Reviewerly/exHarmony huggingface dataset:
Category-Stitch Learning for Union Domain Generalization

Domain generalization aims at generalizing the network trained on multiple domains to unknown but related domains. Under the assumption that different domains share the same classes, previous works can build relationships across domains. However, in realistic scenarios, the change of domains is always followed by the change of categories, which raises a difficulty for collecting sufficient aligned categories across domains. Bearing this in mind, this article introduces union domain generalization (UDG) as a new domain generalization scenario, in which the label space varies across domains, and the categories in unknown domains belong to the union of all given domain categories. The absence of categories in given domains is the main obstacle to aligning different domain distributions and obtaining domain-invariant information. To address this problem, we propose category-stitch learning (CSL), which aims at jointly learning the domain-invariant information and completing missing categories in all domains through an improved variational autoencoder and generators. The domain-invariant information extraction and sample generation cross-promote each other to better generalizability. Additionally, we decouple category and domain information and propose explicitly regularizing the semantic information by the classification loss with transferred samples. Thus our method can breakthrough the category limit and generate samples of missing categories in each domain. Extensive experiments and visualizations are conducted on MNIST, VLCS, PACS, Office-Home, and DomainNet datasets to demonstrate the effectiveness of our proposed method.
A gaze‐based interaction method for large‐scale and large‐space disaster scenes within mobile virtual reality

Abstract A three‐dimensional (3D) visualization of disaster scenes based on mobile virtual reality (VR) can improve the application scenarios and emergency service capabilities of traditional 3D visualization of disaster scenes. Because a smartphone needs to be placed into a mobile head‐mounted display, conventional touch scene interaction cannot be used by mobile VR, and the user's gaze usually serves as the default scene interaction method. However, the existing gaze‐based interaction methods for mobile VR scenes are passive scene interaction methods and cannot meet the basic interaction requirement for actively roaming through and exploring large‐scale and large‐space disaster scenes. Therefore, this study focuses on gaze‐based mobile VR interactions to satisfy the various interaction requirements of large‐scale and large‐space disaster scenes. First, a dynamic user interface (UI) generation method for gaze interaction in large‐scale and large‐space disaster scenes is proposed to solve the problem of the active exploration of mobile VR disaster scenes. Second, disaster scene exploration and disaster information query methods based on a dynamic UI and gaze are proposed. Finally, using a flood disaster as an example, a prototype system and associated experiments are discussed. As indicated by the experimental results, the gaze‐based mobile VR interaction methods addressed in this study can effectively support users in actively roaming through and exploring large‐scale and large‐space disaster scenes, disaster simulation analysis, and the interactive querying of disaster information within mobile VR, making the effective interaction of mobile VR disaster scenes possible.
Edge optimized and personalized lifelogging framework using ensembled metaheuristic algorithms

The fostered use of smart wearables for lifelogging daily activities has fuelled massive data generation. Lack of personalization, massive network traffic, increased latency, and high vulnerability to missing and noisy data are the significant impediments that existing frameworks face. This paper proposes a user-personalized and edge-optimized four-layer framework for lifelogging activities to address these impediments. A lightweight Edge Intelligence (EI) module with low computation requirements is designed to reduce data transmission to the cloud, lowering energy consumption. A novel Max Score Pooling (MSP) algorithm based on ensembled metaheuristic algorithms is proposed to provide a user-specific and optimized set of features. MSP optimized Decision Tree (MSP-DT) classifier is developed for real-time activity recognition in the Spark environment. The classifier's performance is calibrated regularly, making the framework resilient to sensor failure. Experiments demonstrate that the proposed framework can recognize 12 physical activities of different subjects with a mean accuracy of 97.67% and 47.66% reduction in transmitted data.
A practical framework for cyber defense generation, enforcement and evaluation

