Reviewerly/exHarmony huggingface dataset:
Radiative transfer equation-based color prediction and color adjustment strategies

This paper discusses different strategies for color prediction and matching. Although many groups use the two-flux model (i.e., the Kubelka–Munk theory or its extensions), we introduce a solution of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>P</mml:mi> <mml:mi>N</mml:mi> </mml:msub> </mml:mrow> </mml:math> approximation for the radiative transfer equation (RTE) with modified Mark boundaries for the prediction of the transmittance and reflectance of turbid slabs with or without a glass layer on top. To demonstrate the capabilities of our solution, we have presented a way to prepare samples with different scatterers and absorbers where we can control and predict the optical properties and discussed three color-matching strategies: the approximation of the scattering and absorption coefficient, the adjustment of the reflectance, and the direct matching of the color value <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>∗<!-- ∗ --></mml:mo> </mml:msup> <mml:msup> <mml:mi>a</mml:mi> <mml:mo>∗<!-- ∗ --></mml:mo> </mml:msup> <mml:msup> <mml:mi>b</mml:mi> <mml:mo>∗<!-- ∗ --></mml:mo> </mml:msup> </mml:math> .
Deep learning method for pinhole array color image reconstruction

The following paper proposes a combination of a supervised encoder-decoder neural network with coded apertures. Coded apertures provide improved sensitivity and signal-to-noise ratio (SNR) in planar images. The unique array design of this method overcomes the spatial frequency cutoff found in standard multi-pinhole arrays. In this design, the pinholes were positioned to minimize loss in spatial frequencies. The large number of pinholes results in significant overlapping on the detector. To overcome the overlapping issue, reconstruction of the object from the obtained image is done using inverse filtering methods. However, traces of duplications remain leading to a decline in SNR, contrast, and resolution. The proposed technique addresses the challenge of image distortion caused by the lack of accuracy in the inverse filter methods, by using a deep neural network. In this work, the coded aperture is combined with a deep convolutional neural network (CNN) to remove noise caused by pinhole imaging and inverse filter limitations. Compared to only using Wiener filtering, the proposed method delivers higher SNR, contrast, and resolution. The imaging system is presented in detail with experimental results that illustrate its efficiency.
Dynamic Pattern Recognition Model Based on Neural Network Response to Signal Fluctuation

We have proposed a model of dynamic retrieval in associative memory based on temporal input/output correlations under a stimulus-response open scheme of neural networks. This mechanism is different from that of the conventional stationary Hopfield model in which the input signal is used only as information for the initial state of the network. Building upon the fundamental properties of the proposed model, in this paper, we newly evaluate the dependence of identification performance on the signal fluctuation level and on the number of stored patterns by introducing an accuracy rate for known (stored) and unknown (non-stored) patterns, based on the network correlation to the input signal with fluctuation. The results indicate that the dynamic scheme of network response to a fluctuating signal leads to increased efficacy and usefulness.
A Comprehensive Security Review on Cloud Computing

Both in academics and industry, cloud computing technology is gaining traction. Despite its significance and promise for increased IT effectiveness and cost savings, legal ambiguities around information processing, particularly across major economies, remain on both the client and supplier sides. In the perspective of this, this article attempts to provide a review of contemporary published research on privacy challenges and legal frameworks for data security in cloud platforms. We choose to concentrate on data flow between both the U. S. and the European Union due to a general intricacy of international law. Our findings indicated considerable disparities in jurisdiction and security requirements awareness between these two countries. As a result, we've identified a significant multitude of challenges that have to be explored in future Cloud Technology development.
Phase demodulation of single frame projection fringe pattern based on deep learning

Fringe projection structured-light 3D measurement technology is an important tool in 3D digital measurement and detection in intelligent manufacturing. However, the contradiction still exists between the measurement accuracy and the measurement speed. It has great significance to introduce the U-Net network to predict wrapped phase by only using single amplitude-modulation fringe pattern. The influence of the U-Net network structure on the fringe pattern feature extraction is analyzed in detail and the structure and hyper-parameters of the classical U-Net network are improved for the phase demodulation task. It is concluded that the multi-scale symmetric structure design can provide a solution for preserving the phase information of edges and jump regions. To verify the effectiveness of the proposed method, the trained U-Net model and traditional methods are both employed to process the same fringe pattern in the contrast experiment. Then the advantages of the deep learning method, multi-step phase-shifting method and the Fast Fourier transform method (FFT) are compared. The results show that the phase demodulation accuracy of the proposed method still reached the accuracy obtained by more than three fringe patterns when the available information was only one fringe image. This method can replace the traditional FFT method in dynamic measurement applications and retain the accuracy of static measurement.
Short-Iteration Constant-Time GCD and Modular Inversion

