Reviewerly/exHarmony huggingface dataset:
Complementary relations of entanglement, coherence, steering, and Bell nonlocality inequality violation in three-qubit states

We put forward complementary relations of entanglement, coherence, steering inequality violation, and Bell nonlocality for arbitrary three-qubit states. We show that two families of genuinely entangled three-qubit pure states with a single parameter exist, and they exhibit maximum coherence and steering inequality violation for a fixed amount of negativity, respectively. It is found that the negativity is exactly equal to the geometric mean of bipartite concurrences for the three-qubit pure states, although the negativity is always less than or equal to the latter for three-qubit mixed states. Moreover, the complementary relation between negativity and first-order coherence for tripartite entanglement states is established. Furthermore, we investigate the close relation between the negativity and the maximum steering inequality violation. In addition, the complementary relation between negativity and the maximum Bell-inequality violation for arbitrary three-qubit states is obtained. The results provide reliable evidence of fundamental connections among entanglement, coherence, steering inequality violation, and Bell nonlocality.
Teachers’ Professional Knowledge to Develop STEM Integrated Tasks

This study presents developments on the professional knowledge needed to implement STEM hands-on tasks by teachers. Research was developed in the framework of a teachers’ professional development programme designed to provide knowledge for them to be capable of implementing this kind of tasks. With a qualitative methodology, we verified that teachers need to attain a specialized theoretical and technical knowledge to develop STEM integrated hands-on tasks and effectively implement them in class.
GARNet: Global-aware multi-view 3D reconstruction network and the cost-performance tradeoff

Deep learning technology has made great progress in multi-view 3D reconstruction tasks. At present, the mainstream solutions adopt different ways to fusion the features from several views. Among them, attention-based aggregation function performs relatively well and stably, however, it still has an obvious shortcoming the strong independence of each view during predicting the weights for merging leads to a lack of adaption of the global state. In this paper, we propose a global-aware attention-based fusion approach that builds the correlation between each branch and the global feature to provide a comprehensive foundation for weights inference. On the basis of this, we design a complete reconstruction algorithm. Experiments on ShapeNet verify that our method outperforms existing SOTA methods. Furthermore, we propose a view-reduction method based on maximizing diversity and discuss the cost-performance tradeoff of our model to achieve a better performance when facing a heavy input amount and limited computational cost.
Countermeasures against ‘SIM-Swapping’ Crime through Criminal Script Analysis

본 연구는 국내에서 발생하는 심스와핑 범죄의 모델과 범죄 취약 요소를 분석하고, 대응 정책을 제안하는 것을 목표로 한다. 이를 위해서 본 연구에서는 최근 발생한 심스와핑 범죄 사례를 바탕으로 범죄 스크립트 분석을 수행하였다. 그 결과 심스와핑 범죄가 범죄 준비, 복제 심 제작, 단말기 변경 인증, 일회용 OTP 탈취, 가상자산 탈취, 범행자금 세탁 순으로 이루어진다는 것을 확인하였고, 각 과정에서 이동통신사의 데이터 보안 취약 요소, 비인가된 단말기 변경 승인 요청을 거절할 수 있는 검증 알고리즘의 부재, 다중 요소 인증의 인증요소간 강한 의존성 등 범죄유발 요소가 존재함을 파악하였다. 이러한 연구 결과를 토대로 심스와핑 범죄 예방을 위하여 외부 보안 감사 제도 도입, 단말기 변경의 이상 탐지/차단 시스템의 도입, 국내 산업 전반에서 사용 중인 다중 요소 인증의 각 요소 독립성 강화 등 기술적, 법적 정책을 제언하였다. This study aims to analyze the model and vulnerability factors of swapping crimes in Korea and propose countermeasures. To this end, this study conducted a crime script analysis based on recent cases of sim-swapping crimes, and found that sim-swapping crimes consist of crime preparation, clone sim production, terminal change authentication, one-time OTP theft, virtual asset theft, and criminal money laundering. In each process, we identified the existence of crime-inducing factors such as data security vulnerabilities of mobile operators, the lack of verification algorithms to reject unauthorized device change approval requests, and the strong dependence between authentication factors in multi-factor authentication, Based on these findings, we recommended technical and legal policies to prevent sim-swapping crimes, such as introducing an external security audit system, introducing a system for detecting and blocking abnormalities in terminal changes, and strengthening the independence of each element of multi-factor authentication used throughout the domestic industry.
A method for rapid machine learning development for data mining with doctor-in-the-loop

