Reviewerly/exHarmony huggingface dataset:
3D-gARt—A New Gachapon 3D-printed Toy Played with Augmented Reality and Story Narration

We propose a new toy idea in which players can print out a 3D model from a digital file, acquired randomly from an online "gachapon", and then view its connected story and digital contents using an augmented reality (AR) application. This model acts as a kind of interactive object that can respond to players in an appropriate simulated situation. An advantage of our proposal is that we train the digital contents with the entire 3D model without the need for 2D markers. Thus, a player can scan the 3D object directly after printing without any additional color marks. This presents a potentially new gachapon mechanic in that we can trade digital content with objects in online vending machines. It can also provide new marketing channels to increase product values. This integration of AR, 3D-printing, manga, and gachapon technologies enables players to truly merge the virtual world with reality.
Cerebral hemorrhage segmentation with energy functional based on anatomy theory

In the computer-aided diagnosis of brain injury, the segmentation of lesions can provide help for subsequent diagnosis and treatment. For many CT images of cerebral hemorrhage, there are irregular blood clots, whose spatial location, shape, and area are changeable. Besides, the blood clots are sometimes connected with the skull (called a cranial connection), which can add to the difficulty and complexity of segmentation. In this paper, the anatomy and imaging features are used to analyze blood clots at cranial connection and disconnection in CT images. Then, a novel energy functional based on the imaging and anatomy features is proposed, which combines shape and area constraints of a cerebral hemorrhage. The proposed method is verified on the actual CT images of 42 patients with cerebral hemorrhage in the Second Affiliated Hospital of Dalian Medical University, which obtains good segmentation results. It is proved that the proposed method has good performance under the evaluation indicators of Dice coefficient, Jaccard coefficient, and recall.
Development of Single-board Computer-based Self-Driving Car Model using CNN-Controlled RC Car

One of the critical stages in developing an autonomous car is to test the AI controller model using an autonomous car prototype. However, the actual size prototype cars are quite expensive and may be dangerous if the fault in controlling action has occurred. Therefore, this research aims to prototype a small-scale autonomous car using an RC car with a single-board computer. AI is built by using the convolutional neural network (CNN) method. The camera images were used as sensor input (training step), and the steering wheel angle and the car's speed as output. The first stage of this method is to collect training data carried out by recording images, steering angles, and car speed when the car is running on its track. The amount of data taken is 3000, 6000, 12000, and 24000 data. Another step is to perform deep learning training to the model, which has variations in convolution layers as 2, 3, 4, and 5. The next stage is testing the prediction of steering angle and car speed from image data using the trained model with lighting and the color of the obstacles varied. The test found that the model with 24000 data and three convolution layers produced the slightest absolute prediction error at 0.18257.
Procedural generation of synthetic multiplex immunohistochemistry images using cell-based image compression and conditional generative adversarial networks

Collecting substantial amounts of tissue image data is expensive, time consuming and often restricted by sample availability. This data sparsity limits the use of neural networks for image classification and knowledge extraction for many medical and biological applications. In this work, we present a novel method to procedurally generate synthetic fluorescent multiplex immunohistochemistry (fm-IHC) images using human-defined rules and a conditional Generative Adversarial Network (cGAN). Our method consists of three steps: First, we compress high-resolution fm-IHC images using a cell-based compression method (Cell2Grid) in which every cell is represented by just one pixel. We subsequently train a cGAN to reverse this compression. In a second step, we define a procedural algorithm for the generation of synthetic Cell2Grid images. We therefore (1) define rules for the generation of large-scale tissue morphology, (2) define the relative abundance of cell phenotypes in each tissue segment, and (3) define marker distributions for each phenotype. In a third step, we apply the trained cGAN to convert synthetic Cell2Grid images into fm-IHC images. As a showcase, we generated synthetic fm-IHC images of murine pancreatic islets in different stages of insulitis consisting of seven antibody color channels. We derived rules for the procedural algorithm using only 59 real sample images and trained the cGAN on 1,575 unlabeled fm-IHC image crops. We show that our method provides full control over tissue morphology, cell composition, and image ground truth and can support the training of image classification networks.
Big Data Analysis for E-Trading System

The e-commerce platform will largely be used to buy, sell, and distribute goods and services through the internet and other networks. A buyer may compare a product with another seller when a store is only open during business hours. Internet shopping is accessible around the clock, seven days a week. It is expected that the e-commerce sector would be a massive one since most businesses will be implementing their operations online. Using this approach, consumers will be able to evaluate several items and choose the one that best fits their needs and budgets. Nine parts will be completed in my project module. There are going to be two primary roles. Admins and users have separate sections. Admins get quick and easy access to product updates.
Technical Services and COVID-19: Review of the Literature

