Reviewerly/exHarmony huggingface dataset:
Self-Learning Machines Based on Hamiltonian Echo Backpropagation

A physical self-learning machine can be defined as a nonlinear dynamical system that can be trained on data (similar to artificial neural networks) but where the update of the internal degrees of freedom that serve as learnable parameters happens autonomously. In this way, neither external processing and feedback nor knowledge of (and control of) these internal degrees of freedom is required. We introduce a general scheme for self-learning in any time-reversible Hamiltonian system. It relies on implementing a time-reversal operation and injecting a small error signal on top of the echo dynamics. We show how the physical dynamics itself will then lead to the required gradient update of learnable parameters, independent of the details of the Hamiltonian. We illustrate the training of such a self-learning machine numerically for the case of coupled nonlinear wave fields and other examples.2 MoreReceived 26 May 2021Revised 4 June 2023Accepted 11 July 2023DOI:https://doi.org/10.1103/PhysRevX.13.031020Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Open access publication funded by the Max Planck Society.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Physical SystemsArtificial neural networksHamiltonian systemsNonlinear wavesNonlinear DynamicsInterdisciplinary PhysicsNetworks
UX of data

This forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations. --- Juho Kim and Henriette Cramer, Editors
Improving cybersecurity awareness using phishing attack simulation

Abstract This paper aims to present a case study of the cybersecurity awareness level of the workers in a financial institution in Thailand. In this study, there were 2 phases of cyber drills using a spear-phishing email. The first phase was conducted with more than 20,300 workers. It was found that there were 23.25% of potential victims. However, the second phase was conducted four months later with more than 20,200 workers after performing knowledge transfer. The number of workers who opened phishing emails in the second phase decreased to 6.76% only. Therefore, it can be indicated that an appropriate cybersecurity knowledge transfer can reduce a large number of potential victims and cyber threats that may occur in such organizations.
Suffix array for multi-pattern matching with variable length wildcards

Approximate multi-pattern matching is an important issue that is widely and frequently utilized, when the pattern contains variable-length wildcards. In this paper, two suffix array-based algorithms have been proposed to solve this problem. Suffix array is an efficient data structure for exact string matching in existing studies, as well as for approximate pattern matching and multi-pattern matching. An algorithm called MMSA-S is for the short exact characters in a pattern by dynamic programming, while another algorithm called MMSA-L deals with the long exact characters by the edit distance method. Experimental results of Pizza &amp; Chili corpus demonstrate that these two newly proposed algorithms, in most cases, are more time-efficient than the state-of-the-art comparison algorithms.
Single Image Motion Deblurring Based On Modified DenseNet

Motion deblurring is one of the important research problems in image processing. Images get blurred due to camera shakes or moving objects during the camera exposure time. In this paper, we propose a deep convolutional neural network based on DenseNet to handle the blind deconvolution problem. The introduction of atrous convolution allows us to expand the receptive field when extracting the image features globally and locally. Besides, we propose both pixel level and perceptual level loss function to better describe the optimization procedure. Visual and quantitative comparisons are made to evaluate the sharp images between the state-of-the-art method and ours, revealing the effectiveness of our work.
Globalisation and Female Economic Participation in Sub-Saharan Africa

This study assesses the relationship between globalisation and the economic participation of women (EPW) in 47 Sub-Saharan African countries for the period 1990-2013. EPW is measured with the female labour force participation and employment rates. The empirical evidence is based on Panel-corrected Standard Errors and Fixed Effects regressions. The findings show that the positive effect of the overall globalisation index on EPW is dampened by its political component and driven by its economic and social components, with a higher positive magnitude from the former or economic globalisation. For the most part, the findings are robust to the control for several structural and institutional characteristics. An extended analysis by unbundling globalisation shows that the positive incidence of social globalisation is driven by information flow (compared to personal contact and cultural proximity) while the positive effect of economic globalisation is driven by actual flows (relative to restrictions). Policy implications are discussed with some emphasis on how to elevate women’s social status and potentially reduce their victimisation to male dominance.
A Novel Memory-hard Password Hashing Scheme for Blockchain-based Cyber-physical Systems

