Reviewerly/exHarmony huggingface dataset:
Scalable true random number generator using adiabatic superconductor logic

Alternative computing such as stochastic computing and bio-inspired computing holds promise for overcoming the limitations of von Neumann computers. However, one difficulty in the implementation of such alternative computing is the need for a large number of random bits at the same time. To address this issue, we propose a scalable true-random-number generating scheme that we refer to as XORing shift registers (XSR). XSR generates multiple uncorrelated true random bitstreams using only two true random number generators as entropy sources and can thus be implemented by a variety of logic devices. Toward superconducting alternative computing, we implement XSR using an energy-efficient superconductor logic family, adiabatic quantum-flux-parametron (AQFP) logic. Furthermore, to demonstrate its performance, we design and observe an AQFP-based XSR circuit that generates four random bitstreams in parallel. The results of the experiment confirm that the bitstreams generated by the XSR circuit exhibit no autocorrelation and that there is no correlation between the bitstreams.
Adaptive Continuous-Variable Quantum Key Distribution with Discrete Modulation Regulative in Free Space

The finite sampling bandwidth of an analog-to-digital converter has a negative effect on the continuous-variable quantum key distribution (CVQKD), which leaves a potential loophole for an eavesdropper and weakens the practical security of the system. To compensate for the loss in free space, we deploy an adaptive optics (AO) unit in the detector of the CVQKD system with discrete modulations. Since the closed-loop control bandwidth of the embedded AO unit can be optimized with the sampling frequency, the practical security of the system can be enhanced in terms of the secret key rate. The security analysis is demonstrated on the basis of the derived secret key rate with numerical simulations, providing a feasible implementation of CVQKD in realistic free-space environments.
A flexible continuous-wave quantum cryptography scheme with zero-trust security for Internet of Things

As quantum computing techniques develop rapidly, the security of classical communication, which is usually based on public key encryption algorithm, is under great threat. Therefore, a key establishment method with physics base is demanding, especially for Internet of Things devices, where energy and computational power is quite limited. In this article, we present a flexible continuous-wave quantum cryptography scheme for Internet of Things systems. In this configuration, the IoT controller contains a narrow linewidth laser as a real local oscillator. Thus, it is capable of working as either a host or a client in quantum key distribution with remote servers, and efficiently generating quantum random numbers for quantum key distribution, as well as one time pad communication with deployed sensors. The security of the scheme is analyzed under the assumption of collective attacks in the asymptotic regime, and feasibility is theoretically verified with typical channel and commercial device parameters.
Overview of deep learning based pedestrian attribute recognition and re-identification

Pedestrian attribute recognition (PAR) and re-identification (ReID) are important works in the area of computer vision, which are widely used in intelligent surveillance and are of great significance to the creation of smart life. The purpose of this article is to focus on organizing a review of ReID based on deep learning and analyze the associations between PAR and ReID. Firstly, we summarize the major ideas of Attribute-Assisted ReID and compare the differences in datasets and algorithmic concerns between the two areas. Secondly, we introduce a wide range of representative ReID methods. By analyzing some cutting-edge researches, we summarize their specific network structure, loss function design, and effective training tricks. Reference methods and solutions are provided for the main challenges of ReID, such as cloth-changing, domain adaptation, occlusion condition, resolution changes, etc. Finally, we conclude the performance and characteristics of the SOTA methods, obtain inspiration and prospects for future research directions, and demonstrate the effectiveness of Attribute-Assisted ReID.
[Identification of Soil Heavy Metal Sources Around a Copper-silver Mining Area in Ningxia Based on GIS].

This study area was based on the catchment area of the Yaoxianzi ditch located in the arid region of western China. A total of 194 topsoil samples of 0-20 cm depth were collected using the mesh distribution method. The contents of nine heavy metals (Ni, Cu, Zn, As, Ag, Cr, Cd, Hg, and Pb) were determined using ICP-MS. The source and spatial distribution of heavy metals were analyzed using PMF and IDW. Spatial autocorrelation and clustering and outlier analysis were performed using the Spatial Statistical Analysis tool of ArcGIS. The main sources and distribution areas of heavy metals in the soil were obtained through comprehensive analysis. In the study area, the average values of Hg, Ag, Cd, and Pb were 20.48, 3.13, 2.23, and 1.12 times the background values, and the maximum values of Cd, Cu, Pb, and As were 10.92, 5.52, 2.03, and 1.39 times the filter values, respectively. The coefficients of variation of Cu, Cd, Pb, and Hg were ordered as Cu(283.23%)>Cd(224.77%)>Pb(144.40%)>Hg(67.12%) and were closely affected by human activities. The heavy metals in the soil around mining areas came from four main sources:natural parent material (32%), the mixed source of mining activities and transportation (17.1%), the mixed source of industrial activities and atmospheric sedimentation (40.3%), and the mixed source of agricultural activity and putting sandy gravel in farmland (10.6%). Cr and Ni, As and Cu, Hg, and Cd could represent these four sources of heavy metal pollution, respectively. The main sources of soil heavy metal pollution were mining activities and agricultural activities. The heavily contaminated areas were distributed in the mining areas in the south of the study area and in the planting areas in the eastern, central, and northwestern parts of the study area.
Simulated wrapped phase optimizes phase retrieval in phase-shifting interferometry

