Reviewerly/exHarmony huggingface dataset:
Camera Animation for Immersive Light Field Imaging

Among novel capture and visualization technologies, light field has made significant progress in the current decade, bringing closer its emergence in everyday use cases. Unlike many other forms of 3D displays and devices, light field visualization does not depend on any viewing equipment. Regarding its potential use cases, light field is applicable to both cinematic and interactive contents. Such contents often rely on camera animation, which is a frequent tool for the creation and presentation of 2D contents. However, while common 3D camera animation is often rather straightforward, light field visualization has certain constraints that must be considered before implementing any variation of such techniques. In this paper, we introduce our work on camera animation for light field visualization. Different types of conventional camera animation were applied to light field contents, which produced an interactive simulation. The simulation was visualized and assessed on a real light field display, the results of which are presented and discussed in this paper. Additionally, we tested different forms of realistic physical camera motion in our study, and based on our findings, we propose multiple metrics for the quality evaluation of light field visualization in the investigated context and for the assessment of plausibility.
Multilevel Noise Contrastive Network for Few-Shot Image Denoising

In recent years, most denoising methods based on deep Convolutional Neural Networks heavily rely on massive noisy-clean image pairs. Collecting massive noisy-clean image pairs is expensive and not practical in real scenes. Currently, few-shot learning has been applied to many areas to cope with the absence data. The few-shot learning, however, in image denoising severely suffers from domain gap problems, including dataset domain gap and feature domain gap, especially for the real noisy images. Therefore, this paper proposes a Multi-level Noise Contrastive Network (MNC-Net) performing few-shot image denoising. MNC-Net consists of two training stages: i) using contrastive learning to self-supervise the training of Multi-level Noise Contrastive Learner (MNCL) on the pure synthetic noisy images with multiple Gaussian noise levels to ease the acute dataset domain gap, and ii) features generated by the MNCL on limited data are fused to the second stage and alleviate the feature domain gap using our proposed denoising network. Specifically, the MNCL consists of a Contrastive Feature Extractor (CFE) and a Contrastive Feature Projector (CFP). MNCL learns the rich and complex content-invariant degradations and general multiple-level noise representations. The denoising network in the second stage is composed of Guider Feature Encoder (GFE) and Adaptive Denoising Decoder (ADD). The GFE uses contrast features from CFE to guide the produced representations on the specific input noisy images. Then, such output features are fed into the ADD to adaptively denoise the noisy images on the corresponding noise distribution. To our best knowledge, this work is the first attempt to jointly use the few-shot learning and contrastive learning in the deep denoising field. Extensive experiments on CBSD68, Kodak24, Set12, SIDD, and DND show that our method achieves promising denoising performances in the absence of data.
Automated Exposures Selection for High Dynamic Range Structured-Light 3-D Scanning

Structured light scanning has gained widespread applications in industrial metrology; however, measuring objects that have large surface-reflectivity variations is challenging. For this purpose, the high dynamic range (HDR) technique has become a standard method to fuse features under multiple exposures. Presently, in HDR measurement, manual or cumbersome exposure selection strategies are used. This article reports a new exposure selection approach. An image quality metric is designed to evaluate captured images and suppress regional overexposure, based on which a multiple-exposures selection strategy is developed. Unlike existing methods for single-optimal exposure selection, in our approach, pixel filtering is introduced to locate eligible pixels and candidate pixels in next exposures. Experimental results with industrial parts having large surface-reflectivity variations demonstrated that compared with HDR with 15 exposures (equal time intervals), our proposed approach achieved a similar surface coverage rates (97.4% versus 98.1%) and measurement accuracy (0.043 versus 0.041 mm), but only took 3–4 exposures averagely.
Phase estimation via riesz transform in laser speckle interferometry for large-area damage imaging

