Reviewerly/exHarmony huggingface dataset:
Effective camera calibration by using phase-shifting fringe patterns

Authors:
raw_author_name: Chengxiang Liu
raw_affiliation_string: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, China
raw_author_name: Fulin Liang
raw_affiliation_string: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, China
raw_author_name: Zongze Wu
raw_affiliation_string: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, China
raw_author_name: Xiaopin Zhong
raw_affiliation_string: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, China

Abstract: The accuracy of conventional target-based camera calibration depends on the detection of feature points. To improve the calibration accuracy in the flat field, this paper proposes an innovative calibration method based on encoded phase-shifting fringe (PSF) patterns. By using arbitrary quadrilateral interpolation, the relationship between a point on the virtual phase plane and the corresponding four original phase points is determined, establishing a mapping between the simplified original phase field and the virtual phase field. This spatial relationship can be used for accurate camera calibration. The proposed research investigates the details of the PSF based camera calibration method and demonstrates its accuracy and effectiveness through experiments. The evaluation of this method includes the checkerboard edge length variance analysis and the linear distance analysis of the checkerboard fit, both of which serve as validation metrics. Camera distortion is described by the phase distribution, and the average phase error after correction is less than 0.001 pixels (px).
Learning disentangled representations for controllable human motion prediction

Authors:
raw_author_name: Chunzhi Gu
raw_affiliation_string: University of Fukui, Fukui-shi, Fukui 9108507, Japan
raw_author_name: Jun Yu
raw_affiliation_string: Niigata University, Niigata-shi, Niigata 9502181, Japan
raw_author_name: Chao Zhang
raw_affiliation_string: University of Fukui, Fukui-shi, Fukui 9108507, Japan

Abstract: Generative model-based motion prediction techniques have recently realized predicting controlled human motions, such as predicting multiple upper human body motions with similar lower-body motions. However, to achieve this, the state-of-the-art methods require either subsequently learning mapping functions to seek similar motions or training the model repetitively to enable control over the desired portion of body. In this paper, we propose a novel framework to learn disentangled representations for controllable human motion prediction. Our task is to predict multiple future human motions based on the past observed sequence, with the control of partial-body movements. Our network involves a conditional variational auto-encoder (CVAE) architecture to model full-body human motion, and an extra CVAE path to learn only the corresponding partial-body (e.g., lower-body) motion. Specifically, the inductive bias imposed by the extra CVAE path encourages two latent variables in two paths to respectively govern separate representations for each partial-body motion. With a single training, our model is able to provide two types of controls for the generated human motions: (i) strictly controlling one portion of human body and (ii) adaptively controlling the other portion, by sampling from a pair of latent spaces. Additionally, we extend and adapt a sampling strategy to our trained model to diversify the controllable predictions. Our framework also potentially allows new forms of control by flexibly customizing the input for the extra CVAE path. Extensive experimental results and ablation studies demonstrate that our approach is capable of predicting state-of-the-art controllable human motions both qualitatively and quantitatively.
Label-Weighted Graph-Based Learning for Semi-Supervised Classification Under Label Noise

Authors:
raw_author_name: Naiyao Liang
raw_affiliation_string: School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Zuyuan Yang
raw_affiliation_string: School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Junhang Chen
raw_affiliation_string: School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Zhenni Li
raw_affiliation_string: School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Shengli Xie
raw_affiliation_string: School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, China

Abstract: Graph-based semi-supervised learning (GSSL) is a quite important technology due to its effectiveness in practice. Existing GSSL works often treat the given labels equally and ignore the unbalance importance of labels. In some inaccurate systems, the collected labels usually contain noise (noisy labels) and the methods treating labels equally suffer from the label noise. In this paper, we propose a novel label-weighted learning method on graph for semi-supervised classification under label noise, which allows considering the contribution differences of labels. In particular, the label dependency of data is revealed by graph constraints. With the help of this label dependency, the proposed method develops the strategy of adaptive label weight, where label weights are assigned to labels adaptively. Accordingly, an efficient algorithm is developed to solve the proposed optimization objective, where each subproblem has a closed-form solution. Experimental results on a synthetic dataset and several real-world datasets show the advantage of the proposed method, compared to the state-of-the-art methods.
Multiclass autoencoder-based active learning for sensor-based human activity recognition

