Reviewerly/exHarmony huggingface dataset:
On the Maximum Radius of Polynomial Lens Distortion

Polynomial radial lens distortion models are widely used in image processing and computer vision applications to compensate for when straight lines in the world appear curved in an image. While polynomial models are used pervasively in software ranging from PhotoShop to OpenCV to Blender, they have an often overlooked behavior: polynomial models can fold back onto themselves. This property often goes unnoticed when simply warping to undistort an image. However, in applications such as augmented reality where 3D scene geometry is projected and distorted to overlay an image, this folding can result in a surprising behavior. Points well outside the field of view can project into the middle of the image. The domain of a radial distortion model is only valid up to some (possibly infinite) maximum radius where this folding occurs. This paper derives the closed form expression for the maximum valid radius and demonstrates how this value can be used to filter invalid projections or validate the range of an estimated lens model. Experiments on the popular Lensfun database demonstrate that this folding problem exists on 30% of lens models used in the wild.
Cone Beam CT Series Images Rigid Registration for Temporomandibular Joint via Self-supervised Learning Network

Registration of the temporomandibular joint (TMJ) cone beam CT (CBCT) images plays an important role in the medical treatment of temporomandibular joint disorders (TMD) and related diseases. To highlight changes in the condyle bone of TMJ, accurate CBCT images registration is still a challenging work. In this paper, we proposed a self-supervised learning network to realize rigid registration for the TMJ CBCT series images. Without adopting the method of optimization iteration and similarity measurement, the transformation parameters of the rigid registration are directly regressed through our network. Then the warped image is obtained through spatial transformer network. The experimental results also proved the feasibility of this method, and it can greatly improve the accuracy and processing speed of rigid registration.
Applicability of Face Recognition in Real-World Scenarios

Face recognition has become one of the most popular computer technologies in the last ten years, backed by deep convolutional neural networks. It exploits its wide application in various commercial fields. Face recognition technology is relatively mature nowadays. Scientists and engineers have already implemented the necessary functions like face detection, landmarks extraction, and face identification. This paper will depict the necessary steps of those functions, and some popular face recognition libraries will also be introduced in Chapter 3. The paper aims to discuss the parameter settings of the open-source library face_recognition under real-world scenarios with distinct requirements and limitations. Finally, the paper will also go into two cases to analyze the influence of specific parameters and discuss some appropriate settings under different situations.
Support Vector Machine Optimization Using Secant Hyperplane Kernel

In the field of machine learning, one of the famous algorithms is support vector machines (SVM). It has been used to solve classification and as well as regression problems. The most crucial part of SVM is the kernels. There are several widely used kernel functions. A well-designed kernel will help in increasing the accuracy of classification by SVM. Main aim of this paper is analysing and designing of customized kernels. After exploration of all the kernels, a new kernel function has been designed. The proposed work includes the theoretical analysis of the interpretation of kernels to feature space. Further, an experiment was done on four different data sets collected from UCI. The observed improvements identified with the proposed kernel are compared with the existing linear, RBF and polynomial kernels. However, these best results are not universal and not fixed.
Bibliometric Analysis and Data Visualization of Archival Science Journal Literature (1971–2020)

Literature publications in archival science have been critical to the recognition of archival science as an independent discipline. The public access to archives has been popularized with the rise of information freedom since 1970, and the research of international archival study has increased year by year. In recent years, data visualization of literature has become the trend of bibliometric analysis. However, there is still a lack of data visualization analysis about international archival science research results. Therefore, this study used a Scopus database for international journal papers retrieval and employed a bibliometric method to analyze the achievements of international journal articles. The publication quantity, research topics, authors, and journal characteristics were conducted and analyzed. The visualization tools were also taken to present literature keyword distribution and the relationship of subject concepts. The analysis of archival science research literature in international journals could show the whole picture of research trends and changes of archival science in the past 50 years (1971–2020).
Key Components of Deep Metric Learning

The emergence of early metric learning algorithms improved the distance-based classifier, the distance-based clustering and the performance of feature dimensionality reduction. Compared with classic metric learning, deep metric learning can effectively use massive training data, can perform non-linear mapping of input features, and can effectively use deep learning models and technologies that have developed rapidly in recent years. In this survey we review recent deep metric learning. We review commonly used loss functions, and point key components of deep metric learning.
Image Deblurring Using Local Gaussian Models Based on Noise and Edge Distribution Estimation