Classifying free-text from historical databases into research-compatible formats is a barrier for clinicians undertaking audit and research projects. The aim of this study was to (a) develop interactive active machine-learning model training methodology using readily available software that was (b) easily adaptable to a wide range of natural language databases and allowed customised researcher-defined categories, and then (c) evaluate the accuracy and speed of this model for classifying free text from two unique and unrelated clinical notes into coded data. A user interface for medical experts to train and evaluate the algorithm was created. Data requiring coding in the form of two independent databases of free-text clinical notes, each of unique natural language structure. Medical experts defined categories relevant to research projects and performed ‘label-train-evaluate’ loops on the training data set. A separate dataset was used for validation, with the medical experts blinded to the label given by the algorithm. The first dataset was 32,034 death certificate records from Northern Territory Births Deaths and Marriages, which were coded into 3 categories: haemorrhagic stroke, ischaemic stroke or no stroke. The second dataset was 12,039 recorded episodes of aeromedical retrieval from two prehospital and retrieval services in Northern Territory, Australia, which were coded into 5 categories: medical, surgical, trauma, obstetric or psychiatric. For the first dataset, macro-accuracy of the algorithm was 94.7%. For the second dataset, macro-accuracy was 92.4%. The time taken to develop and train the algorithm was 124 minutes for the death certificate coding, and 144 minutes for the aeromedical retrieval coding. This machine-learning training method was able to classify free-text clinical notes quickly and accurately from two different health datasets into categories of relevance to clinicians undertaking health service research.
Quantifying high-dimensional spatial entanglement with a single-photon-sensitive time-stamping camera

High-dimensional entanglement is a promising resource for quantum technologies. Being able to certify it for any quantum state is essential. However, to date, experimental entanglement certification methods are imperfect and leave some loopholes open. Using a single-photon-sensitive time-stamping camera, we quantify high-dimensional spatial entanglement by collecting all output modes and without background subtraction, two critical steps on the route toward assumptions-free entanglement certification. We show position-momentum Einstein-Podolsky-Rosen (EPR) correlations and quantify the entanglement of formation of our source to be larger than 2.8 along both transverse spatial axes, indicating a dimension higher than 14. Our work overcomes important challenges in photonic entanglement quantification and paves the way toward the development of practical quantum information processing protocols based on high-dimensional entanglement.
All-optical synaptic neuron based on add-drop microring resonator with power-tunable auxiliary light

We propose and demonstrate an all-optical synaptic neuron based on an add-drop microring resonator (ADMRR) with power-tunable auxiliary light. Dual neural dynamics of passive ADMRRs, having spiking response and synaptic plasticity, are numerically investigated. It is demonstrated that, by injecting two beams of power-tunable and opposite-direction continuous light into an ADMRR and maintaining their sum power at a constant value, linear-tunable and single-wavelength neural spikes can be flexibly generated, in virtue of the nonlinear effects triggered by perturbation pulses. Based on this, a weighting operation system based on cascaded ADMRRs is designed; it enables implementation of real-time weighting operations at a number of wavelengths. This work provides a novel, to the best of our knowledge, approach for integrated photonic neuromorphic systems based entirely on optical passive devices.
Modeling of thin-film interference filters on structured substrates: microfacet-based BSDF versus ray tracing

We compare two model approaches for the ray optical description of PV modules with coloring based on an interference layer system on the inside of the cover glass. The light scattering is described by a microfacet-based bidirectional scattering distribution function (BSDF) model on the one hand and ray tracing on the other hand. We show that the microfacet-based BSDF model is largely sufficient for the structures used in the context of the MorphoColor application. A structure inversion shows a significant influence only for extreme angles and very steep structures showing correlated heights and surface normal orientations. Regarding an angle-independent color appearance, the model-based comparison of possible module configurations shows a clear advantage of a structured layer system compared to planar interference layers in combination with a scattering structure on the front side of the glass.
Single-path Bit Sharing for Automatic Loss-aware Model Compression