Even theoretically secure cryptosystems, digital signatures, etc. may not be secure after being implemented on the Internet of Things (IoT) devices and PCs because of Side-Channel Attack ( $$\text{ SCA }$$ ). Since RSA key generation and ECDSA need $$\text{ GCD }$$ computations or modular inversions, which are often computed by Binary Euclidean Algorithm ( $$\text{ BEA }$$ ) or Binary Extended Euclidean Algorithm ( $$\text{ BEEA }$$ ), the $$\text{ SCA }$$ weakness of $$\text{ BEA }$$ and $$\text{ BEEA }$$ becomes serious. For countermeasures, the Constant-Time GCD (CT- $$\text{ GCD }$$ ) and Constant-Time Modular Inversion ( $$\text{ CTMI }$$ ) algorithms are good choices. Modular inversion based on Fermat’s Little Theorem ( $$\text{ FLT }$$ ) can work in constant time but it is not efficient for general inputs. Two $$\text{ CTMI }$$ algorithms, named $$\text{ BOS }$$ and $$\text{ BY }$$ in this paper, are proposed by Joppe W. Bos and Bernstein, Yang respectively, which are based on the idea of $$\text{ BEA }$$ . However, $$\text{ BOS }$$ has complicated computations during one iteration and $$\text{ BY }$$ uses more iterations. Small number of iterations and simple computations during one iteration are good characteristics of a constant-time algorithm. Based on this view, this paper proposes new short-iteration CT- $$\text{ GCD }$$ and $$\text{ CTMI }$$ algorithms over $${\mathbb F}_{p}$$ borrowing a simple idea of $$\text{ BEA }$$ . Our algorithms are evaluated from the theoretical point of view. Compared with $$\text{ BOS }$$ , $$\text{ BY }$$ and the improved version of $$\text{ BY }$$ , our short-iteration algorithms are experimentally demonstrated to be faster than theirs.
Post-Quantum Protocols for Banking Applications

With the NIST competition for Post-Quantum (PQ) algorithms standardization coming to an end, it is urgent to address the question of integrating PQ cryptography in real-world protocols to anticipate difficulties and allow a smooth transition. This is especially true for banking applications where the ecosystem composed of a variety of cards and terminals is heterogeneous. Providing solutions to ensure efficiency and some kind of backward compatibility is mandatory. In this work, we provide the first analysis of card-based payments with respect to these questions. We integrate post-quantum algorithms in existing protocols, and propose hybrid versions. We implement them on banking smart-cards and analyse the impacts on various aspects of the product, from production to actual transactions with terminals. Our work shows that such products are possible, but we identify several issues to overcome in the near feature in order to keep the same level of usability.
Development of attractive learning media 3D pocketbook based on augmented reality of the solar system for college student

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Bondan Ajidewantara, Yesiana Arimurti; Development of attractive learning media 3D pocketbook based on augmented reality of the solar system for college student. AIP Conference Proceedings 27 January 2023; 2540 (1): 090007. https://doi.org/10.1063/5.0106734 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
The validity of markerless augmented reality-based learning media on the concept of cell organelle

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Muhamad Ihsan, Sumiyati Sa'adah, Meti Maspupah; The validity of markerless augmented reality-based learning media on the concept of cell organelle. AIP Conf. Proc. 27 January 2023; 2540 (1): 020020. https://doi.org/10.1063/5.0105748 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Incorporating Physics-Based Models into Data Driven Approaches for Air Leak Detection in City Buses

In this work-in-progress paper two types of physics-based models, for accessing elastic and non-elastic air leakage processes, were evaluated and compared with conventional statistical methods to detect air leaks in city buses, via a data-driven approach. We have access to data streamed from a pressure sensor located in the air tanks of a few city buses, during their daily operations. The air tank in these buses supplies compressed air to drive various components, e.g. air brake, suspension, doors, gearbox, etc. We fitted three physics-based models only to the leakage segments extracted from the air pressure signal and used fitted model parameters as expert features for detecting air leaks. Furthermore, statistical moments of these fitted parameters, over predetermined time intervals, were compared to conventional statistical features on raw pressure values, under a classification setting in discriminating samples before and after the repair of air leak problems. The result of this exploratory study, on six air leak cases, shows that the fitted parameters of the physics-based models are useful for discriminating samples with air leak faults from the fault-free samples, which were observed right after the repair was performed to deal with the air leak problem. The comparison based on ANOVA F-score shows that the proposed features based on fitted parameters of physics-based models outrank the conventional features. It is observed that features of a non-elastic leakage model perform the best.
A study on the implementation plan for integrated curriculum classes for convergence talent education (STEAM): Focusing on physical education and Korean language