The COVID-19 pandemic had a near-immediate impact on libraries worldwide. The majority of the world's libraries shifted their work to online environments, and while this pivot was difficult for all libraries, technical services units were disproportionately impacted given the inherent physical nature of the tasks undertaken by these library units. Research on the impact of COVID-19 and libraries is still developing, but already, technical services units and tasks are being left behind. This literature review explores the early days of COVID-19 guidance for technical services units, tasks, and workers, as well as the lasting impact of COVID-19 on technical work.
Using iMovie for Soundscape Composition in General Music

In this column, I discuss a soundscape composition activity that I implemented with primary/elementary music classes. During this activity, students used the iPad app, iMovie, to record, organize and edit sound to accompany photos of artworks that they had created. Overall, I found iMovie to be a useful app for music composition with general music classes.
Detection of Osteoporosis with DEXA Scan Images using Deep Learning Models

Osteoporosis is a chronic degenerative disease. Postmenopausal women and people over 50 years of age are at the highest risk of developing osteoporosis. If left untreated, osteoporosis can prove to be debilitating and might require hospitalization. So it is crucial to identify the risk of osteoporosis and take appropriate measures before the disease can negatively impact the quality of human life. Many existing osteoporosis classification models are computationally expensive and suffer from low accuracy due to inefficient processing of scan images. In this paper, the best model for predicting the risk of osteoporosis using DEXA scan images is identified. To improve the accuracy and specificity of the diagnosis, the scan images are pre-processed using denoising, image enhancement, and thresholding. Different CNN and hybrid models are trained, tested and the performance of the models is charted to infer the best model that gives promising results. Experimental results show that the proposed approach of pre-processing the images, data augmentation, sampling with Synthetic Minority Over Sampling Technique (SMOTE) and Inception v3 CNN model achieves the highest accuracy of 92.05&#x0025;, specificity of 90.37&#x0025;, the sensitivity of 93.87&#x0025;, Precision of 89.97&#x0025;, and F1 score of 91.88&#x0025;.
On the Convergence of Hybrid Federated Learning with Server-Clients Collaborative Training

State-of-the-art federated learning (FL) paradigms utilize data collected and stored in massively distributed clients to train a global machine learning (ML) model, in which local datasets never leave the devices and the server performs simple model aggregation for better privacy protection. In reality, however, the parameter server often has access to certain (possibly small) amount of data, and it is computationally more powerful than the clients. This work focuses on analyzing the convergence behavior of hybrid federated learning that leverages the server dataset and its computation power for collaborative model training. Different from standard FL where stochastic gradient descent (SGD) is always computed in a parallel fashion across all clients, this architecture enjoys both parallel SGD at clients and sequential SGD at the server, by using the aggregated model from clients as a new starting point for server SGD. The main contribution of this work is the convergence rate upper bounds of this aggregate-then-advance hybrid FL design. In particular, when the local SGD keeps an <tex>$\mathcal{O}(1/t)$</tex> stepsize, the server SGD must adjust its stepsize to scale no slower than <tex>$\mathcal{O}(1/t^{2})$</tex> to strictly outperform local SGD with strongly convex loss functions. Numerical experiments are carried out using standard FL tasks, where the accuracy and convergence rate advantages over clients-only (FEDAVG) and server-only training are demonstrated.
Toddler’s Hand Motion Acquisition with Hand-Made Data Glove

Supporting hand dexterity, which develops throughout infancy, by providing appropriate toys and other means is one of the central tasks for childcare workers. However, a method for easily collecting data on infants&#x2019; hand movements has not been established. To address this issue, we developed a downsized data glove for infants using a bending sensor, which is often used in data gloves for adults. We performed a preliminary experiment to acquire test data. Finally we discuss some issues that are unique to children.
Detection of Cervical Cancer with Texture Analysis using Machine Learning Models

In women, cervical cancer is a commonly occurring more dangerous cancer. 90% of the women suffer from this cancer. The cancer begins in thin, flat, flat epithelial cells on the surface of the external neck of the cervix. With the early detection it is curable. But diagnosis is complicated. To identify the cancer nowadays machine learning algorithms, deep learning algorithms, fuzzy logics and artificial intelligence are used. In this proposed system, to identify the cancer cells, the gray scale images were used. The texture is analyzed with a Gabor filter and high impact features were identified with histogram equalization. SVM used for the classification of cancer and non-cancer cells. The obtained accuracy is 97%.
Performance analysis of decoy state quantum key distribution over underwater turbulence channels

