Reviewerly/exHarmony huggingface dataset:
Solution of the Convex Optimization Problem for Subcarrier Wave Continuous-Variable Quantum Key Distribution Protocol with Discrete Modulation

This paper introduces an approach to solving the convex optimization problem for subcarrier wave continuous-variable quantum key distribution (QKD) protocol using discrete modulation. For the first time, we provide a solution to this problem. By employing semidefinite programming, we have estimated the lower bound of the secure key generation rate in the asymptotic limit, taking into account an optimal attack from potential eavesdroppers. We considered an optical scheme that involves the use of quadrature phase modulation of coherent states and coherent detection based on the interference between the carrier and side frequencies. Our findings demonstrate the feasibility of distributing secure key generation rates over distances exceeding 100 km, even in the presence of realistic excess noise.
Toward Programming a Collaborative Robot by Interacting with Its Digital Twin in a Mixed Reality Environment

In an industrial production context, when a robotic arm is assigned a new task, it must be re-programmed by specialized personnel with the necessary skills and knowledge. In addition, the robot remains offline while being re-programmed, negatively affecting productivity. Mixed reality opens up the opportunity for a production worker with no programming skills to teach a robot by interacting with the hologram of its digital twin, thus not interfering with the physical robot during re-programming. We describe the design and implementation of a mixed-reality interface to instruct trajectories for a robot end-effector by moving the hologram of its digital twin by hand. The interface supports the integrality of this interaction so that the user can configure the end effector location and orientation simultaneously. We also report a user study (n = 14) to characterize this interface in terms of usability, user experience, and estimated temporal efficiency that it can provide when programming robot trajectories.
Testing Boundaries or Feeling Cautious: College Students' Gendered Perspectives on a Flirtatious Conversation*

Role‐taking is the process of mentally and affectively placing the self in the position of another. This study used a mixed‐methods approach to highlight how a traditional sexual script often imposes gendered expectations of role‐taking. To explore role‐taking within flirtatious exchanges, a sample of college students read a Snapchat conversation and then inferred what each character was thinking and feeling. Hooking up was the most common qualitative theme among the undergraduate participants; however, this theme was more prominent when considering the perspective of the male character, and this expectation was strengthened by the presence of emoji. The male character was expected to be excited or aroused, more focused on social cues—such as their roommate not being present—and to have the female character over to study as a way to pressure her for physical intimacy. The participants were more likely to think that the female character just wanted to study, was anxious or nervous, and was more aware of the male character's perspective. These findings highlight the complex nature of conveying sexual interest, which often forefronts desire and agency from a male perspective.
Data Science as an Interdiscipline: Historical Parallels from Information Science

Considerable debate exists today on almost every facet of what data science entails. Almost all commentators agree, however, that data science must be characterized as having an interdisciplinary or metadisciplinary nature. There is interest from many stakeholders in formalizing the emerging discipline of data science by defining boundaries and core concepts for the field. This paper presents a comparison between the data science of today and the development and evolution of information science over the past century. Data science and information science present a number of similarities: diverse participants and institutions, contested disciplinary boundaries, and diffuse core concepts. This comparison is used to discuss three questions about data science going forward: (1) What will be the focal points around which data science and its stakeholders coalesce? (2) Can data science stakeholders use the lack of disciplinary clarity as a strength? (3) Can data science feed into an &ldquo;empowering profession&rdquo;? The historical comparison to information science suggests that the boundaries of data science will be a source of contestation and debate for the foreseeable future. Stakeholders face many questions as data science evolves with the inevitable societal and technological changes of the next few decades.
Resolution Based Incremental Scaling Methodology for CNNs

Designing an optimal CNN for each embedded device with a different resource budget would be time-consuming and inefficient. Network scaling provides a viable solution to tackle this challenge, In this work, we propose a novel network scaling strategy called RBIS (resolution-based incremental scaling). Unlike the previous works that consider the width, depth, and input resolution together, we first find the input resolution candidates on a given hardware platform. For each resolution candidate, we incrementally scale the depth and width of each stage up to limit of available resources. Comparison with other scaling methods proves the superiority of the proposed scaling methodology. RBIS finds a more accurate model by up to 0.53 % for EfficientNet-B1 and 0.67 % for the S3NAS B2 scale.
The Impact of E-money on China's Money Supply