목적: 이 연구는 채육대학생의 우연대처기술, 진로적응성 및 진로준비행동의 관계를 규명하여 취업상황에 직면해있는 체육대학생의 우연대처기술 능력의 현 수준을 파악하고 이들 능력을 이끌어줄 수 있는 교수법의 방향성에 대한 기초적 자료를 제공하는데 그 목적을 두고 진행하였다.방법: 2022년 6월부터 7월에 걸쳐 약 두달간 서울 및 수도권지역에 소재하고 있는 4년제 대학 체육학과 3, 4학년 대학생 500명을 표본집단으로 설정한 후 설문조사를 실시하여 최종 480부를 결과에 사용하였다.결과: 첫째, 체육대학생의 우연대처기술은 진로적응성에 정적영향을 미치는 것으로 나타났다. 둘째, 체육대학생의 우연대처기술은 진로준비행동에 정적영향을 미치는 것으로 나타났다. 셋째, 체육대학생의 진로적응성은 진로준비행동에 정적영향을 미치는 것으로 나타났다.결론: 체육대학생을 교수하는 교수자들은 이들이 우연히 마주한 상황에서 대처할 수 있도록 다양한 상황을 임의로 설정하여 이를 해결하도록 하여 우연대처기술을 습득할 수 있는 교수하는 방안이 모색된다면 다른 학과보다 취업난이 높은 체육대학생들에게 도움이 될 수 있을 것이라 생각된다. Purpose: The purpose of this study was to investigate the relationship between accidental coping skills, career adaptability, and career preparation behavior of students in physical education who are facing employment situations, and to provide basic data on the direction of teaching methods that can lead them.Methods: Between June and July 2022, 500 third and fourth-grade university students in the physical education department of four-year universities located in Seoul and the metropolitan area were set as a sample group, and a survey was conducted and the final 480 copies were used for the results.Results: First, it was found that the accidental coping skills of physical education college students had a positive effect on career adaptability. Second, it was found that the accidental coping skills of physical education college students had a positive effect on career preparation behavior. Third, it was found that career adaptability of physical education college students had a positive effect on career preparation behavior.Conclusion: I think it will be helpful for PE students who have higher employment difficulties than other departments if professors who teach PE students can arbitrarily set various situations and solve them so that they can cope with accidental situations.
An Anomaly Detection-Based Domain Adaptation Framework for Cross-Domain Building Extraction from Remote Sensing Images

Deep learning-based building extraction methods have achieved a high accuracy in closed remote sensing datasets. In fact, the distribution bias between the source and target domains can lead to a dramatic decrease in their building extraction effect in the target domain. However, the mainstream domain adaptation methods that specifically address this domain bias problem require the reselection of many unlabeled samples and retraining in other target domains. This is time-consuming and laborious and even impossible at small regions. To address this problem, a novel domain adaptation framework for cross-domain building extraction is proposed from a perspective of anomaly detection. First, the initial extraction results of images in the target domain are obtained by a source domain-based pre-trained model, and then these results are classified into building mixed and non-building layers according to the predicted probability. Second, anomalous objects in the building layer are detected using the isolation forest method. Subsequently, the remaining objects in the building layer and the objects in the non-building layer are used as positive and negative samples, respectively, to reclassify the mixed layer using the random forest classifier. The newly extracted objects are fused with the remaining objects in the building layer as the final result. Four different experiments are performed on different semantic segmentation models and target domains. Some experimental results indicate that our framework can improve cross-domain building extraction compared to the pre-trained model, with an 8.7% improvement in the F1 metric when migrating from the Inria Aerial Image Labeling dataset to the Wuhan University dataset. Furthermore, experimental results show that our framework can be applied to multiple target domains without retraining and can achieve similar results to domain adaptation models based on adversarial learning.
Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network

With the development of wireless technology, signals propagating in space are easy to mix, so blind detection of communication signals has become a very practical and challenging problem. In this paper, we propose a blind detection method for broadband signals based on a weighted bi-directional feature pyramid network (BiFPN). The method can quickly perform detection and automatic modulation identification (AMC) on time-domain aliased signals in broadband data. Firstly, the method performs a time-frequency analysis on the received signals and extracts the normalized time-frequency images and the corresponding labels by short-time Fourier transform (STFT). Secondly, we build a target detection model based on YOLOv5 for time-domain mixed signals in broadband data and learn the features of the time-frequency distribution image dataset of broadband signals, which achieves the purpose of training the model. The main improvements of the algorithm are as follows: (1) a weighted bi-directional feature pyramid network is used to achieve a simple and fast multi-scale feature fusion approach to improve the detection probability; (2) the Efficient-Intersection over Union (EIOU) loss function is introduced to achieve high accuracy signal detection in a low Signal-Noise Ratio (SNR) environment. Finally, the time-frequency images are detected by an improved deep network model to complete the blind detection of time-domain mixed signals. The simulation results show that the method can effectively detect the continuous and burst signals in the broadband communication signal data and identify their modulation types.
Cybersecurity Risk: The Data

