Reviewerly/exHarmony huggingface dataset:
A New CBIR Model Using Semantic Segmentation and Fast Spatial Binary Encoding

Content Based Image Retrieval (CBIR) is the task of finding similar images from a query one. Since the term similar means here “with the same semantic content”, we propose to explore in this paper, a framework that uses Deep Neural Networks based semantic segmentation networks, coupled with a binary spatial encoding. Such simple representation has several relevant properties: 1) It takes advantage of the state of the art semantic segmentation networks and 2) the proposed binary encoding allows a Hamming distance that requests a very low computation budget resulting to a fast CBIR method. Several experiments achieved on public datasets show that our binary semantic signature leads to increase the CBIR accuracy and reduce the execution time. We study the performance of the proposed approach on six different public datasets: Wang, Corel 10k, GHIM-10K, MSRC-V1, MSRC-V2, Linnaeus.
Electric Wheelchair Control Using Wrist Rotation Based on Analysis of Muscle Fatigue

The wheelchair is one of the most common assistive technologies for people with motor impairment, due to its environmentally friendly features in terms of mobility and comfort. However, the operational method for conventional wheelchair is still inconvenience for people with finger problems. Therefore, in the project, a novel control method to operate an electric wheelchair, using hand gestures ( wrist rotation) is developed. Sixty-five (65) participants were involved in this study. Five hand gestures were considered and studied for forward, backward, right, left, and stop maneuvers while considering human ergonomics factor. In this study, the stop maneuver was determined based on the most comfortable hand position to mitigate a fatigue experience, with two gesture classifying methods further investigated. The first method based on threshold has a promising accuracy of 96%, and 91% precision. This method however requires a calibration every time a new user is introduced. The second method, a Naïve Bayes approach, was observed to solve the problem, as it has about 99% of both the accuracy and the precision. The evaluation of this method was then conducted with six participants that operated the wheelchair to follow the trajectories from start to end. The results showed that the participants comfortably controlled the developed wheelchair system to the goal without any collision. Results from experiments indicate that the proposed approach has high accuracy and the potential to solve the problem related to finger dependencies and hand fatigue.
The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

The paper aims to apply the deep learning-based image visualization technology to extract, recognize, and analyze human skeleton movements and evaluate the effect of the deep learning-based human-computer interaction (HCI) system. Dance education is researched. Firstly, the Visual Geometry Group Network (VGGNet) is optimized using Convolutional Neural Network (CNN). Then, the VGGNet extracts the human skeleton movements in the OpenPose database. Secondly, the Long Short-Term Memory (LSTM) network is optimized and recognizes human skeleton movements. Finally, an HCI system for dance education is designed based on the extraction and recognition methods of human skeleton movements. Results demonstrate that the highest extraction accuracy is 96%, and the average recognition accuracy of different dance movements is stable. The effectiveness of the proposed model is verified. The recognition accuracy of the optimized F-Multiple LSTMs is increased to 88.9%, suitable for recognizing human skeleton movements. The dance education HCI system's interactive accuracy built by deep learning-based visualization technology reaches 92%; the overall response time is distributed between 5.1 s and 5.9 s. Hence, the proposed model has excellent instantaneity. Therefore, the deep learning-based image visualization technology has enormous potential in human movement recognition, and combining deep learning and HCI plays a significant role.
Oversampling for Mining Imbalanced Datasets: Taxonomy and Performance Evaluation

The paper focuses on methods and algorithms for oversampling two-classes imbalanced datasets. We propose a taxonomy for oversampling approaches and review state-of-the-art algorithms. The paper discusses also some strengths and weaknesses of the oversampling methods. A computational experiment aims at comparing the performance of several oversampling algorithms. Conclusions discuss possible directions for future developments in the field of balancing imbalanced datasets to achieve better performance when mining them.
The Dematerialization of Money in the Age of COVID-19 Pandemic: A New Future for Digital Finance?

