Reviewerly/exHarmony huggingface dataset:
Complex Noise-Resistant Zeroing Neural Network for Computing Complex Time-Dependent Lyapunov Equation

Complex time-dependent Lyapunov equation (CTDLE), as an important means of stability analysis of control systems, has been extensively employed in mathematics and engineering application fields. Recursive neural networks (RNNs) have been reported as an effective method for solving CTDLE. In the previous work, zeroing neural networks (ZNNs) have been established to find the accurate solution of time-dependent Lyapunov equation (TDLE) in the noise-free conditions. However, noises are inevitable in the actual implementation process. In order to suppress the interference of various noises in practical applications, in this paper, a complex noise-resistant ZNN (CNRZNN) model is proposed and employed for the CTDLE solution. Additionally, the convergence and robustness of the CNRZNN model are analyzed and proved theoretically. For verification and comparison, three experiments and the existing noise-tolerant ZNN (NTZNN) model are introduced to investigate the effectiveness, convergence and robustness of the CNRZNN model. Compared with the NTZNN model, the CNRZNN model has more generality and stronger robustness. Specifically, the NTZNN model is a special form of the CNRZNN model, and the residual error of CNRZNN can converge rapidly and stably to order 10−5 when solving CTDLE under complex linear noises, which is much lower than order 10−1 of the NTZNN model. Analogously, under complex quadratic noises, the residual error of the CNRZNN model can converge to 2∥A∥F/ζ3 quickly and stably, while the residual error of the NTZNN model is divergent.
Container Performance and Vulnerability Management for Container Security Using Docker Engine

Containers have evolved to support microservice architecture as a low-cost alternative to virtual machines. Containers are increasingly prevalent in the virtualization landscape because of better working; containers can bear considerably less overhead than the conventional hypervisor-based component virtual machines. However, containers directly communicate with the host kernel, and attackers can co-locate containers in the host system quicker than virtual machines. This causes significant security issues in container technology. The security hardening system is currently targeted at implementing universal access management regulations that make it difficult to assess the required procedure for accessing containers. Security mechanisms include an explicit awareness of the purpose and actions of the container and entail manual interaction and configuration. A user-friendly container protection scheme implemented an access policy to comply with its anticipated and legitimate application performance. In this study, container technology constraints have been overcome by proposing a unique Docker-sec mechanism. Docker-sec uses four mechanisms; the original collection has been improved during container runtime by additional rules that constrain the capacity of the container, further representing the applications in practice, file system, processes, network isolation, and vulnerability scanning of Docker images over different workload. Different vulnerabilities have been scanned with a CVE severity level. Results showed that inter-container communication with the system is more secure containers from zero vulnerabilities with an overhead of 3.45%.
Trajectory Planning of Dual-Robot Cooperative Assembly

Efficiency can be improved through the cooperation of a dual-robot during assembly. However, how to effectively plan a simple and smooth path in a dynamic environment is a prominent problem in the process of dual-robot cooperative assembly. In this paper, a method based on RRT-Connect algorithm for trajectory planning and post-processing for trajectory optimization is proposed. This method takes full advantage of the excellent solution ability of RRT-Connect algorithm in the complex environment so as to obtain the initial path successfully. Through post-processing, the problem of RRT-Connect non-convergence to target is optimized. We use two 6-DOF industrial robots to build an experimental platform and design a dual-robot cooperative assembly system. According to the given task, the system can generate the original collision-free path based on RRT-Connect algorithm. Then the original path is simplified by Floyd algorithm and smoothed by multi-segment Bezier curve. Finally, the time parameter is sequenced for all the path points based on the iterative method, and the effective trajectory is obtained. The experimental results show that the algorithm proposed in this paper can effectively plan and optimize the trajectory of dual-robot. Compared to other methods, this approach has a higher success rate and less planning time.
Towards Process Centered Architecting for Quantum Software Systems

Quantum Software Engineering (QSE) is a recent trend - focused on unifying the principles of quantum mechanics and practices of software engineering - to design, develop, validate, and evolve quantum age software systems and applications. Software architecture for quantum computing (a.k. a. quantum software architectures (QSA)) supports the design, development, and maintenance etc. phases of quantum software systems using architectural components and connectors. QSA can enable quantum software designers and developers to map the operations of Qubits to architectural components and connectors for implementing quantum software. This research aims to explore the role of QSAs by investigating (i) architectural process having architecting activities, and (ii) human roles that can exploit available tools to automate and customise architecture-centric implementation of quantum software. Results of this research can facilitate knowledge transfer, enabling researchers and practitioners, to address challenges of architecture-centric implementation of quantum software systems.
Avoiding Negative Transfer for Semantic Segmentation of Remote Sensing Images

