Reviewerly/exHarmony huggingface dataset:
Real-Time Object Detection and Tracking Based on Embedded Edge Devices for Local Dynamic Map Generation

Authors:
raw_author_name: Kyoungtaek Choi
raw_affiliation_string: Department of Robotics Engineering, Daegu Catholic University, 13-13, Hayang-ro, Hayang-eup, Gyeongsan-si 38430, Republic of Korea
raw_author_name: Ho Gi Jung
raw_affiliation_string: Department of Electronic Engineering, Korea National University of Transportation, 50 Daehak-ro, Chungju-si 27469, Republic of Korea
raw_author_name: Jae Kyu Suhr
raw_affiliation_string: Department of Intelligent Mechatronics Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea

Abstract: This paper proposes a camera system designed for local dynamic map (LDM) generation, capable of simultaneously performing object detection, tracking, and 3D position estimation. This paper focuses on improving existing approaches to better suit our application, rather than proposing novel methods. We modified the detection head of YOLOv4 to enhance the detection performance for small objects and to predict fiducial points for 3D position estimation. The modified detector, compared to YOLOv4, shows an improvement of approximately 5% mAP on the Visdrone2019 dataset and around 3% mAP on our database. We also proposed a tracker based on DeepSORT. Unlike DeepSORT, which applies a feature extraction network for each detected object, the proposed tracker applies a feature extraction network once for the entire image. To increase the resolution of feature maps, the tracker integrates the feature aggregation network (FAN) structure into the DeepSORT network. The difference in multiple objects tracking accuracy (MOTA) between the proposed tracker and DeepSORT is minimal at 0.3%. However, the proposed tracker has a consistent computational load, regardless of the number of detected objects, because it extracts a feature map once for the entire image. This characteristic makes it suitable for embedded edge devices. The proposed methods have been implemented on a system on chip (SoC), Qualcomm QCS605, using network pruning and quantization. This enables the entire process to be executed at 10 Hz on this edge device.
A Convolutional Neural Network-Based Auto-Segmentation Pipeline for Breast Cancer Imaging

Authors:
raw_author_name: Abu Bakr Azam
raw_affiliation_string: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
raw_author_name: Qi Cao
raw_affiliation_string: School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK
raw_author_name: Lihui Huang
raw_affiliation_string: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
raw_author_name: Yuan Xie
raw_affiliation_string: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
raw_author_name: Yiyu Cai
raw_affiliation_string: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore

Abstract: Medical imaging is crucial for the detection and diagnosis of breast cancer. Artificial intelligence and computer vision have rapidly become popular in medical image analyses thanks to technological advancements. To improve the effectiveness and efficiency of medical diagnosis and treatment, significant efforts have been made in the literature on medical image processing, segmentation, volumetric analysis, and prediction. This paper is interested in the development of a prediction pipeline for breast cancer studies based on 3D computed tomography (CT) scans. Several algorithms were designed and integrated to classify the suitability of the CT slices. The selected slices from patients were then further processed in the pipeline. This was followed by data generalization and volume segmentation to reduce the computation complexity. The selected input data were fed into a 3D U-Net architecture in the pipeline for analysis and volumetric predictions of cancer tumors. Three types of U-Net models were designed and compared. The experimental results show that Model 1 of U-Net obtained the highest accuracy at 91.44% with the highest memory usage; Model 2 had the lowest memory usage with the lowest accuracy at 85.18%; and Model 3 achieved a balanced performance in accuracy and memory usage, which is a more suitable configuration for the developed pipeline.
Keyword Data Analysis Using Generative Models Based on Statistics and Machine Learning Algorithms

Authors:
raw_author_name: Sunghae Jun
raw_affiliation_string: Department of Data Science, Cheongju University, Cheongju 28503, Chungbuk, Republic of Korea

Abstract: For text big data analysis, we preprocessed text data and constructed a document–keyword matrix. The elements of this matrix represent the frequencies of keywords occurring in a document. The matrix has a zero-inflation problem because many elements are zero values. Also, in the process of preprocessing, the data size of the document–keyword matrix is reduced. However, various machine learning algorithms require a large amount of data, so to solve the problems of data shortage and zero inflation, we propose the use of generative models based on statistics and machine learning. In our experimental tests, we compared the performance of the models using simulation and practical data sets. Thus, we verified the validity and contribution of our research for keyword data analysis.
UViT: Efficient and lightweight U-shaped hybrid vision transformer for human pose estimation

