Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Modified Fuzzy PD Controller for the Speed Control of a Brushed DC Motor

In this work, a modified fuzzy PD controller and a conventional fuzzy PD controller were designed and implemented for the speed control of a brushed DC motor. The work made use of a 12 V encoder DC motor of 4000 RPM on no load. The motor was controlled from MATLAB/Simulink environment interfaced with Arduino Uno board through a Cytron DC10MD motor driver. Mamdani inference style was adopted for the design of the fuzzy logic controllers (FLC). 21 and 25 fuzzy rule sizes were experimented on each controller. The performances of the two FLCs were compared using the criteria of rise time, settling time, percentage overshoot and steady state error. The investigations were carried out using 20 g loads on each of the controllers. The conventional fuzzy PD was noted to have significant steady state error. The modified fuzzy PD achieved 50% reduction of the steady state error of the conventional fuzzy for the same DC motor under the same experimental conditions.
Secured Digital Oblivious Pseudorandom and Linear Regression Privacy for Connected Health Services

The security and privacy of the users have become considerable apprehensions owing to the association of the Internet of Things (IoT) devices in diverse applications. Cyber threats are being procured at an eruptive speed, making the overall security and privacy proceedings insufficient. Therefore, everybody on the Internet is a by-product of hackers. Consequently, Machine Learning (ML) algorithms generate precise outputs from wide-reaching complicated databases. The outcomes being procured can predict and ascertain susceptibilities in IoT-based systems. A Secured Digital Oblivious Pseudorandom and Linear Regression Privacy Learning (SDOP-LRPL) method for connected health services is proposed in this work. The SDOP-LRPL method is designed into three sections. First, with the Health Analytics dataset provided as input, relevant features for connected health services are obtained by utilizing the Distinctive Nearest Neighbor Confidence Feature Selection model. Second, a secured authentication model is designed using a Digital Oblivious Pseudorandom Signature with the appropriate feature. Finally, further communication processing between users (i.e., patients and doctors) is performed via the Linear Regression Privacy Preservation Communication model with the authenticated users. This method acquires essential sensitive medical healthcare data via IoT devices. Data analysis is performed via machine learning methods to ensure security and privacy for connected health services. The experimental results reveal that the proposed method meets the efficiency and proper authentication accuracy for connected health services with minimum response time and a higher rate of security as well.
Virtual Reality as a Powerful Persuasive Technology to Change Attitude During the Spread of COVID-19

Virtual Reality is a powerful simulation tool that has the potential to play a key role in several areas. In this article, we focus on different simulation tools and on different ways of providing negative feedback for persuasive purposes through simulated experiments. The persuasive objective that we consider concerns the persuasion towards the right behavior, we are based on the emotional aspect in order to introduce a fairly high level of anxiety as negative feedback with techniques that we will address in what follows, to fight against the spread of COVID-19, in order to strengthen the safety and health of citizens. In general, this research aims to develop a VR application that aims to change the behavior of citizens and encourage persuasive researchers to explore immersive virtual environments.
A Security System for National Network

Recently, cybercrime attacking the national network has increased. National infrastructure such as water purification plants, power plants, and substations are operated by using programmable logic controllers (PLC). PLC controls through the Industrial Control Systems/Supervisory Control And Data Acquisition (ICS/SCADA), which receives operational commands and sends operational states by communication means such as Ethernet and Modbus. However, the environment of ICS/SCADA, called Industrial IoT, is vulnerable to security attacks unlike recently developed technologies such as IoT devices, systems, and networks. Therefore, this study proposes a new security system to strengthen industrial firewalls. We developed an interface, unauthorized access blocking algorithm, and real-time defense system for system access control. The performance of the proposed system was verified by external organizations. Five performance indicators were measured to validate the proposed system. All indexes were achieved to 100%. We hope that this study and results will help block and defend against cyberattacks.
A Two-Way Atomic Exchange Protocol for Peer-to-Peer Data Trading

Various types of data are generated every day, and the amount of generated data is growing exponentially. People are interested in extracting the value of data. Some valuable data among them can be viewed as digital products to be traded. For example, with the outbreak of COVID-19, patients’ personal health records, electronic medical records and travel history become important and valuable information for epidemic prevention. On the other hand, the license keys for high-priced software such as EDA (Electronic Design Automation) tools also have value and can be considered as tradable products. However, the trust between two parties in the trading process becomes an issue. Consumers do not pay until providers give the data while providers are not willing to do since they distrust that consumers will pay after receiving the data. In this paper, we propose a Blockchain-based protocol for data trading with zero-knowledge proofs. To protect the data and maintain their value, Zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) is included that the provider can convince the consumer of the correctness and security of the data without revealing the details before receiving the payment. Predefined agreements between both parties in smart contracts are executed automatically. When the data is valid, the provider receives the payment, and in the meantime, the consumer has the ability to obtain the purchase data. Otherwise, the payment is refunded to the consumer immediately if the provider cheats. This approach employs the method of two-way exchange, as known as Delivery versus Payment (DvP) in physical commodity trading and ensures the rights and benefits for both parties. The whole process is decentralized for the purpose of constructing fair data trading without any trusted third party and ensuring system availability.
A Multi-layered Ontological Approach to Blockchain Technology