Phase retrieval is crucial in phase-shifting interferometry and other phase measurement techniques. However, in noisy wrapped phase maps with high steepness, discontinuities arise and cause phase unwrapping errors. To solve this problem, this Letter presents a phase retrieval method based on a simulated wrapped phase. By establishing the correspondence between the simulated and measured interferograms, the difference in wrapped phases between them can be obtained. The difference in wrapped phase map, which has sparse and wide interference fringes, has a higher reliability of phase unwrapping. The proposed method not only possesses high phase retrieval accuracy but it also simplifies the processing of interferograms. Furthermore, the layout of all interferometric systems, the parameters of optical components, and the model of the measured object are known, so the proposed method can be used as a reference for phase retrieval.
Evaluation of Financial Management Information System Using Modification of the Delone &amp; Mclean Model During the COVID-19 Pandemic

Purpose: This study aims to look at the impact of the Covid-19 pandemic on the implementation of SIMAKU, measure the success of SIMAKU implemented by the Universitas Muhammadiyah Metro from the perception of users, and see what obstacles are experienced during system implementation. Theoretical Framework: Accounting information system (AIS) consists of three basic elements: the system, information and accounting. Design/Methodology/Approach: The study uses a qualitative research approach based on the concept of "going exploring" which involves the in-depth and case-oriented study of several cases or single cases using descriptive study and case study analysis methods. Furthermore, the sources of this research amounted to 12 respondents divided into two, namely: information users and data inputs. Findings: The results of the study concluded that the Covid-19 pandemic did not have a significant effect on the implementation of the work unit program within the UMM and the implementation of the SIMAKU UMM application as a whole was quite good. Research, Practical &amp; Social implications: The benefit of this research is to evaluate the implementation of the Financial Management Information System (SIMAKU) which has been used at the UMM for more than five years so that this system can continue to be useful for its users on an ongoing basis. Implications/Originality/Value: The originality of this study is examines the impact of the Covid-19 pandemic on SIMAKU implementation and identifies errors in the implementation of successful systems and information systems models in user or user perception used by (DeLone &amp; McLean, 2003).
Optical Fiber Vibration Signal Identification Method Based on Improved YOLOv4

In the traditional peripheral-security-early-warning system, the endpoint detection and pattern recognition of the signals generated by the distributed optical fiber vibration sensors is completed step-by-step and in an orderly manner. The method by which these two processes may be placed end-to-end in a network model and processed simultaneously to improve work efficiency has increasingly become the focus of research. In this paper, the target detection algorithm combines the endpoint-detection and pattern-recognition processes of the vibration signal, which can not only quickly locate the start and end vibration positions of the signal but also accurately identify a certain type of signal. You Only Look Once v4 (YOLOv4) is one of the most advanced target detection algorithms, achieving the optimal balance of speed and accuracy. To reduce the complexity of the YOLOv4 model and solve the dataset’s unbalanced sample classification problem, we use a deep separable convolution (DSC) network and a focal loss function to improve the YOLOv4 model. In this paper, the five kinds of signals collected in real-time are visualized as two different datasets in oscillograph and time-frequency diagrams as detection objects. According to the experimental results, we obtained 98.50% and 93.48% mean Average Precision (mAP) and 84.8 and 69.9 frames per second (FPS), respectively, which are improved compared to YOLOv4. Comparing the improved algorithm with other optical fiber vibration signal recognition algorithms, the mAP and FPS values were improved, and the detection speed was about 20 times faster than that of other algorithms. The improved algorithm in this paper can quickly and accurately identify the vibration signal of external intrusion, reduce the false-alarm rate of the early-warning system, and improve the real-time detection rate of the system while ensuring high recognition accuracy.
Markov chain Monte Carlo enhanced variational quantum algorithms

