Reviewerly/exHarmony huggingface dataset:
When Cyber-Physical Systems Meet AI: A Benchmark, an Evaluation, and a Way Forward

Cyber-Physical Systems (CPS) have been broadly deployed in safety-critical domains, such as automotive systems, avionics, medical devices, etc. In recent years, Artificial Intelligence (AI) has been increasingly adopted to control CPS. Despite the popularity of AI-enabled CPS, few benchmarks are publicly available. There is also a lack of deep understanding on the performance and reliability of AI-enabled CPS across different industrial domains. To bridge this gap, we present a public benchmark of industry-level CPS in seven domains and build AI controllers for them via state-of-the-art deep reinforcement learning (DRL) methods. Based on that, we further perform a systematic evaluation of these AI-enabled systems with their traditional counterparts to identify current challenges and future opportunities. Our key findings include (1) AI controllers do not always outperform traditional controllers, (2) existing CPS testing techniques (falsification, specifically) fall short of analyzing AI-enabled CPS, and (3) building a hybrid system that strategically combines and switches between AI controllers and traditional controllers can achieve better performance across different domains. Our results highlight the need for new testing techniques for AI-enabled CPS and the need for more investigations into hybrid CPS to achieve optimal performance and reliability. Our benchmark, code, detailed evaluation results, and experiment scripts are available on https://sites.google.com/view/ai-cps-benchmark.
URLLC occasional large time delay prediction based on unbalanced regression and LSTM

Occasional large time delay is one of the bottlenecks for 5G applied to the industrial field. Accurate measurement, analysis, and prediction of communication network latency are of great significance. Due to the strong randomness and small number of samples in ultra-reliable and low latency communication (URLLC), the prediction task of occasional large time delay is very challenging. This paper proposes an URLLC occasional large time delay prediction method based on unbalanced regression algorithms and long–short term memory (LSTM). We first decompose the original sequence by using the variational mode decomposition (VMD) to obtain relatively stable component sequences. The parameters of the VMD are automatically optimized by grasshopper optimization algorithm (GOA). In order to improve the prediction effect of URLLC occasional large time delay, we introduce unbalanced regression algorithms. Before the VMD decomposition of data samples, the SMOGN is introduced to balance the number of large-time delay samples and common-time delay samples. Next we calculate the weight of each sample based on the rarity of each sample point and our proposed LDSWeight. Finally, LSTM is used to complete cost-sensitive learning. The experimental results show that the URLLC occasional large time delay prediction method proposed in this paper has better prediction accuracy than other methods.
Secure forensic data transmission system in cloud database using fuzzy based butterfly optimization and modified <scp>ECC</scp>

Abstract Data forensic is the investigation of digital data which illustrates how it is created and utilized. It is composed of detection, preservation, retrieval, and analyzing digital information. These data are stored in different domains. When storing the data in a cloud database it is ineluctable to circumvent the data leakage. Several deep learning approaches were conducted to resist data leakage. However, optimized security was not obtained. To address the aforementioned issues we have proposed a novel modified elliptic curve cryptography based secure forensic data transmission system (MESFDT) in the cloud database. The conventional ECC selects the keys used for the encryption and decryption process randomly. This might cause the generation of countless keys and can be easily hacked by a malicious user. Therefore we adopted fuzzy based butterfly optimization algorithm (FBOA) for the generation of keys. Thus the ECC is modified to form modified elliptic curve cryptography (MECC). The proposed method is comprised of a MECC integration unit, wireless transceiver, USB interface, MECC unit, smart card reader. This helps to transfer the forensic data from the user to the cloud database securely and also circumvent the duplication of data. The experimental study depicts that the proposed method increases the security of forensic data and mitigates the encryption and decryption time more gently than the other existing approaches.
Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum

This paper presents an end-to-end deep convolutional neural network (CNN) model for carrier signal detection in the broadband power spectrum, so-called spectrum center net (SCN). By regarding the broadband power spectrum sequence as a one-dimensional (1D) image and each subcarrier on the broadband as the target object, we can transform the carrier signal detection problem into a semantic segmentation problem on a 1D image. Here, the core task of the carrier signal detection problem turns into the frequency center (FC) and bandwidth (BW) regression. We design the SCN to classify the broadband power spectrum as inputs and extract the features of different length scales by the ResNet backbone. Then, the feature pyramid network (FPN) neck fuses the features and outputs the fusion features. Next, the RegNet head regresses the power spectrum distribution (PSD) prediction for FC and the corresponding BW prediction. Finally, we can achieve the subcarrier targets by applying non-maximum suppressions (NMS). Moreover, we train the SCN on a simulation dataset and validate it on a real satellite broadband power spectrum set. As an improvement of the fully convolutional network-based (FCN-based) method, the proposed method directly outputs the detection results without post-processing. Extensive experimental results demonstrate that the proposed method can effectively detect the subcarrier signal in the broadband power spectrum as well as achieve higher and more robust performance than the deep FCN- and threshold-based methods.
Variants of recurrent learning vector quantization

