Reviewerly/exHarmony huggingface dataset:
Proposal for a Comprehensive (Crypto) Asset Taxonomy

Developments in the distributed ledger technology have led to new types of assets with a broad range of purposes. Although some classification frameworks for common instruments from traditional finance and some for these new, so-called cryptographic assets already exist and are used, a holistic approach to integrate both worlds is missing. The present paper Our research is part of the FinTech programme supported by Finnova, Inventx, Swiss Bankers Prepaid Services, and Swisscom. fills this research gap by identifying 14 attributes, each of which is assigned different characteristics, that can be used to classify all types of assets in a structured manner. Our proposed taxonomy, which is an extension of existing classification frameworks, summarises these findings in a morphological box and is tested for practicability by classifying exemplary assets like cash and bitcoin. The final classification framework can help to ensure that the various stakeholders, such as investors or supervisors, have a consistent view of the different types of assets, and in particular of their characteristics, and also helps to establish standardised terminology.
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks

We study the problem of PAC learning one-hidden-layer ReLU networks\nwith $k$ hidden units\non $\R^d$ under Gaussian marginals in the presence of additive label noise. \nFor the case of positive coefficients, we give the first polynomial-time algorithm \nfor this learning problem for $k$ up to $\tilde{\Omega}(\sqrt{\log d})$. \nPreviously, no polynomial time algorithm was known, even for $k=3$.\nThis answers an open question posed by~\cite{Kliv17}. Importantly,\nour algorithm does not require any assumptions about the rank of the weight matrix\nand its complexity is independent of its condition number. On the negative side,\nfor the more general task of PAC learning one-hidden-layer ReLU networks with positive or negative coefficients, \nwe prove a Statistical Query lower bound of $d^{\Omega(k)}$. Thus, we provide a \nseparation between the two classes in terms of efficient learnability.\nOur upper and lower bounds are general, extending to broader families of activation functions.
Solving SAT (and MaxSAT) with a quantum annealer: Foundations, encodings, and preliminary results

Quantum annealers (QAs) are specialized quantum computers that minimize objective functions over discrete variables by physically exploiting quantum effects. Current QA platforms allow for the optimization of quadratic objectives defined over binary variables (qubits), also known as Ising problems. In the last decade, QA systems as implemented by D-Wave have scaled with Moore-like growth. Current architectures provide 2048 sparsely-connected qubits, and continued exponential growth is anticipated, together with increased connectivity. We explore the feasibility of such architectures for solving SAT and MaxSAT problems as QA systems scale. We develop techniques for effectively encoding SAT –and, with some limitations, MaxSAT– into Ising problems compatible with sparse QA architectures. We provide the theoretical foundations for this mapping, and present encoding techniques that combine offline Satisfiability and Optimization Modulo Theories with on-the-fly placement and routing. Preliminary empirical tests on a current generation 2048-qubit D-Wave system support the feasibility of the approach for certain SAT and MaxSAT problems.
Analyzing Deep Learning Model Inferences for Image Classification using OpenVINO

It may be desirable to execute deep learning model inferences on an integrated GPU at the edge. While such GPUs are much less powerful than discrete GPUs, it is able to deliver higher floating-point operations per second than a CPU located on the same die. For edge devices, the benefit of moving to lower precision with minimal loss of accuracy to obtain higher performance is also attractive. Hence, we chose 14 deep learning models for image classification to evaluate their inference performance with the OpenVINO toolkit. Then, we analyzed the implementation of the fastest inference model of all the models. The experimental results are promising. Compared to the performance of full-precision (FP32) models, the speedup of the 8-bit (INT8) quantization ranges from 1.02 to 1.56 on an Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Xeon <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> 4-core CPU, and the speedup of the FP16 models ranges from 1.1 to 2 on an Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Iris <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> Pro GPU. For the FP32 models, the GPU is on average 1.5X faster than the CPU.
Dynamics of entangled networks of the quantum Internet

Entangled quantum networks are a fundamental of any global-scale quantum Internet. Here, a mathematical model is developed to quantify the dynamics of entangled network structures and entanglement flow in the quantum Internet. The analytical solutions of the model determine the equilibrium states of the entangled quantum networks and characterize the stability, fluctuation attributes, and dynamics of entanglement flow in entangled network structures. We demonstrate the results of the model through various entangled structures and quantify the dynamics.
Social Group Recommendation With TrAdaBoost