Network pruning and quantization are proven to be effective ways for deep model compression. To obtain a highly compact model, most methods first perform network pruning and then conduct quantization based on the pruned model. However, this strategy may ignore that the pruning and quantization would affect each other and thus performing them separately may lead to sub-optimal performance. To address this, performing pruning and quantization jointly is essential. Nevertheless, how to make a trade-off between pruning and quantization is non-trivial. Moreover, existing compression methods often rely on some pre-defined compression configurations ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , pruning rates or bitwidths). Some attempts have been made to search for optimal configurations, which however may take unbearable optimization cost. To address these issues, we devise a simple yet effective method named Single-path Bit Sharing (SBS) for automatic loss-aware model compression. To this end, we consider the network pruning as a special case of quantization and provide a unified view for model pruning and quantization. We then introduce a single-path model to encode all candidate compression configurations, where a high bitwidth value will be decomposed into the sum of a lowest bitwidth value and a series of re-assignment offsets. Relying on the single-path model, we introduce learnable binary gates to encode the choice of configurations and learn the binary gates and model parameters jointly. More importantly, the configuration search problem can be transformed into a subset selection problem, which helps to significantly reduce the optimization difficulty and computation cost. In this way, the compression configurations of each layer and the trade-off between pruning and quantization can be automatically determined. Extensive experiments on CIFAR-100 and ImageNet show that SBS significantly reduces computation cost while achieving promising performance. For example, our SBS compressed MobileNetV2 achieves 22.6× Bit-Operation (BOP) reduction with only 0.1% drop in the Top-1 accuracy.
EfficientNet-based electromagnetic attack on AES cipher chips

This paper presents a bypass attack on the Field Programmable gate array (FPGA) cryptographic chip Advanced encryption standard (AES) encryption algorithm. Since the accuracy of half-byte classification is low and the model is slow to converge and easy to overfit when using deep learning, this experiment proposes for the first time to introduce the deep learning network EfficientNet model into the field, which only targets the electromagnetic leakage information of the hardware during encryption for any half-byte, when the plaintext and ciphertext are unknown. This is followed by a "divide and conquer" approach to the entire key, where each half-byte is attacked to obtain the entire key. The accuracy of the model used in this study was found to be about 17% higher than that of common networks such as ResNet and DenseNet, and the model converged better.
How social interactions can affect Modern Code Review

Introduction Modern Code Review (MCR) is a multistage process where developers evaluate source code written by others to enhance the software quality. Despite the numerous studies conducted on the effects of MCR on software quality, the non-technical issues in the MCR process have not been extensively studied. This study aims to investigate the social problems in the MCR process and to find possible ways to prevent them and improve the overall quality of the MCR process. Methodology To achieve the research objectives, we applied the grounded theory research shaped by GQM approach to collect data on the attitudes of developers from different teams toward MCR. We conducted interviews with 25 software developers from 13 companies to obtain the information necessary to investigate how social interactions affect the code reviewing process. Results Our findings show that interpersonal relationships within the team can have significant consequences on the MCR process. We also received a list of possible strategies to overcome these problems. Discussion Our study provides a new perspective on the non-technical issues in the MCR process, which has not been extensively studied before. The findings of this study can help software development teams to address the social problems in the MCR process and improve the overall quality of their software products. Conclusion This study provides valuable insights into the non-technical issues in the MCR process and the possible ways to prevent them. The findings of this study can help software development teams to improve the MCR process and the quality of their software products. Future research could explore the effectiveness of the identified strategies in addressing the social problems in the MCR process.
Towards Better User Studies in Computer Graphics and Vision

Online crowdsourcing platforms have made it increasingly easy to perform evaluations of algorithm outputs with survey questions like "which image is better, A or B?", leading to their proliferation in vision and graphics research papers. Results of these studies are often used as quantitative evidence in support of a paper's contributions. On the one hand we argue that, when conducted hastily as an afterthought, such studies lead to an increase of uninformative, and, potentially, misleading conclusions. On the other hand, in these same communities, user research is underutilized in driving project direction and forecasting user needs and reception. We call for increased attention to both the design and reporting of user studies in computer vision and graphics papers towards (1) improved replicability and (2) improved project direction. Together with this call, we offer an overview of methodologies from user experience research (UXR), human-computer interaction (HCI), and applied perception to increase exposure to the available methodologies and best practices. We discuss foundational user research methods (e.g., needfinding) that are presently underutilized in computer vision and graphics research, but can provide valuable project direction. We provide further pointers to the literature for readers interested in exploring other UXR methodologies. Finally, we describe broader open issues and recommendations for the research community.
Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting

With well-selected data, homogeneous diffusion inpainting can reconstruct images from sparse data with high quality. While 4K colour images of size $$3840\, \times \,2160$$ can already be inpainted in real time, optimising the known data for applications like image compression remains challenging: Widely used stochastic strategies can take days for a single 4K image. Recently, a first neural approach for this so-called mask optimisation problem offered high speed and good quality for small images. It trains a mask generation network with the help of a neural inpainting surrogate. However, these mask networks can only output masks for the resolution and mask density they were trained for. We solve these problems and enable mask optimisation for high-resolution images through a neuroexplicit coarse-to-fine strategy. Additionally, we improve the training and interpretability of mask networks by including a numerical inpainting solver directly into the network. This allows to generate masks for 4K images in around 0.6 s while exceeding the quality of stochastic methods on practically relevant densities. Compared to popular existing approaches, this is an acceleration of up to four orders of magnitude.
Latent-Space Disentanglement with Untrained Generator Networks for the Isolation of Different Motion Types in Video Data

Isolating different types of motion in video data is a highly relevant problem in video analysis. Applications can be found, for example, in dynamic medical or biological imaging, where the analysis and further processing of the dynamics of interest is often complicated by additional, unwanted dynamics, such as motion of the measurement subject. In this work, it is empirically shown that a representation of video data via untrained generator networks, together with a specific technique for latent space disentanglement that uses minimal, one-dimensional information on some of the underlying dynamics, allows to efficiently isolate different, highly non-linear motion types. In particular, such a representation allows to freeze any selection of motion types, and to obtain accurate independent representations of other dynamics of interest. Obtaining such a representation does not require any pre-training on a training data set, i.e., all parameters of the generator network are learned directly from a single video.
Dual Compensation Residual Networks for Class Imbalanced Learning

Learning generalizable representation and classifier for class-imbalanced data is challenging for data-driven deep models. Most studies attempt to re-balance the data distribution, which is prone to overfitting on tail classes and underfitting on head classes. In this work, we propose Dual Compensation Residual Networks to better fit both tail and head classes. Firstly, we propose dual <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Feature Compensation Module</b></i> (FCM) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Logit Compensation Module</b></i> (LCM) to alleviate the overfitting issue. The design of these two modules is based on the observation: an important factor causing overfitting is that there is severe <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature drift</i> between training and test data on tail classes. In details, the test features of a tail category tend to drift towards feature cloud of multiple similar head categories. So FCM estimates a multi-mode feature drift direction for each tail category and compensate for it. Furthermore, LCM translates the deterministic feature drift vector estimated by FCM along intra-class variations, so as to cover a larger effective compensation space, thereby better fitting the test features. Secondly, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Residual Balanced Multi-Proxies Classifier</b></i> (RBMC) to alleviate the under-fitting issue. Motivated by the observation that re-balancing strategy hinders the classifier from learning sufficient head knowledge and eventually causes underfitting, RBMC utilizes uniform learning with a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">residual path</b> to facilitate classifier learning. Comprehensive experiments on Long-tailed and Class-Incremental benchmarks validate the efficacy of our method.
Research on Miners' Human Posture Detection Algorithm Based on MMPose

With the continuous improvement of intelligence in the coal industry, intelligent video analysis and judgment of dangerous behaviors that have occurred can no longer meet the needs of human posture detection in underground mines.In this paper, a human posture detection algorithm based on MMPose is proposed to determine the body posture of personnel under the mine at different times, in order to provide effective information for standardizing miner operations, disaster early warning, and accident rescue.The algorithm is based on MMPose technology, which improves and optimizes the existing shortcomings, facilitating the determination of the body posture of underground personnel at different times, and solving the low accuracy issues of occlusion image detection, multi-person image detection, and dark environment detection.The designed algorithm achieves labeling of 17 key points such as eyes, nose, ears, shoulders, elbows, hands, legs, knees, and feet, and can detect various postures such as standing upright, standing sideways, sitting, squatting, and walking.Experiments have shown that this algorithm has a high accuracy in human posture detection and prediction, and can better mark the key points of the human body for occluded images, multi-person images, and dark environments, meeting the real-time requirements of underground personnel posture detection.
Reflections on the privacy protection of e-CNY

With the rapid development of fintech, digital economy, and blockchain technology, the problems of digital currency in privacy protection need to be paid attention to and solved urgently.We start with the characteristics and issuing mode of e-CNY.Then, the influence of e-CNY at home and abroad is investigated as an attempt, upon which the existing or potential threats and challenges in privacy security are analyzed.Finally, based on the above research, we put forward feasible suggestions and prospects for e-CNY.