In 2009, blockchain led the foundation of an era of cryptocurrencies with Bitcoin playing the role of avant-garde in the series. This phenomenal technology behind Bitcoin has been visualized by many as having far reaching impacts not only in finance and economy, but also in other sectors. This decade has witnessed the emergence of different types of blockchain, associated platforms and a plethora of applications without any standardization of the associated concepts. To facilitate a common interpretation of this technology among its users, we present a multi-layered blockchain ontology that can be leveraged for constructing a structured knowledge management and representation system in this domain. We propose a new Ontology Development Life Cycle and a methodology for the design of such complex ontologies of emerging technologies like blockchain. We devised a prototype tool leveraging the proposed methodology that will enable implementing a multi-layered modularized ontology for any domain and design applications for that domain.
Blockchain Industry Implementation and Use Case Framework: A Survey

This paper explains BC Technology (BCT) and concepts in simple terms. It explores concepts and BC usability outside of currency. The focus of this research paper is to describe BC technology, surveyed business applications and the framework for identifying BC valid opportunities. It also discussed BC security issues and challenges.
Three-Dimensional Large Deformation Reconstruction for Slender Wing-Like Structures from Strain Measurements

Aiming at large deformation of flexible wing-like structures, an algorithm that incorporates geometrically nonlinearity is developed for three-dimensional shape reconstruction based on strain measurements. A certain number of strain measurements distributed on the structural surface are used to estimate the generalized strains and the warping deformation, which can further determine the position of an arbitrary material point in the structure by a series of recursion relations. A simple tube model and a more realistic composite wing model are investigated separately to demonstrate the present method in reconstructing deformed shapes under various applied loads. The modal method is used to calculate the deformation as a comparison. The results have demonstrated the high accuracy and capability of the present method in three-dimensional shape reconstruction, especially for large deformation problems. It also demonstrate that the warping effect has a significant impact on predicting the twist angle, especially when the bending deformation is large.
eMudra: A Leftover Foreign Currency Exchange System Utilizing the Blockchain Technology

The global travel industry leaves almost every traveller with some amount of leftover foreign currency at the end of the trip. Most travellers are unable to use these leftover foreign currencies efficiently and profitably because exchange bureau rates do not favour the traveller nor accept low denomination coins. In this paper, we explore existing currency exchange systems for leftover foreign currency and consider prevalent challenges. We propose an innovative system, eMudra, for exchanging cash based leftover foreign currency by integrating smart kiosk-based systems with peer-to-peer currency exchange utilizing the concept of blockchain technology alleviating the challenges. A key component of our system advocates the role of wearable technology for secure identification with a kiosk-based transactions user interface. We have implemented, tested and evaluated eMudra components—the User Wallet and the Transaction Management Network as a permissioned consortium blockchain while simulating the kiosk interface and associated physical bill and coin exchange. Leftover foreign currency lying unused in the wallets and drawers of travellers is significant and can easily add up to billions if considered globally. Our blockchain based system, eMudra, can play an important role in bringing these currencies back into the global economy by making money deposit, management and exchange process easy and lucrative.
Overview of Reinforcement Learning for Person Re-Identification

For intelligent surveillance, the issue of person re-identification has attracted extensive research interest due to its great academic value and broad application prospect. This issue aims to build the correspondence between human appearances across deployed non-overlapping cameras. However, the captured human images are inevitably influenced and deteriorated by various factors, such as illumination variations, pose changes, viewpoint alterations, partial occlusions, and surrounding clutters, which largely barricade the progress of re-identification performance. While, owing to the development and prosperity of reinforcement learning, researchers have made a big breakthrough in this issue. Motivated by the research breakthrough, this paper provides an overview of reinforcement learning for person re-identification. The contributions of this paper can mainly be summarized as follows: (a) introducing the background and challenges of person re-identification, and describing the framework and characteristics of general reinforcement learning methodology; (b) surveying the latest and most representative researches on reinforcement learning methods for this issue, and analyzing the mechanisms and essences of these methods; (c) clarifying the advantages of reinforcement learning, discussing its potential problems, proposing the conceptual schemes around it, and thus illuminating its future trend for this issue.
Step-by-Step Decoding of Binary Quasi-Reversible BCH Codes

