Reviewerly/exHarmony huggingface dataset:
A Novel Block Chain Method for Urban Digitization Governance in Birth Registration Field: A Case Study

Even though digitization is widely recognized as one of the most imperative trends in achieving effective urban governance, digital infrastructure remains far from the global trend in many African countries. This paper proposes a novel, resilient data manipulation architecture model called the Birth Notification Verification Model (BNVM) using blockchain and smart contracts. The proposed solution was evaluated in a real-world use case scenario in Ghana. The model, which is based on the Ten Civil Registration and Vital Statistics (CRVS) Framework, focuses on the initial inputs for birth registration at the birth notification level. The approach presented in this study paves the way for the creation of decentralized, secure, transparent, and automated systems for civil registration. The application of a smart contract architecture that blends a centralized design with an on-chain and off-chain architecture is further supported by this, providing more evidence of its viability. It offers a safe verification framework for the Ghana Birth and Death Registry based on smart contract technology and can guarantee a birth notification as proof of birth certificate registration in accordance with international standards. The findings provide insight into the use of blockchain technology in public registry institutions. Furthermore, exploring its adoption and implementation in Sub-Saharan Africa contributes to the growing field of blockchain technology research and demonstrates how the concept will address long-standing issues with corruption and security in developing countries.
An IoT-Based Smart Device to Monitor and Analyse the Performance of Athletes

In this day and age, technology and its functionalities are changing the way the world functions on a day-to-day basis, not only towards the betterment of this world but also towards the betterment of its inhabitants. This system is proposed with the intention of equipping athletes from underprivileged backgrounds with low-cost performance monitoring devices which would help them to monitor and analyse their performance, as well as to take the necessary future course of action. The proposed system makes use of an IoT-based low-cost WSN—Particle Argon which collects and stores the data in the Particle Cloud which can be used for real-time analysis as well as to predict the future course of action with the help of predictive analysis algorithm—linear regression. The proposed athlete monitoring system using predictive analysis algorithm (AMSPLA) is used to screen the basic health parameters of the patient, such as heart pulse rate and blood oxygen level as well as acceleration. These parameters are used to indicate the changes caused due the effort undertaken by the athlete. Furthermore, a report could be generated on the basis of the athlete’s performance, which can be used by their coaches to analyse their performance and also to facilitate future course of action.
Preliminaries

This chapter covers three major topics: the fundamentals of the mathematics, the basics of x-ray physics, and essentials of artificial intelligence (AI) and deep learning (DL). The objective is to provide a general review of the important mathematics tools and the background knowledge on x-ray physics used throughout the book, as well as to provide introductory information on AI and DL. The chapter serves mainly as a refresher. Readers who have not been previously exposed to these topics will find a list of recommended reference materials at the end of the chapter so that detailed studies can be performed.
Chinese electronic medical record named entity recognition model based on pre-training and multi-task learning

The Chinese Electronic Medical Records (EMR) lacks abundant annotated data. This severely limits the performance of Named Entity Recognition (NER) models in this domain. We propose a Chinese electronic medical record named entity recognition model based on pre-training and multi-task learning (Pt-Mt) to solve this problem. The model first fine-tunes the improved pre-trained model Roberta on different medical datasets, so that Roberta better fits the characteristics of the medical field and can learn features on different datasets. At the same time, the Chinese Word Segmentation (CWS) task is added as an auxiliary task of the NER model for joint training, which enhances the model's ability to distinguish entity boundaries. Finally, the NER of Chinese EMR based on Roberta and multi-task learning has achieved good results on the CCKS2017, CCKS2019, and CCKS2020.
Election Fairness and the Role of the Media: Focusing on the Election Report Deliberation System

Considering the importance and influence of the media in elections, the request to ensure fairness in elections can also be applied to the media. Accordingly, the election reporting deliberation system was prepared to protect the fairness of election reporting. According to the Public Official Election Act, the Election Broadcasting Deliberation Committee, the Election Articles Deliberation Committee, and the Internet Election Reporting Deliberation Committee review the fairness of election reporting and decide to take proper measures. However, the scope of the Internet media outlets to which the deliberation of Internet election report should be applied is not clear, and the scope could be broadened according to the decision of the Deliberation Committee. In addition, there is a problem that the freedom of political expression may be excessively reduced in cases where the area of freedom of speech is not clearly distinguished with the area of election reporting. There are also problems that fairness standards are not clear, there is a limit to specificity, and it is not easy to judge in applying to actual cases. Moreover, internet media and new media have characteristics such as openness, easy access, non-exclusiveness, interactivity. Therefore, it may not be appropriate to apply the same regulations to the internet media as those for existed media. As the influence of internet media grows in the election process, it is required to ensure fairness. However, it is necessary to consider the characteristics of new media different from the existed media. For online expression in the election process, the diversity and openness of the media should be utilized rather than approached from a regulatory point of view. Through this, it is possible to seek to protect fairness in election.
The Combination of GRU and Dense Block for Image Denoising Network