Reducing the feature distribution shift caused by the factor of visual-environment changes, namely as VE-changes, is a hot issue in domain adaptation learning. However, in the semantic segmentation task of remote sensing imageries, besides VE-changes, the change of semantic-scenes (SS-changes) is another factor raising domain gap, which brings the label distribution shift. For example, although urban and rural share the same landcover label, there is still a gap in label distribution. If there is little relation that can be found in neither feature nor label space, forcibly adapting to a new domain could have a high risk of negative transfer. Hence, we propose a new Transitive Domain Adaptation method for Remote Sensing images (TDARS). Firstly, we introduce an intermediate domain to enlarge the relation between the given source and target domains. Secondly, we learn from primary and non-primary confident classes to increase the likelihood of transferring valuable information. As a result, TDARS enables the given source and target domains to be connected through the selected intermediate domain and performs effective knowledge transfer among all domains. The proposed method is evaluated on three domain adaptation datasets of remote sensing images. Extensive experiments show the approach can effectively handle the domain shift problem from remote sensing images compared to other state-of-the-art domain adaptation methods.
Deep learning for LiDAR-only and LiDAR-fusion 3D perception: a survey

The perception system for robotics and autonomous cars relies on the collaboration among multiple types of sensors to understand the surrounding environment. LiDAR has shown great potential to provide accurate environmental information, and thus deep learning on LiDAR point cloud draws increasing attention. However, LiDAR is unable to handle severe weather. The sensor fusion between LiDAR and other sensors is an emerging topic due to its supplementary property compared to a single LiDAR. Challenges exist in deep learning methods that take LiDAR point cloud fusion data as input, which need to seek a balance between accuracy and algorithm complexity due to data redundancy. This work focuses on a comprehensive survey of deep learning on LiDAR-only and LiDAR-fusion 3D perception tasks. Starting with the representation of LiDAR point cloud, this paper then introduces its unique characteristics and the evaluation dataset as well as metrics. This paper gives a review according to four key tasks in the field of LiDAR-based perception: object classification, object detection, object tracking, and segmentation (including semantic segmentation and instance segmentation). Finally, we present the overlooked aspects of the current algorithms and possible solutions, hoping this paper can serve as a reference for the related research.
Digital image correlation-aided non-destructive buckling load prediction of cylindrical shells

The buckling capacity of a thin cylindrical shell depends on the unknown existed imperfections. Therefore, the prediction of the shell buckling load is still challenging if not impossible. In this paper, the prediction of critical buckling load of axially compressed cylindrical shell is investigated based on the novel non-destructive probing method. In order to find the proper locations for probing, we have combined the digital image correlation (DIC) technique with the non-destructive probing procedures. We have designed and built a 360° three-dimensional (3D) DIC system to reveal the displacement fields of the cylindrical shells and especially the commercial aluminum cans under axial compressions. Through the combination of experiments, numerical simulations and theoretical analysis, nonlinear force-indentation responses of the imperfect shells with non-zero local Gaussian curvatures to the point indentation were analyzed to guide the choice of the proper locations for the indentation. Our results demonstrate that the buckling load prediction of imperfect cylinders is possible if the probing is done sufficiently close to the dominating imperfection location, which could be detected and localized using the DIC technique.
Synthesizing theories of human language with Bayesian program induction

Abstract Automated, data-driven construction and evaluation of scientific models and theories is a long-standing challenge in artificial intelligence. We present a framework for algorithmically synthesizing models of a basic part of human language: morpho-phonology, the system that builds word forms from sounds. We integrate Bayesian inference with program synthesis and representations inspired by linguistic theory and cognitive models of learning and discovery. Across 70 datasets from 58 diverse languages, our system synthesizes human-interpretable models for core aspects of each language’s morpho-phonology, sometimes approaching models posited by human linguists. Joint inference across all 70 data sets automatically synthesizes a meta-model encoding interpretable cross-language typological tendencies. Finally, the same algorithm captures few-shot learning dynamics, acquiring new morphophonological rules from just one or a few examples. These results suggest routes to more powerful machine-enabled discovery of interpretable models in linguistics and other scientific domains.
SeparaFill: Two generators connected mural image restoration based on generative adversarial network with skip connect

