Reviewerly/exHarmony huggingface dataset:
Transfer Learning on EfficientNet for Maritime Visible Image Classification

The state-of-the-art in computer vision has significantly improved over the last few years as a result of the rapid development of deep learning techniques, the existence of large labeled datasets like ImageNet, and GPU acceleration developments. With the fast advancement of pre-trained models, transfer learning and fine-tuning are becoming more popular strategies for saving time and solving the problem of a shortage of data in image processing. For ship recognition, it is still a big challenge to obtain large labeled datasets like ImageNet. In this paper, we experiment with classifying the Marvel vessel dataset by reducing the training samples by half using transfer learning and fine-tuning strategies based on pre-trained EfficientNet (B0B5) as a backbone network to reduce training time and complexity. We also trained the most famous Deep Convolution neural network architectures, ResNet-152 and InceptionV3, and then compared the different architectures' accuracy results for the selected samples of the Marvel dataset. We achieved a significant improvement in classification accuracy compared to the previous state-of-the-art results for the Maritime Vessel (Marvel) dataset using the EfficientNet B5 architecture, with the highest accuracy of 91.60%.
A Fast Matching Method of Trusted Whitelist Based on Bloom Filter

In the process of whitelist checking in the operating system kernel, the main problem is how to put the relatively large amount of whitelist data information into the relatively limited kernel memory space (because the kernel module generally runs entirely in memory and does not directly access large external memory in order to maintain speed) and how to maintain a relatively fast lookup speed. This paper provides a trusted whitelist fast matching device and method to provide a function for fast trusted whitelist comparison and matching in an operating system kernel module with limited memory and limited time to reduce system lag and less available memory due to the introduction of a trusted metric mechanism. To solve the above technical problems, this paper provides a trusted whitelist fast matching device, which introduces a probabilistic algorithm called “Bloom filter” to reduce the time complexity of whitelist retrieval to a constant order and the space complexity to an order of magnitude lower than the above deterministic algorithm, while combining the features of trusted metrics.
Halftoning-Based Fragile Watermarking Approach for Digital Image Self-Recovery

This paper proposes a halftoning-based fragile watermarking approach for digital image tamper detection and self-recovery. The Set Partitioning in Hierarchical Trees (SPIHT) algorithm and halftoning technique are employed to generate the primary recovery bits and secondary recovery bits, respectively. On basis of that, the authentication bits are generated. The Arnold Cat Map and diagonal mapping are then applied to further improve the accuracy of tamper detection and ensure the quality of self-recovery. The experimental results have been conducted to demonstrate the superiorities of the proposed approach in imperceptibility and recovery capability.
A New PoW Consensus of Blockchain Based on Legendre Sequence

PoW consensus can tolerate Byzantine failures by utilizing the cryptographic Hash function as the mining core. However, there are still risks because attacks on those Hash functions are reported from time to time. Alternatively, we present a new mining model based on the Legendre sequence, a binary sequence with good pseudo-randomness. In comparison to the traditional Hash function, the randomness of the Legendre sequence has been rigorously proven, providing a higher level of security for the mining algorithm. Experiments show that the proposed mining model achieves the same level throughput of Bitcoin blockchain while providing a better security guarantee.
360-Degree Image Super-Resolution Based on Single Image Sample and Progressive Residual Generative Adversarial Network

The restriction of network resources has forced cloud Virtual Reality service providers to only transmit low-resolution 360-degree images to Virtual Reality devices, leading to unpleasant user experience. Deep learning-based single image super-resolution approaches are commonly used for transforming low-resolution images into high-resolution versions, but these approaches are unable to deal with a dataset which has an extremely low number of training image samples. Moreover, current single image training models cannot deal with 360-degree images with very large image sizes. Therefore, we propose a 360-degree image super-resolution method which can train a super-resolution model on a single 360-degree image sample by using image patching techniques and a generative adversarial network. We also propose an improved Generative Adversarial Network (GAN) model structure named Progressive Residual GAN (PRGAN), which learns the image in a rough-to-fine way using progressively growing residual blocks and preserves structural and textural information with multi-level skip connections. Experiments on a street view panorama image dataset prove that our image super-resolution method outperforms several baseline methods in multiple image quality evaluation metrics, meanwhile keeping the generator model computational efficient.
Multi-scale Visual Aggregation Residual Network for Super-Resolution