In recent years, group recommendation has become a research hotspot and focus in online social network community. Currently, several deep-learning-based approaches are leveraged to learn preferences of groups for items and predict the next items in which groups may be interested. Yet, their recommendation performance is still unsatisfactory due to the sparse group-item interactions. In order to address this problem, in this article, we introduce an effective model, namely Social Group Recommendation model with TrAdaBoost (SGRTAB), to raise the performance of group recommendation in online social networks. The SGRTAB model includes two stages: data preprocessing (DP) and model optimization (MO). In DP, SGRTAB produces inputs for MO and implements three related tasks: extracting individual features, handling group data via GloVe, and utilizing user contribute ratings to their own groups, whereas in MO, SGRTAB implements group preference learning with the assistance of user preference learning based on the TrAdaBoost algorithm. Specifically, SGRTAB can effectively absorb the knowledge of user preferences into the process of group preference learning through the idea of transferring-ensemble learning. Moreover, extensive experiments on four real-world data sets indicate that the proposed SGRTAB model significantly outperforms the state-of-the-art baselines for social group recommendation.
Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers

‘‘Qubit routing” refers to the task of modifying quantum circuits so that they satisfy the connectivity constraints of a target quantum computer. This involves inserting SWAP gates into the circuit so that the logical gates only ever occur between adjacent physical qubits. The goal is to minimise the circuit depth added by the SWAP gates. In this article, we propose a qubit routing procedure that uses a modified version of the deep Q-learning paradigm. The system is able to outperform the qubit routing procedures from two of the most advanced quantum compilers currently available (Qiskit and t \( | \) ket \( \rangle \) ), on both random and realistic circuits, across a range of near-term architecture sizes (with up to 50 qubits).
Transaction Privacy Preservations for Blockchain Technology

Blockchain is defined as a technology comprising of logic unit of work done in form of transactions where any read or write operation can be carried out and stored in form of blocks where any financial accounts can be accessed by different servers. This is widely used technology due to its tremendous applicability such as independent verification without the involvement of any third party, no alteration or deletion of data, use of cryptography for secure financial accounts, sequential recording of transactions, pure transparent process of transaction. Blockchain follows both private and public type of architecture. In public model, anyone can read and write while in private only authorized users have the access. Security plays a vital role where secure transactions are to be carried out. Main principles of security such as confidentiality which implies the secrecy of send message, integrity which implies the consistency of send and received message and availability which implies information required must be available for reference, which must be preserved in any security-based model. In order to reduce the impact of attacks, various security algorithms such as cryptography, ring signature, Non-interactive Zero- Knowledge Proof, CryptoNote and Monero, Homomorphic Cryptosystem system are introduced in the paper.
Toward Representing Research Contributions in Scholarly Knowledge Graphs Using Knowledge Graph Cells

There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. Toward this end, in this work, we propose a novel semantic data model for modeling the contribution of scientific investigations. Our model, i.e. the Research Contribution Model (RCM), includes a schema of pertinent concepts highlighting six core information units, viz. Objective, Method, Activity, Agent, Material, and Result, on which the contribution hinges. It comprises bottom-up design considerations made from three scientific domains, viz. Medicine, Computer Science, and Agriculture, which we highlight as case studies. For its implementation in a knowledge graph application we introduce the idea of building blocks called Knowledge Graph Cells (KGC), which provide the following characteristics: (1) they limit the expressibility of ontologies to what is relevant in a knowledge graph regarding specific concepts on the theme of research contributions; (2) they are expressible via ABox and TBox expressions; (3) they enforce a certain level of data consistency by ensuring that a uniform modeling scheme is followed through rules and input controls; (4) they organize the knowledge graph into named graphs; (5) they provide information for the front end for displaying the knowledge graph in a human-readable form such as HTML pages; and (6) they can be seamlessly integrated into any existing publishing process thatsupports form-based input abstracting its semantic technicalities including RDF semantification from the user. Thus RCM joins the trend of existing work toward enhanced digitalization of scholarly publication enabled by an RDF semantification as a knowledge graph fostering the evolution of the scholarly publications beyond written text.
Optimization of FireNet for Liver Lesion Classification