Authors:
raw_author_name: Biao Li
raw_affiliation_string: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China; School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, China
raw_author_name: Wenyi Li
raw_affiliation_string: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China; School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, China

Abstract: Pose estimation plays a crucial role in human-centered vision applications and has advanced significantly in recent years. However, prevailing approaches use extremely complex structural designs for obtaining high scores on the benchmark dataset, hampering edge device applications. In this study, an efficient and lightweight human pose estimation problem is investigated. Enhancements are made to the context enhancement module of the U-shaped structure to improve the multi-scale local modeling capability. With a transformer structure, a lightweight transformer block was designed to enhance the local feature extraction and global modeling ability. Finally, a lightweight pose estimation network— U-shaped Hybrid Vision Transformer, UViT— was developed. The minimal network UViT-T achieved a 3.9% improvement in AP scores on the COCO validation set with fewer model parameters and computational complexity compared with the best-performing V2 version of the MobileNet series. Specifically, with an input size of 384×288, UViT-T achieves an impressive AP score of 70.2 on the COCO test-dev set, with only 1.52 M parameters and 2.32 GFLOPs. The inference speed is approximately twice that of general-purpose networks. This study provides an efficient and lightweight design idea and method for the human pose estimation task and provides theoretical support for its deployment on edge devices.
Practical security of continuous-variable quantum key distribution with an optical amplifier

Authors:
raw_author_name: Yi Zheng
raw_affiliation_string: School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, Shaanxi, China
raw_author_name: Yiliang Wang
raw_affiliation_string: School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, Shaanxi, China
raw_author_name: Haobin Shi
raw_affiliation_string: School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, Shaanxi, China
raw_author_name: Wei Pan
raw_affiliation_string: School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, Shaanxi, China

Abstract: In a practical continuous-variable quantum key distribution (CVQKD) system, the optical amplifier can be used to improve the performance of the system by compensating for the imperfections of the detectors. However, the eavesdropper Eve can actively utilize a reverse external magnetic field to deteriorate the performance of the optical amplifier, which may affect the practical security of the system. In this paper, we investigate the practical security of a CVQKD system with an optical amplifier under the effect of the reverse external magnetic field. Based on the investigation of the parameter estimation under the above influence, we find that the evaluation of the secret key rate may be overestimated, which will open a security loophole for Eve to successfully launch an intercept-resend attack in the practical CVQKD system with an optical amplifier. In order to close this security loophole, we can monitor the reverse external magnetic field or change the type of the optical isolator to make the secret key rate of the system evaluated exactly.
Variational approach to unique determinedness in pure-state tomography

Authors:
raw_author_name: Chao Zhang
raw_affiliation_string: Department of Physics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
raw_author_name: Xuanran Zhu
raw_affiliation_string: Department of Physics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
raw_author_name: Bei Zeng
raw_affiliation_string: Department of Physics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

Abstract: In pure-state tomography, the concept of unique determinedness (UD), the ability to uniquely determine pure states from measurement results, is crucial. This study presents a variational approach to examining UD, offering a robust solution to the challenges associated with the construction and certification of UD measurement schemes. We put forward an effective algorithm that minimizes a specially defined loss function, enabling the differentiation between UD and non-UD measurement schemes. This leads to the discovery of numerous optimal pure-state Pauli measurement schemes across a variety of dimensions. Additionally, we discern an alignment between uniquely determined among pure states and uniquely determined among all states in qubit systems when utilizing Pauli measurements, underscoring its intrinsic robustness under pure-state recovery. We further interpret the physical meaning of our loss function, bolstered by a theoretical framework. Our study not only propels the understanding of UD in quantum state tomography forward, but also delivers valuable practical insights for experimental applications, highlighting the need for a balanced approach between mathematical optimality and experimental pragmatism.
Classification of high-dimensional imbalanced biomedical data based on spectral clustering SMOTE and marine predators algorithm

