Reviewerly/exHarmony huggingface dataset:
Qubit-controlled directional edge states in waveguide QED

Abstract We propose an in-situ tunable chiral quantum system, composed of a quantum emitter coupled to a waveguide based on the Rice-Mele model (where we alternate both the on-site potentials and tunnel couplings between sites in the waveguide array). Specifically, we show that the chirality of photonic bound state, that emerges in the bandgap of the waveguide, depends only on the energy of the qubit; a parameter that is easy to tune in many artificial atoms. In contrast to previous proposals that have either shown imperfect chirality or fixed directionality, our waveguide quantum electrodynamics scheme achieves both perfect chirality and the capability to switch the directionality on demand with just one tunable element in the device. We also show that our model is easy to implement in both state-of-the-art superconducting circuit and quantum dot architectures. The results show technological promise in creating long-range couplers between qubits while maintaining, in principle, zero crosstalk.
A Systematic Review of Citation Recommendation Over the Past Two Decades

A citation is a reference to the source of information used in an article. Citations are very useful for students and researchers to locate relevant information on a topic. Proper citation is also important in the academic ethics of article writing. Due to the rapid growth of scientific works published each year, how to automatically recommend citations to students and researchers has become an interesting but challenging research problem. In particular, a citation recommendation system can assist students to identify relevant papers and literature for academic writing. Citation recommendation can be classified into local and global citation recommendation depending on whether a specific local citation context is given; e.g., the text surrounding a citation placeholder. This article provides a systematic review on global citation recommendation models and compares the reviewed methods from the traditional topic- based models to the recent models embedded with deep neural networks, aiming to summarize this field to facilitate researchers working on citation recommendation.
Dynamic Gesture Recognition Based on Three-Stream Coordinate Attention Network and Knowledge Distillation

Gesture recognition has always been one of the important research directions in the field of computer vision.The dynamic gesture has the problems of complex backgrounds and many interference factors.The gesture recognition model based on deep learning usually has high computational cost and poor real-time performance.In addition, deep learning models are limited to recognizing existing categories in the training set and their performance largely depends on the amount of labeled data.To address the above problems, this paper presents a dynamic gesture recognition method named 3SCKI based on a three-stream coordinate attention (CA) network, knowledge distillation, and image-text contrastive learning.Specifically, 1) CA is utilized for feature fusion to make the model focus more on target gestures and reduce background interference, 2) traditional knowledge distillation loss is improved to reduce the amount of calculation and improve the real-time performance.Specifically, the guidance function is added to make the student network only learn the classification probability correctly identified by the teacher network, and 3) multi-granularity context prompt template integration method is proposed to construct an improved CLIP visual language model MG-CLIP.It aligns text and visual concepts from the image level to the object level to the part level.Through comparative learning of image features and text features, gesture classification is performed, enabling the model to identify image categories that have not appeared during the training phase.The proposed method is evaluated on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD).The results show that our proposed method can obtain recognition rates of 65.87% on the validation set of IsoGD.For single mode data, 3SCKI can obtain the state-of-the-art recognition accuracy on RGB, Depth, and Optical Flow data (61.22%,58.84%, and 50.30% of the validation set of IsoGD, respectively).
Key Reduction in Multi-Key and Threshold Multi-Key Homomorphic Encryptions by Reusing Error

As cloud computing and AI as a Service are provided, it is increasingly necessary to deal with privacy sensitive data. To deal with the sensitive data, there are two cases of outsourcing process: i) many clients participate dynamically ii) many clients are pre-determined. The solutions for protecting sensitive data in both cases are the multi-key homomorphic encryption (MKHE) scheme and the threshold multi-key homomorphic encryption (TMKHE) scheme. However, these schemes may be difficult for clients with limited resources to perform MKHE and TMKHE. In addition, due to the large size of the evaluation keys, in particular multiplication and rotation keys, the communication between the clients and server that provide outsourcing service increases. Also, the size of the evaluation keys that the server must hold is tremendous, in particular, for the multiplication and rotation keys, which are essential for bootstrapping operation. In this paper, we propose a variant of MKHE and TMKHE with reduced evaluation keys. To reduce the size of the evaluation keys, we propose a variant of ring learning with errors (RLWE), called RLWE reusing errors (ReRLWE). ReRLWE generates other components by reusing the error that is used when generating an RLWE sample. We prove that RLWE can be reduced to ReRLWE and propose modified evaluation keys under the ReRLWE assumption, which are the modified multiplication and rotation keys. For MKHE, multiplication and rotation keys are reduced by 66% and 25%, respectively. For TMKHE, a multiplication and rotation keys are reduced by 50% and 25%, respectively.
A Novel Algebraic Inverse Kinematics Based Approach to Gaze Control in Humanoid Robots