In recent years, deep learning techniques, and in particular convolutional neural networks (CNNs) methods have demonstrated a superior performance in image classification and visual object recognition. In this work, we propose a classification of four types of liver lesions, namely, hepatocellular carcinoma, metastases, hemangiomas, and healthy tissues using convolutional neural networks with a succinct model called FireNet. We improved speed for quick classification and decreased the model size and the number of parameters by using fire modules from SqueezeNet. We have used bypass connection by adding it around Fire modules for learning a residual function between input and output, and to solve the vanishing gradient problem. We have proposed a new Particle Swarm Optimization (NPSO) to optimize the network parameters in order to further boost the performance of the proposed FireNet. The experimental results show that the parameters of FireNet are 9.5 times smaller than GoogLeNet, 51.6 times smaller than AlexNet, and 75.8 smaller than ResNet. The size of FireNet is reduced 16.6 times smaller than GoogLeNet, 75 times smaller than AlexNet and 76.6 times smaller than ResNet. The final accuracy of our proposed FireNet model was 89.2%.
Meditation for level of institutional quality to combat income inequality through financial development

Abstract Institutional role to perpetuate the relationship between financial development and income inequality has significant importance from policy perspectives. The broadening income inequality and its procurement through financial sector is attracting the worldwide attention of researchers now days. This study is an attempt to investigate the relationship of income inequality and financial development varying with level of institutional quality. To this end, unbalanced panel data set for 124 countries covering the time span 1990–2015 is used applying the instrumental variable threshold regression approach. The estimated results highlight that embedded efficient institutional framework of a country assures more income equality with financial development. Moreover, GDP growth increases the income inequality if institutional quality is below the threshold level while educational attainment is a major factor to decrease it. In respect of policy implications, financial sector may be fruity to overcome income inequality if policy makers bring the level of institutional quality up to threshold level of that country.
Self-organizing migrating algorithm with clustering-aided migration

This paper proposes a novel migration strategy for Self-organizing Migrating Algorithm (SOMA), which combines advantages of the explorative All-To-Random migration with new exploitation focused All-To-Cluster-Leaders strategy. The main goal of this novel innovation to SOMA is to deliver competitive results, not only on the latest CEC 2020 benchmark set on a single objective bound-constrained numerical optimization. The proposed algorithm variant was titled SOMA-CL, and it has manifested notable potential in such demanding challenges. The results of the proposed algorithm were compared and tested for statistical significance against two other SOMA variants.
Statistical properties of sketching algorithms

Sketching is a probabilistic data compression technique that has been largely developed by the computer science community. Numerical operations on big datasets can be intolerably slow; sketching algorithms address this issue by generating a smaller surrogate dataset. Typically, inference proceeds on the compressed dataset. Sketching algorithms generally use random projections to compress the original dataset, and this stochastic generation process makes them amenable to statistical analysis. We argue that the sketched data can be modelled as a random sample, thus placing this family of data compression methods firmly within an inferential framework. In particular, we focus on the Gaussian, Hadamard and Clarkson-Woodruff sketches and their use in single-pass sketching algorithms for linear regression with huge samples. We explore the statistical properties of sketched regression algorithms and derive new distributional results for a large class of sketching estimators. A key result is a conditional central limit theorem for data-oblivious sketches. An important finding is that the best choice of sketching algorithm in terms of mean squared error is related to the signal-to-noise ratio in the source dataset. Finally, we demonstrate the theory and the limits of its applicability on two datasets.
VaultPoint: A Blockchain-Based SSI Model that Complies with OAuth 2.0

An identity management including authentication and authorization in a network environment is a critical security factor. Various models for identity management have been developed continually, from the silo model to the federated model and to the recently introduced self-sovereign identity (SSI) model. In particular, SSI makes users manage their own information by themselves independently of any organizations. SSI utilizes the newly emerged blockchain technology and many studies of it are in progress. However, SSI has not had wide public use because of its low compatibility and inconvenience. This is because it involves an unfamiliar user experience and an immature process. To solve this problem, this paper proposes a new blockchain-based SSI model that complies with the popular and mature standard of OAuth 2.0. Using blockchain, the proposed model secures users’ data sovereignty where users can use and control their own information in a decentralized manner, instead of depending on a specific monopolistic service-providers. Users and clients who are familiar with the existing OAuth can easily accept the proposed model and apply it, which makes both usability and scalability of the model excellent. This paper confirmed the feasibility of the proposed model by implementing it and a security analysis was performed. The proposed model is expected to contribute to the expansion of both blockchain technology and SSI.
Hotspots and Trends of Virtual Reality, Augmented Reality and Mixed Reality in Education Field