Authors:
raw_author_name: Hyunseo Park
raw_affiliation_string: School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
raw_author_name: Gyeong Ho Lee
raw_affiliation_string: School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
raw_author_name: Jaeseob Han
raw_affiliation_string: School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
raw_author_name: Jun Kyun Choi
raw_affiliation_string: School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

Abstract: Leveraging the enormous amounts of real-world data collected through Internet of Things (IoT) technologies, human activity recognition (HAR) has become a crucial component of numerous human-centric applications, with the aim of enhancing the quality of human life. While the recent advancements in deep learning have significantly improved HAR, the process of labeling data continues to remain a significant challenge due to the substantial costs associated with human annotation for supervised model training. Active learning (AL) addresses this issue by strategically selecting informative samples for labeling during model training, thereby enhancing model performance. Although numerous approaches have been proposed for sample selection, which consider aspects of uncertainty and representation, the difficulties in estimating uncertainty and exploiting distribution of high-dimensional data still pose a major issue. Our proposed deep learning-based active learning algorithm, called Multiclass Autoencoder-based Active Learning (MAAL), learns latent representation leveraging the capacity of Deep Support Vector Data Description (Deep SVDD). With the multiclass autoencoder which learns the normal characteristics of each activity class in the latent space, MAAL provides an informative sample selection for model training by establishing a link between the HAR model and the selection model. We evaluate our proposed MAAL using two publicly available datasets. The performance results demonstrate the improvements across the overall active learning rounds, achieving enhancements up to 3.23% accuracy and 3.67% in the F1 score. Furthermore, numerical results and analysis of sample selection are presented to validate the effectiveness of the proposed MAAL compared to the alternative comparison methods.
Multi-valued model checking IoT and intelligent systems with commitment protocols in multi-source data environments

Authors:
raw_author_name: Ghalya Alwhishi
raw_affiliation_string: Concordia University, 1455 Boul. de Maisonneuve Ouest, Montreal, Canada
raw_author_name: Jamal Bentahar
raw_affiliation_string: Concordia University, 1455 Boul. de Maisonneuve Ouest, Montreal, Canada; Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
raw_author_name: Ahmed Elwhishi
raw_affiliation_string: University of Doha for Science and Technology, 24449 Arab League St, Doha, Qatar
raw_author_name: Witold Pedrycz
raw_affiliation_string: Istinye University, Vadistanbul 4A Blok, 34396, Sariyer/Istanbul, Turkey; King Abdulaziz University, 21589, Jeddah, Saudi Arabia; Polish Academy of Sciences, 00-901, Warsaw, Poland; University of Alberta, 116 St and 85 Ave, Edmonton, Canada
raw_author_name: Nagat Drawel
raw_affiliation_string: Concordia University, 1455 Boul. de Maisonneuve Ouest, Montreal, Canada

Abstract: In today's world of connectivity, various domains use different multi-sensor Internet of Things (IoT) and Intelligent Systems (IS) applications. These applications involve extensive interactions between thousands or millions of components in open environments, which challenges verifying their reliability and efficiency. This paper introduces the first investigation in verifying IoT applications and intelligent systems in multi-source data settings with multi-agent commitment protocols in uncertain or inconsistent environments. Specifically, we present original and efficient solutions for modeling and verifying these systems with conditional and unconditional commitment protocols under uncertain or inconsistent settings. We introduce 4v-CTLc and 4v-CTLcc, multi-valued logics of commitment for reasoning about inconsistency over these systems that expand 3v-CTLc and 3v-CTLcc for reasoning about uncertainty. Moreover, we introduce new reduction algorithms for reducing our multi-valued model checking problems to the two-valued ones. To implement these algorithms, we develop two new automatic verification tools. The first tool translates the multi-valued logics to CTL and automatically interacts with the NuSMV model checker. The second tool translates these logics to the two-valued versions, CTLc and CTLcc, and automatically interacts with the MCMAS+ model checker. We apply our verification approaches to a Smart Home, a Smart Hospital and a Smart Mortgage system with multi-source data as case studies using sets of properties, including safety, liveness and reachability. The experimental results obtained by the proposed multi-valued model checking techniques proved these techniques' high efficiency and applicability to modeling and verifying intelligent and autonomous multi-source data systems.
Image restoration via joint low-rank and external nonlocal self-similarity prior

Authors:
raw_author_name: Wei Yuan
raw_affiliation_string: School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
raw_author_name: Han Liu
raw_affiliation_string: School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
raw_author_name: Lili Liang
raw_affiliation_string: School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
raw_author_name: Wenqing Wang
raw_affiliation_string: School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
raw_author_name: Ding Liu
raw_affiliation_string: School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China

