Reviewerly/exHarmony huggingface dataset:
A framework for interoperability between models with hybrid tools

Complex system development and maintenance face the challenge of dealing with different types of models due to language affordances, preferences, sizes, and so forth that involve interaction between users with different levels of proficiency. Current conceptual data modelling tools do not fully support these modes of working. It requires that the interaction between multiple models in multiple languages is clearly specified to ensure they keep their intended semantics, which is lacking in extant tools. The key objective is to devise a mechanism to support semantic interoperability in hybrid tools for multi-modal modelling in a plurality of paradigms, all within one system. We propose FaCIL, a framework for such hybrid modelling tools. We design and realise the framework FaCIL, which maps UML, ER and ORM2 into a common metamodel with rules that provide the central point for management among the models and that links to the formalisation and logic-based automated reasoning. FaCIL supports the ability to represent models in different formats while preserving their semantics, and several editing workflows are supported within the framework. It has a clear separation of concerns for typical conceptual modelling activities in an interoperable and extensible way. FaCIL structures and facilitates the interaction between visual and textual conceptual models, their formal specifications, and abstractions as well as tracking and propagating updates across all the representations. FaCIL is compared against the requirements, implemented in crowd 2.0, and assessed with a use case. The proof-of-concept implementation in the web-based modelling tool crowd 2.0 demonstrates its viability. The framework also meets the requirements and fully supports the use case.
Blockchain and Cryptocurrency: Development Without Regulation?

Blockchain is a technology based on a chain of interconnected blocks containing transaction history and user data. Blockchain permitted the creation of crypto-currency. Among its advantages: decentralization, transparency, and integrity. This technology has increased especially with the advent of covid19 with the accentuation of the wave of digitalization. Classic finance systems felt overwhelmed by events and tried to catch up with this new wave, by creating their crypto-currency, and embarking on this new world of digital finance where regulation and control are non-existent. Many central banks see the introduction of central bank digital currencies (MDBC) as a response to these challenges. But the phenomenal expansion of these crypto-currencies could present risks in terms of transmission of monetary policy, monetary creation, and financial stability. In this work, we will present the blockchain, the evolution of crypto-currency, and the reaction classic finance systems to this wave of digitalization of transactions, and especially to an absence of regulation.
Storage State Analysis and Extraction of Ethereum Blockchain Smart Contracts

In migrating and upgrading an Ethereum smart contract, it is necessary to transfer both the code as well as the stored data. Various methods attempt to migrate or upgrade a smart contract, but they are mostly manual, error-prone, and applicable only before deployment. Further, they have challenges in extracting the storage state of complex mapping data structures along with their keys. In this work, we present Smartmuv as an automatic source-code-based static analysis tool to analyze and extract the state from the storage-trie of smart contracts. Based on the abstract syntax tree and the control flow graphs of the Solidity source code, the tool analyzes each state variable including mapping types along the inheritance hierarchy. It also provides the upgrade algorithm that initializes the extracted state in the constructor of new smart contract. Smartmuv safely approximates the origin of the keys used in the mapping to extract values and has been able to extract the mapping state of 23,673 smart contracts with 95.7% overall precision. Moreover, we also validate the Smartmuv’s extracted state with the third-party tool Etherscan.
Negative result about the construction of genuinely entangled subspaces from unextendible product bases

Unextendible product bases (UPBs) provide a versatile tool with various applications across different areas of quantum information theory. Their comprehensive characterization is thus of great importance and has been a subject of vital interest for over two decades now. An open question asks about the existence of UPBs, which are genuinely unextendible, i.e., they are not extendible even with biproduct vectors. In other words, the problem is to verify whether there exist genuinely entangled subspaces (GESs), subspaces composed solely of genuinely multiparty entangled states, complementary to UPBs. We solve this problem in the negative for many sizes of UPBs in different multipartite scenarios. In particular, in the all-important case of equal local dimensions, we show that there are always forbidden cardinalities for such UPBs, including the minimal ones corresponding to GESs of the maximal dimensions.
Dual Projective Zero-Shot Learning Using Text Descriptions