There has been an increasing interest of integrating blockchain into cyber-physical systems (CPS). The design of password hashing schemes (PHSs) is in the core of blockchain security. However, no existing PHS seems to meet both the requirements of sufficient security and small code size for blockchain-based CPSs. In this article, a novel memory-hard PHS based on the classic PBKDF2 is proposed. Evaluation results show that the proposed scheme is promising for blockchain-based CPS, as it manages to provide enhanced security in comparison to PBKDF2 with limited increase in code size.
Analysis on the Weight initialization Problem in Fully-connected Multi-layer Perceptron Neural Network

In this paper, the weight initialization problem in fully connected multi-layer perceptron neural networks (MLPNN) is studied. A structure as 8-10-1 MLP neural network is taken as the experimental object. The training and predicting dataset selected in this paper is California housing price dataset. Eight special weight initialization methods are analyzed and five of them are tested by simply using the basic neural network with structure as 8-10-1 MLP to simulate one common neural network, visualize and compare the weight change process when using random weight initialization and its special weight initialization method. This paper implements the aim that identifying the influence of their special weight initialization method on their weight change process, focusing on what specific problems are solved by these special weight initialization methods to improve the convergence speed and accuracy and verifying the hypothesis that these specific weight initialization method will generate better result with high convergence speed and accuracy than random weight initialization.
Towards Strengthening Deep Learning-based Side Channel Attacks with Mixup

In recent years, various deep learning techniques have been exploited in side channel attacks, with the anticipation of obtaining more satisfactory attack results. Most of them con-centrate on improving network architectures or putting forward novel metrics, assuming that there are adequate profiling traces available to train an appropriate neural network. However, in practical scenarios, profiling traces are probably insufficient, which makes the network learn deficiently and compromises attack performance. In this paper, we investigate a kind of data augmentation technique, called mixup, and first propose to exploit it in deep-learning based side channel attacks, for the purpose of expanding the profiling set and facilitating the chances of mounting a successful attack. We utilize mixup to generate new traces and perform Correlation Power Analysis for generated traces and original traces. The analysis reveals that the leakage location and leakage intensity between them are consistent. In view of this observation, we consider it feasible to add generated traces to the original profiling set. Our verifying experiments show that mixup is truly capable of enhancing attack performance especially for insufficient profiling traces. Specifically, when the size of the training set is decreased to 30% of the whole set, mixup can almost reduce required attacking traces to half. We test three mixup parameter values and conclude that generally all of them can bring about improvements. Besides, we compare three leakage models and surprisingly discover that least significant bit model, which is less frequently used in previous works, actually surpasses prevalent identity model and hamming weight model in terms of attack results.
Security System for Railway Crossings using Machine Learning

The train accidents are very devastating incidents which results in serious injuries or even deaths. Mainly in India, the people don't know the exact timing of closing the railway crossing gates. Due to this many get stuck between the gates while train is passing, which may lead severe accidents. The gates are also manually operated which takes a lot of time. Therefore, it needs to improve security system at railway crossing gates as the technology moves towards the higher level. In this paper we will solve the issue of manually operated railway crossing gates by transforming it into fully automatic system operated through wirelessly by station master using Arduino UNO and ultrasonic sensor. The second objective is to detect the number of persons and vehicles remain on the tracks after closing of gates by the help of OpenCV (Open Source Computer Vision Library) with buzzer and led bulb connected to it to give alerts during opening and closing of gates. In addition to this, a dot matrix display is connected with the gates to show time of closing of gate. Firstly, the system approaches to solve the time consumption issue of manually operated railway gateway system. Secondly, to reduce the number of accidents or any casualties and continuously checking the status after closing of gates. The timer shows the time for closing of railway crossing gates to avoid people trapped between the gates.
Sparse Representations-based depth images quality assessment

The conventional 2D metrics can be used for measuring the quality of depth maps, but none of them is considered to be efficient and is not accurate when used for evaluating 3D quality. In this paper, we propose a new full reference objective metric, called Sparse Representations-Mean Squared Error (SR-MSE), which efficiently evaluates the depth maps compression distortions. It adaptively models the reference and compressed depth maps in a mixed redundant transform domain dedicated to depth features. Then, it computes the mean squared error between the sparse coefficients issued from this modeling. As a benchmark of quality assessment, we perform a subjective evaluation test for depth maps compressed using the latest 3D High Efficiency Video Coding standard at various bitrates. We compare the subjective results with the proposed and conventional objective metrics. Experimental results demonstrate that the proposed SR-MSE, compared to the conventional image quality assessment metrics, yields the highest correlated scores to the subjective ones.
Visual Query Posing in Multimedia Web Document Retrieval