Abstract: Recent studies have revealed that joint priors, such as joint sparsity and external nonlocal self-similarity (ENSS) prior and joint low-rank and sparsity prior, are extremely effective in various image inverse problems. Few works, however, make use of both low-rank and ENSS priors. With this in mind, in this paper we propose a new joint prior, namely LRENSS prior, which utilizes low-rank and ENSS priors jointly in a unified framework, and successfully adapt the proposed LRENSS prior to image restoration problems. Specifically, low-rank and ENSS priors are bridged by treating ENSS prior as dictionaries for structural sparse representation. Further, an elegant block coordinate descent method is developed to solve the corresponding optimization problem. The proposed LRENSS prior is validated on image denoising and image deblurring tasks. Experimental results illustrate that the proposed LRENSS prior has better performance than other state-of-the-art algorithms in both qualitative and quantitative assessments.
BSELA: A Blockchain Simulator with Event-Layered Architecture

Authors:
raw_author_name: Bo Cui
raw_affiliation_string: College of Computer Science, Inner Mongolia University, Hohhot, China; Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing, Hohhot, China

Abstract: Blockchain technology has found widespread use in various fields, including finance, healthcare, the Internet of Things, and supply chain management, due to its ability to meet technical and non-technical requirements. Therefore, it is crucial for system designers to evaluate the performance and characteristics of a given blockchain platform before deploying it. One way to achieve this is through the use of blockchain simulators. However, existing simulators have limitations in areas such as model scaling, event scheduling, time control, object model evaluation, and simulation magnitude. To address these limitations, we propose a Blockchain Simulator based on Event-Layered Architecture (BSELA) in this paper. BSELA comprises multiple functional modules with effective model scalability, with the core being our proposed Event-Layered Architecture. This architecture introduces a new event-driven scheduling mechanism for discrete-event simulation, which improves the efficiency, stability, and maintainability of event scheduling during blockchain simulation. Furthermore, we propose a time advancement mechanism for subsequent events based on event rounds, which improves the accuracy of time advancement, simulation complexity, and real-time demand handling capability during the simulation. We begin by validating the efficiency of our simulator by comparing experiments with those of existing blockchain simulators. We then validate the accuracy of BSELA through simulation experiments on blockchain network connectivity, block propagation latency, and the number of INV messages propagated per hour. We validated the accuracy of the experimental results by conducting a comparison with Bitcoin data. Finally, in order to address the research gap in the performance of blockchain systems at the underlying peer-to-peer network level and in other fields, we designed and experimentally validated the node-trust network construction mechanism by adjusting the simulator's blockchain object model. Our experiments show that this tuning mechanism improves data transfer efficiency by 30%–40%, and also improves security. The design and experiments of the BSELA tuning mechanism validate the scalability and flexibility of our simulator for blockchain research.
Hybrid unsupervised paradigm based deformable image fusion for 4D CT lung image modality

Authors:
raw_author_name: Muhammad Zafar Iqbal
raw_affiliation_string: School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
raw_author_name: Imran Razzak
raw_affiliation_string: School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
raw_author_name: Abdul Qayyum
raw_affiliation_string: National Heart and Lung Institute, Imperial College London, United Kingdom
raw_author_name: Thanh Thi Nguyen
raw_affiliation_string: Faculty of Information Technology, Monash University, Melbourne, Australia
raw_author_name: M. Tanveer
raw_affiliation_string: Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
raw_author_name: Arcot Sowmya
raw_affiliation_string: School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

Abstract: Deformable image registration plays a critical role in various clinical applications (e.g., image fusion, atlas creation, and tumors targeting). In radiation therapy, especially in the context of fast registration of computed tomography (CT) lung image modalities, it is used to determine the geometric transformation by relating the anatomic points in two images. The main challenge lies in effectively addressing the nonlinear large and small deformation between the inspiration and expiration phases. In this work, we propose an unsupervised hybrid paradigm-based registration network (HPRN) for the registration of 4D CT lung images without relying on ground truth data. The proposed HPRN exhibits effective learning of multi-scale and multi-resolution features, leading to the computation of a more accurate Deformation Vector Field (DVF). Furthermore, we incorporate the regularization, image similarity and Jacobian determinant loss functions, which results in improving capability in dealing with complex large and small deformations. We evaluate the effectiveness of the proposed model on the publicly accessible DIRLab 4DCT lung image dataset, which shows the effectiveness of the proposed framework by achieving better Target Registration Error (2.04±1.42mm) compared to other state-of-the-art unsupervised image registration algorithms.
A versatile device for implementing the optical quantum gates in multiple degrees of freedom