Decoy state quantum key distribution protocols have been studied for atmospheric, fiber, and satellite links; however, those results are not directly applicable to underwater environments with different channel characteristics. In this paper, we investigate the fundamental performance limits of decoy state BB84 protocol over turbulent underwater channels and provide a comprehensive performance characterization. We adopt a near field analysis to determine the average power transfer over a turbulent underwater path and use this to obtain a lower bound on the secret key rate. We quantify the performance of decoy BB84 protocol in different water types assuming various turbulence conditions. We further investigate the effect of system parameters such as transmit aperture size and detector field of view on the performance.
Novel Visual Secret Sharing Scheme for the QR Code with Meaningful Shares

Visual secret sharing (VSS) scheme can share the secret image with several shares. Shares are performed by the decoding operation to restore the secret image. VSS scheme is mainly used for the secure transmission of images. Quick response (QR) code is often used by people on the Internet. It can be encrypted by the VSS scheme to enhance the security of its transmission. The share is meaningless that will arouse the suspicion of the attacker. The security of the VSS scheme is decreased. This paper proposes a novel VSS scheme for the QR code with meaningful images to solve this problem. Two continue tone images are converted to halftone images. The halftone image is modified to generate the share. Every share is meaningful, not meaningless. Two shares are performed the decoding operation to restore the secret QR code. The restored QR code can be correctly decoded by the general decoder. Experimental results prove that the proposed scheme is feasible.
Design of IoT Based VPN Gateway for Home Network

With the rapid development of technology, individuals can keep with their handy devices like notebooks, tablet computers, and smart cell phones for doing their personal and professional works regardless of the place and location. In addition, they have the potential to connect people to many services on the market to gain access. Security is one of the issues with the user accessing the data through the connected network. In essence, the public network is more vulnerable and the information transferred to systems is not properly encrypted. Furthermore, the network is accessible to anyone those who have the understanding of the computer network. The intruders may use the public network to plough the freedom users from surfing internet by blocking with their websites or advertisements. A Virtual Private Network (VPN) is a network that uses their encryption mechanism to link two different networks together using public telecommunications and to providing services through World Wide Web. In this paper, a prototype model is designed for VPN gateway connection using Raspberry Pi which in turn to connect the house network with the ISP network. Through this a VPN network services like security and scalability is guaranteed to the home systems using the proposed prototype.
Integrating robust feature detection methodology with in-house DIC for identification and correlation of natural patterns on large structures

While developing a novel digital image correlation (DIC)-based NDT method, one has to integrate an automatic and robust feature detection method with the DIC technique. Several studies in the past employed various algorithms such as SIFT, SURF and BRISK with DIC for feature detection and correlation initiation purposes. However, our study shows that the performance of available algorithms is subjected to the image of interest from a particular field experiment. Therefore, the selection of the feature detection algorithms is an essential step towards accurate and efficient processing. We have developed a methodology that applies various feature detection algorithms (namely SIFT, SURF, BRISK, ORB and KAZE) and selects the most accurate, efficient and repeatable algorithm for detecting unique natural patterns. Moreover, the methodology is integrated with an in-house 3D-DIC program to identify as well as correlate natural patterns to obtain in-plane and out-of-plane displacements of large structures. The combined methodology is successfully applied and verified by performing field experiments with a light tower of 10m height and a utility-scale wind turbine. It is observed that the developed methodology is robust enough to detect natural patterns accurately and efficiently. It has also been demonstrated that the technique is successful with the determination of 3D displacements and natural frequencies of the large structures.
A Ring Learning with Errors-Based Ciphertext-Policy Attribute-Based Proxy Re-Encryption Scheme for Secure Big Data Sharing in Cloud Environment

Owing to the huge volume of big data, users generally use the cloud to store big data. However, because the data are out of the control of users, sensitive data need to be protected. The ciphertext-policy attribute-based encryption scheme can not only effectively control the access of big data, but also decrypt the ciphertext as long as the user's attributes satisfy the access structure of ciphertext, so as to realize one to many big data sharing. When the user's attributes do not satisfy the access structure of ciphertext, the attribute-based proxy re-encryption scheme can be used for big data sharing. The ciphertext-policy attribute-based proxy re-encryption (CP-ABPRE) scheme combines the characteristics of the ciphertext-policy attribute-based encryption scheme and proxy re-encryption scheme. In a CP-ABPRE scheme, on the one hand, the data owner can use the ciphertext-policy attribute-based encryption scheme to encrypt the big data for cloud storage, to realize the access control of the big data. On the other hand, the proxy (cloud service provider) can convert ciphertext under one access structure into ciphertext under another access structure, thus realizing big data sharing between users of different attribute sets. In this article, we modify the existing attribute-based encryption scheme based on Ring Learning With Errors (RLWE), add re-encryption key generation algorithm, re-encryption ciphertext generation algorithm, and re-encryption ciphertext decryption algorithm, and construct CP-ABPRE scheme. In the construction of the re-encryption key, we introduce a random vector and hide the vector in the key by threshold technology. Finally, a CP-ABPRE scheme supporting threshold access structure is constructed based on RLWE. Compared with the existing attribute-based proxy re-encryption schemes, our scheme has smaller public parameters, can encrypt multiple plaintext bits at a time, and can resist selective access structure and chosen plaintext attack, so it is more suitable for big data sharing in cloud environment.
Hourly Work of 3D Microstructural Visualization of Dual Phase Steels by SliceGAN