Authors:
raw_author_name: Xiwen Qin
raw_affiliation_string: Graduate School, Changchun University of Technology, Changchun, China; School of Mathematics and Statistics, Changchun University of Technology, Changchun, China
raw_author_name: Siqi Zhang
raw_affiliation_string: School of Mathematics and Statistics, Changchun University of Technology, Changchun, China
raw_author_name: Xiaogang Dong
raw_affiliation_string: School of Mathematics and Statistics, Changchun University of Technology, Changchun, China
raw_author_name: Hongyu Shi
raw_affiliation_string: School of Mathematics and Statistics, Changchun University of Technology, Changchun, China
raw_author_name: Liping Yuan
raw_affiliation_string: School of Mathematics and Statistics, Changchun University of Technology, Changchun, China

Abstract: The research of biomedical data is crucial for disease diagnosis, health management, and medicine development. However, biomedical data are usually characterized by high dimensionality and class imbalance, which increase computational cost and affect the classification performance of minority class, making accurate classification difficult. In this paper, we propose a biomedical data classification method based on feature selection and data resampling. First, use the minimal-redundancy maximal-relevance (mRMR) method to select biomedical data features, reduce the feature dimension, reduce the computational cost, and improve the generalization ability; then, a new SMOTE oversampling method (Spectral-SMOTE) is proposed, which solves the noise sensitivity problem of SMOTE by an improved spectral clustering method; finally, the marine predators algorithm is improved using piecewise linear chaotic maps and random opposition-based learning strategy to improve the algorithm’s optimization seeking ability and convergence speed, and the key parameters of the spectral-SMOTE are optimized using the improved marine predators algorithm, which effectively improves the performance of the over-sampling approach. In this paper, five real biomedical datasets are selected to test and evaluate the proposed method using four classifiers, and three evaluation metrics are used to compare with seven data resampling methods. The experimental results show that the method effectively improves the classification performance of biomedical data. Statistical test results also show that the proposed PRMPA-Spectral-SMOTE method outperforms other data resampling methods.
Preparation of cavity-Fock-state superpositions by reinforcement learning exploiting measurement backaction

Authors:
raw_author_name: Yves Bérubé-Lauzière
raw_affiliation_string: Institut quantique and Département de génie électrique et de génie informatique, Faculté de génie, Université de Sherbrooke, Sherbrooke, Québec, J1K 2R1, Canada

Abstract: Preparation of bosonic and general cavity quantum states usually relies on using open-loop control to reach a desired target state. In this work, a measurement-based feedback approach is used instead, exploiting the nonlinearity of weak measurements alongside a coherent drive to prepare these states. The extension of previous work on Lyapunov-based control is shown to fail for this task. This prompts for a different approach, and reinforcement learning (RL) is resorted to here for this purpose. With such an approach, cavity-eigenstate superpositions can be prepared with fidelities over $98%$ using only the measurements backaction as the nonlinearity, while naturally incorporating detection of cavity photon jumps. Two different RL frameworks are analyzed: an off-policy approach recently introduced called truncated quantile critic (TQC) and the on-policy method commonly used in quantum control, namely proximal policy optimization. It is shown that TQC performs better at reaching higher target state fidelity preparation.
A simple probabilistic neural network for machine understanding

Authors:
raw_author_name: Rongrong Xie
raw_author_name: Matteo Marsili

Abstract: Abstract We discuss the concept of probabilistic neural networks with a fixed internal representation being models for machine understanding. Here, ‘understanding’ is interpretted as the ability to map data to an already existing representation which encodes an a priori organisation of the feature space. We derive the internal representation by requiring that it satisfies the principles of maximal relevance and of maximal ignorance about how different features are combined. We show that, when hidden units are binary variables, these two principles identify a unique model—the hierarchical feature model—which is fully solvable and provides a natural interpretation in terms of features. We argue that learning machines with this architecture possess a number of interesting properties, such as the continuity of the representation with respect to changes in parameters and data, the possibility of controlling the level of compression and the ability to support functions that go beyond generalisation. We explore the behaviour of the model with extensive numerical experiments and argue that models in which the internal representation is fixed reproduce a learning modality which is qualitatively different from that of traditional models, such as restricted Boltzmann machines.
A variable clustering approach for overdispersed high-dimensional count data using a copula-based mixture model

Authors:
raw_author_name: Edwin R. van den Heuvel
raw_affiliation_string: Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

