Reviewerly/exHarmony huggingface dataset:
Practical zoom camera calibration method for close-range photogrammetry

Compared to a conventional fixed focus lens, a zoom lens is more flexible and adaptable. However, the challenges involved in precise calibration for a zoom camera prevent its widespread use in close-range photogrammetry. A practical calibration method for a zoom camera is proposed. The zoom-focus model is established through dimension reduction of the setting variables, which is represented as a set of functions of the zoom setting. The zoom and focus settings are updated in real time for objects at different measurement depths. The calibration process only requires the zoom camera to observe the control points distributed in the designed calibration field with several combinations of zoom and focus settings. All the coefficients of the zoom-focus model can be solved by a nonlinear joint optimization. Experimental results have proved that the proposed method is effective for close-range photogrammetry.
Distributed Cooperative Complete Coverage Path Planning in an Unknown Environment Based on a Heuristic Method

This paper proposes a distributed collaborative complete coverage path planning (CCPP) algorithm based on a heuristic method to solve a CCPP problem for multiple agents in an unknown environment. Based on the relationship between path length, energy consumption and number of turns, the algorithm instructs the agents to autonomously plan their respective paths in real time by giving the priority of directions. Simulation experiments show that the proposed CCPP algorithm can guarantee efficient collision-free complete coverage compared with related approaches.
Stereo matching algorithm using census transform and segment tree for depth estimation

This article proposes an algorithm for stereo matching corresponding process that will be used in many applications such as augmented reality, autonomous vehicle navigation and surface reconstruction. Basically, the proposed framework in this article is developed through a series of functions. The final result from this framework is disparity map which this map has the information of depth estimation. Fundamentally, the framework input is the stereo image which represents left and right images respectively. The proposed algorithm in this article has four steps in total, which starts with the matching cost computation using census transform, cost aggregation utilizes segment-tree, optimization using winner-takes-all (WTA) strategy, and post-processing stage uses weighted median filter. Based on the experimental results from the standard benchmarking evaluation system from the Middlebury, the disparity map results produce an average low noise error at 9.68% for nonocc error and 18.9% for all error attributes. On average, it performs far better and very competitive with other available methods from the benchmark system.
Multi-preset video encoder bitrate ladder prediction

Determining optimal resolution for any possible combination of video title and subscriber bandwidth limit is a critical problem to address in streaming and Video on Demand (VoD) services. Approaches such as per-title encoding aim at addressing this problem through computationally complex encoding passes for constructing content-adaptive bitrate ladders. This paper proposes a machine learning (ML)-based method that accelerates such approaches by estimating bitrate ladders of a multi-preset encoder. To do so, reference content-adaptive bitrate ladder using the fastest possible preset of a given encoder is constructed exhaustively. Then, an offline-trained regressor is deployed to transform the fast-preset ladder into the slow-preset ladder that is needed for actual encoding pass. The experiment, carried out on the official optimized codec implementation of the Versatile Video Coding (VVC) standard (VVenC), shows a significant performance improvement in terms of compression efficiency and complexity trade-off. In particular, the proposed method can offer 91% ladder construction complexity reduction at the cost of only 0.88% coding efficiency drop in terms of BD-BR.
Yoga Posture Detection Using Machine Learning

Yoga is the combination of physical, mental, and spiritual attributes. Its practice renders discipline to life. Its practice also releases stress and makes humans happier. Yoga contains different breathing techniques, asanas, and meditation techniques. These postures and techniques bring peace to the mind. To practice a pose, the pose is selected and then uploaded as captured by the webcam after the pose capturing is done. Then in the background, the parallel image processing fetches the key points like knee, eye, and hand after the selection of key points. A skeleton of the yoga pose is done; then it will show the match pose of yoga that you had done using which you can measure the accuracy of your pose. RGB camera is used for yoga pose detection system and it obtains overall accuracy with real-time prediction. We have proposed a system that can observe human body part movement and also keep track of the accuracy of different yoga poses. To detect joint points of the human body in real-time, we used Live Webcam and from those joint points to calculate the accuracy of a specific yoga pose for the user.
Research on Power Prediction of Runoff Small Hydropower Cluster Based on Fusion Modeling