Abstract SliceGAN is used to generate a 3D microstructure image from one or three 2D images in ferrite‐martensite dual phase steels. This method accelerates 3D visualization of microstructure to be hourly task. The 2D images sliced from the SliceGAN‐3D image seem to be quite similar with the original 2D images. The volume fraction of martensite in SliceGAN‐3D image is in good agreement with that in experimentally reconstructed 3D image.
An effective chronic lymphocytic leukemia detection method using hybrid optimization aware random multimodal deep learning

Abstract The leukemia represents a blood cancer that begins from bone marrow and spread to bloodstream and other limbs. The leukocytes pose various morphologies and size that makes it complex to detect and segment exactly. To consider this issue, this article develops a novel and efficient method for chronic lymphocytic leukemia (CLL) detection with blood smear images. Here, the preprocessing is done with Type 2 fuzzy and cuckoo search‐based filter for discarding artifacts and noise contained in the images. The cell segmentation is done with deep fuzzy clustering for producing the segments from the preprocessed blood smear images. The discovery of CLL is done with random multimodal deep learning (RMDL). The RMDL training is done with proposed Jaya‐competitive swarm optimization (Jaya‐CSO) algorithm. The developed Jaya‐CSO is devised by combining Jaya optimization and competitive swarm optimizer (CSO). Hence, the proposed Jaya‐CSO‐based RMDL is employed for discovering lymphocytic leukemia. The proposed Jaya‐CSO‐based RMDL offered improved performance with highest accuracy of 95.1%, highest true negative rate of 95.4% and highest true positive rate of 95.2%.
Potential of Sentinel-2 MSI data in targeting rare earth element (Nd3+) bearing minerals in Esfordi phosphate deposit, Iran

Modern-day devices, transport, and green energy require growing supply of rare earth elements (REEs), which subsequently provide the basics for attempts on developing the efficient and innovative methods for their detection and exploration. Remote sensing data / techniques provide great possibilities to quick and non‑destructive exploration of such deposits, even in relatively unreachable and socially sensitive places. The diagnostic absorption features of REEs are generated in the visible to shortwave infrared (VNIR-SWIR) region of the electromagnetic spectrum, among which, the most prominent absorption features of neodymium (Nd), which could be a key pathfinder for REEs, are displayed in the VNIR region. This study assesses the capability of the Sentinel-2 MSI data for detecting absorption feature of Nd from space. This process was successfully examined and verified in the Esfordi phosphate deposit, Central Iran, and resulted the direct mapping of REE (Nd). For this purpose, firstly, the potential of the Sentinel-2 MSI data for detecting REEs was theoretically evaluated, and then, the adaptive coherence estimator (ACE) algorithm was applied for targeting Nd-bearing zones, using the field spectra of apatite as reference. Field works, X-ray diffraction (XRD), spectroscopy, and the geochemical analyses supported the achievements. The results demonstrated that Sentinel-2 MSI can capably detect the 740 nm absorption band of Nd. Generally, this research attained high overall accuracy of 88.64% for the generated map using in situ collected data for verification.
Adversarial Attacks Defense Method Based on Multiple Filtering and Image Rotation

Adversarial examples in an image classification task cause neural networks to predict incorrect class labels with high confidence. Many applications related to image classification, such as self-driving and facial recognition, have been seriously threatened by adversarial attacks. One class of the existing defense methods is the preprocessing-based defense which transforms the inputs before feeding them to the system. These methods are independent of the classification models and have excellent defensive effects under oblivious attacks. An image filtering method is often used to evaluate the robustness of adversarial examples. However, filtering induces the loss of valuable features that reduce the classification accuracy and weakens the adversarial perturbation. Furthermore, the fixed filtering parameters cannot effectively defend against the adversarial attack. This paper proposes a novel defense method based on different filter parameters and randomly rotated filtered images. The output classification probabilities are statistically averaged, which keeps the classification accuracy while removing the perturbation. Experimental results show that the proposed method improves the defense capability of various models against diverse kinds of oblivious adversarial attacks. Under the adaptive attack, the transferability of the adversarial examples among different models is significantly reduced.