Abstract: In this paper, we propose a latent variable model for the analysis and clustering of high-dimensional correlated and overdispersed count data. We use a set of random effects to capture within-group correlations and a copula to address between-group correlations. Since maximum likelihood (ML) estimation is computationally intensive for high-dimensional data, we propose a fast moment-based estimation procedure. Additionally, we implement the proposed estimation procedure into a clustering algorithm, borrowing ideas from the K-means algorithm. Based on a simulation study, we demonstrate that the estimation procedure shows good stability and precision. Furthermore, it is computationally fast, unlike the ML approach which shows serious convergence issues. In addition, the clustering algorithm with our moment-based estimation procedure can identify the simulated grouped data structure. The approach is further illustrated with a high-dimensional RNA-Seq dataset of commercial potatoes.
Pose Measurement Method Based on Machine Vision and Novel Directional Target

Authors:
raw_author_name: Dongri Shan
raw_affiliation_string: School of Electronics and Information, Aerospace Information Technology University, Jinan 250200, China; School of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, China
raw_author_name: Zhihao Zhu
raw_affiliation_string: School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, China
raw_author_name: Xiaofang Wang
raw_affiliation_string: School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, China
raw_author_name: Peng Zhang
raw_affiliation_string: School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, China

Abstract: Aiming at the shortcomings of the existing machine vision pose measurement technology, a pose measurement method based on monocular vision and a cooperative target is proposed. A planar target designed with circles and rings as the main body is dedicated to object pose measurement, and a feature point coordinate extraction and sorting algorithm is designed for this target to effectively extract image features on the target. The RANSAC algorithm and topology-based fitting for the intersection method are used to optimise data processing, further improving the accuracy of feature point coordinate extraction and ultimately achieving high-precision measurement of object poses. The experimental results show that the measurement accuracy of the roll angle perpendicular to the optical axis can reach 0.02°, and the repeatability can reach 0.0004° after removing the systematic error; the measurement accuracy of the pitch angle can reach 0.03°, and the repeatability can go to 0.002° after removing the systematic error. The measurement range of the pitch angle is [−30°, +30°]; the measurement range of the roll angle is [−179°, +179°]. The experimental results show that the system has high measurement accuracy and meets the requirements of high-precision measurement.
Liger: Interleaving Intra- and Inter-Operator Parallelism for Distributed Large Model Inference

Authors:
raw_author_name: Jiangsu Du
raw_affiliation_string: Sun Yat-sen University, Guangzhou, China
raw_author_name: Jiazhi Jiang
raw_affiliation_string: Sun Yat-sen University, Guangzhou, China
raw_author_name: Shenggan Cheng
raw_affiliation_string: National University of Singapore, Singapore, Singapore
raw_author_name: Dan Huang
raw_affiliation_string: Sun Yat-sen University, Guangzhou, China
raw_author_name: Yutong Lu
raw_affiliation_string: Sun Yat-sen University, Guangzhou, China

Abstract: Distributed large model inference is still in a dilemma where balancing cost and effect. The online scenarios demand intraoperator parallelism to achieve low latency and intensive communications makes it costly. Conversely, the inter-operator parallelism can achieve high throughput with much fewer communications, but it fails to enhance the effectiveness.
Image Steganography and Style Transformation Based on Generative Adversarial Network

Authors:
raw_author_name: Li Li
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Xinpeng Zhang
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Kejiang Chen
raw_affiliation_string: CAS Key Laboratory of Electro-Magnetic Space Information, University of Science and Technology of China, Hefei 230027, China
raw_author_name: Guorui Feng
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Deyang Wu
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Weiming Zhang
raw_affiliation_string: CAS Key Laboratory of Electro-Magnetic Space Information, University of Science and Technology of China, Hefei 230027, China

Abstract: Traditional image steganography conceals secret messages in unprocessed natural images by modifying the pixel value, causing the obtained stego to be different from the original image in terms of the statistical distribution; thereby, it can be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial-Intelligence-Generated Content (AIGC) becoming popular on social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image-style-transformation neural network with a steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder–decoder model, and finally, generates a styled image containing the secret messages at the same time. An adversarial training technique was applied to enhance the imperceptibility of the generated art-style stego image from plain-style-transferred images. The lack of the original cover image makes it difficult for the opponent learning steganalyzer to identify the stego. The proposed approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of three bits per pixel for a color image, according to the experimental results.