Authors:
raw_author_name: Wuhong Zhang
raw_affiliation_string: Department of Physics, Xiamen University, Xiamen, 361005, China

Abstract: Optical quantum logic gates are the basic elements for building optical quantum computer. Usually, to implement different logic gates, different optical elements and frameworks are needed. Here, we propose a versatile device that a modified Sagnac interferometer composed of a spatial light modulator (SLM) for implementing multiple optical gates within a classical light framework. We demonstrate that the device can manipulate the polarization and full-field spatial modes of photons to perform multi-qubit logic operations. And versatile conversions between different logic gates are achieved by simply altering the hologram imposed on SLM. Our device shows a good performance in exploring the photon's full degrees of freedom, which makes it an appropriate candidate for integrated optical computing.
Variational Quantum Linear Solver-based Combination Rules in Dempster–Shafer Theory

Authors:
raw_author_name: Hao Luo
raw_affiliation_string: Institute of Fundamental and Frontier Science, UESTC, Chengdu, 610054, China
raw_author_name: Qianli Zhou
raw_affiliation_string: Institute of Fundamental and Frontier Science, UESTC, Chengdu, 610054, China
raw_author_name: Yong Deng
raw_affiliation_string: Institute of Fundamental and Frontier Science, UESTC, Chengdu, 610054, China; School of Medicine, Vanderbilt University, Nashville, 37240, USA

Abstract: Dempster–Shafer Theory (DST), as a method of handling uncertain information, is widely used in decision-making and information fusion. But the issue of exponential computational complexity limits its real-time application. Since quantum computers with its natural parallel computing capability can theoretically achieve speedup, an HHL-based algorithm for implementing DST operations on quantum circuits has been proposed. However, the algorithm is impossible to be implemented with high accuracy due to the limited performance of quantum computers in the current noisy intermediate-scale quantum (NISQ) era. In response to that, we utilize an up-to-date scheme Variational Quantum Linear Solver (VQLS) to conduct DST operations by solving the linear system. Besides, VQLS further reveals the structural consistency of DST with quantum computation, which enables faster circuit configuration and computation. In this paper, we first realize the transformation in belief functions by VQLS and then extends it to a methods of implementing combination rules. Simulated under a realistic classification task, the feasibility and accuracy of the proposed VQLS-based method are verified. The new VQLS-based method proposed is able to achieve combination rules at a lower error level with fewer quantum resources, which is more suitable for the NISQ era.
Privacy-Preserving Neural Networks for Smart Manufacturing

Authors:
raw_author_name: Daniel Finke
raw_affiliation_string: The Pennsylvania State University The Applied Research Laboratory, , University Park, PA 16802
raw_author_name: Hui Yang
raw_affiliation_string: The Pennsylvania State University Industrial and Manufacturing Engineering, , University Park, PA 16802

Abstract: Abstract The rapid advance in sensing technology has expedited data-driven innovation in manufacturing by enabling the collection of large amounts of data from factories. Big data provides an unprecedented opportunity for smart decision-making in the manufacturing process. However, big data also attracts cyberattacks and makes manufacturing systems vulnerable due to the inherent value of sensitive information. The increasing integration of artificial intelligence (AI) within smart factories also exposes manufacturing equipment susceptible to cyber threats, posing a critical risk to the integrity of smart manufacturing systems. Cyberattacks targeting manufacturing data can result in considerable financial losses and severe business disruption. Therefore, there is an urgent need to develop AI models that incorporate privacy-preserving methods to protect sensitive information implicit in the models against model inversion attacks. Hence, this paper presents the development of a new approach called mosaic neuron perturbation (MNP) to preserve latent information in the framework of the AI model, ensuring differential privacy requirements while mitigating the risk of model inversion attacks. MNP is flexible to implement into AI models, balancing the trade-off between model performance and robustness against cyberattacks while being highly scalable for large-scale computing. Experimental results, based on real-world manufacturing data collected from the computer numerical control (CNC) turning process, demonstrate that the proposed method significantly improves the ability to prevent inversion attacks while maintaining high prediction performance. The MNP method shows strong potential for making manufacturing systems both smart and secure by addressing the risk of data breaches while preserving the quality of AI models.