Deep neural networks with residual network structure have achieved great progress in the fields of super-resolution and have greatly improved the performance of super-resolution. However, in order to obtain higher reconstruction accuracy, simply increasing the depth of the network will aggravate the complexity of the network. In this paper, an efficient multi-scale visual aggregation residual network (MVARN) structure is developed for exploiting abundant image detail features, which mimics the visual aggregation mechanism of human eyes. Within the framework of the residual network, we design convolution kernels of different sizes to exploit image detail features by the manner of gradually increasing the range of receptive fields. We show that by utilizing the residual network framework with the multi-scale convolution (MSC) kernels, the image detail feature is capable of being exploited effectively. The feature extracted from different scales can complement and fuse each other to obtain the desired image information. Experimental results show that our proposed method achieves visually pleasing results and is superior to some of the state-of-the-art methods.
Image Caption Method Based on Graph Attention Network with Global Context

Faster-RCNN is commonly used to extract image features in image-to-text generative models since the development of deep learning, although the extraction procedure is time-consuming. Existing approaches extract fixed-size grid features and then use language models to generate image captions, but they only focus on the grid spatial location features without considering grid feature interaction and image global features. An image caption method based on graph attention network with global context is proposed to generate higher-quality image captions. By building a grid feature interaction graph, a multi-layer convolutional neural network is utilized for visual encoding, and the grid features and entire image features of a given image are retrieved. Then, using the graph attention network, which includes a global node and many local nodes, the feature extraction problem is changed into a node classification problem, and the global and local features can be completely utilized after updating and optimization. Finally, the Transformer-based decoding module makes use of the enhanced visual features to provide image captions. The Microsoft COCO dataset is used for experiments evaluation. The experimental results demonstrate that the image caption method based on graph attention network with global context successfully captures the global and local features of the image and achieves 133.1% CIDEr, significantly improving quality of image caption.
FPLotto: A fair blockchain-based lottery scheme for privacy protection

Aiming at the existence of a trusted third party in the traditional lottery and the privacy leakage of the current blockchain lottery scheme, we propose a fair blockchain-based lottery scheme for privacy protection, called FPLotto. Benefiting from a Lagrange interpolating polynomial over a finite field and a verifiable random function, lottery ticket of all users fairly contributes to the generation of winning numbers which is transparent and verifiable to anyone. The discrete logarithm, the one-way property of hash function and VRF provide security and avoid deliberate control of the lottery draw. Moreover, the mixing protocol based on zero-knowledge proofs hides lottery amount and bonus amount, and cuts off the connection among the lottery buyers and the winners for protecting the privacy of users. The scheme guarantees fairness, verifiability, and privacy without a trusted third party. Additionally, we propose a method of aggregation verification based on PLONK reducing the operating cost of lottery agency and speeding up the draw phase.
Support Vector Machine: Melanoma Skin Cancer Diagnosis based on Dermoscopy Image

Melanoma is one of the most deadly types of skin cancer. Until now, the diagnosis of melanoma skin cancer is still using the biopsy method, which is the procedure of taking a small portion of tissue from the patient's body to be examined using a microscope. This method is considered less effective because finding the type of melanoma skin cancer requires a long preparation and a slightly longer wound healing time. Suppose this melanoma can be diagnosed from the start for patients suffering from malignant melanoma tumors. In that case, it can be treated immediately so that the percentage of patient recovery will be higher. Therefore, in this study, a rapid dermoscopy image computerized analysis system was designed so that it can be used to classify the type of melanoma image or not (nevus) with Gray Level Co-occurrence Matric (GLCM). For the classification method used, the support vector machine (SVM) is one of the supervised learning that is usually used for classifications such as (Support Vector Classification) and regression (Support Vector Regression). This method is believed to be quite good and accurate in performing dermoscopy image recognition which is used as input and for image processing. The system for diagnosing melanoma will be implemented in a graphic user interface that will facilitate the user interface experience. The results of this study are information about skin cancer from the image, whether it is melanoma or not (nevus) which can help dermatologists diagnose melanoma quickly. In this study, 2589 data training was conducted, and testing was carried out on 192 testing data. The test results obtained an accuracy rate of 83% and an error percentage of 17%.
Food Items Prediction Using Sentimental Analysis

In today’s scenario, feedback, opinions and reviews provided on the web, social media etc., has proven to be an indirect promotion for the products, companies etc., which acts as an x-factor for increasing the sales of those items or gaining the popularity of the particular domains. This research work has proposed an autonomous feedback system to the food items available at the Chandigarh University using sentimental analysis, that will help the customer to judge the items as a good or bad based on the opinion score of the items. Here, a database has been created by using an initial survey on the previous customers for the food courts located at the University. For comparative analysis, five machine learning algorithms viz., Naıve Bayes, Naıve Bayes using TF-IDF techniques, Multi-nomialNB, XG Boost, and Cat Boost were selected. Naıve Bayes was selected as a baseline for the comparative sentimental analysis of the food items using the CUFC corpus. Experimental analysis shows the highest accuracy (80.40%) using the Cat-Boost algorithm, thus providing the improvement of 18.80% over the baseline feature. Further, this study will also help the customers to select the food items based on the opinions scores and will be also provided to the shopkeepers for improving the items as per feedback received. Thus, imparting and integrating a latest technology for food-oriented applications.
Real Time Detection and Conversion of Gestures to Text and Speech to Sign System