A direct phase estimation (DPE) via Riesz transform from a speckle pattern (images) for visualizing barely visible impact damage (BVID) occurring in layered composites is proposed in this paper. Rather than complex optical hardware setup for phase extraction in commercial digital speckle pattern interferometry (DSPI) or shearography (SG) systems, the proposed numerical (software) DPE implementation via Riesz transform with a log-Gabor filter enables a compact, portable, and cost-effective system for large-area real-time nondestructive inspection (NDI) due to its simplicity, stability, and efficiency. Using speckles in laser speckle interferometry (LSI) speckle pattern is intrinsically noisy; the correlation-based phase signatures derived from Riesz transform followed by the log-Gabor filter is implemented to supplant the conventional phase-difference algorithm to localize damage regions, especially in the case where the high-power diode laser (low coherence) can cause serious phase drift. By enveloping the monogenic signal through Riesz transform for direct phase retrieval is more intuitive than TPS or SPS. This yields visualization of the damage feature with higher fidelity. Consequently, a phase-correlation algorithm becomes a more robust operation to cope with speckle images from the interferometry. This full-field optical SG system was demonstrated and verified in an impacted damaged honeycomb composite panel using the proposed phase-correlation algorithm under thermal stress loading for large-area inspection in near real-time. The visualized damage images enhanced by principal component analysis (PCA) agreed with images from pulse laser/LDV and X-ray CT scan. The DPE algorithm has a promising potential to apply on any optical metrology for instantaneous phase estimation and can combine with selected imaging conditions for multi-damage detection.
Enhancing User Experience of Eye-Controlled Systems: Design Recommendations on the Optimal Size, Distance and Shape of Interactive Components from the Perspective of Peripheral Vision

For special populations with motor impairments, eye-controlled interaction may be the only way for them to communicate with the outside world. Because of the dominance of vision in the motor mechanism, eye-controlled interaction has high usability and important research value. During eye-controlled interaction, the visual channel needs to receive information from the graphical user interface (GUI) and transmit the user's eye-controlled instructions, which overburdens the visual channel and reduces the efficiency of eye-controlled interaction. This study presents an ergonomic experiment to study how to design interactive GUI components in an eye-controlled user interface. The experiments were conducted based on the shape, size, and distance (from the object to the center of the screen) of the visual interactive components. The experiment comprised three parts: (1) the pre-experiment determined the evaluation index and selected the icon material; (2) the formal experiment was a three-factor within-subjects experiment, which included a search task using participants' peripheral vision; and (3) after the experiment, subjective evaluations were conducted using a questionnaire. The results showed that the shape, size, and distance of the interactive object significantly affected the reaction time, and the size factor significantly affected the movement time of the eye-controlled interaction. Finally, combined with the results of the subjective evaluation, we concluded that the recommended sizes of the interactive components were 2.889°, 3.389°, and 3.889°, and the recommended distances were 5.966° and 8.609°. Additionally, designers should utilize components with simple concrete shapes as much as possible to improve user recognition efficiency. Our study provides enlightening recommendations on how to design components in eye-controlled interactive interfaces, and has great guiding significance for building design standards of the eye-controlled systems.
Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs

Microcalcification clusters (MCs) are among the most important biomarkers for breast cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning studies on digital breast tomosynthesis (DBT) are focused on detecting and classifying lesions, especially soft-tissue lesions, in small regions of interest previously selected. Only about 25% of the studies are specific to MCs, and all of them are based on the classification of small preselected regions. Classifying the whole image according to the presence or absence of MCs is a difficult task due to the size of MCs and all the information present in an entire image. A completely automatic and direct classification, which receives the entire image, without prior identification of any regions, is crucial for the usefulness of these techniques in a real clinical and screening environment. The main purpose of this work is to implement and evaluate the performance of convolutional neural networks (CNNs) regarding an automatic classification of a complete DBT image for the presence or absence of MCs (without any prior identification of regions). In this work, four popular deep CNNs are trained and compared with a new architecture proposed by us. The main task of these trainings was the classification of DBT cases by absence or presence of MCs. A public database of realistic simulated data was used, and the whole DBT image was taken into account as input. DBT data were considered without and with preprocessing (to study the impact of noise reduction and contrast enhancement methods on the evaluation of MCs with CNNs). The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance. Very promising results were achieved with a maximum AUC of 94.19% for the GoogLeNet. The second-best AUC value was obtained with a new implemented network, CNN-a, with 91.17%. This CNN had the particularity of also being the fastest, thus becoming a very interesting model to be considered in other studies. With this work, encouraging outcomes were achieved in this regard, obtaining similar results to other studies for the detection of larger lesions such as masses. Moreover, given the difficulty of visualizing the MCs, which are often spread over several slices, this work may have an important impact on the clinical analysis of DBT images.
Quantum Protocol for Secure Multiparty Logical AND With Application to Multiparty Private Set Intersection Cardinality

Like addition, subtraction, multiplication and division, logical AND is a fundamental arithmetic operation in scientific computing. In this paper, we first consider and define a new privacy-preserving problem, i.e., Secure Multiparty Logical AND (SMLA), in which multiple participants can jointly compute the logical AND of their respective private bits. Furthermore, we present a novel quantum SMLA protocol by employing phase-matching quantum conference key agreement and perfect quantum encryption, and design the corresponding quantum circuits. The proposed quantum SMLA protocol can ensure information-theoretical security. What’s more, we design a private grouping strategy to reduce the communicational complexity and accordingly present an improved quantum SMLA protocol with the linear communicational rounds. In addition, we investigate its applications and design a feasible quantum Multiparty Private Set Intersection Cardinality (MPSI-CA) protocol based on the improved quantum SMLA protocol, in which multiple participants jointly compute the intersection cardinality without revealing their respective private sets. Finally, we verify the correctness and the feasibility of the proposed quantum protocols by circuit simulations in IBM Qiskit.
Sentiment Analysis on Twitter Data Using Term Frequency-Inverse Document Frequency