Abstract The characteristics of the runoff hydropower station are to generate power completely according to the natural runoff, and the output and power generation change with the change of the natural flow. The cluster power prediction of the runoff hydropower station is conducive to improving the safety of the power grid operation and improving the utilization rate of hydropower. This paper analyzes the operation characteristics of runoff hydropower stations, summarizes the prediction direction of mainstream runoff hydropower, analyzes the characteristics of the hydrological prediction model represented by the Xin'an River model, and puts forward a study on the power prediction technology of runoff small hydropower cluster based on fusion modeling. Based on the detailed analysis of watershed runoff characteristics, a prediction model based on multi-watershed observation characteristics is proposed, which combines the traditional hydrological model with the LSTM model to form a combined prediction model suitable for runoff hydropower. The method is universal and innovative.
Distillability Problem of 4×4 Monomial Matrices

Abstract It is a fundamental problem in quantum information whether a particular quantum state of a composite system is entangled. It has enormous potential in quantum error correction, quantum cryptography, and quantum teleportation applications. This problem can be transferred in the form of a mathematical conjecture called the distillation conjecture. In the first section of this paper, relevant physical and mathematical information is presented, including basic linear algebra knowledge, the statement, and concrete applications of multiple mathematical knowledge like conjugate, eigenvalue, and singular value. Then, we introduce the distillation conjecture in a mathematical version for a more precise mathematical analysis. In an effort to make more significant headway in proving the conjecture, we selected some theories and findings relating to the Kronecker product, Kronecker sum, eigenvalue, and singular value, then evaluated and grouped them. In addition, we provided multiple proofs of the conjecture under varying conditions and made numerous attempts and hypotheses regarding how to establish the conjecture.
BeyondCorp

This chapter focuses on Google’s approach to the zero-trust networking concept.
DeYOLOv3: An Optimal Mass Detector for Advanced Breast Cancer Diagnostics

Mass detection is an essential phase in any breast cancer diagnosis procedure. This paper proposes an optimal mass detector based on a novel YOLOv3 framework that can handle both detection and classification of mass in full-field digital mammograms just in one single pass through a deep convolution neural network (DCNN). To tackle the conflict between classification and regression tasks that remained in the original model, we replace the coupled detection head with decoupled one. It can be observed in the result that our proposed model yields better performance than other advanced detection models.
Quantum‐Error‐Rejection for Hyperentanglement Transmission without Calibrated Reference Frames

Abstract In quantum communication, the channel noise and the misalignment of the reference frames between the communication parties will lead to the failure of quantum state transmission. Here an alignment‐free spatial‐polarization hyperentanglement transmission scheme is provided for hyperentangled photons. In this scheme, before the spatial‐polarization hyperentanglement is transmitted through the fiber channel, it is first encoded as a time‐bin entanglement with the same polarization. After the photons pass through the noise channel, the polarization errors caused by reference frames misalignment and channel noise can be corrected by time‐bin entanglement. In principle, by implementing this scheme, the communication parties can share the original hyperentangled state, and the success probability can approach unity. The scheme is robust to random channel noise and reference frames misalignment, and the decoherence effect caused by the misalignment of the reference frames between the communication parties can be completely suppressed by implementing this scheme.
Fast and Robust Nondestructive Parity Meter for Cat‐State Qubits via Reverse Engineering and Optimal Control

Abstract In this paper, a protocol for nondestructive parity meter (NPM) of two cat‐state qubits is proposed. The physical model contains two cavities and an auxiliary qubit. For each cavity, the quantum information is encoded on the odd and even cat state, forming a cat‐state qubit. By adjusting the strength of the driving field on the auxiliary qubit, an effective Hamiltonian for cavity‐selective transition is derived. Moreover, reverse engineering based on the effective Hamiltonian is applied to find the evolution path, and the systematic‐error‐sensitivity nullification method is used to select proper parameters of the evolution path. In this way, robust control fields are designed to combat the influence of the systematic errors. Furthermore, the effects of random noise and decoherence on the fidelity of the protocol are considered. Numerical simulations show that the protocol is insensitive to the experimental imperfection, including systematic errors, random noise and decoherence. Therefore, the protocol is expected to expand the vision of realizing NPM.
Enhanced sentiment analysis based on improved word embeddings and XGboost