Electronic money alters the form of currency, reduces the demand for cash, alters the manner in which transactions are settled, and alters the quantity of reserves held by our commercial banks. Electronic money makes the relevant factors acting on the base money supply more complex, which will undoubtedly make it more difficult for the central bank to achieve control over the base money, thereby further weakening the central bank's ability to manipulate the money supply and affecting the effectiveness of the central bank's monetary policy target of the money supply. Therefore, a correct understanding of the impact of electronic money development on the base money supply is crucial for enhancing the effectiveness of monetary policy in China in light of the expanding scale of electronic money in China.
Prediction of K-Means Multivariable Rice Production Center Pattern

Abstract West Sumatra is one of the provinces that have the potential to be used as a rice production center in Indonesia. However, the determination of local production centers is still justified based on conventional methods. This study aims to obtain historical data on rice production center variables, process data using the K-Means algorithm (RStudio), and justify rice production center areas in West Sumatra in a given year based on the acquisition of recommendation values (centroids). The results showed that in 2010-2020, the best number of clusters (elbow graph) was K=3. The highest recommendation value of the final centroid lies in centroid 3 so 8 recommendations for districts that are the center of rice production are obtained, including Padang Pariaman Regency, Pesisir Selatan Regency, Tanah Datar Regency, Agam Regency, Solok Regency, Fifty Cities Regency, Pasaman Regency, and South Solok Regency. Furthermore, of the eight districts included in the Governor’s Decree no. 525-757-2021 concerning the determination of food areas. Based on the 2019 irrigation network performance index as a decision-supporting variable, the highest index value reached the Batang Surantih irrigation area, Pesisir Selatan Regency, with an index value of 15 (100%).
Denial-of-Service Attack Defense Strategy for Continuous Variable Quantum Key Distribution via Deep Learning

In the practical Continuous Variable Quantum Key Distribution (CVQKD) system, there is a large gap between the ideal theoretical model and the actual physical system. There are still some inevitable flaws, which give quantum hackers the opportunity to manipulate the channel in complex communication environments and launch Denial of Service attacks on the quantum channel. Therefore, a DoS attack-aware defense scheme for the CVQKD system based on convolutional neural networks (CNN) is proposed. The simulation results show that the proposed model can effectively detect DoS attacks launched by quantum hackers in CVQKD system in a complex communication environment, and the model has strong robustness due to the addition of the attention mechanism module. In addition, multiple sets of comparative experiments show that compared with the existing artificial neural network model, the CNN-based model has higher accuracy and stability.
An architectural technical debt index based on machine learning and architectural smells

A key aspect of technical debt (TD) management is the ability to measure the amount of principal accumulated in a system. The current literature contains an array of approaches to estimate TD principal, however, only a few of them focus specifically on architectural TD, but none of them satisfies all three of the following criteria: being fully automated, freely available, and thoroughly validated. Moreover, a recent study has shown that many of the current approaches suffer from certain shortcomings, such as relying on hand-picked thresholds. In this paper, we propose a novel approach to estimate architectural technical debt principal based on machine learning and architectural smells to address such shortcomings. Our approach can estimate the amount of technical debt principal generated by a single architectural smell instance. To do so, we adopt novel techniques from Information Retrieval to train a learning-to-rank machine learning model (more specifically, a gradient boosting machine) that estimates the severity of an architectural smell and ensure the transparency of the predictions. Then, for each instance, we statically analyse the source code to calculate the exact number of lines of code creating the smell. Finally, we combine these two values to calculate the technical debt principal. To validate the approach, we conducted a case study and interviewed 16 practitioners, from both open source and industry, and asked them about their opinions on the TD principal estimations for several smells detected in their projects. The results show that for 71% of instances, practitioners agreed that the estimations provided were <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">representative</i> of the effort necessary to refactor the smell.
Blockchain based E - Cheque Clearing and Verification

This study work suggests a cutting-edge electronic check clearance system that is scalable. It is built on the blockchain, and all banks interested in using it must sign up for the proposed blockchain-based framework in order to provide their clients the speedier cheque clearance service. The suggested e-cheque solution is free from numerous security threats like e-cheque manipulation, double spending, and forged e-cheques. The suggested solution allows for the electronic or physical deposit of the e-cheque through teller machines. Because only 32.2% of nodes, on average, engage in the proposed trust-based consensus mechanism and because the number of messages exchanged during each consensus process is significantly lower than with the PoW strategy, the proposed system is highly scalable
Design of LEGO Parts Automatic Sorting Scheme