The vision system of humanoid robots is one of the essential components that allow them to do complex activities. This has led to numerous studies on artificial gaze stabilization, many of which are based on biological vision system reflexes. The gaze stabilization solution includes image capture and processing, inverse kinematics, and feedback control to achieve the desired gaze behavior. Generally, the multi-solution problem of finding the desired gaze direction and the inverse kinematics problem of finding the desired joints are solved by using cost functions and numerical solvers. This results in disadvantages that include long processing time, uncertain number of iterations, and risk of numerical instability. In this work, an alternative algebraic method is introduced by exploiting the cascading structure of the neck and eye. By using direct equations instead of numerical solving, the multi-solution and inverse kinematics problems are solved utilizing the geometrical structure and the proposed virtual configurations. Moreover, a sliding mode controller is designed to move the neck-eye joints to their desired positions. The developed scheme is simulated in the MATLAB/SIMULINK environment. Results for various scenarios show that the system can effectively gaze at different stationary and moving targets, showing human-like gaze behavior. In terms of timing, the desired gaze direction is quickly achieved and stabilized, whereas the neck-eye system dynamics stay longer. For one of the simulation cases presented, it took 0.3 seconds to achieve the desired gaze direction for the dynamics of more than 1 second.
Bone‐GAN: Generation of virtual bone microstructure of high resolution peripheral quantitative computed tomography

Data-driven development of medical biomarkers of bone requires a large amount of image data but physical measurements are generally too restricted in size and quality to perform a robust training.This study aims to provide a reliable in silico method for the generation of realistic bone microstructure with defined microarchitectural properties. Synthetic bone samples may improve training of neural networks and serve for the development of new diagnostic parameters of bone architecture and mineralization.One hundred-fifty cadaveric lumbar vertebrae from 48 different male human spines were scanned with a high resolution peripheral quantitative CT. After prepocessing the scans, we extracted 10,795 purely spongeous bone patches, each with a side length of 32 voxels (5 mm) and isotropic voxel size of 164 μm. We trained a volumetric generative adversarial network (GAN) in a progressive manner to create synthetic microstructural bone samples. We then added a style transfer technique to allow the generation of synthetic samples with defined microstructure and gestalt by simultaneously optimizing two entangled loss functions. Reliability testing was performed by comparing real and synthetic bone samples on 10 well-understood microstructural parameters.The method was able to create synthetic bone samples with visual and quantitative properties that effectively matched with the real samples. The GAN contained a well-formed latent space allowing to smoothly morph bone samples by their microstructural parameters, visual appearance or both. Optimum performance has been obtained for bone samples with voxel size 32 × 32 × 32, but also samples of size 64 × 64 × 64 could be synthesized.Our two-step-approach combines a parameter-agnostic GAN with a parameter-specific style transfer technique. It allows to generate an unlimited anonymous database of microstructural bone samples with sufficient realism to be used for the development of new data-driven methods of bone-biomarkers. Particularly, the style transfer technique can generate datasets of bone samples with specific conditions to simulate certain bone pathologies.
Optimized deep learning vision system for human action recognition from drone images

There are several benefits to constructing a lightweight vision system that is implemented directly on limited hardware devices. Most deep learning-based computer vision systems, such as YOLO (You Only Look Once), use computationally expensive backbone feature extractor networks, such as ResNet and Inception network. To address the issue of network complexity, researchers created SqueezeNet, an alternative compressed and diminutive network. However, SqueezeNet was trained to recognize 1000 unique objects as a broad classification system. This work integrates a two-layer particle swarm optimizer (TLPSO) into YOLO to reduce the contribution of SqueezeNet convolutional filters that have contributed less to human action recognition. In short, this work introduces a lightweight vision system with an optimized SqueezeNet backbone feature extraction network. Secondly, it does so without sacrificing accuracy. This is because that the high-dimensional SqueezeNet convolutional filter selection is supported by the efficient TLPSO algorithm. The proposed vision system has been used to the recognition of human behaviors from drone-mounted camera images. This study focused on two separate motions, namely walking and running. As a consequence, a total of 300 pictures were taken at various places, angles, and weather conditions, with 100 shots capturing running and 200 images capturing walking. The TLPSO technique lowered SqueezeNet's convolutional filters by 52%, resulting in a sevenfold boost in detection speed. With an F1 score of 94.65% and an inference time of 0.061 milliseconds, the suggested system beat earlier vision systems in terms of human recognition from drone-based photographs. In addition, the performance assessment of TLPSO in comparison to other related optimizers found that TLPSO had a better convergence curve and achieved a higher fitness value. In statistical comparisons, TLPSO surpassed PSO and RLMPSO by a wide margin.
Efficient Container Management Scheme Based on Deep Learning Model

