Reviewerly/exHarmony huggingface dataset:
Scene Text Detection with Gradient Auto Encoders

Text serves as an excellent persistent communication medium for unambiguous and precise information exchange. Text could help us to describe any scene. Hence, it would be ideal to understand scene text to accurately identify and understand a scene image or video. Variations such as script, font, color, scale, lighting, angle of view and other distortions make scene text understanding a challenge. Detecting and localizing the possible text, could improve the task of text understanding. Though decades of research had attempted to address the problem, still it is an open area. For instance, requirement of high-performance computation platform, large training dataset and longer training process. We have attempted to train our auto encoder based text detector to precisely localize text with minimum training on a small dataset and limited computational resources. The idea involves computation of morphological gradient to enhance text on the scene image and to feed it to a gradient auto encoder neural network to locate possible text components. The proposed detector can detect text across multiple languages and it is robust against the variations such as scale, orientation, font, and lighting. The results are promising. The proposed method achieves an F-measure of 0.75 and 0.76 on MRRC dataset and MSRA-TD500 dataset respectively, after training with 167 images.
Deep Dilated Convolutional Network for Single Image Dehazing

The visual quality of the images gets decreased due to bad weather conditions. The image captured under hazy weather conditions have serious attenuation in terms of color and saturation. In addition, these hazy images have very low contrast and the visual quality will be drastically poor. Moreover, object detection in hazy environment is too challenging. So, single image dehazing is a demanding, challenging and ill-posed problem. In this paper, we propose a 9-layer convolutional neural network with deep dilated filters of different dilation rates to achieve an end-to-end mapping from haze image to haze free image. Exponential expansion of receptive field is possible with the dilated filters without increasing the model complexity. Furthermore, the dilated convolutional layers help for efficient model compactness. We did experiments on synthetic dataset and on naturally obtained hazy images. The results show that our network achieves outstanding performance over the existing algorithms in terms of PSNR, SSIM and visual quality.
An Explainable Transfer Learning Based Approach for Detecting Face Mask

COVID-19 has made a serious impact throughout the world. Wearing a face mask properly in public is considered an effective strategy to prevent infection. To contribute to community health, this work intends to develop an accurate real-time system for detecting non-mask faces in public. The proposed system can be used by law enforcement authorities to monitor and enforce the proper use of masks. A novel transfer learning-based method to detect face masks efficiently is proposed in this paper. Inspired by EfficientNetV2, a transfer learning model has been proposed to use for detecting whether a person is wearing a mask or not. To our knowledge, the EfficientNetV2 has not been used for detecting face masks. The model building is done based on two standard face mask datasets. The images in the first dataset consist of multiple people wearing masks, not wearing masks, and wearing the mask incorrectly. The second dataset consists of masked faces and faces without masks. To validate the generalization capability of the proposed model, the trained model is tested on two new standard datasets. In addition to that, the testing is done on a dataset created by ourselves. The proposed model performs well even on images with distortions such as blurred and noisy images. The model predicts whether the person in the input image is wearing a mask or not and also the correctness of mask-wearing with significantly better accuracy than the existing methods. The explainability of the proposed model is explained using a class activation map.
Integration of GAN and Adaptive Exposure Correction for Shadow Removal

Shadow removal from images and videos is an essential task in computer vision that concentrates on detecting the shadow generated by the obstructed light source, and obtains realistic shadow-free results. In this paper, we present a method based on generative adversarial networks (GANs) for shadow removal by supervised learning. Specifically, we train two generators and two discriminators to learn the mapping between shadow and shadow-free image domains. We employ generative adversarial constraints with cycle consistency and content constraints to learn the mapping efficiently. We also propose an adaptive exposure correction module to handle the over-exposure problem in the shadow area of the result. We additionally present a method for improving the quality of benchmark datasets and eventually achieving better shadow removal results. We also show ablation studies to analyze the importance of the ground-truth data with the adaptive exposure correction module in the proposed framework and explore the impact of using different learning strategies in the presented method. We validate the approach on the available large-scale benchmark Image Shadow Triplets dataset (ISTD), and show quantitative and visual improvements in the state-of-the-art results.
HD-VAE-GAN: Hiding Data with Variational Autoencoder Generative Adversarial Networks

This manuscript proposes an end-to-end trainable model, VAE-GAN, engineered to hide messages (image) inside a container (image). The model consists of an embedder network (to hide a message inside the container) and an extractor network(to extract the hidden message from the encoded image). In the proposed method, we employ the generative power of a variational autoencoder with adversarial training to embed images. At the extractor, a vanilla convolutional network with adversarial training has provided the best results with clean extracted images. To analyse the noise sensitivity of the model, the encoded image is subjected to multiple attacks, and it is established that the proposed method is inherently robust towards attacks like Gaussian blurring, rotation, noise, and cropping. However, the model can be trained on any possible attacks to reduce noise sensitivity further. In this manuscript, we explore the application of hiding images inside images, but the method can be extended to hide various combinations of data hiding.
Anomaly Detection in ATM Vestibules Using Three-Stream Deep Learning Approach

Anomalies are abnormal events that deviate from typical behavioral patterns. Video anomaly detection is the problem of identifying anomalies in video feeds. ATM vestibules are one of the critical places where such anomalies must be detected. The problem lies around how we represent a video and further perform analysis on it to predict an anomaly. Another problem is the unavailability of data for this task specific to the ATM vestibule. To tackle these, this paper uses a three-stream deep learning architecture and contributes two novel datasets: box annotated image and temporal annotated video dataset. The three streams correspond to contextual, spatial and motion information. It is first-of-its-kind attempt to leverage box annotated image dataset for finetuning object detection models to detect ATM class and temporal annotated video dataset to train the model for video anomaly detection in ATM vestibule. The presented work achieves a recall score of 0.93, and false positive rate of 0.13.
Secure keys data distribution based user-storage-transit server authentication process model using mathematical post-quantum cryptography methodology