Abstract Mural is an important component of culture and art of Dunhuang in China. Unfortunately, these murals had been ruined or are being ruined by some diseases such as cracking, hollowing, falling off, mildew, dirt, and so on. Existing image restoration algorithms have problems such as incomplete repair and disharmonious texture during large-area repair, so the effect of mural image disease area repair is poor. Due to lack of a standard mural datasets, Dunhuang mural datasets are created in the paper. Meanwhile, our network architecture SeparaFill is proposed which connects two generators based on U-Net. Based on the characteristics of the painting, the contour line pixel area of the mural image is innovatively separated from the content pixel area. Firstly, the contour restoration generator network with skip connect and hierarchical residual blocks is employed to repair contour lines. Then, the color mural image is repaired by the content completion network with guide of the repaired contour. Full resolution branches and generator branches of the U type are exploited in content completion generators. Convolution layers of different kernel sizes are fused to improve the reusability of the underlying features. Finally, global and local discriminant networks are applied to determine whether the repaired mural image is authentic in terms of both the modified and unmodified areas. The proposed SeparaFill shows good performance in restoring the line structure of the damaged mural images and retaining the contour information of the mural images. Compared with existing restoration algorithms in mural real damage repair experiment, our algorithm increases the peak signal-to-noise ratio (PSNR) by an average of 1.1–4.3 dB and the structural similarity (SSIM) values were slightly improved. Experimental results reveal the good performance of the proposed model, which can contribute to digital restorations of ancient murals.
MsVRL: Self-Supervised Multiscale Visual Representation Learning via Cross-Level Consistency for Medical Image Segmentation

Automated medical image segmentation for organs or lesions plays an essential role in clinical diagnoses and treatment plannings. However, training an accurate and robust segmentation model is still a long-standing challenge due to the time-consuming and expertise-intensive annotations for training data, especially 3-D medical images. Recently, self-supervised learning emerges as a promising approach for unsupervised visual representation learning, showing great potential to alleviate the expertise annotations for medical images. Although global representation learning has attained remarkable results on iconic datasets, such as ImageNet, it can not be applied directly to medical image segmentation, because the segmentation task is non-iconic, and the targets always vary in physical scales. To address these problems, we propose a Multi-scale Visual Representation self-supervised Learning (MsVRL) model, to perform finer-grained representation and deal with different target scales. Specifically, a multi-scale representation conception, a canvas matching method, an embedding pre-sampling module, a center-ness branch, and a cross-level consistent loss are introduced to improve the performance. After pre-trained on unlabeled datasets (RibFrac and part of MSD), MsVRL performs downstream segmentation tasks on labeled datasets (BCV, spleen of MSD, and KiTS). Results of the experiments show that MsVRL outperforms other state-of-the-art works on these medical image segmentation tasks.
Representation learning for knowledge fusion and reasoning in Cyber–Physical–Social Systems: Survey and perspectives

The digital deep integration of cyber space, physical space and social space facilitates the formation of Cyber–Physical–Social Systems (CPSS). Knowledge empowers CPSS to be capable of solving complex tasks and providing intelligent services. Knowledge representation, fusion, and reasoning are complementary in the knowledge graph and worthy of continuous exploration and in-depth research. Since the concept of knowledge representation learning has been proposed, various models have been developed and demonstrated their superior ability in knowledge fusion and reasoning. In this paper, we investigate the progress and function of representation learning models adopted in knowledge fusion and reasoning, and extract their commonalities and characteristics, which is a perspective that has never been developed before, to provide new ideas for scholars and realize the mutual promotion of knowledge fusion and reasoning. This paper comprehensively reviews classic methods and investigates advanced and emerging works, which are published in authoritative journals or conferences. Furthermore, to overcome the limitations of existing methods, we propose an integrated knowledge representation learning and application framework for CPSS and a series of tensor-based knowledge fusion and reasoning models. Finally, four future directions are discussed in this paper, we analyze the challenges faced by them and suggest promising solutions.
Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model

Human physical activity recognition from inertial sensors is shown to be a successful approach for monitoring elderly individuals and children in indoor and outdoor environments. As a result, researchers have shown significant interest in developing state-of-the-art machine learning methods capable of utilizing inertial sensor data and providing key decision support in different scenarios. This paper analyzes data-driven techniques for recognizing human daily living activities. Therefore, to improve the recognition and classification of human physical activities (for example, walking, drinking, and running), we introduced a model that integrates data preprocessing methods (such as denoising) along with major domain features (such as time, frequency, wavelet, and time–frequency features). Following that, stochastic gradient descent (SGD) is used to improve the performance of the extracted features. The selected features are catered to the random forest classifier to detect and monitor human physical activities. Additionally, the proposed HPAR system was evaluated on five benchmark datasets, namely the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE databases. The experimental results show that the HPAR system outperformed the present state-of-the-art methods with recognition rates of 90.18%, 91.25%, 91.83%, 90.46%, and 92.16% from the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE datasets, respectively. The proposed HPAR model has potential applications in healthcare, gaming, smart homes, security, and surveillance.
Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data