The container orchestration platform provides services and applications to users by automatically managing containers in cloud-native clusters. However, the conventional container management technique operates based on a reactive mechanism, so it is difficult to guarantee availability for a rapidly changing workload, and a problem arises in which resources are wasted. Therefore, this study proposes an efficient container management scheme (ECMS) that guarantees high availability and scalability of cloud services and applications composed of various workloads and minimizes idle resources. ECMS predicts future workload by training accumulated past resource usage metrics on a deep learning model. ECMS creates a container by scaling the size of the resource to prevent the generation of idle resources, then selects a node that guarantees high availability and deploys the container. Lastly, it provides scalability of service by performing horizontal resource container autoscaling based on proactive mechanism.
Design of Reliable Electronic Document Based on HTML and Blockchain

An electronic document based on HTML via digital communication is the mainstream in a mobile environment, and the document containing personal legal information gets to be delivered in this way. However, HTML does not have the content integrity verification specification; therefore, it is hard to prove the content integrity once an electronic document has been delivered. We have designed the Reliable Electronic Document System using a metatag definition in HTML to store a content digest value and a metadata definition in a blockchain to store the PKI certificate to verify the content integrity. Furthermore, we confirmed that the Reliable Electronic Document System generates a reliable document and verifies content integrity. As a result, we expect this system to be used in customer communication by providing a reliable electronic document.
Conclusion

While the projects in this book have now concluded, your AR journey doesn’t have to end here! There are many ways for you to continue expanding your knowledge and experience in this field.
Automatic Classification for Representative Spatio-temporal-Based Event Document Using Machine Learning

As the use of social media and online news media increases, the amount of text data online is rapidly increasing. As the use of spatio-temporal information increases, research on extracting spatio-temporal information from text data and detecting events is being actively conducted. However, the document also contains insignificant and trivial spatio-temporal information that is not relevant to the subject. Therefore, it is important to extract the spatio-temporal-based events that describe the core topic of the document. In this study, spatio-temporal-based events related to the core topic of the document are defined as ‘representative spatio-temporal-based events’. We propose a machine learning-based document classifier to classify documents containing representative spatio-temporal-based events. To train the proposed document classifier, guidelines for building training data were prepared, and 10,000 training data were constructed. Experimental results showed that the accuracy of traditional machine learning was not high.
Vehicle Detection Algorithm Based on Video Image Processing in Intelligent Transportation System

With the rapid development of the automobile industry, the problem of traffic congestion has become increasingly prominent. In order to reduce traffic accidents, improve road transportation efficiency and ensure road traffic safety, there is an urgent need for intelligent processing of vehicle real-time dynamic detection research. In this paper, the research of vehicle detection algorithm based on video image processing in the intelligent transportation system is aimed at real-time detection of vehicles through the algorithm to ensure driving safety. This article mainly uses experimental method and comparative method to analyze the application of several different algorithms in detection. The experimental results show that the minimum feature vector of the background difference method in vehicle detection is 32, but the highest accuracy is 98.4%. This shows that the background difference method is very useful in image processing.
KoBART-Based Hybrid Literacy Improvement System for Each Level for Efficient Reading

The digital literacy of 15 years-old students in Korea is the lowest among OECD countries. This is not a decrease in student’s literacy, but an increase in the number of students who cannot read. In this paper, we proposed a system that identifies the number of user summary keywords, presents step-by-step summaries, and provides improvement to learn and grow. First, receives the original text, KoBART identifies the number of key keywords. After that, the user's summary is input, and the number of key keywords is determined by comparing it with the summary summarized by artificial intelligence. We present a summary of three levels (Levels 1, 2, 3) using KoBART and TextRANK algorithm. ROUGE demonstrates the reliability of the model by performing a performance measurement of the developed model. In addition, it compares the artificial intelligence summary with the user’s summary to identify the user’s levels. Finally, an AI summary is presented according to the level identified by the user summary. In other words, it induces interest in reading through step-by-step text summaries. It is possible to learn the ability to grasp the core of the text and to expect improvement in literacy and learning ability.
A Memory Replay-Based Continual Learning Utilizing Class Representative and Class Boundary Data