Learning Vector Quantization (LVQ) and its cost-function-based variant called Generalized Learning Vector Quantization (GLVQ) are powerful, yet simple and interpretable Machine Learning (ML) algorithms for multi-class classification. Even though GLVQ is an effective tool for classifying vectorial data in its native form, it is not particularly suited to handle raw sequence data of potentially different lengths. Usually, this problem is addressed by manually engineering fixed-length features from the raw data or by employing recurrent networks. Therefore, a natural idea is to try to incorporate recurrent units for data processing into the GLVQ network structure. The processed data can then be compared in a latent space for classification decisions. Yet, not much work has been done in direction as far as we are aware. Existing methods to handle sequential data in the framework of GLVQ are rather unsophisticated and severely outdated. In this paper, we provide a general framework for incorporating recurrent structures in an LVQ network and derive two classification models as variants of Recurrent Learning Vector Quantization, namely RecLVQ and LVQRNN. We also demonstrate the abilities of such approaches on illustrative classification problems.
Whole-Body Keypoint and Skeleton Augmented RGB Networks for Video Action Recognition

Incorporating multi-modality data is an effective way to improve action recognition performance. Based on this idea, we investigate a new data modality in which Whole-Body Keypoint and Skeleton (WKS) labels are used to capture refined body information. Unlike directly aggregated multi-modality, we leverage distillation to adapt an RGB network to classify action with the feature-extraction ability of the WKS network, which is only fed with RGB clips. Inspired by the success of transformers for vision tasks, we design an architecture that takes advantage of both three-dimensional (3D) convolutional neural networks (CNNs) and the Swin transformer to extract spatiotemporal features, resulting in advanced performance. Furthermore, considering the unequal discrimination among clips of a video, we also present a new method for aggregating the clip-level classification results, further improving the performance. The experimental results demonstrate that our framework achieves advanced accuracy of 93.4% with only RGB input on the UCF-101 dataset.
A novel family of edge preserving anisotropic filters

In this article, new anisotropic image filters are introduced and their performances are compared with existing isotropic and anisotropic filters. The developed filters were examined according to their image noise removing performances in terms of standard image quality metrics, as well as the edge preserving properties of the filtered images. Mathematical inferences of anisotropic filters are made based on the minimization of the Polyakov action energy integral. New anisotropic metrics are found by means of Finsler metrics that minimize the corresponding integral. The new metrics perform filtering by updating the image with anisotropic Laplace-Beltrami flow. After filtering, it was observed that the introduced metrics perform well against other anisotropic metrics. It is also observed that the developed New Randers, New Normalized Miron, and New Metric filters preserve edges better than other filters, making it a plausible noise removal tool prior to edge detection in image processing. The source codes of proposed filters are publicly available at https://github.com/HAYDARKILIC.
Strategies for Reuse and Sharing among Data Scientists in Software Teams

Effective sharing and reuse practices have long been hallmarks of proficient software engineering. Yet the exploratory nature of data science presents new challenges and opportunities to support sharing and reuse of analysis code. To better understand current practices, we conducted interviews (N=17) and a survey (N=132) with data scientists at Microsoft, and extract five commonly used strategies for sharing and reuse of past work: personal analysis reuse, personal utility libraries, team shared analysis code, team shared template notebooks, and team shared libraries. We also identify factors that encourage or discourage data scientists from sharing and reusing. Our participants described obstacles to reuse and sharing including a lack of incentives to create shared code, difficulties in making data science code modular, and a lack of tool interoperability. We discuss how future tools might help meet these needs.
Passive blind quantum computation with heralded single-photon sources

Blind quantum computation (BQC) is one of the most promising alternatives to solve the shortage of quantum resources in the implementation of quantum computations. However, in most reported studies on decoy-state BQCs, the source intensities are actively modulated, which, unfortunately, results in side-channel leakage and undermines system security. In this paper, we present a passive decoy-state BQC scheme with a heralded single-photon source that circumvents this problem skillfully, improving system security. Moreover, simulation results show that our passive scheme can even outperform other active schemes in the generation efficiency of blind states when using state-of-the-art equipment.
Geometrical structure preservation joint with self-expression maintenance for adaptive graph learning