With the continuous development of the economy and the growing differentiation of young people's interests, people are willing to spend a lot of energy and money on their favorite games to get a better experience. Nowadays, Lego has entered more and more families. However, due to too many types and quantities of Lego parts, people usually do not know how to effectively organize these parts after each game. Most people choose to throw all these parts into the box, but this will make the parts very confused and seriously affect the game experience when playing next time. To solve this problem, we try to design and develop a Lego classification device to automatically classify different types of Lego parts. We try to use a variety of technical solutions for comparison, from multiple dimensions such as cost calculation, technical implementation details and risk management to comprehensively compare, and finally select the best design scheme.
Reform and Innovation in Guzheng Education Teaching Model

The reform and innovation of the Guzheng (Chinese zither) education and teaching model are of great significance in cultivating students' music literacy, creativity, and comprehensive abilities. The traditional Guzheng education model emphasizes technical training but often overlooks students' personalized development and holistic cultivation. The reformed and innovative teaching model can better stimulate students' creativity and expressive abilities by encouraging their participation in activities such as composition and performance. Personalized learning and teaching are important directions for Guzheng education reform, tailoring instructional content and progress to students' interests and characteristics while providing personalized feedback and guidance. Integration of modern technological methods is also a crucial aspect of Guzheng education and teaching model reform, utilizing online resources for learning and communication, as well as employing multimedia technologies such as audio and video for teaching and demonstration purposes, thereby offering diverse, convenient learning approaches and teaching resources. Through reform and innovation, Guzheng education can better meet students' needs and promote their comprehensive development in music literacy, creativity, and overall abilities.
Automatic Classification of Frequency-Modulated Radar Waveforms Under Multipath Conditions

Techniques for automatic modulation classification (AMC) of radar signals are crucial for spectrum sensing, passive bistatic radars and electronic intelligence (ELINT). Most of existing AMC algorithms were evaluated using solely synthetic data. Meanwhile, signals intercepted in real-life scenarios are extra distorted due to the multipath propagation. This paper presents the novel, pattern recognition AMC method for frequency modulated radar signals, with improved resistance to noise and multipath. This has been achieved by a multi-stage feature selection process, involving over a dozen of popular radar signal metrics. Based on the feature selection results, the two advanced waveform features were utilized in the designed algorithm, i.e. quasi-maximum likelihood (QML) instantaneous frequency estimate and fractional Fourier transform (FrFT) profile. The proposed AMC method has been evaluated using both synthetic and real data. Obtained results show that proposed classification framework achieves overall accuracy of 93.6% for a set of real-life signals acquisitions, corrupted both by noise and multipath influence.
Investigating the Impact of Online Learning Clubs on Student Motivation and Self-Efficacy in Science: An Experimental Study

Introduction. Russian studentsʼ natural science literacy lags behind that of their counterparts in other countries, largely due to a lack of interest in the subject area and low self-confidence. Digital technologies offer new opportunities for basic and additional education that can help address these challenges and increase student engagement in the natural sciences. However, not all digital learning formats have been thoroughly examined in terms of their impact on studentsʼ engagement and learning outcomes. This research aims to evaluate the effectiveness of additional online science clubs in enhancing motivation and self-efficacy among schoolchildren in this subject area. Materials and Methods. To investigate the issue, we conducted an experiment involving 1 023 students in grades 7–9 from the Khangalassky ulus of the Republic of Sakha. The experimental group consisted of students randomly selected to participate in online science club on the GlobalLab platform, while the control group continued with regular studies. Using structural equation modelling and instrumental variable methods, we have analyzed data on changes in the level of motivation and self-efficacy of both groups, while also controlling for socio-demographic characteristics and overall curiosity levels of the students. Results. The findings indicate that online clubs have the potential to increase motivation and self-efficacy in natural science subjects, but their widespread use requires further investigation. The comparison of results between the control and experimental groups revealed a slight improvement in both general and subject motivation, as well as subject self-efficacy for students in the experimental group. The instrumental variable approach proved a more significant impact of online club participation on motivation and self-efficacy but only in the specific subject in which the child was engaged. At the same time, the study revealed a rather low interest among students to participate in online science club as extracurricular activity. Discussion and Conclusion. The findings of the study support the notion that online clubs are effective in enhancing studentsʼ motivation and self-efficacy in natural sciences. However, the research also identified certain limitations in the use of additional education outside of school. For instance, students may lack a clear understanding of their participation goals, which can affect their interest in engaging in such activities. Thus, the results of the study provide a basis for evaluating the potential of online formats in additional education and for making recommendations for their optimal use.
A monocular image depth estimation method based on weighted fusion and point‐wise convolution