This chapter analyzes the dematerialization of money in the age of COVID-19 pandemic. Although this process has become more visible following the pandemic, the dematerialization of money is not a new phenomenon. Over time, money was turned into fiat money, scriptural money, electronic money, and recently to digital money, namely in the context of cryptocurrency market. However, contrary to what would be expected, it was found that the growing trend toward the use of cryptocurrencies has been reversed with the emergence of the pandemic. The comparison between the pre-pandemic and the pandemic period shows the mean growth rates of daily trading volumes for six of the major cryptocurrencies (Bitcoin, Dogecoin, Ethereum, BitcoinCash, Ripple, and Litecoin) are considerably lower during the pandemic. This situation is particularly evident in the case of Ethereum, which from a mean growth rate of trading volumes of 0.54% in the pre-pandemic period, recorded during the pandemic a decrease of −0,575%. Significant drops were also observed in Bitcoin, Dogecoin, and Litcoin. Even so, digital payments seem to be the future, namely through the launch of the so-called Central Bank Digital Currency. We can take the digital euro as a good example of this future reality.
The Effect of Typographic Modifications on Pupil and Blink Behavior in Dyslexic and Non-Dyslexic Readers

This paper examines the effect of four typographic modification variables among a group of dyslexic and non-dyslexic adults (18 -45 y) during English reading.The eye movement recordings of 14 participants dyslexic (n = 2) and non-dyslexic (n = 14) readers were analyzed by examining the following parameters: pupil diameter, blink count, blink duration and blink frequency.The typographic modification variables assessed co comprise font color, background color, line spacing and font type.It was identified that the following modifications 1) font type, 2) background color, 3) line spacing 4) font color impacted eye movements and induced a visually stressful environment for the dyslexic reader.The results substantiates that the manipulation of typographic presentation of English language text can evoke atypical pupil and blink behavior in dyslexic individuals when compared to non-dyslexic individuals.The findings contribute to the creation of an eye movement profile for dyslexic individuals that can identify optimal settings for viewing electronic reading stimuli.
Cultural policy of stimulating young people’s reading commitment: priorities of the digital era

The article deals with an analysis of the specificities of young peoples communicative behavior and values significant for intensification of their reading commitment. Empirical data of the research Reading Commitment of the Student Youth of the Southern Urals: Motivations and Practices of the Forming of Regional Intellectual Resources during the Digital Era (2021) are used. The priorities of cultural policy of stimulating the Internet-oriented audiences reading commitment are outlined. A necessity of monitoring various manifestations of the reading commitment within the non-institutional area, mainly in the network realm, is grounded in order to identify the specificities of reading practices generated by the reality of the digital era, as well as to harmonize both virtual and real worlds.
PHOTOCATALYST COATING OF MNO-FE2O3PS FROM NATURAL MATERIALS

The present invention relates to an Photocatalyst coating of MnO-Fe2O3/PS from natural materials. Photocatalyst is a method for reducing liquid pollutant materials using ultraviolet (UV) light to produce oxidizing and reducing species on the surface of the catalyst. The catalyst used is MnO-Fe2O3/PS. The tools used are HEM-3D (High Energy Milling Ellipse-3D Mention), XRF (X-Ray Fluorescene), XRD (X-Ray Difraction) and SEM (Scanning Electron Microscope). The precursors were made by adding 0.5 grams of polystyrene and 0.4 grams of MnO powder and Fe2O3 powder. The coating is carried out by using the spin coating method and temperature variations using a furnace. The result of this research is that the MnO-Fe2O3/PS nanocomposite coating made has the best photocatalytic properties at a temperature of 60ºC with a grain size of 168.73 nm. photocatalyst coating of nanocomposite MnO-Fe2O3/PS to produce an energy gap of 2.4 electron volts.
Research on urban landscape big data information processing system based on ordinary differential equations

Abstract In view of the increasing system management requirements of garden landscapes in the construction of smart cities, this paper combines ordinary differential equations with big data and other information technologies. Design ideas of smart city garden big data information management and display system under differential equation. By analyzing the research literature, combined with space syntax, network text analysis and other methods, the theoretical framework of urban landscape planning method based on ordinary differential equations based on big data is constructed, and the aspect planning of urban garden landscape information management module, three-dimensional display module and recommendation module is determined. content. The experimental results show that the user interaction rate when using the system of this paper for information management of urban gardens shows a significant upward trend compared with the traditional system, and the average user interaction rate is higher than 40%. The application of ordinary differential equations and big data technology guides participatory planning and promotes the improvement of the information management level of smart city garden landscape. The designed information processing system has higher user experience and comfort.
Anonymous Identity Based Broadcast Encryption against Continual Side Channel Attacks in the State Partition Model

In the past 10 years, many side-channel attacks have been discovered and exploited one after another by attackers, which have greatly damaged the security of cryptographic systems. Since no existing anonymous broadcast encryption scheme can resist the side-channel attack, the paper presents an anonymous identity-based broadcast encryption against continual side-channel attacks in the state partition model (CLR-SS-AIBBE). Based on split-state technology, the proposed scheme divides the private key into two states, and the decryption operations are correspondingly divided into two steps. Based on the three static hypotheses for a bilinear group with composite order, the proposed scheme can be proved to be fully secure by the dual system encryption technology in the standard model. The leakage ratio about the private key can reach 1/3.
HOW TO DESIGN INCLUSIVE LITERACY AND NUMERACY LEARNING IN ELEMENTARY SCHOOLS?