• The geometrical structure is explored in two different ways. • Self-expressive learning is seamlessly integrated into LPP. • The cluster interweaving problem of LPP is alleviated. Locality preserving projection (LPP) is a subspace learning method that uses pairwise distance to measure the similarity between data points. However, when data points from different clusters are adjacent, the pairwise distance may not accurately reflect the similarity between data points. In this study, a novel graph learning model is proposed to alleviate this problem; it is called adaptive graph learning with geometrical structure preservation and self-expression maintenance (GEAGL), in which a discriminative LPP method is used to extract the geometrical structure of data. By integrating self-expressive learning into the LPP method, a similarity graph that preserves the geometrical structure and self-expressive properties of data can be adaptively learned. The learned similarity graph tends to consist of a block diagonal structure when data points are extracted from independent linear subspaces, which alleviates the cluster interweaving problem. In this study, the relationship between the proposed method and k-means clustering was revealed, and the geometrical structure preservation property of the proposed method was theoretically analyzed. Finally, a graph-based clustering method was developed based on the similarity graph produced by GEAGL. Experimental results of benchmark datasets demonstrate the superiority of the proposed method in comparison with state-of-the-art methods.
Online Adaptive Kernel Learning with Random Features for Large-scale Nonlinear Classification

In the field of support vector machines, online random feature map algorithms are very important methods for large-scale nonlinear classification problems. At present, the existing methods have the following shortcomings: (1) If only the hyperplane vector is updated during learning while the random feature components are fixed, there is no guarantee that these online methods can adapt to the change of data distribution shape when the data is coming one by one. (2) When the kernel is selected improperly, the samples mapped to an inappropriate space may not be well classified. In order to overcome these shortcomings, considering the fact that iteratively updating random feature components can make data better fit in the current space and lead to the flexible adjustment of the kernel function, random features based online adaptive kernel learning (RF-OAK) is proposed for large-scale nonlinear classification problems. Theoretical analysis of the proposed algorithm is also provided. The experimental results and the Wilcoxon signed-ranks test show that in terms of test accuracy, the proposed method is significantly better than the state-of-the-art online feature mapping classification methods. Compared with the deep learning algorithms, the training time of RF-OAK is shorter. In terms of test accuracy, RF-OAK is better than online algorithm and comparable with offline algorithms.
MixPatch: A New Method for Training Histopathology Image Classifiers

CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in the medical domain. The objective of this study is to propose a new training method, called MixPatch, designed to improve a CNN-based classifier by specifically addressing the prediction uncertainty problem and examine its effectiveness in improving diagnosis performance in the context of histopathological image analysis. MixPatch generates and uses a new sub-training dataset, which consists of mixed-patches and their predefined ground-truth labels, for every single mini-batch. Mixed-patches are generated using a small size of clean patches confirmed by pathologists while their ground-truth labels are defined using a proportion-based soft labeling method. Our results obtained using a large histopathological image dataset shows that the proposed method performs better and alleviates overconfidence more effectively than any other method examined in the study. More specifically, our model showed 97.06% accuracy, an increase of 1.6% to 12.18%, while achieving 0.76% of expected calibration error, a decrease of 0.6% to 6.3%, over the other models. By specifically considering the mixed-region variation characteristics of histopathology images, MixPatch augments the extant mixed image methods for medical image analysis in which prediction uncertainty is a crucial issue. The proposed method provides a new way to systematically alleviate the overconfidence problem of CNN-based classifiers and improve their prediction accuracy, contributing toward more calibrated and reliable histopathology image analysis.
A Cluster Analysis Model for PhD Dissertation Quality Based on the Depth Algorithm

Doctoral education is an important part of higher education and plays an important role in cultivating many higher talents for the country. In doctoral education, thesis writing is an indispensable teaching section. The quality of the doctoral thesis will directly affect the graduation of doctoral students. Therefore, the analysis of the quality of the doctoral thesis is a typical aspect of the evaluation of doctoral education work. This paper aims to study the quality clustering analysis of the doctoral dissertation based on the depth algorithm. This paper establishes a doctoral paper quality cluster analysis model based on the depth algorithm and carries out the doctoral paper quality cluster analysis experiment based on this model. After the experiment, the main factors affecting the quality of doctoral dissertation were also analyzed. The conclusion is the following: The accuracy rate of the doctoral paper quality cluster analysis model based on the depth algorithm has reached 88.5%.
End-to-End Training of Deep Neural Networks in the Fourier Domain

