Reviewerly/exHarmony huggingface dataset:
Approximation Theory of Wavelet Frame Based Image Restoration

In this paper, we analyze the error estimate of a wavelet frame based image restoration method from degraded and incomplete measurements. We present the error between the underlying original discrete image and the approximate solution which has the minimal $\ell_1$-norm of the canonical wavelet frame coefficients among all possible solutions. Then we further connect the error estimate for the discrete model to the approximation to the underlying function from which the underlying image comes.
Bitcoin and Ethereum -- A Decade of Risk/Return Analysis

Bitcoin was the first cryptocurrency in the world launched in 2009 while Ethereum was the second one launched in 2015. This paper evaluates the risk/return analysis for both the cryptocurrencies using CMBI Bitcoin and CMBI Ethereum indices as their proxies since their inception and compares them to 10 traditional asset classes -- U.S. stocks, developed country stocks, emerging market stocks, gold, commodities, energy, U.S. bonds, global bonds, U.S. real estate and global real estate. Both the cryptocurrencies had very high risk, absolute returns and risk-adjusted performance (Sharpe, Sortino and Omega Ratios) compared to other traditional asset classes. Ethereum and Bitcoin also had very high cumulative returns and generated much more wealth compared to other asset classes. However, both the cryptocurrencies also had very high maximum drawdown and monthly downside deviation compared to traditional asset classes. Overall, Bitcoin and Ethereum are extremely high risk, high reward plays for investors.
A Preliminary Study on the Teaching Mode of Interactive VR Painting Ability Cultivation

This paper introduces the advantages and characteristics of VR painting compared with traditional painting and analyzes its application prospects. Based on the experience in interactive VR painting teaching, we initially explored the mode of training VR painting talents and summarized the current challenges VR painting faces.
Can AI Replace Conventional Markerless Tracking? A Comparative Performance Study for Mobile Augmented Reality Based on Artificial Intelligence

AR is struggling to achieve its maturity for the mass market. Indeed, there are still many challenging issues that are waiting to be discovered and improved in AR related fields. Artificial Intelligence seems the more promising solution to overcome these limitations; indeed, they can be combined to obtain unique and immersive experiences. Thus, in this work, we focus on integrating DL models into the pipeline of AR development. This paper describes an experiment performed as comparative study, to evaluate if classification and/or object detection can be used an alternative way to track objects in AR. In other words, we implemented a mobile application that is capable of exploiting AI based model for classification and object detection and, at the same time, project the results in AR environment. Several off-the-shelf devices have been used, in order to make the comparison consistent, and to provide the community with useful insights over the opportunity to integrate AI models in AR environment and to what extent this can be convenient or not. Performance tests have been made in terms of both memory consumption and processing time, as well as for Android and iOS based applications.
Re: Preliminary reports of augmented-reality assisted craniofacial bone fracture reduction

I have read with great interest Lin and colleagues' article on the use of augmented reality (AR) in zygomaticomaxillary complex (ZMC) fractures fixation.1 Augmented Reality (AR) is a rapidly advancing technology that is gaining acceptance and application in a variety of surgical fields. There have been numerous studies evaluating the precision and accuracy of AR-guided navigation. This study contributes to the body of evidence demonstrating the exciting potential of augmented reality in craniomaxillofacial surgery.
Oaldpc: Oversampling Approach Based on Local Density Peaks Clustering for Imbalanced Classification

Oversampling techniques have been favored by researchers in improving imbalanced classification. Multiple variations of oversampling methods are developed to handle imbalances between minority and majority classes. Yet, most of them fail to overcome imbalances within the minority class while at the same time avoiding noise generation. Recently, clustering-based oversampling methods are proposed against imbalances within the minority class and (or) noise generation. However, they create more synthetic samples in large-size or high-density regions, can not remove noise in the original data, rely on many parameters and fail to handle non-spherical data. Besides, adopted clustering methods are suitable for imbalanced data. To advance the state of the art, this paper proposes a novel oversampling approach based on local density peaks clustering (OALDPC). First, a new parameter-free local density peaks clustering (LDPC) is proposed to partition data into separated sub-clusters. Second, a new parameter-free LDPC-based noise filter is proposed to remove suspicious noise. Third, a new sampling weight is proposed by weighting the sample’s number and density of each minority class sub-cluster. Four, a new parameter-free interpolation based on the sampling weight and LDPC is proposed to create synthetic minority class samples. Extensive experiments with real and artificial data sets prove that OALDPC consistently outperforms popular oversampling methods.
Asymmetric Vision Transformers for Multi-Label Classification