In the last few years, the techniques implemented to satisfy users' information needs are changing due to the heterogeneity of web contents, which are getting more wealthy of multimedia contents. The search engines adapt at information needs of users and provides a multi-modal search interface that allows the users to specify the image and text predominantly. Nevertheless, it is not easy to find systems that integrate visual and textual query with reasonable accuracy. Over the years, many authors proposed several frameworks that develop and improve search engines. Moreover, the use of ontology in semantic research had good results and, in an image retrieval system, the deep learning obtained excellent outcomes. In this paper, we present a novel approach for visual query posing to perform a visual and textual query using only images.
The constricted mayfly optimization algorithm

In this paper, an improvement was proposed to increase the capability of optimization for the newly raised mayfly optimization (MO) algorithm. Simulation experiments were carried out and the results verified that the constriction factors for the particle swarm optimization (PSO) algorithm would also be capable in improving the MO algorithm, whereas with ignorable incensements at most times.
Chinese Event Subject Extraction in the Financial Field Integrated with BIGRU and Multi-head Attention

Abstract Event subject extraction was to extract subjects of specific event types. For the traditional BiLSTM network, the threshold is complicated, the required parameters are many, and the time cost is high. This paper is oriented to the financial field and proposes a method of introducing a multi-head attention mechanism based on the BIGRU network to extract event subjects. First, the text is vectorized, and then the word vector obtained is input into the BIGRU network to learn the context features, and introduce a multi-head attention mechanism to extract the depth feature values of the text. Finally, a comparative experiment is conducted on the data set. The method in this paper achieves an Accuracy value of 80.47% and an F1 value of 89.18%. The result is better than the control group, indicating that the model proposed in this paper can effectively improve the accuracy of extracting Chinese event subjects.
On quantum reading, quantum illumination, and other notions

In this brief note, I clarify the crucial differences between three different protocols of quantum channel discrimination, after some confusion has appeared in recent literature.
Blockchain‐based smart contract for issuance of country of origin certificate for Indian Customs Exports Clearance

Summary The imports and exports are the major activity of any customs department of the country. Customs officers have to verify all the documents as per the customs act of the country, requirements, and give clearance for exports and imports. But due to the limited period and ease of doing business initiatives, it is impossible to verify each produced paper. Sometimes, forged documents may be produced by parties, and based on illegal documents; illegal exports and imports can be taken place. Due to existing message exchange and application program interface (API)‐based information interchange among participating parties having their centralized server, the possibility of delay in message and authenticity of the sender, information mutation may occur. So the concept of blockchain overcomes these issues by enabling secured, encrypted, immutable transactions among participating parties such as exporter/importer, banks, customs, shipping lines, international parties, and so forth. Thus, this paper proposes a blockchain‐based Business process model and notation (BPMN) framework and smart contract for the country of origin certificate management system against forged certificate in Indian customs and international usage and usage of IPFS (Inter Planetary File System) for sharing multilateral agreement files on the blockchain network.
Artificial intelligence in clinical decision-making for diagnosis of cardiovascular disease using epigenetics mechanisms

This chapter provides an overview of machine learning, a mainstream discipline of artificial intelligence. Machine learning is discussed in the context of medical research in general and epigenetics research in cardiology and cardiovascular research in particular. The chapter begins with an overview of machine learning concepts. Main stages of the machine learning workflow including the description of the most popular machine learning techniques used in cardiovascular medicine are presented. In order to reflect the importance of machine learning in biomedical research, selected machine learning applications for disease prediction and diagnosis are reviewed.
Orthogonality of isometries in the conformal model of the 3D space

Motivated by questions on orthogonality of isometries, we present a new construction of the conformal model of the 3D space using just elementary linear algebra. In addition to pictures that can help the readers to understand the conformal model, our approach allows to obtain matrix representation of isometries that can be useful, for example, in applications of computational geometry, including computer graphics, robotics, and molecular geometry.
Autonomous Robotic Escort Incorporating Motion Prediction and Human Intention

This paper presents a technique that allows a robot to escort a human to their destination. Unlike tracking where the robot follows the human from behind, the proposed technique locates the robot in front of the human by incorporating human intention in addition to conventional motion prediction. Human head pose is used as an effective past-proven implicit indicator of intention. A particle filter allows accurate estimation and prediction of the non-Gaussian human trajectory. The predicted pose from both the human motion and intention determines the robot control action which leads to efficient autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention model reduces human position prediction error by approximately 40% when turning. Experimental validation with an omnidirectional mobile robotic platform shows successful and effective escorting compared to the conventional techniques.
Efficient Hardware Implementation for Quantum key Distribution Protocol using FPGA