This study is an exploratory analysis of applying natural language processing techniques such as Term Frequency-Inverse Document Frequency and Sentiment Analysis on Twitter data.The uniqueness of this work is established by determining the overall sentiment of a politician's tweets based on TF-IDF values of terms used in their published tweets.By calculating the TF-IDF value of terms from the corpus, this work displays the correlation between TF-IDF score and polarity.The results of this work show that calculating the TF-IDF score of the corpus allows for a more accurate representation of the overall polarity since terms are given a weight based on their uniqueness and relevance rather than just the frequency at which they appear in the corpus.
Use of cyber attack and defense agents in cyber ranges: A case study

With the ever-changing cybersecurity landscape, the need for a continuous training for new cybersecurity skill sets is a requirement. Such continuous training programs can be delivered on platforms like cyber ranges. Cyber ranges support training by providing a simulated or emulated representation of a computer network infrastructure, besides additional training and testing services. Cyber attack and defense skills can be gained by attacking and defending a simulated or an emulated infrastructure. However, to provide a realistic training in such infrastructures, there is a need for necessary friction in the environment. Human teams, playing both attackers’ and defenders’ roles, provide this friction. Involving human teams in large-scale cybersecurity exercises is relatively inefficient and not feasible for standardizing training because different teams apply different tactics. Currently, the proposed solutions for cyber range training platforms focus on automating the deployment of the cybersecurity exercise infrastructure but not on the execution part. This leaves a room for improving exercise execution by adding realism and efficiency. This research presents an agent-based system that emulates cyber attack and defense actions during cybersecurity exercise execution; this helps provide realistic and efficient cybersecurity training. To specify agents’ behavior and decision making, a new formal model, called the execution plan (EP), was developed and utilized in this work.
Addressing modern and practical challenges in machine learning: a survey of online federated and transfer learning

Abstract Online federated learning (OFL) and online transfer learning (OTL) are two collaborative paradigms for overcoming modern machine learning challenges such as data silos, streaming data, and data security. This survey explores OFL and OTL throughout their major evolutionary routes to enhance understanding of online federated and transfer learning. Practical aspects of popular datasets and cutting-edge applications for online federated and transfer learning are also highlighted in this work. Furthermore, this survey provides insight into potential future research areas and aims to serve as a resource for professionals developing online federated and transfer learning frameworks.
Shear Sonic Prediction Based on DELM Optimized by Improved Sparrow Search Algorithm

In the geophysical exploration field, the sonic log (DT) and shear sonic log (DTS) are frequently used as quick and affordable procedures for reservoir evaluation. Due to the high acquisition costs, DTS is only accessible in a few wells within an oil/gas field. Numerous attempts have been made to establish a precise relationship between DTS and other petrophysical data. In this study, a method based on the deep extreme learning machine optimized by the improved sparrow search algorithm (ISSA-DELM) is proposed to improve the accuracy and stability of the DTS prediction. Firstly, the deep extreme learning machine (DELM) model is constructed by combining the extreme learning machine and the autoencoder algorithm. Secondly, aimed at the defects of the sparrow search algorithm (SSA), an improved sparrow search algorithm (ISSA) with the firefly search disturbance is proposed by merging the iterative strategy of the firefly algorithm and applied to optimize the initial input weights of the DELM. Finally, the ISSA-DELM is applied to the prediction of the DTS in a block of the Ordos Basin in China. The quantitative prediction results show that the RMSE, MAE, and R-square predicted by the ISSA-DELM model are 6.1255, 4.1369, and 0.9916, respectively. The comprehensive performance is better than the ELM, the DELM, and the DELM optimized by the optimization algorithms, such as the genetic algorithm (GA), the particle swarm optimization (PSO), and the SSA. Therefore, it can be concluded that the method provides an effective method for missing DTS estimation.
FAIR Guidelines and Data Regulatory Framework for Digital Health in Nigeria