Abstract Variational quantum algorithms have the potential for significant impact on high-dimensional optimization, with applications in classical combinatorics, quantum chemistry, and condensed matter. Nevertheless, the optimization landscape of these algorithms is generally nonconvex, leading the algorithms to converge to local, rather than global, minima and the production of suboptimal solutions. In this work, we introduce a variational quantum algorithm that couples classical Markov chain Monte Carlo techniques with variational quantum algorithms, allowing the former to provably converge to global minima and thus assure solution quality. Due to the generality of our approach, it is suitable for a myriad of quantum minimization problems, including optimization and quantum state preparation. Specifically, we devise a Metropolis–Hastings method that is suitable for variational quantum devices and use it, in conjunction with quantum optimization, to construct quantum ensembles that converge to Gibbs states. These performance guarantees are derived from the ergodicity of our algorithm’s state space and enable us to place analytic bounds on its time-complexity. We demonstrate both the effectiveness of our technique and the validity of our analysis through quantum circuit simulations for MaxCut instances, solving these problems deterministically and with perfect accuracy, as well as large-scale quantum Ising and transverse field spin models of up to 50 qubits. Our technique stands to broadly enrich the field of variational quantum algorithms, improving and guaranteeing the performance of these promising, yet often heuristic, methods.
Surrogate-assisted evolutionary neural architecture search with network embedding

Abstract To accelerate the performance estimation in neural architecture search, recently proposed algorithms adopt surrogate models to predict the performance of neural architectures instead of training the network from scratch. However, it is time-consuming to collect sufficient labeled architectures for surrogate model training. To enhance the capability of surrogate models using a small amount of training data, we propose a surrogate-assisted evolutionary algorithm with network embedding for neural architecture search (SAENAS-NE). Here, an unsupervised learning method is used to generate meaningful representation of each architecture and the architectures with more similar structures are closer in the embedding space, which considerably benefits the training of surrogate models. In addition, a new environmental selection based on a reference population is designed to keep diversity of the population in each generation and an infill criterion for handling the trade-off between convergence and model uncertainty is proposed for re-evaluation. Experimental results on three different NASBench and DARTS search space illustrate that network embedding makes the surrogate model achieve comparable or superior performance. The superiority of our proposed method SAENAS-NE over other state-of-the-art neural architecture algorithm has been verified in the experiments.
Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models

Antibiotic-resistant bacteria and antibiotic resistance genes (ARGs) are pollutants of worldwide concern that seriously threaten public health and ecosystems. Machine learning (ML) prediction models have been applied to predict ARGs in beach waters. However, the existing studies were conducted at a single location and had low prediction performance. Moreover, ML models are "black boxes" that do not reveal their predictions' internal nuances and mechanisms. This lack of transparency and trust can result in serious consequences when using these models in high-stakes decisions. In this study, we developed a gradient boosted regression tree based (GBRT) ML model and then described its behavior using six explainable artificial intelligence (XAI) model-agnostic explanation methods. We used hydro-meteorological and qPCR data from the beaches in South Korea and Pakistan and developed ML prediction models for aac (6'-lb-cr), sul1, and tetX with 10-fold time-blocked cross-validation performances of 4.9, 2.06 and 4.4 root mean squared logarithmic error, respectively. We then analyzed the local and global behavior of the developed ML model using four interpretation methods. The developed ML models showed that water temperature, precipitation and tide are the most important predictors for prediction of ARGs at recreational beaches. We show that the model-agnostic interpretation methods not only explain the behavior of the ML model but also provide insights into the behavior of the ML model under new unseen conditions. Moreover, these post-processing techniques can be a debugging tool for ML-based modeling.
Model-independent nonlinearity rectification algorithm using a phase-probability-equalization-based look-up table

In the fringe projection system, nonlinearities often result in severe artifacts, such as the gamma effect and the phase-shifting error. Most previous methods can only eliminate the nonlinearity of a particular model. Additionally, the problems of coupling nonlinearities are difficult to solve. Therefore, this paper proposes a model-independent nonlinear rectification algorithm. By applying phase probability equalization (PPE) on several complete periods of a flat area, we built a look-up table (LUT) between the phase error and the wrapped phase, and retrieved an accurate phase with the subtraction of a searched phase error. The simulation and experimental results show that, compared with the traditional full-field PPE algorithm, the proposed algorithm is more robust to the object height distributions and has better rectification on incomplete fringe periods. Besides, the proposed algorithm also has higher efficiency because of the characteristics of local processing and noniterative characteristics.
QuadStream