Zero-shot learning (ZSL) aims to recognize image instances of unseen classes solely based on the semantic descriptions of the unseen classes. In this field, Generalized Zero-Shot Learning (GZSL) is a challenging problem in which the images of both seen and unseen classes are mixed in the testing phase of learning. Existing methods formulate GZSL as a semantic-visual correspondence problem and apply generative models such as Generative Adversarial Networks and Variational Autoencoders to solve the problem. However, these methods suffer from the bias problem since the images of unseen classes are often misclassified into seen classes. In this work, a novel model named the Dual Projective model for Zero-Shot Learning (DPZSL) is proposed using text descriptions. In order to alleviate the bias problem, we leverage two autoencoders to project the visual and semantic features into a latent space and evaluate the embeddings by a visual-semantic correspondence loss function. An additional novel classifier is also introduced to ensure the discriminability of the embedded features. Our method focuses on a more challenging inductive ZSL setting in which only the labeled data from seen classes are used in the training phase. The experimental results, obtained from two popular datasets—Caltech-UCSD Birds-200-2011 (CUB) and North America Birds (NAB)—show that the proposed DPZSL model significantly outperforms both the inductive ZSL and GZSL settings. Particularly in the GZSL setting, our model yields an improvement up to 15.2% in comparison with state-of-the-art CANZSL on datasets CUB and NAB with two splittings.
Application of Virtual Reality Technology Based on Artificial Intelligence in Sports Skill Training

With the rapid development of science and technology and economy, it has a great impact on people’s lifestyle and habits. Fewer and fewer people will actually carry out offline sports activities, and some will choose gyms and other places for offline exercise, but the high cost of gyms limits most people’s desire to exercise. Our country has been actively introducing policies or measures to advocate physical exercise, but people’s enthusiasm is not high or improper training leads to frequent physical injury. In view of the current situation and pain points of sports skill training, this paper first discusses the possibility of the overall application of virtual reality technology based on artificial intelligence in sports skill training. Secondly, it explores the application of virtual reality technology based on artificial intelligence in tennis, which is a highly skilled sports training. Finally, combined with the practical application of virtual reality technology based on artificial intelligence in sports skills training, it proves the advantages and influence of applying virtual reality technology based on artificial intelligence in sports skills training in the future.
The Heavy Mineral Map of Australia: Vision and Pilot Project

We describe a vision for a national-scale heavy mineral (HM) map generated through automated mineralogical identification and quantification of HMs contained in floodplain sediments from large catchments covering most of Australia. The composition of the sediments reflects the dominant rock types in each catchment, with the generally resistant HMs largely preserving the mineralogical fingerprint of their host protoliths through the weathering-transport-deposition cycle. Heavy mineral presence/absence, absolute and relative abundance, and co-occurrence are metrics useful to map, discover and interpret catchment lithotype(s), geodynamic setting, magmatism, metamorphic grade, alteration and/or mineralization. Underpinning this vision is a pilot project, focusing on a subset from the national sediment sample archive, which is used to demonstrate the feasibility of the larger, national-scale project. We preview a bespoke, cloud-based mineral network analysis (MNA) tool to visualize, explore and discover relationships between HMs as well as between them and geological settings or mineral deposits. We envisage that the Heavy Mineral Map of Australia and MNA tool will contribute significantly to mineral prospectivity analysis and modeling, particularly for technology critical elements and their host minerals, which are central to the global economy transitioning to a more sustainable, lower carbon energy model.
Task scheduling to a virtual machine using a multi‐objective mayfly approach for a cloud environment

Summary Cloud computing has been progressively popular in the arenas of research and business in the recent years. Virtualization is a resource management approach used in today's cloud computing environment. Virtual Machine (VM) migration algorithms allow for more dynamic resource allocation, as well as improvement in computing power and communication capability in cloud data centers. This necessitates an intelligent optimization approach to VM allocation design for an improved performance of application. In this article, a multi‐objective optimal design approach is proposed to tackle the tasks of VM allocation. Multi‐Objective Optimization (MOO) is a strategy adopted by several methods to handle tasks and workflow scheduling issues that deal with numerous opposing goals. In the cloud computing context, effective task scheduling is critical for achieving cost effective implementation as well as resource utilization. To address the optimal solution, this article proposes an entropy‐based multi objective mayfly algorithm is assessed using a convergence pattern in MOO. The model is tested by implementing in a cloud simulator and results prove that the recommended model has an improved performance with regard to factors such as time and utilization rate.
Application of Deep Learning Workflow for Autonomous Grain Size Analysis