Abstract Adopting the FAIR Guidelines—that data should be Findable, Accessible, Interoperable and Reusable (FAIR)—in the health data system in Nigeria will help protect data against use by unauthorised parties, while also making data more accessible to legitimate users. However, little is known about the FAIR Guidelines and their compatibility with data and health laws and policies in Nigeria. This study assesses the governance framework for digital and health/eHealth policies in Nigeria and explores the possibility of a policy window opening for the FAIR Guidelines to be adopted and implemented in Nigeria's eHealth sector. Ten Nigerian policy documents were examined for mention of the FAIR Guidelines (or FAIR Equivalent terminology) and the 15 sub-criteria or facets. The analysis found that although the FAIR Guidelines are not explicitly mentioned, 70% of the documents contain FAIR Equivalent terminology. The Nigeria Data Protection Regulation contained the most FAIR Equivalent principles (73%) and some of the remaining nine documents also contained some FAIR Equivalent principles (between 0–60%). Accordingly, it can be concluded that a policy window is open for the FAIR Guidelines to be adopted and implemented in Nigeria's eHealth sector.
FAIR Equivalency with Regulatory Framework for Digital Health in Ethiopia

Abstract This paper investigates whether or not there is a policy window for making health data ‘Findable’, ‘Accessible’ (under well-defined conditions), ‘Interoperable’ and ‘Reusable’ (FAIR) in Ethiopia. The question is answered by studying the alignment of policies for health data in Ethiopia with the FAIR Guidelines or their ‘FAIR Equivalency’. Policy documents relating to the digitalisation of health systems in Ethiopia were examined to determine their FAIR Equivalency. Although the documents are fragmented and have no overarching governing framework, it was found that they aim to make the disparate health data systems in Ethiopia interoperable and boost the discoverability and (re)usability of data for research and better decision making. Hence, the FAIR Guidelines appear to be aligned with the regulatory frameworks for ICT and digital health in Ethiopia and, under the right conditions, a policy window could open for their adoption and implementation.
Distributionally Robust State Estimation for Nonlinear Systems

Uncertainties unavoidably exist in modeling for nonlinear systems: state equation, measurement equation, and/or noises statistics might be uncertain. Such model mismatches render the performance of nominally optimal state estimators being deteriorated or even unsatisfactory. Therefore, robust filters that are insensitive to modeling uncertainties have to be designed. The challenge is to quantitatively describe the uncertainties and then design accordingly efficient robust filters. Since uncertainties in nominal models make prior state distributions and likelihood distributions uncertain as well, this article proposes a distributionally robust particle filtering framework for nonlinear systems subject to modeling uncertainties. Specifically, we use worst-case prior state distributions (near the nominal prior state distributions) to generate prior state particles and/or determine their weights. Likewise, worst-case likelihood distributions (near the nominal likelihood distributions) are used to evaluate the worst-case likelihoods of prior state particles at given measurements. The “worst-case” scenario is quantified by entropy of distributions, and maximum entropy distributions are found in balls centered at nominal distributions with radii defined by statistical similarity measures such as moments-based similarity, Wasserstein distance, and Kullback-Leibler divergence. We prove that Gaussian approximation filters (e.g., unscented/cubature/ensemble Kalman filter) are distributionally robust in the sense that they use maximum entropy prior state distributions and maximum entropy likelihood distributions. Moreover, we show that the distributionally robust particle filtering framework provides a likelihood evaluation method for general nonlinear measurement equation with non-additive and non-multiplicative measurement noises. At last, we discuss measurement outlier treatment strategies in the distributionally robust particle filtering framework.
Systematic Survey on Visually Meaningful Image Encryption Techniques

Due to advancements in technology, digital images are widely used in various applications like medical field, military communication, remote sensing, etc.These images may contain sensitive and confidential information.Therefore, images are required to be protected from unauthorized access.Many image protection techniques have been proposed in past years.The most common technique to protect the images is encryption.In this technique, a secret key and an encryption algorithm are used to change the plain image into an encrypted image.The encrypted image looks like a noisy image and can easily attracts the attacker's attention.If image gets captured and stacked, sensitive information can be revealed.In this regard, Visually Meaningful Encrypted Image (VMEI) technique is developed, which initially encrypts the original image and then hides it into a reference image.The final encrypted image looks like a normal image.Hence, VMEI technique provides more security as compared to simple image encryption techniques.Therefore, a systematic survey on existing VMEI techniques is presented in this paper.The VMEI techniques are divided into different categories based on their characteristics.Moreover, this paper elaborates and investigates the improvements and analyses performed on VMEI techniques based on various evaluation parameters.These evaluation parameters are divided into different categories such as security attacks, encryption key attacks, quality analysis, and noise attacks.Finally, this paper discusses the potential applications and the future challenges of VMEI techniques.