It is challenging to enforce and evaluate cyber-defenses for large networks. The current state-of-the-art approaches on defense enforcement and evaluations are manually performed by a security expert and they are executed separately without using a defined workflow. Moreover, the cyber defense generation, enforcement, and evaluation require costs, time, and effort of the security experts to effectively identify attacks and the best set of defenses to deploy. To effectively address these challenges, we propose a novel defense automation framework named CD-GEE (Cyber Defense Generation, Enforcement and Evaluation). CD-GEE can perform automated Cyber defense generation, defense enforcement and security evaluation without human intervention. CD-GEE works in the following five phases: (1) host/network data collection, (2) graphical security models construction, (3) cyber defense generation & selection, (4) defense deployment, and (5) defense evaluation and report. To show the usability and applicability of CD-GEE, we perform experiments on different networks including Amazon’s Elastic Compute Cloud. Besides, we also evaluate the performance and the effectiveness of the defenses deployed via our proposed framework and tool. We show that the CD-GEE measures the security posture of the network before and after defense deployment and evaluation. We also showed that our proposed framework performed better than without the framework in enhancing the security of networks on both the Amazon Elastic Compute Cloud and simulation network environment.
K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Image segmentation plays an important role in daily life. The traditional K-means image segmentation has the shortcomings of randomness and is easy to fall into local optimum, which greatly reduces the quality of segmentation. To improve these phenomena, a K-means image segmentation method based on improved manta ray foraging optimization (IMRFO) is proposed. IMRFO uses Lévy flight to improve the flexibility of individual manta rays and then puts forward a random walk learning that prevents the algorithm from falling into the local optimal state. Finally, the learning idea of particle swarm optimization is introduced to enhance the convergence accuracy of the algorithm, which effectively improves the global and local optimization ability of the algorithm simultaneously. With the probability that K-means will fall into local optimum reducing, the optimized K-means hold stronger stability. In the 12 standard test functions, 7 basic algorithms and 4 variant algorithms are compared with IMRFO. The results of the optimization index and statistical test show that IMRFO has better optimization ability. Eight underwater images were selected for the experiment and compared with 11 algorithms. The results show that PSNR, SSIM, and FSIM of IMRFO in each image are better. Meanwhile, the optimized K-means image segmentation performance is better.
A homomorphic non-subsampled contourlet transform based ultrasound image despeckling by novel thresholding function and self-organizing map

The speckle noise leaves an adverse effect on ultrasound images due to which loss of information occurs. Hence this paper proposes a homomorphic Non-Subsampled Contourlet Transform (NSCT) based ultrasound image despeckling technique using a novel thresholding function, bilateral filter, and self-organizing map (SOM). The bilateral filter is utilized over the low-pass NSCT sub-band for speckle component removal and sharp features. To get better noise suppression and edge preservation, a novel thresholding function is proposed and performed over the high-pass NSCT sub-band. In the proposed method, Kohonen’s SOM is implemented as a post-processing step for deblurring purposes. The significance of the proposed scheme is also tested where it was found that using Kohonen’s SOM as post-processing works better than without post-processing. Experimental outcomes were also evaluated on real speckled ultrasound images and synthetic added speckled noisy images. The results are evaluated and compared using visual analysis and performance metrics using with and without reference images. For more critical analysis, intensity profile along a line and experts observation were also evaluated to find the performance analysis of the proposed methodology. From all experimental and comparative evaluations, it was found that the proposed approach gives better outcomes compared to similar and recent methods.
Polarization-based underwater image enhancement using the neural network of Mueller matrix images

In the paper, a method of underwater image enhancement based on the neural network of Mueller matrix images is presented. The novelty of our study lies in the employment of the neural network based on Mueller matrix imaging, which can provide complete information of the target, to conduct underwater image enhancement. To establish the dataset, we obtain the Mueller matrix images of different objects under different water turbidity. We utilize an improved neural network based on U-net and a loss function using the high-level feature extractor to enhance the underwater images. The method does not require the physical model of the underwater scene because of the use of deep learning. The enhancement of objects with different materials and textures is obvious, which illustrates that the proposed method is effective and robust. Our method is superior compared with the state-of-the-art methods in terms of quantitative measures and visual quality.
A wrapper-based feature selection approach to investigate potential biomarkers for early detection of breast cancer

Breast cancer (BC) biomarkers can radically improve the early detection in patients and, as a result, reduce mortality rate, whether for detecting individuals at increased risk of developing cancer or in the screening process. Finding a successful biomarker for breast cancer would be a fast and low-cost first solution to predicting BC, and it could potentially lead to a decline in the global BC mortality rate. However, biomarker exploration translates into the role of feature ranking and selection in machine learning terminology. This study explores the influence of using a particular biomarker or combinations of different biomarkers as predictors for breast cancer. Three different classification algorithms were integrated with a sequential backward selection model: support vector machine (SVM), random forests (RF), and Decision Trees (DTs). The result shows that the optimal set of biomarkers comprises Glucose, Resistin, homo, BMI, and Age using the SVM model. The sensitivity and specificity were 0.94 and 0.90, respectively and the 95% confidence interval for the AUC was [0.89, 0.98]. The result indicates that Glucose, Resistin, homo, BMI, and Age combined can serve as a crucial BC biomarker in BC screening and detection.
The noise covariances of linear Gaussian systems with unknown inputs are not uniquely identifiable using autocovariance least-squares