Image deblurring is important to improve the quality of the image captured from a camera. To well deblur an image, it is an important issue to estimate the distributions of noise and edges, especially for the methods based on maximum likelihood estimation (MLE). In this paper, first, we propose a method for noise estimation based on the smooth prior. That is, for most of natural images, there should be some smooth regions. Then, after predicting the smooth regions and determining their gradient distributions, the noise distribution of the image can be well estimated. Then, based on the estimated noise and the additivity property of the Gaussian distributions, the local edge distributions can be determined. Then, the parameters of the MLE model can be assigned adaptively by the ratio of the standard deviations of edge and noise distributions. Experiments show that the proposed image deblurring algorithm based on the local Gaussian model and smooth prior has better performance than state-of-the-art image deblurring algorithms, including conventional and learning based ones.
A Provisional Labels-Reduced, Real-Time Connected Component Labeling Algorithm for Edge Hardware

Connected component labeling (CCL) is an important operation in computer vision tasks, widely used for real-time image processing on edge hardware. The existing connected component labeling algorithms always generate plenty of provisional labels. Solving the equivalence of provisional labels needs much memory access and processing time. The greater difficulty is that the processing time varies with the number and shape of the connected components. To solve the problems, this brief proposes a new two-scan CCL algorithm. It consists of novel provisional label predicting and provisional label assignment methods based on predicting table, linked lists, and equivalent table. The provisional label predicting method reduces the number of provisional labels effectively. The provisional label assignment method can implement quickly the assignment operation of CCL by combination of the first scan and the second scan with fixed processing time. We have implemented the proposed algorithm on Field Programmable Gate Arrays (FPGA) for raster scan images. The results show that its processing speed can achieve 726fps@225MHz for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$640\times 480$ </tex-math></inline-formula> image resolution, and its resource utilization is less than 15% of compared implementation. It is suitable for real-time edge applications. Compared with other algorithms, the proposed algorithm reduces provisional labels efficiently and completes the CCL operation with a fixed number of iterations. Therefore, it shortens processing time and makes the processing time constant.
Weakly supervised Branch Network with Template Mask for Classifying Masses in 3D Automated Breast Ultrasound

Automated breast ultrasound (ABUS) is being rapidly utilized for screening and diagnosing breast cancer. Breast masses, including cancers shown in ABUS scans, often appear as irregular hypoechoic areas that are hard to distinguish from background shadings. We propose a novel branch network architecture incorporating segmentation information of masses in the training process. By providing the spatial attention effect, the branch network boosts the performance of existing neural network classifiers, helping to learn meaningful features around the mass. For the segmentation information, we leverage the existing radiology reports without additional labeling efforts. The reports should include the characteristics of breast masses, such as shape and orientation, and a template mask can be created in a rule-based manner. Experimental results show that the proposed branch network with a template mask significantly improves the performance of existing classifiers.
CacheGuard: A Behavior Model Checker for Cache Timing Side-Channel Security: (Invited Paper)

Defending cache timing side-channels has become a major concern in modern secure processor designs. However, a formal method that can completely check if a given cache design can defend against timing side-channel attacks is still absent. This study presents CacheGuard, a behavior model checker for cache timing side-channel security. Compared to current state-of-the-art prose rule-based security analysis methods, CacheGuard covers the whole state space for a given cache design to discover unknown side-channel attacks. Checking results on standard cache and state-of-the-art secure cache designs discovers 5 new attack strategies, and potentially makes it possible to develop a timing side channel-safe cache with the aid of CacheGuard.
Digital wall for sensitive data protection