Traditional grain size determination in materials characterization involves microscopy images and a laborious process requiring significant manual input and human expertise. In recent years, the development of computer vision (CV) has provided an alternative approach to microstructural characterization with preliminary implementations greatly simplifying the grain size determination process. Here, an end-to-end workflow to measure grain size in microscopy images without any manual input is presented. Following the ASTM standards for grain size determination, results from the line intercept (Heyn's method) and planimetric (Saltykov's method) approaches are used as the baseline. A pre-trained holistically nested edge detection (HED) model is used for CV-based edge detection, and the results are further compared to the classic Canny edge detection method. Post-processing was performed using open-source image processing packages to extract the grain size. In optical microscope images, the pre-trained HED model achieves much higher accuracy than the Canny edge detection method while reducing the image processing time by one to two orders of magnitude compared to traditional methods. The effects of morphological operations on the predicted grain size accuracy are also explored. Overall, the proposed end-to-end convolutional neural network (CNN)-based workflow can significantly reduce the processing time while maintaining the same accuracy as the traditional manual method.
Recognition Method of Massage Techniques Based on Attention Mechanism and Convolutional Long Short-Term Memory Neural Network

Identifying the massage techniques of the masseuse is a prerequisite for guiding robotic massage. It is difficult to recognize multiple consecutive massage maps with a time series for current human action recognition algorithms. To solve the problem, a method combining a convolutional neural network, long-term neural network, and attention mechanism is proposed to identify the massage techniques in this paper. First, the pressure distribution massage map is collected by a massage glove, and the data are enhanced by the conditional variational auto-encoder. Then, the features of the massage map group in the spatial domain and timing domain are extracted through the convolutional neural network and the long- and short-term memory neural network, respectively. The attention mechanism is introduced into the neural network, giving each massage map a different weight value to enhance the network extraction of data features. Finally, the massage haptic dataset is collected by a massage data acquisition system. The experimental results show that a classification accuracy of 100% is achieved. The results demonstrate that the proposed method could identify sequential massage maps, improve the network overfitting phenomenon, and enhance the network generalization ability effectively.
Target layer regularization for continual learning using Cramer-Wold distance

We propose an effective regularization strategy (CW-TaLaR) for solving continual learning problems. It uses a penalizing term that is based on measuring the dissimilarity between two probability distributions defined on a target layer of an underlying neural network shared by all tasks. We consider two complementary ways of modeling a target layer distribution that our strategy preserves while learning a new task, both of them trained and applied with the use of the Cramer-Wold distance. Simultaneously, we do not require remembering previous tasks datasets. We perform experiments involving several common supervised frameworks, which prove the competitiveness of the CW-TaLaR method in comparison to a few existing state-of-the-art continual learning models.
Challenges in Tracing Proxy Addresses by Mining Bitcoin Fraternize Service Transactions

Bitcoin plays a major role in digital online transactions with decentralized scattered Peer-to-Peer systems. Cryptocurrency framework permits individuals to exchange with proxy addresses in Fraternize services. Nowadays, Bitcoin users have been increasing rapidly due to various Fraternize services. Due to this, some illicit activities were happening by creating unknown addresses. We surveyed machine learning algorithms like Decision Tree and Random Forest to classify illicit transactions in the Bitcoin network which helps to improve True Positive rate (TPR), and also reviewed various Fraternize services to identify proxy addresses and trace the ownership of Bitcoin transactions. Furthermore, examines the challenges of tracing the proxy addresses in the Bitcoin ecosystem after fund transaction. For further implementation, Bitcoin Transaction Datasets were acquired from Kaggle.
New Image Encryption Algorithm based on Pixel Confusion-Diffusion using Hash Functions and Chaotic Map

Information privacy and security has become a necessity in the rapid growth of computer technology. A new algorithm for image encryption is proposed in this paper; using hash functions, chaotic map and two levels of diffusion process. The initialization key for chaos map is generated with the help of two hash functions. The initial seed for these hash functions is the sum of rows, columns and pixels across the diagonal of the plain image. Firstly, the image is scrambled using quantization unit. In the first level of diffusion process, the pixel values of the scrambled image are XOR with the normalized chaotic map. Odd pixel value is XOR with an even bit of chaotic map and even pixel is XOR with an odd bit of chaotic map. To achieve strong encryption, the image undergoes a second level of diffusion process where it is XOR with the map a finite number of times. After every round, the pixel array is circular shifted three times to achieve a strong encrypted image. The experimental and comparative analysis done with state of the art techniques on the proposed image encryption algorithm shows that it is strong enough to resist statistical and differential attacks present in the communication channel.
Multi‐objective task scheduling in cloud computing