Multi-label image classification (MLIC) aims to distinguish multiple objects within a single image, which is challenging due to the co-occurrence of different objects and cluttered backgrounds. Most of existing works leverage label dependencies and/or object-specific region relationships to build their models. However, these methods usually fail to fully captureÂ the fine-grained details among different regions. Inspired by the success of vision transformer (ViT), we propose an asymmetric vision transformer to tackle the challenge in MLIC task, which optimizes the embedding to fuse global and local information by asymmetric loss. The virtues of local information stepped from attention module in transformer can capture label-related object regions and those semantic-related discriminative regions help us to focus on the details of each single object. While long-range global information will concern on multi-region which depict the whole picture of relations among multiple objects. Specifically, by introducing asymmetric loss function to optimizing global and local embedding matrix, our system can balance the probabilities of positive and negative samples, which is especially important for multi-label classification. Extensive experiments on three multi-label classification datasets (VOC2007, VOC2012, and MS-COCO) well demonstrate the superiority of our approach against other state-of-the-art methods.
Secure Edge Computing Vulnerabilities in Smart Cities Sustainability Using Petri Net and Genetic Algorithm-Based Reinforcement Learning

The Industrial Internet of Things (IIoT) revolution has emerged as a promising network that enhanced information dissemination about the city’s resources. This city's resources are wirelessly connected with different constrained devices (such as sensors, robotics, and actuators). However, the communication of this wireless information is threatened by several malicious attacks, cyber-attacks, and hackers. This is due to unsecured IIoT networks that were exposed as a potential back door entry point for the attacks. Consequently, this study aims at develop a security framework for the edge computing vulnerabilities in the smart cities’ sustainability using Petri Net and Genetic Algorithm-Based Reinforcement Learning (GARL). First, a common trust model for addressing information outflows in the network using distributed authorization algorithm is proposed. This algorithm implemented on a secure framework modelling in Petri Net called secure trust-aware philosopher privacy and authentication (STAPPA) for mitigation of the privacy breach in the networks. The Genetic Algorithm-based Reinforcement Learning (GARL) is used for the optimization of the searching, detection of anomalies, and shortest route during the agent learning in the environment. The results of detection accuracy rate obtained over a secure framework using reinforcement learning are 96%, 97% and 99% during simulation in the network environment. The sensitivity of the detection rate is 0.1667, 0.6250, and 0.4783, while the specificity is 0.9794. The result of the GARL simulation model obtained shows the best distance of 238.84 * 10-3 fitness when the searching space is optimized by reducing the number of chromosomes to 10 in the model. These approaches help to detect anomalous, and prevent unauthorized users from accessing edge computing components in the city architecture.
Bounded Pool Mining and the Bounded Bitcoin Price

We present a simple model featuring the supply side of the Bitcoin ecosystem, i.e. the market structure of "mining". Our model suggests that a large mining pool entertains the trade-off between compromising the network given its market power and attracting sufficient hashrate delegations from the dispersed miners. We show that a mining pool therefore finds it optimal to be self-constrained in size, though it maintains a positive probability of compromising the network in equilibrium. As a result, the bounded market concentration in pooled mining leads to a bounded range of the Bitcoin price fluctuations. Our results suggest that the risk of having large mining pools for the well-functioning of the Bitcoin network is less of a concern. We show a range of other supply side factors shift the Bitcoin price bandwidthÂ and document important empirical evidence that is consistent with our model predictions.
IMPROVING READING COMPREHENSION USING STORY IMPRESSION ON THE SECOND SEMESTER STUDENTS' AT UNIVERSITAS BHINNEKA PGRI

isolation. Rather, it is targeted at the students learning and analyzing the whole text, at how it is constructed to achieve its purpose (Carrell, 1996: 26)The design of this research is a classroom action research with cycle model since this research is planned to improve the students’ Reading Comprehension at the second semester of Universitas Bhinneka PGRI using Story Impression. Action research is a form of collective self-reflective enquiry undertaken by participants in social situations in order to improve the rationality and justice of their own social or educational practices, as well as their understanding of those practices and the situations in which the practices are carried out (Carr and Kemmis 1988: 5-6 in Smith, 2007). According to the result of Test Cycle I was done in cycle I, it was found that the students who could reach the passing grade were 21 students (84%) and 4 students (16%) were failed. The criteria of success which had been determined was 80% of the students and the standard value was 80. So, the action in cycle 1 was successful.The suggestion for the students by using Story Impression, the students can know much Reading Comprehension and it makes students more active in teaching and learning process, for English lecturer should use Story Impression as alternative way in teaching Reading Comprehension and for future researcher as the reference to make a further research.
Data-Driven Surveillance