Abstract The existing monocular depth estimation methods based on deep learning have difficulty in estimating the depth near the edges of the objects in an image when the depth distance between these objects changes abruptly and decline in accuracy when an image has more noises. Furthermore, these methods consume more hardware resources because they have huge network parameters. To solve these problems, this paper proposes a depth estimation method based on weighted fusion and point‐wise convolution. The authors design a maximum‐average adaptive pooling weighted fusion module (MAWF) that fuses global features and local features and a continuous point‐wise convolution module for processing the fused features derived from the (MAWF) module. The two modules work closely together for three times to perform weighted fusion and point‐wise convolution of features of multi‐scale from the encoder output, which can better decode the depth information of a scene. Experimental results show that our method achieves state‐of‐the‐art performance on the KITTI dataset with δ 1 up to 0.996 and the root mean square error metric down to 8% and has demonstrated the strong generalisation and robustness.
Unified Scaling-Based Pure-Integer Quantization for Low-Power Accelerator of Complex CNNs

Although optimizing deep neural networks is becoming crucial for deploying the networks on edge AI devices, it faces increasing challenges due to scarce hardware resources in modern IoT and mobile devices. This study proposes a quantization method that can quantize all internal computations and parameters in the memory modification. Unlike most previous methods that primarily focused on relatively simple CNN models for image classification, the proposed method, Unified Scaling-Based Pure-Integer Quantization (USPIQ), can handle more complex CNN models for object detection. USPIQ aims to provide a systematic approach to convert all floating-point operations to pure-integer operations in every model layer. It can significantly reduce the computational overhead and make it more suitable for low-power neural network accelerator hardware consisting of pure-integer datapaths and small memory aimed at low-power consumption and small chip size. The proposed method optimally calibrates the scale parameters for each layer using a subset of unlabeled representative images. Furthermore, we introduce a notion of the Unified Scale Factor (USF), which combines the conventional two-step scaling processes (quantization and dequantization) into a single process for each layer. As a result, it improves the inference speed and the accuracy of the resulting quantized model. Our experiment on YOLOv5 models demonstrates that USPIQ can significantly reduce the on-chip memory for parameters and activation data by ~75% and 43.68%, respectively, compared with the floating-point model. These reductions have been achieved with a minimal loss in mAP@0.5—at most 0.61%. In addition, our proposed USPIQ exhibits a significant improvement in the inference speed compared to ONNX Run-Time quantization, achieving a speedup of 1.64 to 2.84 times. We also demonstrate that USPIQ outperforms the previous methods in terms of accuracy and hardware reduction for 8-bit quantization of all YOLOv5 versions.
Comparison of Training Strategies for Autoencoder-Based Monochromatic Image Denoising

Monochromatic images are used mainly in cases where the intensity of the received signal is examined. The identification of the observed objects as well as the estimation of intensity emitted by them depends largely on the precision of light measurement in image pixels. Unfortunately, this type of imaging is often affected by noise, which significantly degrades the quality of the results. In order to reduce it, numerous deterministic algorithms are used, with Non-Local-Means and Block-Matching-3D being the most widespread and treated as the reference point of the current state-of-the-art. Our article focuses on the utilization of machine learning (ML) for the denoising of monochromatic images in multiple data availability scenarios, including those with no access to noise-free data. For this purpose, a simple autoencoder architecture was chosen and checked for various training approaches on two large and widely used image datasets: MNIST and CIFAR-10. The results show that the method of training as well as architecture and the similarity of images within the image dataset significantly affect the ML-based denoising. However, even without access to any clear data, the performance of such algorithms is frequently well above the current state-of-the-art; therefore, they should be considered for monochromatic image denoising.