&lt;span lang="EN-US"&gt;Sentiment analysis is a well-known and rapidly expanding study topic in natural language processing (NLP) and text classification. This approach has evolved into a critical component of many applications, including politics, business, advertising, and marketing. Most current research focuses on obtaining sentiment features through lexical and syntactic analysis. Word embeddings explicitly express these characteristics. This article proposes a novel method, improved words vector for sentiments analysis (IWVS), using XGboost to improve the F1-score of sentiment classification. The proposed method constructed sentiment vectors by averaging the word embeddings (Sentiment2Vec). We also investigated the Polarized lexicon for classifying positive and negative sentiments. The sentiment vectors formed a feature space to which the examined sentiment text was mapped to. Those features were input into the chosen classifier (XGboost). We compared the F1-score of sentiment classification using our method via different machine learning models and sentiment datasets. We compare the quality of our proposition to that of baseline models, term frequency-inverse document frequency (TF-IDF) and Doc2vec, and the results show that IWVS performs better on the F1-measure for sentiment classification. At the same time, XGBoost with IWVS features was the best model in our evaluation.&lt;/span&gt;
A Blockchain-Based IoT Terminal Trust Mechanism Management Scheme

Abstract Traditional centralized Internet of Things (IoT) management cannot effectively defend against internal attacks from compromised devices due to its own limitations, while distributed IoT node management has certain requirements for the communication environment and resource consumption of the nodes themselves. And the trust authentication of the nodes cannot be easily resolved in a distributed manner. This paper presents a blockchain-based trust management solution. Trust management of nodes based on blockchain also greatly reduces resource consumption. Specifically, the trust value is derived by weighting the historical trust value and the direct trust value, so that the evaluation node does not rely solely on historical trust data to derive its own independent trust evaluation. A separate system node level mechanism exists in the scheme, and the system increases the system node level trust value for the node when this evaluation reaches a trusted level. If this evaluation is malicious, the system node-level trust value for the evaluation is initialized. Security analysis and experiments have shown that this scheme can effectively detect malicious nodes and takes less time.
Channel transformer U-Net: an automatic and effective skeleton extraction network for electronic speckle pattern interferometry

The fringe skeleton extraction method may be the most straightforward method for electronic speckle pattern interferometry (ESPI) phase extraction. Due to ESPI fringe patterns having the characteristics of high noise, low contrast, and different fringe shapes, it is very difficult to extract skeletons from ESPI fringe patterns with high accuracy. To deal with this problem, we propose a skeleton extraction method based on deep learning, called channel transformer U-Net, for directly extracting skeletons from noisy ESPI fringe patterns. In the proposed method, the advanced channel-wise cross fusion transformer module is integrated into the design of deep U-Net architecture, and a loss function by combining binary cross entropy loss and poly focal loss is proposed. In addition, a marking algorithm is proposed for phase extraction, which can realize automatic identification of a skeleton line. The effectiveness of the above proposed algorithms has been verified by computer-simulated and real-dynamic ESPI measurements. The experimental results demonstrate that the proposed channel transformer U-Net can obtain accurate, complete, and smooth skeletons in all cases. The accuracy of the skeleton extraction obtained by our proposed network can reach 0.9878, and the correlation coefficient value can reach 0.9905. The skeleton line automatic marking algorithm has strong universality.
A 64×64 integrated photonic accelerator

In this work we present our integrated photonic accelerator in commercial silicon photonics chip serving for large-scale optical computing and AI applications. Specifically, we demonstrate its supremacy in low latency optical Ising application.
Multispectral Image Denoising via Structural Tensor Sparsity Promoting Model