In our paper “Cybersecurity Risk” (Florackis, C., Louca, C., Michaely, R. and M. Weber. 2023. The Review of Financial Studies, Volume 36, Issue 1, Pages 351-407; https://doi.org/10.1093/rfs/hhac024) we construct a novel firm-level measure of cybersecurity risk using textual analysis of cybersecurity-risk disclosures in “Item 1A. Risk Factors” section of 10-K statements. The measure successfully identifies firms extensively discussing cybersecurity risk in their 10-K, displays intuitive relations with quantitative measures of cybersecurity risk disclosure language, exhibits a positive trend over time, is more prevalent among industries relying more on information technology systems, correlates with several characteristics linked to firms hit by cyberattacks and, importantly, predicts future cyberattacks. After providing evidence that our measure captures exposure to cybersecurity risk, we show that cybersecurity risk is priced in the cross-section of stock returns. Here, we publicly release our measure and hope that this allow for more subtle investigations and spur new research in this area.
A Novel Blockchain-Enabled Supply-Chain Management Framework for Xinjiang Jujube: Research on Optimized Blockchain Considering Private Transactions

Excellent jujube supply-chain management is of great significance to the development of the Xinjiang jujube industry. However, traditional jujube supply-chain management is faced with the dilemmas of opaque connection of transaction flow and capital flow information, unreliable product traceability information, and jujube farmers’ lack of bargaining power. In this research, we propose a jujube supply-chain-management framework based on blockchain. Hyperledger fabric is the distributed solution platform of this research. The current blockchain-based traceability framework for agri-food emphasizes the construction process and ignores the performance and characteristics of the framework. This research optimizes the blockchain-network-topology architecture and storage cost of writing data. This not only solves the traceability of jujube, but also fills the gap in previous research on the traceability framework. Moreover, transactions are innovatively divided into common transactions and private transactions. Private transactions have enhanced the bargaining power of jujube farmers. Through the analysis of benchmark tests, the effectiveness and feasibility of the framework are verified.
Lightweight and edge-preserving speckle matching network for precise single-shot 3D shape measurement

Single-shot three-dimensional (3D) shape measurement method based on digital speckle correlation (DSC) has high measurement precision and only requires one pair of speckle images. Nevertheless, the matching of speckles is time-consuming and may lead to some data defects at large gradient variations and edge regions. This paper proposes an end-to-end speckle matching network to achieve fast, precise, and edge-preserved 3D measurement. Compared with other networks, the proposed network reduces the redundant channels in the Siamese feature-extraction subnetwork and constructs cost volume by group-wise correlation, making it possible to provide efficient representations at low computational consumption, thus being suitable for high-precision 3D measurement in large resolution. A lightweight 3D stacked hourglass subnetwork is used to aggregate cost information and furthermore, an edge refinement module is integrated by leveraging the left speckle image as a guide to regular edge details. A reliable speckle binocular dataset is prepared with a specialized device in a simple procedure and the labels are produced from the DSC method. Experiments demonstrate that the proposed network can produce dense disparity maps with subpixel precision and reduce single-shot inference time to 0.13 s with less GPU occupation, which is a significant improvement compared with the traditional DSC method and other learning-based methods. Moreover, the experiment validates that measurement precision of the reconstructed plane can reach less than 0.03 mm, meeting the requirements of most industrial applications.
Current State of Blockchain and Cryptocurrency for Major US Cities

This analysis provides an overview of the current state of blockchain and cryptocurrency in several major cities in the United States, including the legal and regulatory framework, as well as the level of adoption and acceptance among businesses and consumers. The analysis covers cities such as New York City, San Francisco, Chicago, Los Angeles, and Miami, and examines any recent developments or changes in government policies towards cryptocurrency in these areas. The results show that while some cities, particularly those with a strong technology sector, have a high level of cryptocurrency adoption and acceptance, others have yet to fully embrace the technology. Additionally, the analysis highlights that different cities may have varying regulations and policies regarding cryptocurrency usage and trade. Overall, the analysis provides a comprehensive look at the current state of cryptocurrency in several major cities in the United States.