Empirical facts show that literacy and numeracy abilities are very diverse. This study aims to analyze the design of inclusive learning according to the diversity of students' abilities. The research location is SDN Selebung 2, Central Lombok Regency. The type of research used is a case study. Collecting data using observation, interviews, questionnaires, and documentation. The results of the study found that the inclusive literacy and numeracy learning model was teaching at the right level (TaRL). Students are divided into several groups according to their ability level. The stages of the TaRL learning model include initial assessment, grouping students, and learning according to level. The application of the level-based learning model shows an improvement in the learning process. Indicators of the success of the application of the model can be seen from the increase in students' literacy and numeracy skills, as well as the large number of students who give positive responses to ability level-based learning.
Qubitization of Bosons

A binary mapping from Fock space of bosonic state to qubits is given. Based on the binary mapping, we construte an algorithm of qubitization of bosons with complexity O(log(N)). As an example, the algorithm of qubitization of bosons in matrix product state to simulate real time dynamics of Yukawa coupling is realized. The calculation error bar is estimated by random sampling method. This proposal may be achieved in superconductivity noisy intermediate--scale quantum computer not far future.
Quantum computational finance: martingale asset pricing for incomplete markets

A derivative is a financial security whose value is a function of underlying traded assets and market outcomes. Pricing a financial derivative involves setting up a market model, finding a martingale (``fair game") probability measure for the model from the given asset prices, and using that probability measure to price the derivative. When the number of underlying assets and/or the number of market outcomes in the model is large, pricing can be computationally demanding. We show that a variety of quantum techniques can be applied to the pricing problem in finance, with a particular focus on incomplete markets. We discuss three different methods that are distinct from previous works: they do not use the quantum algorithms for Monte Carlo estimation and they extract the martingale measure from market variables akin to bootstrapping, a common practice among financial institutions. The first two methods are based on a formulation of the pricing problem into a linear program and are using respectively the quantum zero-sum game algorithm and the quantum simplex algorithm as subroutines. For the last algorithm, we formalize a new market assumption milder than market completeness for which quantum linear systems solvers can be applied with the associated potential for large speedups. As a prototype use case, we conduct numerical experiments in the framework of the Black-Scholes-Merton model.
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption

Most knowledge graphs (KGs) are incomplete, which motivates one important research topic on automatically complementing knowledge graphs. However, evaluation of knowledge graph completion (KGC) models often ignores the incompleteness -- facts in the test set are ranked against all unknown triplets which may contain a large number of missing facts not included in the KG yet. Treating all unknown triplets as false is called the closed-world assumption. This closed-world assumption might negatively affect the fairness and consistency of the evaluation metrics. In this paper, we study KGC evaluation under a more realistic setting, namely the open-world assumption, where unknown triplets are considered to include many missing facts not included in the training or test sets. For the currently most used metrics such as mean reciprocal rank (MRR) and Hits@K, we point out that their behavior may be unexpected under the open-world assumption. Specifically, with not many missing facts, their numbers show a logarithmic trend with respect to the true strength of the model, and thus, the metric increase could be insignificant in terms of reflecting the true model improvement. Further, considering the variance, we show that the degradation in the reported numbers may result in incorrect comparisons between different models, where stronger models may have lower metric numbers. We validate the phenomenon both theoretically and experimentally. Finally, we suggest possible causes and solutions for this problem. Our code and data are available at https://github.com/GraphPKU/Open-World-KG .
Finding Derivatives from an Equipotential Graph

We examine how upper-division physics students find derivatives from an equipotential graph in an individual interview setting.We focus specifically on identifying the different kinds of behavior that students engage in when finding a derivative from an equipotential graph, and also on the representational elements that students use or introduce during their work.We find that the students were able to find the derivative successfully using a ratio-of-small-changes approach.Students engaged in behavior like sketching straight lines or arrows on the given graph as a way to choose points for calculating a ratio and to keep one variable constant for the derivative.We also saw students make sense of the equipotential graph and reinterpret the derivative they found using those representations using two other graphical representations: cross-sections and three-dimensional plastic surfaces.We find these results encouraging because the students had studied equipotential graphs and derivatives as ratiosof-small-changes as part of their junior-level electrostatics, suggesting that such a course is effective at helping students develop representational fluency for working with multivariable derivatives.
Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models