Data is a primary asset for any organization. Most of the companies pays their money in protecting data stored on local or global storage. Main problem came in to exist when data transmitted from one source to another through wireless media. The existing techniques like Digital Certificate or Cryptography will perform secure transmission as well as the authentication for sensitive data between sender and receiver. This authentication and Secure transmission are not good enough to protect data in the unsecured wireless network. We need an enhanced approach that will create an unbreakable wall for an Intruder to detect data in the carrier medium. The proposed approach provides a way to hide the data into the specific domain of the carrier object to provide security through its surrounding samples. The results obtained through the proposed concept are too good for carrier media like image audio and video. Propose concept first creates a Region of Interest (ROI) from carrier object which is smaller than in the size of the entire carrier. The secret data is embedded into this ROI and other pixels for samples of carriers kept untouched. These untouched samples protect the sensitive data in ROI. However, the proposed concept is more flexible to use any kind of data hiding approaches like 1 bpp. 2 bpp or 8 bpp Max. The results shown in the result analysis are based on 1 bpp and 2 bpp levels of data hiding which indicates that the distortion that happened in a carrier is minimum and it’s more effective in reversible data hiding process. Propose concept implemented on image as a carrier media. But it’s very flexible to extend it to other kinds of media like audio and video. The basic intentions behind to choose image as a carrier media is it’s an error tolerance capacity which is much more better than audio and video.
Improving Face Recognition for Mask Wearers Using Data Augmentation of Left–Right Image Flipping and Rotation

Wearing a mask hides half of a face, making it difficult to recognize using face recognition. This raises the problem of it being impossible to identify the whereabouts of a person because their face cannot be recognized. In this paper, we aim to improve the recognition rate by using a learning-based method combining an NVIDIA pre-trained model and face images with and without masks. Furthermore, we aim to improve the recognition rate by utilizing data augmentation to increase the number of training data. Experimental results show that the recognition rate of face images improved.
Railway Pedestrian Intrusion Detection Using Onboard Forward-Viewing Camera

The railway environment changes frequently and trains run fast. The railway-assisted driving system plays an essential role in railway safety. Pedestrians are prone to intrusion when the train enters and exits the platform. Therefore, we propose an algorithm for real-time detection of pedestrian intrusion in the track using onboard video. First, we use the yolov5 object detection algorithm to detect whether there is a pedestrian intrusion. If there is an intrusion, the Fast-SCNN algorithm is called to segment the railway track area. The compound algorithm judges whether the detected object is in the railway track area and reports an abnormality if there is a pedestrian intrusion. The detection speed of the compound detection algorithm proposed in this paper is 34-90FPS (frames per second), and the accuracy rate is 98.17%, which meets the requirements of real-time detection of track perimeter safety and assists driving in stations.
Unsupersived Image Texture Transfer Based On Generative Adversarial Network

The application of Generative Adversarial Network to image texture transfer is a hot research area in computer vision. We propose an unsupervised image texture transfer method in this paper, in which we develop a texture loss network using VGG19 model on the basis of generative adversarial network. To quantify the image texture information, we use the mean and variance of multiple low-level feature layers in this network. The texture loss refers to the differences in texture features between the input image and target image, acting on the generator for updating the parameters. Furthermore, using a U-net full convolutional network in the generator is an effective way to preserve the multi-scale features of the input image. In loss function, the reconstruction loss is calculated by the generated image and the target image in their grayscale space. We use a variety of unpaired datasets for experiment with unsupervised training. The results show that compared with other texture transfer networks, this method can better constrain the transfer performance of the generator and achieve the goal of arbitrary texture transfer.
Process Management for Admission Control to Access Available Resources for Delay Sensitive Service in Fog-to-Cloud Architecture

Resource availability and accessibility in fog-to-cloud computing platforms is vital to facilitate delay-critical services. Cloud and fog collaborations for resource sharing can help to meet the service level time-critical requirements by making the available resources accessible. In this paper process management scheme is proposed for secure storage access considering a fog-to-cloud architecture. Failure criteria and service request arrival control are described. The results demonstrate the suitability of the process management scheme to be integrated to the fog-to-cloud architecture.
Densely-packed Object Detection via Hard Negative-Aware Anchor Attention

In this paper, we propose a novel densely-packed object detection method based on advanced weighted Hausdorff distance (AWHD) and hard negative-aware anchor (HNAA) attention. Densely-packed object detection is more challenging than conventional object detection due to the high object density and small-size objects. To overcome these challenges, the proposed AWHD improves the conventional weighted Hausdorff distance and obtains an accurate center area map. Using the precise center area map, the proposed HNAA attention determines the relative importance of each anchor and imposes a penalty on hard negative anchors. Experimental results demonstrate that our proposed method based on the AWHD and HNAA attention produces accurate densely-packed object detection results and comparably outperforms other state-of-the-art detection methods. The code is available at ${\color{Blue} \text{here}}$.
Privacy Preserving of Two Collaborating Parties Using Fuzzy C-Means Clustering