This chapter describes current information and research marketing practices with AI. More or less covertly, marketers collect, aggregate, and analyse consumers’ data from different offline and online sources and use intelligent algorithmic systems to derive new knowledge about their preferences and behaviours. Consumers know little about how marketers operate with their data, what knowledge is extracted, and with whom this is shared. A new profound asymmetry emerges between consumers and marketers, where power is not only related to the market and contracts but also to the possibility of knowing consumers' lives. Existing privacy and data protection only succeed to a limited extent in limiting surveillance practices and reducing these asymmetries. The main reasons for this are found in the limited effectiveness of the dominant protection paradigm of informed consent and the conceptualisation of information as personal data.
Algorithmic Marketing

This chapter provides an overview of the “algorithmic business” concept. The latter is a socio-technical notion describing the use of intelligent computational processes by business entities which allow making assessments, predictions and decisions about consumers. The phenomenon represents the last stage of the evolution of marketing technologies caused mainly by the increase in consumer data and powerful new applications based on machine learning and cognitive computing to analyse such data and make automated decisions in real-time. The chapter includes an analysis of the historical development, leading technologies, current applications, and new organisational models.
Detecting Illicit Ethereum Accounts Based on Their Transaction History and Properties and Using Machine Learning

The Ethereum blockchain has been subject to an increasing fraudulent activity during the recent years that hinders its democratization. To detect fraudulent accounts, previous works have exploited supervised machine learning algorithms. We can identify two main approaches. The first approach consists in representing transaction records as a graph in order to apply node embedding algorithms. The second approach consists in calculating statistics based on the amount and time of transactions realized. The former approach leads to better results at this day. However, transactional data approaches - based on time and data only - are not used to their full potential. This paper adopts a transactional data approach by expanding feature calculation to every transaction properties. We study three classification models: XGBoost, SVM Classifier and Logistic Regression and operate a feature selection protocol to highlight the most significant features. Our model results in a 26 features dataset providing an f-score of 0.9654.
Combining Static and Contextual Features: The Case of English Tweets

In recent years, social media networks have emerged as a veritable source for gathering information in various disciplines. This enables the extraction and analysis of users’ feelings, opinions, emotions and reactions to different topics. Pre-trained Neural Language Models (LMs) have yielded outstanding scores on a variety of Natural Language Processing (NLP) related tasks across different languages. In this paper, we propose that the efficiency of contextual word vectors using LMs for social media can be further boosted by incorporating static word embeddings that are specifically trained on social media (e.g., Twitter). We demonstrate that the combination of static word embeddings with contextual word representations is indeed effective for building an enhanced Sentiment Analysis system for English tweets.
Identifying Incentives for Extortion in Proof of Stake Consensus Protocols

A distributed consensus algorithm is at the core of what makes cryptocurrencies a decentralised ledger; they are the tools that facilitate the agreement between millions of users worldwide on what the playing rules are going to be, as well as the punishments and rewards for (dis)obeying them. The first cryptocurrency, Bitcoin, popularised proof-of-work puzzle-solving algorithm, in the form of block mining to process and validate transactions on the blockchain. However, several limitations with proof-of-work, such as enormous energy demand, significant (and increasing) computational power requirement, and lack of scalability, led blockchain enthusiasts and researchers to construct alternatives. One prominent alternative mechanism proposed is the proof-of-stake; a mechanism that does not rely on the mining power but the amount of stake owned by a node, allowing randomly selected validators to create blocks and verify blocks created by other validators. A proof-of-stake mechanism naturally results in the formation of staking pools, whereby multiple stakeholders can pool their resource to earn rewards. In this paper we explore the likely evolution of a competitive staking pool market. We pay particular attention to the importance of security. Staking pools could be subject to a range of attacks by malicious actors and so secure staking pools are essential for a well functioning proof-of-stake currency.
Effect of Entropy Reshaping of the IP Identification Covert Channel on Detection

Randomly IP Identification (IP ID) header field could be exploited as a covert channel. This research aims to trigger the developers of the network intrusion prevention systems to consider the role of the IP ID covert channel entropy adaptation on detection. So, the paper proposes a new method for reshaping the entropy of the IP ID covert channel (REIPIC). It iteratively treats the frequencies of occurrence pattern of the IP ID covert values within a sliding window of a specific size, to resemble their counterparts in the normal case. Then, an entropy-based SVM is used to evaluate REIPIC impacts on the IP ID covert channel detection. Finally, SVM examines the REIPIC capability of reaching an iteration at which the IP ID covert channel could not be identified. REIPIC contributes declaring the new frequencies of occurrence matrix and identifying a conceptual framework that could be invested in other pattern adaptation applications.
Classification of Hate Speech Language Detection on Social Media: Preliminary Study for Improvement