In their daily lives, Deaf-Mute people face numerous challenges when performing simple tasks. One of the many challenges is the ability to communicate with others through sign language. Unfortunately, only 0.2 percent of the population uses and practices ASL (American Sign Language). This divides Deaf-Mute people from those who want to communicate with them. The proposed "Sign Language Translator" system aims to bridge this gap, and it is hoped that it will help Deaf-Mute people converse with people who are unfamiliar with ASL. The system can be used to help non-native speakers learn the language. The goal of the system is to convert speech to American Sign Language by using an artificial neural network known as RNN (Recurrent Neural Network) with LSTM (Recurrent Neural Network - Long Short-Term Memory) trained by a Connectionist Temporal Classification (CTC) neural network. This paper additionally presents gesture-based communication to text/speech model that enables a method for laying out a two-way correspondence without the need of an interpreter. For gesture recognition this paper uses the SSD Mobilenet V2 model.
Detection of Facemask and Temperature Monitoring with Email Notification using Raspberry Pi

Various businesses are confronted with the challenge of selecting the best method to protect their staff and customers from the disruptions to their daily routines. The growth in corona trend has coincided with the opening of various stores, workplaces, and organizations. Facemasks are becoming increasingly required in these situations. for everyone. As a result of Covid-19, the demand for face mask identification technology and temperature recognition is increasing. Because of the COVID-19 pandemic, detecting someone who does not use a mask is challenging. The proposed concept might be deployed at a variety of locations, including school gates, restaurants, bank doors, and airports. The individual's body temperature and face mask must be confirmed before passing the gate. Individuals' faces are recognized by Open CV, which divides them into two groups: those who wear masks and those who do not.
Survey on detection of Leukemia and its subtypes using Machine Learning techniques

A type of malignant growth that begins in the bone marrow of the patient is Leukemia. The creation of extremely strange white platelets in the bone marrow is the proper reason of Leukemia. Recognizing and diagnosing leukemia at the beginning phase i.e. accurate malignant leukocytes differentiation in the disease as early as possible, which is a primary issue in symptomatic field. The utilization of machine learning strategies to examine blood smear images can help in the early discovery of leukemia and the transient recognition of deformity subtypes, permitting treatment to promptly start. Accordingly, this paper gives an outline of machine learning (ML) procedures for recognizing leukemia and its subtypes. This paper presents writing review on the recognizable proof of leukemia and its subtypes. In this field, huge headway is made consistently. This overview can provide them with a new viewpoint on different types of writing, permitting them to foster more effective future examination for their investigations. This overview features a promising future exploration course for applying novel machine learning algorithms in hematology labs to help pathologists and oncologists is solid to recognizing leukemia and its subtypes.
Face Mask Detection and Face Recognition of Unmasked People in Organizations

With the onset of the global pandemic of COVID-19 and with the infection rates on the rise, face masks have become a necessity in public spaces to prevent further spread of the virus. Therefore, it is necessary to develop a system which enforces the need to wear a facemask in public places and identify the ones who are without it. This paper describes a face mask detection and face recognition system. Masked faces are detected using Convolutional Neural Networks and individuals without a face mask are recognized using the LBPH algorithm. The models use OpenCV and TensorFlow and are trained and tested. For doing so, two sets of datasets are used, where the majority of the images are obtained from the Kaggle dataset. 20% of the images are considered for training purposes and other 80% is for testing. The experimental results have confirmed an average face mask detection and face recognition rate of 99%. Accuracy and loss plots for the proposed model are compared with the existing model. Testing for mask detection and facial recognition of masked and unmasked faces are done elaborately.
Water Hardness Prediction using Fuzzy Subtractive Clustering and GRNN

Softcomputing methods e. g. Fuzzy Logic, ANN, GA etc. have proven to be very fast and effective techniques for solving the classification and regression problems. They can be very well applied to both linear and non-linear data. The results can be further improved by combining these techniques to build a hybrid model. The present article addresses the anticipation of hardness of water. The prediction models based on the fuzzy subtractive clustering and GRNN (General Regression Neural Network) techniques have been developed in MATLAB using the available raw data pertaining to the water hardness of lake. A comparative analysis is also presented of the developed models.
The effect of presentation position and multichannel approach on learning performance in the use of an augmented reality–head worn display interface for train-driving training