In this smart and rapidly growing computing world, most of the organizations and companies needs to share necessary information (data) to third parties for their analysis which plays major role in decision making. Any data generally consists of sensitive (personal) information about people and organizations and when the sensitive information to be revealed to third parties, there is always chance of privacy loss. In this regard any organization or an individual requires the application of privacy preserving techniques to ensure data privacy, that means the data to be exchanged between two or more sites, without data loss or privacy violation. When data to be shared between multiple organizations or sites (parties) the privacy issues will be arises more. In this line we choose collaborative fuzzy clustering that gives better solutions to preserve privacy of data while sharing information between multiple parties to achieve combined results. Collaborative clustering is a concept or mechanism to find a common data points along with relationships within data residing at various individual data sites. We propose two ways of collaborative clustering using Fuzzy C-Means Clustering approach, one is Horizontal-PPFCM and other is Vertical-PPFCM. In both proposed methods the information is securely shared between two parties to compute the combined results and privacy also preserved. In fuzzy collaborative clustering two parties generate clusters with mutual collaboration of information they own at each individual sites and a collaborative objective function does all the necessary computations with granular information collected from each party. The overall process flow requires only the secured information and a common objective function for final outcomes. The proposed methods are working well for both horizontal and vertical collaborations and showing better results with assured data privacy.
Evaluating the capacity of deep generative models to reproduce measurable high-order spatial arrangements in diagnostic images

Given the recent interest in the role of deep generative models (DGM) in medical imaging pipelines, it is imperative to evaluate the capacity of such models to generate medically accurate images. Popular methods of evaluation of natural images generated using generative adversarial networks (GANs), a type of DGM, are often applied to medical data. Such methods are insufficient to evaluate anatomical realism, representations of which include high-order spatial information. To our knowledge, no test exists for the faithful replication of spatial statistics beyond the second-order. In this work, purposefully designed stochastic object models (SOMs) are proposed to encode predetermined rules governing the prevalence of features within single images, thus encoding known high-order spatial information within each realization. These SOMs are independent of the network architecture being tested and can also be applied to any new architecture that may be proposed. Two popular GANs are trained on these SOM datasets and the generated images are tested for the encoded statistics. It is observed that although ensemble statistics might be well replicated, this is not necessarily true for realization i.e., per-image statistics. Thus, GAN-generated images might not be ready for clinical use. With the proposed SOMs, the rate of image errors and the rate of feature malformation can be quantified for any architecture, while providing one measure of GAN utility in a diagnostic scenario.
Pearl: Towards Optimization of DNN-accelerators Via Closed-Form Analytical Representation

Hardware accelerators for deep learning are proliferating, owing to their high-speed and energy-efficient execution of deep neural network (DNN) workloads. Ensuring an efficient DNN accelerator design requires a vast design-space exploration of a large number of parameters. However, current exploration frameworks are limited by slow architectural simulations, which limit the number of design points to be examined. To address this challenge, in this paper we introduce Pearl, an analytical representation of executing the DNN inference, mapped to an accelerator. Pearl provides immediate estimates of performance and energy of DNN accelerators, where we incorporate new parameters to capture dataflow mapping schemes beneficial for DNN systems. We model equations that represent utilization rates of the compute fabric for different dataflow mappings. We validate the accuracy of our equations against a state-of-the-art cycle-accurate DNN hardware simulator. Results show that Pearl achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt; 1.0\%$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt; 1.3\%$</tex> average error in performance and energy estimates, respectively, while achieving <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt; 1.2\cdot 10^{7}\times$</tex> simulation speedup. Pearl shows higher average accuracy than existing analytical models that support the simulator. We also leverage Pearl to explore and optimize area-constrained DNN accelerators targeting inference on full HD resolution.
Development of science experiment media kits for elementary school students

Abstract The essence of the current science curriculum (aka curriculum 2013) nationally applied in all elementary schools is scientific approach to improve the students’ skills and knowledge. Based on our need analysis we found that Mechanical experiment series are highly needed. The main goal of this study is to produce media kit for science experiment activities for elementary school students. The media kit consists of experimental tools kit and their experimental guide modules. We applied the 4-D model to achieve the goal. This study is on-going process; this article reports the progress status of the study.