Multispectral images (MSIs) contain more spectral information than traditional 2D images, which can provide a more accurate representation of objects. MSIs are easily affected by various noises when captured by sensors. In recent years, many MSI denoising methods, especially the Kronecker-basis-representation (KBR) method, have achieved great success. KBR uses tensor representation and decomposition to achieve good MSI denoising performance. However, each full band patch (FBP) group is decomposed in this method so that too many dictionary atoms are generated. In this paper, we propose a structural tensor sparsity promoting (STSP) model for MSI denoising. In order to decrease the number of dictionary atoms, we cluster FBP groups and learn orthogonal dictionaries for each class rather than each FBP group. To improve the denoising performance, the structural similarity among FBP groups are utilized in the STSP model by enforcing nonlocal centralized sparse constraint, where the compromise parameter is statistically and adaptively determined. Experimental results on the the CAVE dataset demonstrate that our model outperforms the state-of-art methods in terms of both objective and subjective quality.
Teaching Social Virtual Reality With Ubiq

We share our experiences of teaching virtual reality with Ubiq, an open-source system for building social virtual reality (VR). VR as a subject touches on many areas, including perception, human–computer interaction, and psychology. In our VE module, we consider all aspects of VR. In recent years, networked VR, and in particular social VR, has become increasingly relevant, at the same time as demand for online and hybrid teaching has increased. Commercial social virtual reality systems have proliferated, but for a number of reasons, this has not resulted in systems any more suitable for research and teaching. As a result we created Ubiq, a system for building social VR applications designed first for research and teaching. In this article, we describe how Ubiq came to be, and our experiences of using it in our virtual environments module over the last two years.
Review of Visualization Methods for Categorical Data in Cluster Analysis

The paper focuses on visualization methods suitable for outcomes of cluster analysis of categorical data (nominal data, specifically). Since nominal data have no inherent order, their graphical representation is often challenging or very limited. This paper aims to provide a list of common visualization methods in the domain of cluster analysis of objects characterized by nominal variables. Firstly, the various plot types (such as clustering scatter plot, dendrogram, icicle plot) for cluster analysis are presented, and their suitability for presenting clusters of nominal data is discussed. Then, we study approaches of sorting nominal values on chart axes in such a way that would improve visualization of the data. Lastly, we introduce a simple alternative to cluster scatter plot for nominal data, that makes the final visualization of clustering solution more efficient since the pattern and groups in data are now more apparent. The suggested method is demonstrated in illustrative examples.
Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI

Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H&amp;E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist’s work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue.
Gaussian-modulated continuous-variable quantum key distribution based on untrusted entanglement source

In a practical quantum communication system, the security of signal source of continuous-variable quantum key distribution may be jeopardized due to device flaws and hidden attacks. In this paper, an improved scheme for Gaussian-modulated continuous-variable quantum key distribution based on an untrusted entangled source is proposed. In particular, the entanglement source is placed in an untrusted quantum channel to simulate that it is controlled by an eavesdropper, thereby verifying the security of Gaussian-modulated continuous-variable quantum key distribution in a complex environment. This work in detail analyzes the influence of untrusted entanglement source on practical Gaussian-modulated continuous-variable quantum key distribution system, and the numerical simulation shows that the performance of Gaussian-modulated continuous-variable quantum key distribution will dramatically decrease once the entanglement source has moved out of the sender, and it will slightly rise as the untrusted entanglement source slowly moves away from the sender. This paper further introduces two kinds of optical amplifiers, which are phase-sensitive amplifier and phase-insensitive amplifier, to compensate for the imperfection of the coherent detector. These amplifiers are beneficial to enhancing the quantum efficiency of the receiver’s detector. Specifically, the security key rate of Gaussian-modulated continuous-variable quantum key distribution with homodyne detection can be well improved by phase-sensitive amplifier, and the security key rate of Gaussian-modulated continuous-variable quantum key distribution with heterodyne detection can be well improved by phase-insensitive amplifier. To summary, this paper proposes a scheme for Gaussian-modulated continuous-variable quantum key distribution with untrusted entanglement source, experimental results show that the proposed scheme can generate secure quantum keys even if the Gaussian entanglement source is untrusted, and the two optical amplifiers can effectively improve the quantum efficiency of the detector at the receiver. This work aims to promote the practical process of the Gaussian-modulated continuous-variable quantum key distribution system and provide theoretical guidance for the practical implementation and application of the Gaussian-modulated continuous-variable quantum key distribution system.