Binary quasi-reversible BCH codes whose defining set contains consecutive elements from negative to positive integers have received considerable attention in recent years due to their efficient decoding suitable for a wide range of applications. This paper combines the concept of the weight and quasi-reversible structures to introduce two subclasses of BCH codes: odd-like/even-like quasi-reversible BCH codes. The step-by-step decoding of these codes is developed as follows: First, the weight evaluation of a received polynomial is able to judge whether the number of errors is odd or even, which helps to simplify the decoding processes. Second, based on Chiò’s pivotal condensation process which can be easily implemented in a parallel computing architecture, the determinant calculation of the band matrix instead of Peterson’s matrix in column-echelon form is faster. Third, a newly proposed non-monic error-locator polynomial is sparser than the conventional ones. As a consequence, the theoretical analysis and experimental results validate potential benefits in requiring fewer finite field additions and multiplications used in the decoding of binary odd-like/even-like quasi-reversible BCH codes up to half the minimum distance when compared with the narrow-sense BCH codes with small error-correcting capability.
On Optimality of the Round Function of Rocca

At ToSC 2021, Sakamoto et al. proposed Rocca, an AES-based encryption scheme, for Beyond 5G applications. They presented a class of round functions that achieved impressive performance in software by improving the design strategy for constructing an efficient AES-based round function that was proposed by Jean and Nikolić at FSE 2016. In this paper, we revisit their design strategy for finding more efficient round functions. We add new requirements further to improve speed of Rocca. Specifically, we focus on the number of temporary registers for updating the round function and search for round functions with the minimum number of required temporary registers. As a result, we find a class of round functions with only one required temporary register, while round function of Rocca requires two temporary registers. We show that new round functions are significantly faster than that of Rocca on the latest Ice Lake and Tiger Lake architectures. We emphasize that, regarding speed, our round functions are optimal among the Rocca class of round functions because the search described in this paper covers all candidates that satisfy the requirements of Rocca.
Rethinking The Electric Guitar as an Augmented Instrument

The research analyzes the main electronic and digital ensembles for electric guitar in the last decades. Through this, the sense of augmentation of the instru- ment is deepened, with special emphasis on its integration with the computer. A first section of the research is the interpretation of a series of case studies using Ableton Live software with MAX/MSP, together with a MIDI controller that homogenizes and allows performing the case studies selected for the the- sis: Electric Counterpoint (Steve Reich 1987), La cite des saules (Dufourt 1997), Trash TV Trance (Romitelli 2002) and Not I (Prins 2007). A second section is the development of an augmentation proposal for e-guitar that consists of a separation in the hands-feet interface to be able to control effects, volumetric balances and samples and loops. Finally, a reflection is articulated around the augmentation in the electric guitar and its relationship with the concepts of post digital, Smart instruments, modular flexibility and obsolescence.
Reviews of Popper’s Articles on Logic

This chapter contains the contemporary reviews of Popper’s articles on logic by W. Ackermann, E. W. Beth, H. B. Curry, G. Hasenjaeger, J. G. Kemeny, S. C. Kleene, J. C. C. McKinsey and E. Nagel.
A Secure Data Controller System Based on IPFS and Blockchain

Blockchain is mainly used to store the amount of coins transferred and the information of sender/recipient in text format. Treating such simple information, computing and comparing hashes of the blocks, and maintaining its integrety already burden the system, which causes ‘the limit of block capacity’, the difficulty of putting a large amount of data in one block of the blockchain. This presents a solution to the block capacity problem by efficiently distributing and encrypting files using the IPFS program with which a large amount of data is recorded outside the Blockchain.
A Novel Multi-Stage Attentional Framework for 3d Hand-Object Pose Estimation from Rgb-D Data

3D hand-object pose estimation aims to jointly detect the poses of both hand and the manipulated object, but is challenging due to the depth ambiguity in hand-object interaction (HOI) scenarios and difficulty in generalizing isolated hand or object pose estimation approaches to complex HOI scenarios. In this paper, we propose a novel multi-stage attentional framework for 3D hand-object pose estimation from RGB-D data. In the first stage, we design an efficient spatial-attentional fusion (SAF) module to integrate the 2D heatmaps generated from RGB images with corresponding depth image, which enables better information integration of different modalities as provided by RGB-D data. In the second stage, an HOI-aware channel attentional (HOI-CA) module is introduced to further refine the initial 3D hand and object pose features generated from the integrated information, respectively. Specifically, the HOI-aware features are disentangled to retrieve the hand- and object-related semantic information, therefore improving the 3D pose estimation accuracy of both hand and object. Through extensive experiments on public hand-object datasets, we demonstrate the state-of-the-art performance of the proposed method in complicated HOI scenarios. We also conduct comprehensive ablation studies to validate the effectiveness of our method.
Pre-trained Language Model with Feature Reduction and No Fine-Tuning