Streaming rendered 3D content over a network to a thin client device, such as a phone or a VR/AR headset, brings high-fidelity graphics to platforms where it would not normally possible due to thermal, power, or cost constraints. Streamed 3D content must be transmitted with a representation that is both robust to latency and potential network dropouts. Transmitting a video stream and reprojecting to correct for changing viewpoints fails in the presence of disocclusion events; streaming scene geometry and performing high-quality rendering on the client is not possible on limited-power mobile GPUs. To balance the competing goals of disocclusion robustness and minimal client workload, we introduce QuadStream , a new streaming content representation that reduces motion-to-photon latency by allowing clients to efficiently render novel views without artifacts caused by disocclusion events. Motivated by traditional macroblock approaches to video codec design, we decompose the scene seen from positions in a view cell into a series of quad proxies , or view-aligned quads from multiple views. By operating on a rasterized G-Buffer, our approach is independent of the representation used for the scene itself; the resulting QuadStream is an approximate geometric representation of the scene that can be reconstructed by a thin client to render both the current view and nearby adjacent views. Our technical contributions are an efficient parallel quad generation, merging, and packing strategy for proxy views covering potential client movement in a scene; a packing and encoding strategy that allows masked quads with depth information to be transmitted as a frame-coherent stream; and an efficient rendering approach for rendering our QuadStream representation into entirely novel views on thin clients. We show that our approach achieves superior quality compared both to video data streaming methods, and to geometry-based streaming.
Key Space Enhanced Correlated Random Bit Generation Based on Synchronized Electro-Optic Self-Feedback Loops with Mach–Zehnder Modulators

With the widespread application of big data, the amount of data transmitted through optical networks has been increasing dramatically. Correlated random bit generation (CRBG) is one of the key technologies in secure communication systems to ensure security performance and transmission efficiency. We propose and demonstrate a CRBG scheme based on a Mach–Zehnder modulator (MZM) electro-optic feedback loop to improve the security and speed of communication systems. In this scheme, common-signal-induced synchronization is accomplished to generate wideband complex physical entropy sources, and a private hardware module is employed to perform post-processing and nonlinear transformation of the synchronized signal. The simulation results show that the effective bandwidth of the output chaotic signal is significantly increased to 27.76 GHz, and high-quality synchronization with a correlation coefficient of over 0.98 is reached. A high-rate CRBG of up to 5.3 Gb/s is successfully achieved between two synchronized wideband physical entropy sources, and the hardware key space is enhanced to ∼242, which greatly improves the privacy of physical entropy sources. The proposed scheme provides a promising approach for high-speed private CRBG, which is expected to be used in high-speed secure key distribution and optical communication systems.
Real-time automated detection of older adults' hand gestures in home and clinical settings

There is an urgent need, accelerated by the COVID-19 pandemic, for methods that allow clinicians and neuroscientists to remotely evaluate hand movements. This would help detect and monitor degenerative brain disorders that are particularly prevalent in older adults. With the wide accessibility of computer cameras, a vision-based real-time hand gesture detection method would facilitate online assessments in home and clinical settings. However, motion blur is one of the most challenging problems in the fast-moving hands data collection. The objective of this study was to develop a computer vision-based method that accurately detects older adults' hand gestures using video data collected in real-life settings. We invited adults over 50 years old to complete validated hand movement tests (fast finger tapping and hand opening-closing) at home or in clinic. Data were collected without researcher supervision via a website programme using standard laptop and desktop cameras. We processed and labelled images, split the data into training, validation and testing, respectively, and then analysed how well different network structures detected hand gestures. We recruited 1,900 adults (age range 50-90 years) as part of the TAS Test project and developed UTAS7k-a new dataset of 7071 hand gesture images, split 4:1 into clear: motion-blurred images. Our new network, RGRNet, achieved 0.782 mean average precision (mAP) on clear images, outperforming the state-of-the-art network structure (YOLOV5-P6, mAP 0.776), and mAP 0.771 on blurred images. A new robust real-time automated network that detects static gestures from a single camera, RGRNet, and a new database comprising the largest range of individual hands, UTAS7k, both show strong potential for medical and research applications.The online version contains supplementary material available at 10.1007/s00521-022-08090-8.
Partitioned Active Learning for Heterogeneous Systems

Abstract Active learning is a subfield of machine learning that focuses on improving the data collection efficiency in expensive-to-evaluate systems. Active learning-applied surrogate modeling facilitates cost-efficient analysis of demanding engineering systems, while the existence of heterogeneity in underlying systems may adversely affect the performance. In this article, we propose the partitioned active learning that quantifies informativeness of new design points by circumventing heterogeneity in systems. The proposed method partitions the design space based on heterogeneous features and searches for the next design point with two systematic steps. The global searching scheme accelerates exploration by identifying the most uncertain subregion, and the local searching utilizes circumscribed information induced by the local Gaussian process (GP). We also propose Cholesky update-driven numerical remedies for our active learning to address the computational complexity challenge. The proposed method consistently outperforms existing active learning methods in three real-world cases with better prediction and computation time.