Hemoglobin is one of the most important blood elements, and its optical properties will determine all other optical properties of human blood. Since the refractive index (RI) of hemoglobin plays a vital role as a non-invasive indicator of some illnesses, accurate calculation of it would be of great importance. Moreover, measurement of the RI of hemoglobin in the laboratory is time-consuming and expensive; thus, developing a smart approach to estimate this parameter is necessary. In this research, four viable strategies were used to make a quantitative correlation between the RI of hemoglobin and its influencing parameters including the concentration, wavelength, and temperature. First, alternating conditional expectations (ACE), a statistical approach, was employed to generate a correlation to predict the RI of hemoglobin. Then, three different optimized intelligent techniques-optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)-were used to model the RI. A bat-inspired (BA) algorithm was embedded in the formulation of intelligent models to obtain the optimal values of weights and biases of an artificial neural network, membership functions of the fuzzy inference system, and free parameters of support vector regression. The coefficient of determination, root-mean-square error, average absolute relative error, and symmetric mean absolute percentage error for each of the ACE, ONN, OFIS, and OSVR were found as the measure of each model's accuracy. Results showed that ACE and optimized models (ONN, OFIS, and OSVR) have promising results in the estimation of hemoglobin's RI. Collectively, ACE outperformed ONN, OFIS, and OSVR, while sensitivity analysis indicated that the concentration, wavelength, and, lastly, temperature would have the highest impact on the RI.
A sentiment analysis of the Black Lives Matter movement using Twitter

As more attention is brought to the issue of racial injustice, public sentiments and opinions on racial issues are increasingly important to track. At the same time, recent progress in machine learning and natural language processing methods, coupled with the growing amount of available data for training and analysis, allows researchers to extract sentiments from text data at large scales. We applied a natural language processing framework to study public sentiment surrounding the Black Lives Matter (BLM) movement. Specifically, we used a state-of-the-art BERT model fine-tuned for Twitter sentiment classification to predict the sentiment from approximately 1 million tweets from July 2013 to March 2021 related to BLM. The BERT model was trained on the Sentiment 140 dataset on which it obtained an AUC of 0.97 on the training data and 0.94 on testing data, outperforming other machine learning models. We found that retweet frequency and word count frequency were able to illustrate important themes in the BLM movement as well as indicate events of significant importance to the movement. Additionally, sentiment analysis revealed which of these themes and events were associated with positive public sentiment, such as social justice, and which were associated with negative sentiment, such as police brutality. Our analyses can also be applied to better understand other social and political movements to aid related research and activism.
Promoting a meaningful learning of double integrals through routes of digital tasks

Within a wider project aimed at innovating the teaching of mathematics for freshmen, in this study we describe the design and the implementation of two routes of digital tasks aimed at fostering students' approach to double integrals. The tasks are built on a formative assessment frame and classical works on problem solving. They provide facilitative and response-specific feedback and the possibility to request different hints. In this way, students may be guided to the development of well-connected knowledge, operative and decision-making skills. We investigated the effects of the interaction with the digital tasks on the learning of engineering freshmen, by comparing the behaviours of students who worked with the digital tasks (experimental group, N=19) and students who did not (control group, N=19). We detected that students in the experimental group showed more exibility of thinking and obtained better results in the final exam than students in the control group. The results confirmed the effectiveness of the experimental educational path and offered us interesting indications for further studies.&#x0D; Subject Classification: 97D40, 97U70, 44A45
EANA: Reducing Privacy Risk on Large-scale Recommendation Models

Embedding-based deep neural networks (DNNs) are widely used in large-scale recommendation systems. Differentially-private stochastic gradient descent (DP-SGD) provides a way to enable personalized experiences while preserving user privacy by injecting noise into every model parameter during the training process. However, it is challenging to apply DP-SGD to large-scale embedding-based DNNs due to its effect on training speed. This happens because the noise added by DP-SGD causes normally sparse gradients to become dense, introducing a large communication overhead between workers and parameter servers in a typical distributed training framework. This paper proposes embedding-aware noise addition (EANA) to mitigate the communication overhead, making training a large-scale embedding-based DNN possible. We examine the privacy benefit of EANA both analytically and empirically using secret sharer techniques. We demonstrate that training with EANA can achieve reasonable model precision while providing good practical privacy protection as measured by the secret sharer tests. Experiments on a real-world, large-scale dataset and model show that EANA is much faster than standard DP-SGD, improving the training speed by 54X and unblocking the training of a large-scale embedding-based DNN with reduced privacy risk.