It is known that deep learning models are performing beyond human beings if sufficient size of training data is provided. However, this is usually true only if a model is trained for a single task. If a model is continuously trained on multiple tasks, the model abruptly forgets the previously learned information. To overcome this phenomenon, various continual learning methods have been proposed. In particular, in the class-incremental learning scenario in which no task identification information is given at the inference time, the memory replay method has shown good performances. In general, the memory replay method stores class representative samples to recall previously learned tasks, but this leads to poor performance when a sample can be a representative of multiple classes. Therefore, this paper proposes a method that stores class boundary data as well as class representative data in the memory buffer to improve the performance of the memory replay. For this, a class representative sample is defined as the one of which feature is close to the mean of a certain class, and a class boundary sample is defined to be located between the means of two different classes. The experiments confirm that the proposed method shows higher performance than existing methods, which proves the importance of utilizing class boundary samples in continual learning.
Effective Use of Knowledge Graphs in a Language Representation Model

Methods such as knowledge-enabled language representation model (K-BERT) that help train models using external information, such as knowledge graphs, have recently been proposed in the field of natural language processing. However, adding knowledge that does not match the topic when external information is used may hinder training. Hence, a method is required that adds only knowledge matching the topic of the input data by partitioning the external information by topic. Topic-based knowledge graph BERT (TK-BERT), which uses the existing latent Dirichlet allocation (LDA) model to partition the knowledge, does not consider the contextual information. To compensate for this drawback and effectively use external information, this study uses the BERTopic technique to partition the knowledge graph.
Gaussian Pseudospectral Optimization-Driven Neural Network Planning of Obstacle Avoidance Trajectory

This work proposed an improved Gaussian pseudospectral optimization data-driven neural network trajectory planning algorithm to shorten the planning time and improve the speed of obstacle avoidance trajectory. Firstly, the obstacle avoidance trajectory planning optimal control problem is established by deriving the kinematics model of unmanned vehicle and obstacle model. Gaussian pseudospectral optimization is then derived in detail to tackle the infinite time optimal control problem (OCP) and obtain the obstacle avoidance optimization data. By using the neural network training data set, an intelligent planning method for on-line calculation of obstacle avoidance trajectory is designed. Simulation results show that the proposed method can efficiently decrease trajectory planning time.
Testing Effects

While the Simulator is a convenient way to rapidly preview in-progress effects, it is simply an approximation of how your project will look and behave. For a more accurate depiction, it’s important to properly test how your augmented reality creations feel on the actual device a user would experience it on.
Development of Ubiquitous Flip Learning Model Based on Backward Design

Flip learning is an educational model in which learners watch online lectures at home and perform tasks in the classroom by reversing the traditional teaching method of performing tasks at home after class activities in the classroom. Backward is an understanding-oriented curriculum, and it is a curriculum model that allows students to develop a permanent understanding by first designing an evaluation that was at the last stage in the existing curriculum design. The purpose of this study is to analyze the effectiveness of a backward design-based flip learning class model by developing and applying it in a ubiquitous educational environment. In order to examine the effect of this study, the experimental group was subjected to ubiquitous backward design-based flip learning, and the results of effectiveness perception and satisfaction, learner competency and academic achievement were compared and analyzed through experiments.
Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch

We consider the problem of computing differentially private approximate histograms and heavy hitters in a stream of elements. In the non-private setting, this is often done using the sketch of Misra and Gries [Science of Computer Programming, 1982]. Chan, Li, Shi, and Xu [PETS 2012] describe a differentially private version of the Misra-Gries sketch, but the amount of noise it adds can be large and scales linearly with the size of the sketch: the more accurate the sketch is, the more noise this approach has to add. We present a better mechanism for releasing a Misra-Gries sketch under (ε,δ)-differential privacy. It adds noise with magnitude independent of the size of the sketch size, in fact, the maximum error coming from the noise is the same as the best known in the private non-streaming setting, up to a constant factor. Our mechanism is simple and likely to be practical. We also give a simple post-processing step of the Misra-Gries sketch that does not increase the worst-case error guarantee. It is sufficient to add noise to this new sketch with less than twice the magnitude of the non-streaming setting. This improves on the previous result for ε-differential privacy where the noise scales linearly to the size of the sketch.