Prediction of survival for patients in intensive care units (ICUs) has been subject to intense research. However, no models exist that embrace the multiverse of data in ICUs. It is an open question whether deep learning methods using automated data integration with minimal pre-processing of mixed data domains such as free text, medical history and high-frequency data can provide discrete-time survival estimates for individual ICU patients. We trained a deep learning model on data from patients admitted to ten ICUs in the Capital Region of Denmark and the Region of Southern Denmark between 2011 and 2018. Inspired by natural language processing we mapped the electronic patient record data to an embedded representation and fed the data to a recurrent neural network with a multi-label output layer representing the chance of survival at different follow-up times. We evaluated the performance using the time-dependent concordance index. In addition, we quantified and visualized the drivers of survival predictions using the SHAP methodology. We included 37,355 admissions of 29,417 patients in our study. Our deep learning models outperformed traditional Cox proportional-hazard models with concordance index in the ranges 0.72-0.73, 0.71-0.72, 0.71, and 0.69-0.70, for models applied at baseline 0, 24, 48, and 72 h, respectively. Deep learning models based on a combination of entity embeddings and survival modelling is a feasible approach to obtain individualized survival estimates in data-rich settings such as the ICU. The interpretable nature of the models enables us to understand the impact of the different data domains.
Recognizing multiclass Static Sign Language words for deaf and dumb people of Bangladesh based on transfer learning techniques

Sign language is a language used for communication of the deaf and dumb (D&D) community. To avoid difficulties in communication among themselves and also among normal people, transfer learning-based automatic sign language recognition can play a great role. Transfer learning has been used in many countries in sign language. In Bangladesh, here also some techniques consisting of a convolutional neural network and transfer learning have been used to recognize Bangladeshi Sign language. Those techniques have been used only to sign alphabets, characters, and numbers. In this paper, a transfer learning based automatic sign language recognition system is introduced using Bangladeshi Sign Language (BdSL) words. Very rare research has been done on Bangladeshi Sign Words, and there is an inadequate Bangladeshi Sign Words dataset. This system employs four well-performed transfer learning techniques named VGG16, VGG19, AlexNet and InceptionV3 with pre-trained weights. The accuracy, recall, precision, and F1 score are used to assess the efficiency of the suggested models. The dataset of Bangladeshi sign words has been used in this paper which is consisting of 1105 images. The models show the training accuracy of 99.92%, 99.58%, 98.70% and 97.86% for VGG16, VGG19, InceptionV3 and AlexNet respectively whereas validation accuracy is 92.41%, 91.62%, 88.22% and 84.95% for VGG16, VGG19, InceptionV3 and AlexNet respectively. The proposed transfer learning based on the CNN method demonstrates better performance for the recognition of Bangladeshi Sign Words.
New Aggregation Approaches with HSV to Color Edge Detection

Abstract The majority of edge detection algorithms only deal with grayscale images, while their use with color images remains an open problem. This paper explores different approaches to aggregate color information of RGB and HSV images for edge extraction purposes through the usage of the Sobel operator and Canny algorithm. This paper makes use of Berkeley’s image data set, and to evaluate the performance of the different aggregations, the F -measure is computed. Higher potential of aggregations with HSV channels than with RGB channels is found. This article also shows that depending on the type of image used, RGB or HSV, some methods are more appropriate than others.
VDE-Net: a two-stage deep learning method for phase unwrapping

Phase unwrapping is a critical step to obtaining a continuous phase distribution in optical phase measurements and coherent imaging techniques. Traditional phase-unwrapping methods are generally low performance due to significant noise or undersampling. This paper proposes a deep convolutional neural network (DCNN) with a weighted jump-edge attention mechanism, namely, VDE-Net, to realize effective and robust phase unwrapping. Experimental results revealed that the weighted jump-edge attention mechanism, which is first proposed and simple to calculate, is useful for phase unwrapping. The proposed algorithm outperformed other networks or common attention mechanisms. In addition, an unseen wrapped phase image of a living red blood cell (RBC) was successfully unwrapped by the trained VDE-Net, thereby demonstrating its strong generalization capability.
Cautious weighted random forests

Random forest is an efficient and accurate classification model, which makes decisions by aggregating a set of trees, either by voting or by averaging class posterior probability estimates. However, tree outputs may be unreliable in presence of scarce data. The imprecise Dirichlet model (IDM) provides workaround, by replacing point probability estimates with interval-valued ones. This paper investigates a new tree aggregation method based on the theory of belief functions to combine such probability intervals, resulting in a cautious random forest classifier. In particular, we propose a strategy for computing tree weights based on the minimization of a convex cost function, which takes both determinacy and accuracy into account and makes it possible to adjust the level of cautiousness of the model. The proposed model is evaluated on 25 UCI datasets and is demonstrated to be more adaptive to the noise in training data and to achieve a better compromise between informativeness and cautiousness.