Convolutional networks are commonly used in various machine learning tasks, and they are more and more popularly used in the embedded domain with devices such as smart cameras and mobile phones. The operation of convolution can be substituted by point-wise multiplication in the Fourier domain, which can save operation, but usually, it is applied with a Fourier transform before and an inverse Fourier transform after the multiplication, since other operations in neural networks cannot be implemented efficiently in the Fourier domain. In this paper, we will present a method for implementing neural network completely in the Fourier domain, and by this, saving multiplications and the operations of inverse Fourier transformations. Our method can decrease the number of operations by four times the number of pixels in the convolutional kernel with only a minor decrease in accuracy, for example, 4% on the MNIST and 2% on the HADB datasets.
NC-GNN: Consistent Neighbors of Nodes Help More in Graph Neural Networks

Graph neural networks, as the promising methodology in data mining for graph data, currently attract much attention and are broadly applied in graph-based tasks. Existing GNN methods mostly follow the assumption of homophily, where the connected nodes are similar and share the same labels. Most graphs in the real world can satisfy the assumption. However, for the particular nodes, the situation is not always satisfied. The connections between different-labeled nodes will introduce noise in feature aggregation and result in node representation deviating in the wrong direction. In this paper, we focus on the different-labeled neighbors of labeled nodes in the graphs. By regarding aggregation among neighbors as the procedure of node feature reconstruction, we devise a novel metric neighbor consistency to measure the difference between nodes and their neighborhoods. In this way, we can evaluate the reliability of nodes after aggregation. Furthermore, we propose a novel method, Neighbor Consistent Graph Neural Networks (NC-GNN), to promote the training of graph neural networks by reweighting the influence of labeled nodes based on neighbor consistency scores. Systematic experiments are conducted on benchmark datasets, and the results demonstrate the effectiveness of our method.
Gain compensation across LIDAR scans

High-end Terrestrial Lidar Scanners are often equipped with RGB cameras that are used to colorize the point samples. Some of these scanners produce panoramic HDR images by encompassing the information of multiple pictures with different exposures. Unfortunately, exported RGB color values are not in an absolute color space, and thus point samples with similar reflectivity values might exhibit strong color differences depending on the scan the sample comes from. These color differences produce severe visual artifacts if, as usual, multiple point clouds colorized independently are combined into a single point cloud. In this paper we propose an automatic algorithm to minimize color differences among a collection of registered scans. The basic idea is to find correspondences between pairs of scans, i.e. surface patches that have been captured by both scans. If the patches meet certain requirements, their colors should match in both scans. We build a graph from such pair-wise correspondences, and solve for the gain compensation factors that better uniformize color across scans. The resulting panoramas can be used to colorize the point clouds consistently. We discuss the characterization of good candidate matches, and how to find such correspondences directly on the panorama images instead of in 3D space. We have tested this approach to uniformize color across scans acquired with a Leica RTC360 scanner, with very good results.
Design and Implementation of Path Planning for Wheel-Track Hybrid Mobile Robot

Wheel-track hybrid mobile robot has been widely used because of its high autonomous mobility and its ability to adapt to complex and changeable outdoor environment. Map construction and path planning are the main conditions to achieve its autonomous mobility. Therefore, a wheel-track hybrid robot path planning has been designed and implemented. Simultaneous Localization and Mapping (SLAM) algorithms are used to process the road images captured by the robot’s camera and other sensors. A rapid raster map with Robot Operating System (ROS) was constructed by rapid recognition of key elements from the images with the help of the Oriented Fast and Rotated Brief (ORB) feature vector. The ant colony algorithm with strong optimization ability, high optimization efficiency, and flexible algorithm is used for path planning, and the problem of unfixed location of outdoor obstacles is solved at the same time. The simulation results show that the global performance and convergence speed of the improved algorithm are increased.
Radius of curvature measurement based on iterative correlation coefficient matching strategy with a single-shot interferogram

We proposed an iterative correlation coefficient matching strategy for the high-precision measurement of the radius of curvature (ROC), which only needs a single-frame interferogram. This strategy includes two parts: one is to obtain an initial ROC by using the relationship between the ROC and the diameter of the dark ring; other is to further solve a high-precision value by an iterative method. In the iterative part, we simulated a series of interference patterns with each ROC in the setting range, and calculated the correlation coefficient between the each simulated interferogram and the measured interferogram, and searched the maximum of the correlation coefficient that would be used to confirm a new ROC in the next iteration. When the calculated ROCs in the adjacent iterations meet the termination condition, the high-accuracy ROC can be solved. The feasibility of our proposed method has been verified by simulations and experiments. The errors of the test mirrors with the ROC of 101.6 mm and 1000 mm are 0.013% and 0.024% in experiments, respectively. This research provides a simple and feasible method for realizing the high-precision ROC measurement.