Existing works in optimal filtering for linear Gaussian systems with arbitrary unknown inputs assume perfect knowledge of the noise covariances in the filter design. This is impractical and raises the question of whether and under what conditions one can identify the noise covariances of linear Gaussian systems with arbitrary unknown inputs. This paper considers the above identifiability question using the correlation-based autocovariance least-squares (ALS) approach. In particular, for the ALS framework, we prove that (i) the process noise covariance Q and the measurement noise covariance R cannot be uniquely jointly identified; (ii) neither Q nor R is uniquely identifiable, when the other is known. This not only helps us to have a better understanding of the applicability of existing filtering frameworks under unknown inputs (since almost all of them require perfect knowledge of the noise covariances) but also calls for further investigation of alternative and more viable noise covariance methods under unknown inputs. Especially, it remains to be explored whether the noise covariances are uniquely identifiable using other correlation-based methods. We are also interested to use regularization for noise covariance estimation under unknown inputs, and investigate the relevant property guarantees for the covariance estimates. The above topics are the main subjects of our current and future work.
PPSEB: A Postquantum Public-Key Searchable Encryption Scheme on Blockchain for E-Healthcare Scenarios

In the current E-healthcare scenarios, medical institutions are used to encrypt the information and store it in an Electronic Health Record (EHR) system in order to ensure the privacy of medical information. To realize data sharing, a Public-key Encryption with Keyword Search (PEKS) scheme is indispensable, ensuring doctors search for medical information in the state of ciphertext. However, the traditional PEKS scheme cannot resist the keyword guessing quantum computing attacks, and its security depends on the confidentiality of the secret key. In addition, classical PEKS hand over the search process to a third party, affecting the search results’ accuracy. Therefore, we proposed a postquantum Public-key Searchable Encryption scheme on Blockchain (PPSEB) for E-healthcare scenarios. Firstly, we utilized a lattice-based cryptographic primitive to ensure the security of the search process and achieve forward security to avoid key leakage of medical information. Secondly, we introduced blockchain technology to solve the problem of third-party untrustworthiness in the search process. Finally, through security analysis, we prove the correctness and forward security of the solution in the E-healthcare scenarios, and the comprehensive performance evaluation demonstrates the efficiency of our scheme compared with other existing schemes.
Benchmark of deep learning models for single image super-resolution (SISR)

In this paper we present a study of deep learning models for single image super-resolution (SISR), through the some latest methods used in neural networks for super-resolution, exploring many methods used and proposed. Moreover, this paper presents a global and complete technical benchmark of state-of-the-art machine learning algorithms based on reference metrics (PSNR and SSIM) in the field of visualization and perception. This study involved 53 different neural networks tested on 7 datasets established as reference in the vision domain (Set5, Set14, BSD100, Urban100, DIV2K, Manga109, DIV8K). This work allows us to have a reference to demonstrate the performances and the limits of these algorithms as well as to orient future research in the field of super resolution images in order to develop efficient algorithms. The benchmark covered many neural network architectures (GAN, RNN and Residual Networks), using different techniques and distinct technologies.
Virtual Reality and Aesthetic Experience

The problem of aesthetic experience in a virtual environment could be reformulated as: what can we learn about aesthetics from the perspective of ‘aesthetic experience in virtual environments’, given the specific nature of such an environment? The discourse goes in circles, because it is always from theories elaborated in the field of the so-called ‘real’ that we develop the difference, but it is a process typically philosophical, that, on the other hand, can make sense only if it can be shown that the virtual is an existent being that has an ontological structure of its own. The ontology of this strange object–event, and of its relationship to space–time, must therefore be addressed: what are the conditions of identity for a virtual body? What are its limits? In what sense does it have borders? Also, its specific temporality and its connection to human and computer memories are arguable dimensions that deserve analysis, as they directly affect an ontology of the virtual body, the difference between the virtual and the possible, and the relationship between the human body and the virtual body. However, the specific character of the virtual is to be an intermediate entity between object and event, between thing and image, so that virtual bodies represent a hybrid, interactive world which can be visualized as synthetic image, an immersive hybrid engaging the corporeality of the user and merging with the virtual body’s image; this hybridization, between the body of the spectator–actor and the virtual space in which it is immersed, is difficult to define with singularity.
Drone Forensics: A Case Study on DJI Mavic Air 2