With developments in computer graphics and simulation technology, virtual reality (VR), augmented reality (AR) and mixed reality (MR) have been more widely used in several areas of education. To explore and predict hotspots and research trends of VR, AR and MR in education, knowledge mapping is used to provide visualizations of publishing trends, countries, institutions and author co-citations based on Web of Science database in the last ten years. Nine hotspots and trends, including cognitive load, learning environment, knowledge acquisition, training, gamification, assessment, children education, engineering education, and higher medical education, are discovered in the study to show us future research directions in the education field.
A Survey on Applications of Siamese Neural Networks in Computer Vision

Computer Vision nowadays uses many Deep Learning techniques in order to make the computer learn data representations from images and image sequences (as in videos). One of the important tasks in this respect is learning the similarity between two given images, which can be readily accomplished by learning a similarity criterion between the images. This can be readily accomplished using Siamese Convolutional Neural Networks (Siamese CNNs). Siamese CNNs can learn a similarity criterion between various kinds of image pairs. The paper presents a survey, which deals with the study of some remarkable papers which have used Siamese CNNs and triplet nets (which are a variation of the Siamese CNNs) in order to learn how similar are two images to one another.
Transfer learning-based Object Detection by using Convolutional Neural Networks

Object detection has become an important task for various purposes in our daily lives. Machine learning techniques have been used for this task from earlier but they are used for the classification of image-based species to extract the feature set. This task of deciding the feature set helps to decide the desired object detection. To overcome the object classification problern, this paper proposes a transfer learning-based deep learning method. The different convolutional neural networks (CNN) are studied in this work. Here for the improvement in the result, the majority voting scheme is used. The overall work is carried out on the CUB 200-2011 dataset. The results obtained have shown incredible improvement in the accuracy of the proposed work when compared to the different CNN models.
Security of multiple <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si12.svg"><mml:mi mathvariant="sans-serif">RGB</mml:mi></mml:math> images by RSA cryptosystem combined with FrDCT and Arnold transform

The security, efficiency, and storage of image data are global issues while transmitting images over the Internet. In this paper, we have fabricated a novel approach for security of multiple-color images utilizing asymmetric RSA cryptosystem, reality preserving fractional discrete cosine transform and Arnold transform. In our approach, three indexed images are obtained from three RGB color images by extracting their color maps. These three indexed images are taken as red, green and blue components of an RGB color image. Each element of an RGB image is first encrypted using RSA cryptosystem. The fractional discrete cosine transform is applied on the partially encrypted image which transforms the image data from the time domain to the frequency domain. Finally, Arnold transform dislocates the processed image. The security of the similar encryption schemes available in literature depends upon the secret keys only. If the secret keys are leaked, the encryption scheme is no more secure. Also, these schemes are secure either in the time domain or in the frequency domain. However, the security of our approach depends upon secret keys and their proper arrangements. The proposed method is secure in the time domain as well as in the frequency domain. Due to a single encrypted image, it is easy for storage and transmission over the Internet. Simulation analysis is done with standard examples to conclude that the proposed approach is robust.
Can I believe what I see? Data visualization and trust in the humanities

Questions of trust are increasingly important in relation to data and its use. The authors focus on humanities data and its visualization, through analysis of their own recent projects with museums, archives and libraries internationally. Their account connects the specifics of hands-on digital humanities work to larger epistemological questions. They discuss the sources of potential mistrust, and examine how different expectations and assumptions emerge depending on the use and user of the data; they offer a simple schema through which the implications may be traced. It is argued that vital issues of trust can be engaged with through design, which, rather than being conceived as a cosmetic finish, is seen as contributing insights and questions that affect the whole process. The article concludes with recommendations intended to be useful in both theory and practice.
Smart contract for distributed energy trading in virtual power plants based on blockchain

Abstract The energy system is evolving from smart grid to energy Internet. Virtual power plant (VPP), as an important part of the energy Internet, plays an important role in the distributed energy generation and trading. In this article, a blockchain‐based VPP transaction model is established for the future energy Internet driven by real‐time electricity price. Then the smart contracts for distributed energy trading in VPPs using blockchain technology are proposed, and the key technological difficulties are analyzed and the solutions are given. Experiments show that the proposed model can reflect the supply and demand information in real time, so that two‐way selection can be carried out under the condition of information symmetry when distributed energy is connected to the grid. If our method is applied, we can help distributed energy suppliers set electricity prices, reduce the trust cost and improve the energy trading efficiency. Also, we can help the distributed energy voluntarily participate in VPPs and joint maintain the system, then solve the problem of VPP's coordinated control and scheduling of distributed energy resources.