Summary Cloud computing services are used to fulfill user requests, often expressed in the form of tasks and their execution in such environments requires efficient scheduling strategies that take into account both algorithmic and architectural characteristics. Unfortunately, this problem is known to be NP‐hard in its general form. Despite the fact that several studies have been published in the literature, there are still interesting and relevant questions to be addressed. Indeed, most of the previous studies focus on a single objective and in the case where they deal with a set of objectives, they use a simple compromise function and do not consider how each of the parameters might influence the others. To this end, we propose an efficient task scheduling algorithm which is based on the pollination behavior of flowers and makes use of both Pareto optimality principle and TOPSIS technique to improve the quality of the obtained solutions. Both single and multiobjective optimization variants are investigated. In the latter case, three optimization criteria are considered, namely, minimizing the time makespan or schedule length, the execution cost, and maximizing the overall reliability of the task mapping. Different test‐bed scenarios and QoS metrics were considered and the obtained results corroborate the merits of the proposed algorithm.
Real-time 3D measurement of freeform surfaces by dynamic deflectometry based on diagonal spatial carrier-frequency pattern projection

Deflectometry is a three-dimensional optical technique based on structured light projection for measuring and inspecting specular freeform surfaces. Conventional deflectometry method needs complicated calibration and time-consuming phase-extraction algorithm, which are not suitable for industrial metrology requiring dynamic and high-speed measurement. In this paper, a novel dynamic deflectometry method with a simple system setup and calibration is proposed for the three-dimensional measurement of specular freeform surfaces with high speed and high accuracy. We developed a very simple calibration method using a reference flat mirror which helps to save calibration time and computer resources. We also proposed a one-shot method by using a spatial carrier-frequency diagonal fringe pattern for screen projection along with an algorithm to separate a diagonal phase into vertical and horizontal phases. We extensively evaluated the feasibility of proposed calibration and one-shot method by different simulations and measuring several specular surfaces experimentally. Our novel deflectometry gives similar measurement accuracy compared to traditional contact measurement technique while offering much higher measurement speed. Dynamic surface shape measurement of silicon wafer due to temperature’s variation proved the real-time measurement ability of our proposed method.
EdgeWorkflow: One click to test and deploy your workflow applications to the edge

In recent years, edge computing has become the ideal computing paradigm for various smart systems, such as smart logistics, smart health and smart transportation. This is due to its advantages including fast response times, energy efficiency and cost effectiveness over conventional cloud computing platforms. However, running complex computational scientific workflow tasks is still a very challenging issue at the edge, due to its typical three-layered computing environment consisting of an end device layer, an edge server layer, and a cloud server layer. A large number of recent studies have proposed different solutions for optimizing such computing resource management problems in an edge computing environment. However, since evaluation of most such studies is conducted through simulation, the effectiveness cannot be guaranteed in a real world environment. Therefore, to advance research on efficient execution and deployment problems for real world workflow applications using edge computing, an open-source edge workflow management system with comprehensive empirical evaluation capabilities is urgently required. This paper presents the first edge workflow system (named EdgeWorkflow) that is able to deploy user-created workflow applications to a real-world edge computing environment with "one-click" after optimizing the configuration with the simulation tool. With the aid of EdgeWorkflow, the user can automate the generation of specific edge computing environments, easily model and generate executable workflow applications with a visual modelling tool, effectively select various resource management methods included in the systems or apply their own resource management and task scheduling algorithms, efficiently monitor the statuses of computational tasks and obtain comprehensive reports on the execution results (such as those regarding time, cost and energy). We use an edge computing-based unmanned aerial vehicle (UAV) last-mile delivery system as a real-world case study, and a number of representative scientific workflows are employed for our experiments. Our experimental results show that EdgeWorkflow can effectively evaluate the performance of different resource management and workflow task scheduling algorithms and efficiently deploy and execute user-defined scientific workflow applications to user-specified edge computing environments.
Optimal visual tracking using Wasserstein transport proposals

We propose a novel visual tracking method based on the Wasserstein transport proposal (WTP). In this study, we theoretically derive the optimal proposal function in Markov chain Monte Carlo (MCMC) based visual tracking frameworks. For this objective, we adopt the optimal transport theory in the Wasserstein space and present a new transport map that can transform from a simple proposal distribution to the optimal target distribution. To find the best transport map, we conduct an additional Monte Carlo simulation. Experimental results demonstrate that the proposed method outperforms other state-of-the-art visual tracking methods. The proposed WTP can be substituted with conventional proposal functions in an MCMC framework, and thus can be plugged into any existing MCMC-based visual tracker. • We propose a visual tracker using the optimal proposal function in MCMC. • We use a Wasserstein transport proposal (WTP) as the optimal proposal function. • The proposed WTP is highly applicable to conventional MCMC frameworks.