Pre-trained language models were proven to achieve excellent results in Natural Language Processing tasks such as Sentiment Analysis . However, the number of sentence embeddings from the base model of Bidirectional Encoder from Transformer (BERT) is 768 for a sentence, and there will be more than millions of unique numbers when the dataset is huge, leading to the increasing complexity of the system. Thus, this paper presents the feature reduction of the sentence embeddings classification with BERT to decrease the number of features and complexity by using feature reduction algorithm. With 50% fewer features, the experimental results show that the proposed system improves the accuracy by 1%−2% with 89% lesser GPU memory usage.
Compass Errors in Mobile Augmented Reality Navigation Apps: Consequences and Implications

Augmented reality capabilities on smartphones have led to the development of mobile Augmented Reality (mAR) navigation apps. Whilst some mAR navigation apps annotate navigational points of interest within the camera image, mAR compass apps add only a compass rose and bearing to the image, enabling the device to function as a hand bearing compass. Compass apps do not attempt registration of the camera image, so these apps allow observation of the errors affecting smartphone magnetometers. This paper presents compass deviation (error) curves and offset errors for mAR compass apps on 17 mobile devices. The magnitude of deviation error measured (typically 5–10°) and the apparent transience of compass calibration, over periods of less than an hour, cast doubt on claims that the apps are equivalent to professional navigation compasses, and suitable for navigation use. As an mAR navigation app is unlikely to achieve reliable registration in an open-field context such as yacht navigation, the deviations observed also raise serious questions about the reliability of mAR navigation apps, and identifies real safety concerns for the use of such apps. The paper concludes by speculating on interface designs for mAR navigation apps that could mitigate compass errors in mobile devices, identifying also some issues that require further investigation.
A brief demonstration of a tool for SARAL/AltiKa waveform clustering

This article describes a classification tool to cluster SARAL/AltiKa waveforms. The tool was made using Python scripts. Radar altimetry systems (e.g., SARAL/AltiKa) measures the distance from the satellite centre to a target surface by calculating the satellite-to-surface round-trip time of a radar pulse. An altimeter waveform represents the energy reflected by the earth’s surface to the satellite antenna with respect to time. The tool clusters the altimetric waveforms data into desired groups. For the clustering, we used evolutionary minimize indexing function (EMIF) with k-means cluster mechanism. The idea was to develop a simple interface which takes the altimetry waveforms data from a folder as inputs and provides single value (using EMIF algorithm) for each waveform. These values are further used for clustering. This is a simple light weighted tool and user can easily interact with it.
Do US State Breach Notification Laws Reduce Firm Data Breaches?

We ask whether US state laws requiring notification when firms suffer data breaches actually decrease data breaches. From 2003-2018, 50 states and the District of Columbia enacted breach notification laws (BNLs) mandating firms to notify states after suffering data breaches, undertake breach mitigation, pay fines for non-compliance, and be liable for damages to the public and private individuals. BNLs were supposed to reduce data breaches and create a market for data privacy, wherein firms could strike their preferred balance of data security for and cost to consumers. We find no evidence supporting this supposition. Results from multi-state difference-in-difference analyses indicate no significant change in annual breach counts or breach magnitudes following BNL enactment. Results also indicate no significant long-term change in rates of identity thefts, fraud, and related data misuse. These non-effects persist for different firms, time-periods, and types of BNLs. We conjecture that inconsistent state notification standards and inadequate state dissemination of information about firm leaders and laggards in data security explain BNL ineffectiveness to date. We propose a new US federal regime to address these shortcomings and let a national BNL achieve goals state BNLs clearly failed to meet.
A Critical Inquiry into an Anthropomorphized Robot Entering the Workplace

In this study, we analyse the entry of an anthropomorphized software automation robot into the workplace in a media consultancy company, a technology pioneer in its industry. Applying an affect lens, we explore how organizational members experience the entry of the AI-based robot and seek to understand the reasons behind its fortunes and misfortunes. We go beyond depicting ‘enthusiastic feelings of the management’ and ‘frustration of employees’ and highlight what consequences anthropomorphising AI may have on interaction between humans and machines, and ultimately on success of these technology projects. Understanding human-robot interaction and its consequences is not simply about distinctions between positive and negative affect. It is about distinctions rendered within ongoing interventions into affective organizational life. Our study has implications for understanding the contemporary technologized global economy that often neglects and downplays the role humans must play in what has been described the Second Machine Age.