Abstract This paper presents a new hardware implementation of the quantum key distributed cryptography protocol using Field Programmable Gate Array (FPGA). In many security applications, the software implementations of key distribution algorithms are slow and inefficient. In order to solve this problems, new hardware architecture was proposed to speed up the performance and flexibility of key distribution algorithms. The concurrent computing designs quantum key distribution protocol with reducing the hardware area, producing a high throughput and low latency. It also showed high speed processing and consumed low power. An efficient hardware architectural model for quantum key distribution protocol was developed using very high-speed integrated circuit hardware description language (VHDL). This hardware designed to be used with any quantum key distribution protocol to distribution the secret key securely. The hardware implemented and test with Spartan-3 FPGA board and the results show a throughput of 8 Mbps and efficiency of 1.6 Mbps/slice.
One Method for Implementing Privacy Protection of Electronic Invoices Based on Blockchain

In view of the fact that the blockchain system is essentially a distributed ledger, the data privacy of electronic invoices needs to be protected, and the electronic invoice circulation environment needs security support, we proposed a privacy protection scheme for electronic invoices based on blockchain technology, a block storage scheme based on three-tier storage such as index/element information summary/electronic invoice encrypted data, and a fast and secure access model based on three-tier storage scheme, which solves the conflict between the openness of blockchain technology and the confidentiality of electronic invoices. Firstly, starting from the background and related knowledge of blockchain technology, we analyzed and studied the availability of blockchain technology on electronic invoices, and analyze the security threats and security requirements of the chain on electronic invoices, On this basis, we designed an electronic invoice system model based on blockchain technology, researched the block storage model and data access logical structure of electronic invoices, and gave the relevant implementation scheme and some pseudo code. Secondly, we analyzed the application scenarios and security of electronic invoices based on blockchain technology; Finally, according to the analysis results of the system, the security, excellence and feasibility of the scheme are proved.
Touchless Hand Interaction and Machine Learning Agents to Support Kinesthetic Learning in Augmented Reality

Augmented Reality (AR) learning has successfully been proven to support new learning pedagogies. The pedagogical approach of Kinesthetic Learning or "Learning by Doing" using AR combined with virtual touchless hand interaction and machine learning agents has not been explored in great detail. Fundamentally this exploration of touchless technologies and learning intelligence has taken on new importance in the new post-COVID world which created a need for more interactive and personalized learning solutions. This research presented the implementation of touchless hand interaction and machine learning agents in an AR learning application for teaching the PC assembly in the resource constraint environment.
Experimental analysis of temperature, light intensity, and humidity on rooftop standalone solar power plant

Abstract Rooftop standalone solar technology is a new renewable power generation solution worldwide and can reduce carbon dioxide levels in the environment. However, several factors affect the performance of the power plant, it is highly influenced by weather parameters. This paper shows the design of the rooftop standalone solar power plant and the influence of temperature, light intensity, and environmental humidity on it. The designed system consists of a 200WP photovoltaic, 1260Wh LiFePO4 battery, and an MPPT solar charge controller 40A. This standalone power plant was applied to supply the energy of the home lighting, with 20Watt the total of power. The result shows the design of the rooftop standalone solar power plant. Furthermore, there is an influence of temperature, light intensity, and environmental humidity on the LiFePO4 battery input power.
Ensemble Deep Learning for Brazil Currency Coin Prediction

Abstract In this present fast growing environment the automatic coin reorganization and identification machines has a vital role in all financial allied fields. At present most of coin recognition techniques are depends on physical properties of the coin like length, width, weight etc. Whereas image processing techniques are based on extraction of colour of the coin, edge features of the coin and shape of the coin. For recognition and detection of Brazil currency we have designed Machine Learning, Deep Learning (DL) models in this paper. We have designed various a Machine Learning (ML) models with Convolutional Neural Network for identification and reorganization of Brazil currency. Brazil currency consisting of 5 Centavos, 10 Centavos, 25 Centavos etc. Each of which has different shapes and designs. Different deep learning models are designed and applied ensemble to find the better accuracy. The trained ensemble model is tested on various the datasets which consists of shifting of images, rotation and translated images.