remains the most discussed debt type, though Java's top-five debt types differ from Python and R. Traces of Algorithm Debt were detected in Python and R but not in Java. Finally, Repayment is the most common debt management activity across all the languages. However, Java has more Communication activity in the commit messages containing Test Debt compared to Python and R.The results of this study are crucial to understand the main differences in SATD between languages and, therefore, contributing to better debt management across different paradigms, closing a research gap, and acting as the baseline for future works.
Solarnet: A Single Stage Regression Based Framework for Efficient and Robust Object Recognition in Aerial Images

Object recognition and localization play a crucial role in aerial images and their applications. The aerial images are challenging due to the large aspect ratio, arbitrary orientation, variation in scales, non-uniform and cluttered object distribution. To address these challenges, we propose an efficient and robust model Simultaneous Object Localization and Recognition network (SOLARNet), which is a fusion network that integrates two different sub-networks PixelAttentionDetector (PD) and RotationDetector (RD). The PD considers features from different scales and cluttered objects, while RD handles rotation invariance, giving horizontal and orientated object detection results. The state-of-the-art model fails to achieve higher accuracy when images are adversarially attacked. SOLARNet is not only efficient in terms of accuracy but also robust concerning different adversarial attacks, e.g., Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), which is the crucial factor for any mission-critical system. We have performed experiments on the publicly available DOTA dataset for oriented and horizontal object recognition tasks. Qualitative and quantitative results reported in this paper substantiate the superiority of SOLARNet over other state-of-the-art methodologies.
Potential Web Content Identification and Classification System Using Nlp and Machine Learning Techniques

Nowadays, the volume of educational contents on the world wide web is surging rapidly, challenging users with numerous options of e-Learning contents in various area of interests. This transition paves way for web data mining and classification for identifying the most relevant contents according to the user’s interests and needs. Web mining is a technique to automatically track down and extract the patterns from the data on WWW. The purpose of this paper was to analyze and classify the web content based on keyword inputs resulting a database facilitating a new way of data content recommendation for the users. The proposed work aims to scrape the freely accessible unstructured text content on the search engine and preprocessing it to structured data using NLP methods. The extracted structured data undergoes unsupervised learning algorithm for clustering them to obtain the three classified clustered sets of highly impacted, average and low impacted data contents, which will be further stored in the database for future recommendation of classified web content pages to the users.
Towards a Metrics Suite for the Complexity Analysis of Labview Systems Models

LabVIEW is a popular commercial modeling tool that is often used in systems engineering. LabVIEW also includes a special programming language developed for engineers to help them support the automation of computer-aided systems. Although LabVIEW is widely used in various fields (e.g., industrial design, academic research, and engineering education), there has not been much attention given to the systems models built in LabVIEW (e.g., support for analyzing the complexity of systems models). Our previous work in surveying engineers who use LabVIEW suggests that systems engineers are deeply concerned about the complexity of the LabVIEW systems models that they create. To address the need for additional support in understanding the complexity of LabVIEW systems models, we introduce a metrics suite to assist end-users in characterizing the complexities of LabVIEW systems models from different aspects in this paper. We theoretically validated the metrics using Weyuker’s validation. In addition, our metric suite was applied to 10 LabVIEW models mined from GitHub to empirically evaluate its suitability to support the description of systems model complexities. Our research is one of the first efforts to address complexity analysis of LabVIEW systems models through a software metrics approach.
Adcapsnet: A Novel Efficient and Robust Anomaly Detection Capsule Network Model for Deteriorated Iiot Sensor Images

With the rapid development of the IIoT, the anomalies generated in industrial production, especially the hidden anomalies, such as slight rotation of the sensor images and position reversed sample images, will cause great damage to the normal operation of industry. Detecting anomalies hidden in deteriorated sensor images has increasingly become a hot spot. Although there existed many related researches, there are still some problems that are difficult to deal with such hidden anomalies. This paper proposes an efficient and robust semi-supervised anomaly detection capsule network (ADCapsNet) by integrating two reconstructed components based on CapsNet and two novel operations. Reconstructed parts include a changed convolution structure to better extract the features of the data, and a new added SecondaryCaps layer to better extract spatial relationships for anomaly detection. New operations mainly embody the optimized vector selecting operation for dynamic anomaly detection routing and the scoring operation using modified probability mechanism. Specially, the modified probability mechanism is adopted to widen the score gap between positive and negative samples. This model can accurately identify and output the spatial relationships. Extensive experiments on four data sets show that the ADCapsNet has good performance of anomaly detection for deteriorated IIoT sensor images.
Nonlinear Anisotropic Diffusion Methods for Image Denoising Problems: Challenges and Future Research Opportunities

Nonlinear anisotropic diffusion has attracted a great deal of attention for its ability to simultaneously remove noise and preserve semantic image features. This ability favors several image processing and computer vision applications, including noise removal in medical and scientific images that contain critical features (textures, edges, and contours). Despite their promising performance, methods based on nonlinear anisotropic diffusion suffer from practical limitations that have been lightly discussed in the literature. Our work surfaces these limitations as an attempt to create future research opportunities. In addition, we have proposed a diffusion-driven method that generates superior results compared with classical methods, including the popular Perona-Malik formulation. The proposed method embeds a kernel that properly guides the diffusion process across image regions. Experimental results show that our kernel encourages effective noise removal and ensures preservation of significant image features. We have provided potential research problems to further expand the current results.
Feature Selection Using Rank Revealing Qr Factorization and Genetic Algorithm: Unsupervised and Hybrid Supervised Approaches

The selection of most informative and discriminative features from high-dimensional data has been noticed as an important topic in machine learning and data engineering. Using matrix factorization-based techniques such as nonnegative matrix factorization for feature selection has been emerged as hot topic in feature selection. Finding a subspace, which can span original data while it has dimensions much less than original dimensions of data, is main goal of feature selection using matrix factorization. In this study, rank revealing QR factorization was leveraged for obtaining most informative features as unsupervised feature selection. This technique uses permutation matrix of QR for feature selection which is unique characteristics this factorization matrix. In next step, a hybrid feature selection which is based on the rank revealing QR factorization and genetic algorithm was proposed. In this technique, all redundant features were removed by QR feature selection and then genetic algorithm was applied to find most informative subset of features. Consequently, the proposed algorithm was compared state-of-the-art feature selection in order to have dependable evaluation. Proposed method and other techniques were tested on seven available microarray datasets using KNN, SVM and C4.5 classifiers. In terms of evaluation metrics, the experimental results showed that the proposed method can be comparable with the state-of-the-art feature selection.