In order to better extract image features and effectively remove noise features, we propose an image denoising model based on GRU (Gate Recurrent Unit) and Dense block. The GRU contacts the previous state through the current state, which can more effectively extract noise features from the picture. The Dense block can effectively improve the feature propagation efficiency, reduce the problem of vanishing gradient, and optimize the process of feature acquisition. The denoising performance can be effectively improved through the cascade of GRU and Dense block. Our extensive evaluations on two datasets demonstrate that the proposed model outperforms the state of the art methods under all different noise levels in terms of PNSR, and the visual effects achieved by the proposed model are also better than the competing methods.
The Future

Jacques Attali discusses his work and the perception of AI in the context of humankind and the music industry. The interview explores the definition and state of the artefact in the contemporary music ecosystem and arising technological, ethical, and legal issues. Attali reflects on the technical and cultural predictions outlined in his seminal work Noise and the ongoing responsibility of artists to computer creativity.
The Artist

Holly Herndon shares her vision of the trajected development of AI technologies in music. This chapter explores how AI has inspired Herndon’s collaborations with different artists and musicians. Holly Herndon reflects on how contemporary artists can navigate challenges within the music industry and shares her perspective on the future development and implementation of AI within creative music practice. Holly Herndon explores how music production can be improved by technology. She reviews the human-centred approach and assumes significance of AI being a supplementary tool for creative process. This chapter will investigate the impact of the ‘misleading’ nature of the term AI and the possible consequences it might bring to the art. Holly Herndon will share her personal experience of going through the COVID pandemic in 2019 and reveal the inspiration and project drawn from that. This chapter will discuss copyright and diversity issues. Holly Herndon addresses the copyright court cases and connects the possible implications in law and ethics to the disruptive nature of AI in the music industry. She emphasises her fascination for human voice and its important part in music-creation practice.
The Student

Modern music production techniques are increasingly dependent on AI-driven software. These technological tools present an environment where the artist follows instructions or uses them as inspiration. Both approaches do not require a deep understanding of what is happening inside. They represent the ‘black box’ experience regardless of whether a technical configuration involves that. This chapter discusses how software engineers interpret standard practices in music theory and how this interpretation often contradicts modern approaches to composition. The extent of the programmer’s music background informs the engagement with the technology. During the development of AI music software, the fundamentals of music theory are translated into mathematical language. The resultant challenges that appear are inherited through an algorithmic programming approach. This environment restrains a user’s creativity. How this conflict can be anticipated during the initial development stages can be revealed by an understanding of the essential concept in music theory and the vital phases in the construction of music-related AI systems. A popular approach to creating an AI training dataset is built on public-domain classical music compositions, and reflection is given on its appropriateness as an engineering solution to a musical enquiry.
Social Media Policies for Advertising and Public Relations

The use of social media strategies and tactics in advertising and public relations as a way to engage with audiences provides many benefits to organizations. It also presents numerous challenges. Risks from improper use include violating Federal Trade Commission (FTC) regulations, copyright infringement, and invasion of privacy, to name a few. After a number of brand-damaging incidents, more and more strategic communication practitioners are recommending and drafting policies and guidelines for the appropriate use of social media. While every organization is unique, there are some common principles to follow.
Development of a convolutional neural network joint detector for non-orthogonal multiple access uplink receivers