With the inundation of more cost effective and improved flight performance Unmanned Aerial Vehicles (UAVs) into the consumer market, we have seen more uses of these for both leisure and business purposes. As such, demand for digital forensic examination on these devices has seen an increase as well. This research will explore and discuss the forensic examination process on one of the more popular brands of UAV in Singapore, namely DJI. The findings are from the examination of the exposed File Transfer Protocol (FTP) channel and the extraction of the Data-at-Rest on the memory chip of the drone. The extraction was done using the Chip-Off and Chip-On technique.
Hierarchical quotient space-based concept cognition for knowledge graphs

As part of cognitive intelligence, concept cognition aims to view a concept from a comprehensive perspective and clearly grasp the concept intension (i.e., the typical attributes of the things referred to by the concept). Since knowledge graphs contain many entities that are beneficial for concept cognition, concept cognition for knowledge graphs is a promising research branch of concept cognition. Unlike general concept cognition, which selects all the attributes belonging to the concept and simultaneously eliminates other attributes, concept cognition for knowledge graphs focuses on determining the significance of concept-relevant attributes. Therefore, we propose a method of processing concept cognition for knowledge graphs that utilizes the hierarchical quotient space to determine attribute significance. Specifically, 1) this is the first study to propose and address concept cognition for knowledge graphs. 2) We propose a method of building a hierarchical quotient space for a concept in a knowledge graph that fully considers the characteristics of the concept-relevant attributes in the knowledge graph. 3) We propose using the distance between two hierarchical quotient spaces with and without a certain attribute to determine the significance of the concept-relevant attributes. Finally, the performance of our solution is measured from multiple aspects.
Review on various image encryption schemes

Image information security is a significant examination course in the field of data security. Images unreasonably add to correspondence in this time of interactive media. At the point when a client moves images over an unstable correspondence organization, at that point the total security is a moving matter to monitor the privacy of images. Encryption is the technique by which data is changed over into mystery code that shrouds the data's actual importance. This work contributes the review of various image encryption methods, unique image is changed to trivial irregular clamor signal which would draw consideration and consequently pull in assaults and examination of discrete image encoding procedures, at long last end with end and upcoming works.
Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities

Abstract Several deep learning techniques are utilized for classification of the masses in mammogram images. But the existing method doesnot provide sufficient accuracy . for breast masses classification in mammogram images. In this manuscript, an efficient breast cancer image classification framework utilizing the deep belief network (DBN) through chaotic krill herd optimization (CKHO) algorithm for classification of masses in mammogram images is proposed. Initially the input mammogram images are pre‐processed by altered phase preserving dynamic range compression (APPDRC) method for removing the unwanted noise and artifacts. Then these pre‐processed images are assumed with DBN for classifying that mass in the mammogram images into normal, benign, and malignant lesions. Generally, DBN does not reveal any acceptance of optimization techniques for computing optimal parameters that guarantee an accurate classification. Therefore in this work, proposed CKHO algorithm is employed for optimizing weight parameters of the self‐attention convolutional neural network (SACNN). The simulation process is performed under MATLAB platform. Finally, the proposed DBN with chaotic krill herd optimization algorithm (DBN‐CKHO) attains high accuracy 44.5%, 33.42%, 55.23%, 62.35%, and 43.42% when compared with the existing method such as extreme learning machine classifier with moth flame optimization (ELMC‐MFO), Kernel extreme learning machine classifier with Chaotic salp swarm algorithm (KELMC‐CSSA), Extreme learning machine classifier with fruit fly optimization algorithm (ELMC‐FOA), Kernel extreme learning machine classifier with grasshopper optimization algorithm (KELMC‐GOA) and deep neural network with non‐dominated sorting genetic algorithm‐based classification approach.
Semi-quantum secret sharing in high-dimensional quantum system using product states

To make the quantum secret sharing more practical, semi-quantum secret sharing protocols in two-dimensional quantum system have been extensively investigated. This paper proposed a novel semi-quantum secret sharing protocol in high-dimensional quantum system using product states. In the present protocol, the quantum capabilities of the receivers Bob and Charlie are limited, and all participants do not have to access quantum memory. Security analysis indicates that the present protocol is complete robustness to intercept-measure-resend attack and entangle-measure attack. Comparisons between the present protocol and existing protocols are illustrated, which indicates that the present protocol is more efficient. Furthermore, the key rate in different quantum noisy channels is calculated.
Towards personalised predictive psychiatry in clinical practice: an ethical perspective

Summary Personalised prediction models promise to enhance the speed, accuracy and objectivity of clinical decision-making in psychiatry in the near future. This editorial elucidates key ethical issues at stake in the real-world implementation of prediction models and sets out practical recommendations to begin to address these.