The objective of this study was to determine the differential effects of the presentation position of the augmented reality–head worn display (AR-HWD) interface and the audiovisual-dominant multimodal learning material on learning performance and cognitive load across different learning tasks in training for high-speed train driving. We selected 48 participants to conduct an AR driving training experiment for high-speed trains. The results indicated that whether or not the AR interface was presented next to the learning target did not significantly affect learning performance and cognitive load. In complex learning tasks, such as learning to operate the controller of a high-speed train and to remember the steps of swapping the front and tail of the train, the auditory-led AR learning materials were effective in improving learning performance and reducing cognitive load compared with the visual-led AR learning materials. In the simple task of icon recognition, the visual-led AR learning materials performed better in terms of learning performance and cognitive load. The results of this study can guide the design of AR-HWD interfaces for different learning tasks in training for high-speed train driving, which can improve learning performance and reduce cognitive load during the learning process. HIGHLIGHTSWe studied the position of AR interface presentation and AR multichannel learning for high-speed train-driving training.Whether or not the AR interface was presented next to the target did not have a significant effect on learning performance for that target.Auditory-dominant interfaces outperformed visual-dominant interfaces in complex learning tasks.Visual-dominant interfaces outperformed auditory-dominant interfaces in simple recognition tasks.
Expanding Multimodal Artistic Expression and Appreciation Methods through Integrating Augmented Reality

Abstract Increasing usage of digital technologies in daily life has affected even tech savvy students. With the prominent development of augmented reality (AR) and virtual reality technologies, this study concentrates on the ongoing development of AR in art education and its potential in art education. As an educator in visual arts at a higher education institution, the author explored AR‐integrated curriculum by introducing an AR creation tool (Adobe Aero) and AR education tool (Merge Cube) into visual arts education classes. The lessons were designed with multimodal delivery methods for learning effectiveness with AR, and students learned about the concepts of arts integration and AR technologies. This study examines how newly developed AR‐based curriculum expanded students’ art expression and represented artwork in real and virtual spaces. In outlining the results, this study explains the practice of two‐dimensional (2D) and virtual‐based three‐dimensional (3D) hybrid art creations in AR, and digital storytelling with AR. This study also discusses the positive impacts on students' learning engagement and satisfaction and understanding of layers and spatial structures.
On stylized facts of cryptocurrencies returns and their relationship with other assets, with a focus on the impact of COVID-19

This study provides an empirical analysis on the main univariate and multivariate stylized facts iin return series of the two of the largest cryptocurrencies, namely Ethereum and Bitcoin. A Markov-Switching Vector AutoRegression model is considered to further explore the dynamic relationships between cryptocurrencies and other financial assets. We estimate the presence of volatility clustering, a rapid decay of the autocorrelation function, an excess of kurtosis and multivariate little cross-correlation across the series, except for contemporaneous returns. The analysis covers the pandemic period and sheds lights on the behaviour of cryptocurrencies under unexpected extreme events.
MSEN-GRP: A Geographic Relations Prediction Model Based on Multi-Layer Similarity Enhanced Networks for Geographic Relations Completion

Geographic relation completion contributes greatly to improving the quality of large-scale geographic knowledge graphs (GeoKGs). However, the internal features of a GeoKG used in large-scale GeoKGs embedding are often limited by the weak connectivity between geographic entities (geo-entities). If there is no proper choice in the method of external semantic enhancement, this will often interfere with the representation and learning of the KG. Therefore, we here propose a geographic relation (geo-relation) prediction model based on multi-layer similarity enhanced networks for geo-relations completion (MSEN-GRP). The MSEN-GRP comprises three parts: enhancer, encoder, and decoder. The enhancer constructs semantic, spatial, structural, and attribute-similarity networks for geo-entities, which can explicitly and effectively enhance the implicit semantic associations between existing geo-entities. The encoder can obtain the long path relation dependency characteristics of geo-entities using a mixed-path sampling strategy and can support different optimization schemes for external semantic enhancement. Geo-relations prediction experiments show that the mean reciprocal ranking of this method is significantly higher than those of the traditional TransE DisMult and methods, and Hits@10 is improved by up to 57.57%. Furthermore, the spatial-similarity network has the most significant enhancement effect on geo-relations prediction. The proposed method provides a new way to perform relation completion in sparse GeoKGs.