We present a novel approach to signal detection for Non-Orthogonal Multiple Access (NOMA) uplink receivers using Convolutional Neural Networks (CNNs) in a single-shot fashion. The defacto NOMA detection method is the so-called Successive Interference Cancellation which requires precise channel estimation and accurate successive detection of the user equipment with the higher powers. It is proposed converting incoming packets into 2D image-like streams. These images are fed to a CNN-based deep learning network commonly used in the image processing literature for image classification. The classification label for each packet converted to an image is the transmitted symbols by all user equipment joined together. CNN network is trained using uniformly distributed samples of incoming packets at different signals to noise ratios. Furthermore, let’s performed hyperparameter optimization using the exhaustive search method. Our approach is tested using a modeled system of two user equipment systems in a 64-subcarrier Orthogonal Frequency Division Multiplexing (OFDM) and Rayleigh channel. It is found that a three-layer CNN with 32 filters of size 7×7 has registered the highest training and testing accuracy of about 81. In addition, our result showed significant improvement in Symbol Error Rate (SER) vs. Signal to Noise Ratio (SNR) compared to other state-of-the-art approaches such as least square, minimum mean square error, and maximum likelihood under various channel conditions. When the channel length is fixed at 20, our approach is at least one significant Figure better than the maximum likelihood method at (SNR) of 2 dB. Finally, the channel length to 12 is varied and it is registered about the same performance. Hence, our approach is more robust to joint detection in NOMA receivers, particularly in low signal-to-noise environments
Digital competences of teachers in vocational education in the Czech Republic

With each new generation, a new digital era is also born. There are few professions that can do work without Digital Technologies. Teachers' digital competences in vocational education are therefore key to fulfilling the new professional roles of teachers in response to the ever-changing demands of society. Online teaching in the pandemic era has shown the need for continuous improvement of teachers' digital competences (they apply digital competences in communication; in preparation for teaching or in the teaching process itself). Teachers' digital competences play a significant role in the development of their students' digital competences. Increasing the digital competences of teachers and students' is linked to the development of professional competences. This includes the acquisition of knowledge, skills and habits in the use of digital technologies in the preparation of future teachers at university, but also in teaching practice. In our paper, we offer a few examples of how teachers' digital competences can be enhanced.
INTERNET COMMUNICATIONS AS A RESOURCE OF NTERACTION BETWEEN GOVERNMENT AND SOCIETY

Currently, political Internet communications, represented by social networks, blogs, forums, etc., are being further developed. Social networks are gaining the greatest popularity in the system of interaction between government and society. The essence and typology of Internet communications are presented, communication barriers in the communication system of government and society are considered. The influence of communications on the modification of civil society through the introduction of virtual forms of communication with politicians and the activities of expert platforms is shown.
Domain-Driven Actionable Knowledge Discovery for Traffic Accidents Using Rules Induction

Due to the limitation of the methodologies of traditional data mining to satisfy business expectations, the shift from mining data-centered hidden patterns to domain-driven actionable knowledge discovery has become a significant direction of KDD research [22]. Traditional data mining algorithms and tools face major obstacles and challenges to solve real-life business problems and issues as they fail to provide actions that can be taken by people in business based on generated rules [22]. A small set of rules are generated by standard classification algorithms to form a classifier, but these classification algorithms use domain independent biases and heuristics [2]. This research aimed to propose a new approach to find actionable rules from sets of discovered rules. It focused on how a combination of traditional classification data mining and domain-driven data mining approach could be applied in solving real-life problems related to the field of traffic accidents in UAE. Real-life data were collected and pre-processed using the user’s existing knowledge and needs. Classification using Rules Induction was applied on the domain-driven dataset. The discovered rules from this technique were then summarized, combined, and analyzed. The final set of actionable rules from Classification technique for each class was then generated using a proposed interestingness method. To support such a process, the domain driven in-depth pattern discovery (DDID-PK) framework was followed [9]. Based on experimental results, the extracted domain-driven rules were more interesting and actionable than those produced by the traditional classification technique of data mining. In addition, the integration of data-centered classification technique of data mining to domain-driven approach of data mining and actionable knowledge discovery could help the Dubai police authority to reduce traffic accident severity by formulating new policies and traffic rules based on the domain-driven knowledge extracted from some hidden patterns from real data.
Controllable Free Viewpoint Video Reconstruction Based on Neural Radiance Fields and Motion Graphs

In this paper, we propose a controllable high-quality free viewpoint video generation method based on the motion graph and neural radiance fields (NeRF). Different from existing pose-driven NeRF or time/structure conditioned NeRF works, we propose to first construct a directed motion graph of the captured sequence. Such a sequence-motion-parameterization strategy not only enables flexible pose control for free viewpoint video rendering but also avoids redundant calculation of similar poses and thus improves the overall reconstruction efficiency. Moreover, to support body shape control without losing the realistic free viewpoint rendering performance, we improve the vanilla NeRF by combining explicit surface deformation and implicit neural scene representations. Specifically, we train a local surface-guided NeRF for each valid frame on the motion graph, and the volumetric rendering was only performed in the local space around the real surface, thus enabling plausible shape control ability. As far as we know, our method is the first method that supports both realistic free viewpoint video reconstruction and motion graph-based user-guided motion traversal. The results and comparisons further demonstrate the effectiveness of the proposed method.
Peranan Audit Forensik Dalam Pencegahan Kasus Fraud Penerbitan Izin Usaha Pertambanga (IUP) Pemerintahan Daerah