This study aims to improve performance in the detection of hate speech on social media in Indonesia, particularly Twitter. Until now, the machine learning approach is still very suitable for overcoming problems in text classification and improving accuracy for hate speech detection. However, the quality of the varying datasets caused the identification and classification process to remain a problem. Classification is one solution for hate speech detection, divided into three labels: Hate Speech (HS), Non-HS, and Abusive. The dataset was obtained by crawling Twitter to collect data from communities and public figures in Indonesia. The optimization process of the classification algorithm is carried out by comparing the SVM algorithm, eXtreme Gradient Boosting (XGBoost), and RandomSearchCV. The experiment was carried out through two stages of activity: the classification algorithm using SMOTE and without SMOTE with Stratified K-Fold CV algorithm. The highest performance accuracy with SMOTE is obtained at 90.7% with the SVM model. Whereas without SMOTE, the highest performance accuracy uses the SVM with a value of 87.8%. Furthermore, with the addition of the Stratified K-Fold CV algorithm for non-SMOTE, the train_score is 97.4%, and the test_score is 95.2%. The results show that SMOTE oversampling can increase the accuracy value in detecting hate speech.
Analyze Symmetric and Asymmetric Encryption Techniques by Securing Facial Recognition System

This paper aims to protect the facial recognition system by securing the stored images and preventing unauthorized people from accessing them. Symmetric and asymmetric encryption techniques were proposed for the image encryption process. To ensure the use of the most efficient techniques, thus, compare the results between two popular encryption algorithms. AES was chosen to represent the symmetric cipher and the RSA for asymmetric ciphers. High-resolution face images are encoded by both algorithms, as well as the ability to be analyzed by quantitative parameters such as PSNR, histogram, entropy, and elapsed time. The results showed through the proposed criteria the preference of AES, as it provided distinguished results in image coding in terms of coding quality and accuracy, processing speed and execution, coding complexity, coding efficiency, and homogeneousness. To sum up, symmetric encryption techniques protect the face recognition system faster and better than asymmetric encryption techniques.
Design Principles for Interoperability of Private Blockchains

Interoperability is one of the most promising research areas of blockchain technologies. When state-of-the-art solution architectures are designed, interoperability is expected to address many business challenges and speed up time to market. We see many digital transformation projects integrated with interrelated business processes to improve business performance. In the meantime, we see many businesses and industry solutions that are also built using blockchain technologies. Most of these blockchain solutions are running as stand-alone industry processes as a common practice. There is an increasing demand to interconnect and interoperate these blockchain solutions to enable end-to-end tracking and visibility. This demand also brings the need for the standardization of blockchain interoperability. Until now, we see most of the interoperability research projects concentrating on interconnecting 2 independent blockchain networks or they are mostly built for public cryptocurrency blockchain networks. Most of these projects have less or minimum standardization approaches and do not provide critical business privacy requirements. Part of these research projects is also breaking the general decentralized architecture of blockchain in many ways. Our proposed architectural approach and design principles are aiming to serve as a foundation for future private interoperability projects. Our proposed solution is using smart contracts to design a publish & subscribe architecture. This architecture can be used to integrate 2 or more private blockchains at smart contracts level.
HDFS Improvement Using Shortest Path Algorithms

In the previous version of the article “data processing on distributed systems: storage challenge” we presented a new approach for the storage, management and exploitation of distributed data in the form of small files, such as the case of message exchanges real-time localization in port activity. During this approach, we managed to optimize more than 30% of information management using the classic HADOOP/YARN/HDFS architecture [11]. Considering that in a HADOOP ecosystem with several data processing nodes, access to the right node, containing the desired data, in the optimal time presents a major challenge and very important research avenues for researchers and scientists [15]. In this paper, we will see together that the marriage between mathematical algorithms and computer magic can give us very encouraging and very important results. Indeed, one of the principle that manifests itself is the theory of graphs, especially the calculation of the shortest path to optimally reach the data on a few nodes in an architecture of a few hundred nodes or even thousands [16]. After several research and comparison, Dijkstra’s algorithm is the chosen algorithm for calculating the shortest path in a HADOOP/HDFS system.