Fraud is a latent danger that threatens a country. The occurrence of fraud (cheating) in the public and private sectors has an extraordinary loss impact. Research results from the Association of Certified Fraud Examiners (ACFE) Global show that every year an average of 5% of the organization's revenue becomes a victim of fraud. Fraud is an unlawful act that is intentionally carried out for a specific purpose (manipulation or giving false reports to other parties) carried out by people from inside or outside the organization to gain personal or group benefits that directly or indirectly harm other parties. The research uses qualitative methods with data collection methods using literature studies. The object of this research is secondary data in the form of fraud against licensing by local governments. For this reason, the role of forensic audits is the best way to prevent and detect fraud. Forensic audits have the ability to provide statements in their expertise.
Partial Key Exposure Attacks on RSA with Moduli N=p<sup>r</sup>q<sup>s</sup>

Many fast variants of RSA are designed to speed up encryption and decryption. Prime power RSA (PP-RSA) is one of the most important variants. It is widely used in electronic money trading systems and high-speed programs. At present, the security of PP-RSA with moduli N = p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sup> q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> has not been fully studied. In this paper, we give three powerful attacks based on Coppersmith’s method, applying to the cases when the most significant bits or the least significant bits of the private key are known. Our attacks work in polynomial time. This is the first work on partial key exposure attacks of PP-RSA with moduli N = p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sup> q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> .
Demonstration of a general fault‐tolerant quantum error detection code for (2 <i>n</i> + 1)‐qubit entangled state on IBM 16‐qubit quantum computer

Quantum error detection has always been a fundamental challenge in a fault-tolerant quantum computer. Hence, it is of immense importance to detect and deal with arbitrary errors to efficiently perform quantum computation. Several error detection codes have been proposed and realised for lower number of qubit systems. Here we present an error detection code for a (2n + 1)-qubit entangled state using two syndrome qubits and simulate it on International Business Machines 16-qubit quantum computer for a 13-qubit entangled system. The code is able to detect an arbitrary quantum error in any one of the first 2n qubits of the (2n + 1)-qubit entangled state and detects any bit-flip error on the last qubit of the (2n + 1)-qubit entangled state via measurements on a pair of ancillary error syndrome qubits. The protocol presented here paves the way for designing error detection codes for the general higher number of entangled qubit systems.
An Iterative Fixpoint Semantics for MKNF Hybrid Knowledge Bases with Function Symbols

Hybrid Knowledge Bases based on Lifschitz's logic of Minimal Knowledge with Negation as Failure are a successful approach to combine the expressivity of Description Logics and Logic Programming in a single language. Their syntax, defined by Motik and Rosati, disallows function symbols. In order to define a well-founded semantics for MKNF HKBs, Knorr et al. define a partition of the modal atoms occurring in it, called the alternating fixpoint partition. In this paper, we propose an iterated fixpoint semantics for HKBs with function symbols. We prove that our semantics extends Knorr et al.'s, in that, for a function-free HKBs, it coincides with its alternating fixpoint partition. The proposed semantics lends itself well to a probabilistic extension with a distribution semantic approach, which is the subject of future work.
Effect of source statistics on utilizing photon entanglement in quantum key distribution

We present a novel benchmarking approach to compare photonic entanglement sources based on parametric downconversion and semiconductor quantum dots. We analyze the source statistics and its impact to secure quantum key rate.
Practical entanglement based metropolitan quantum key distribution link

We present a fully operational, real field quantum key distribution link based on entanglement over 50km of deployed optical fiber. We implemented automated synchronization and real time post treatment to continuously generate secret keys.
Free-Electron Radiation in the Form of Optical Cat and GKP States

We show how coherent modulation of free-electron wavefunctions can alter the quantum nature of the light they emit. We utilize this concept to create Schrödinger-cat and GKP states, which are sought-after for fault-tolerant quantum computation.
Quantum Networking Advantage with Graph States

Quantum networks will provide multi-user entanglement to connected nodes. Here we demonstrate a network advantage for quantum conference key agreement between four users from a re-configurable six-photon graph state.
Information Leakage due to Detection Coupling Mismatch

An eavesdropper may gain information by exploiting device imperfections while implementing quantum key distribution protocols. We find information leakage due to coupling mismatch at receiver’s detectors in terms of mutual information between eaves-dropper and receiver.