&lt;abstract&gt; &lt;p&gt;The central remote servers are essential for storing and processing data for cloud computing evaluation. However, traditional systems need to improve their ability to provide technical data security solutions. Many data security challenges and complexities await technical solutions in today's fast-growing technology. These complexities will not be resolved by combining all secure encryption techniques. Quantum computing efficiently evolves composite algorithms, allowing for natural advances in cyber security, forensics, artificial intelligence, and machine learning-based complex systems. It also demonstrates solutions to many challenging problems in cloud computing security. This study proposes a user-storage-transit-server authentication process model based on secure keys data distribution and mathematical post-quantum cryptography methodology. The post-quantum cryptography mathematical algorithm is used in this study to involve the quantum computing-based distribution of security keys. It provides security scenarios and technical options for securing data in transit, storage, user, and server modes. Post-quantum cryptography has defined and included the mathematical algorithm in generating the distributed security key and the data in transit, on-storage, and on-editing. It has involved reversible computations on many different numbers by super positioning the qubits to provide quantum services and other product-based cloud-online access used to process the end-user's artificial intelligence-based hardware service components. This study will help researchers and industry experts prepare specific scenarios for synchronizing data with medicine, finance, engineering, and banking cloud servers. The proposed methodology is implemented with single-tenant, multi-tenant, and cloud-tenant-level servers and a database server. This model is designed for four enterprises with 245 users, and it employs integration parity rules that are implemented using salting techniques. The experimental scenario considers the plain text size ranging from 24 to 8248 for analyzing secure key data distribution, key generation, encryption, and decryption time variations. The key generation and encryption time variations are 2.3233 ms to 8.7277 ms at quantum-level 1 and 0.0355 ms to 1.8491 ms at quantum-level 2. The key generation and decryption time variations are 2.1533 ms to 19.4799 ms at quantum-level 1 and 0.0525 ms to 3.3513 ms at quantum-level 2.&lt;/p&gt; &lt;/abstract&gt;
Fraud Transaction Detection Approach Using Machine Learning Hybrid Techniques

Fraud detection is a crucial task in financial transactions to prevent monetary losses and maintain the integrity of the financial system. Some of the machine learning algorithms that we tested for their efficacy in identifying fraudulent activity include Linear Support Vector Classifier (Linear SVC), Support Vector Classifier with Radial Basis Function Kernel (SVC with RBF Kernel), Logistic Regression, Random Forest, Decision Tree, Naive Bayes, Stacking Classifier (Random Forest + SVM with Logistic Regression), and Voting Classifier (Random Forest + Perceptron Algorithm + Boosting. The performance of these algorithms is evaluated using a publicly available dataset consisting of mobile money transactions. We compare the accuracy, precision, recall, F1-score. Our suggested methods demonstrate the capability to accurately identify fraudulent transactions while keeping the number of false positives reasonably low.
Effectual Cryptography Approaches for Cloud Storage Image Encryption

In recent years and with the great progress of the use of cloud computing and their uses that covered most aspects of modern life as well as provide a variety of services, such as the formation of computing resources, cost control, sustainability, mobility and service flexibility. However, there are challenges to cloud computing, the most important of which is data security and transmission. Cryptographic is the science of protecting data by converting data (plain text) into an incomprehensible format (cipher text) for unauthorized individuals through the use of mathematical techniques. This paper provides work for the most common encryption algorithms that are utilized to encryption of data in cloud computing and presented some of papers that based on the most common cryptographic techniques such as DES, 3DES, Blowfish, AES, RSA, D-H, ECC and others. This way paves the way for finding the suitable encryption algorithm to protect the data in the cloud environment. The AES outperforms the other algorithms in term of encryption time.
Hiding Video in Images: Harnessing Adversarial Learning on Deep 3D-Spatio-Temporal Convolutional Neural Networks

This work proposes end-to-end trainable models of Generative Adversarial Networks (GAN) for hiding video data inside images. Hiding video inside images is a relatively new topic and has never been attempted earlier to our best knowledge. We propose two adversarial models that hide video data inside images: a base model with Recurrent Neural Networks and a novel model with 3D-spatiotemporal Convolutional Neural Networks. Both the models have two distinct networks: (1) An embedder to extract features from the time variate video data and inject them into the deep latent representations of the image. (2) An extractor that reverse-engineers the embedder function to extract the hidden data inside the encoded image. A multi-discriminator GAN framework with multi-objective training for multimedia hiding is one of the novel contributions of this work.
Video Colorization Using Modified Autoencoder Generative Adversarial Networks

Colorization is a visual tool to convey ideas and emotions, especially when it comes to entertainment or film making. It is easy to perceive information from colored images rather than black and white or grayscale images. In the modern digitization era, colorization is even more important, because it helps in-person tracking, objects detection and provides an edge to the new state-of-the-art technologies. This paper presents a method for colorizing grayscale videos using Modified Autoencoder Generative Adversarial Network. The proposed generative model uses modified autoencoders by embedding a DenseNet based architecture. Its property of concatenation of all previous layers helps to increase the color consistency with the increase in the selection of color for an object. Subjective and quantitative results analysis is done using several performance measures. Experimental results show that the proposed model provides an improved and consistent mapping of colors on the video dataset.
Self Similarity Matrix Based CNN Filter Pruning

Abstract. In recent years, most of the deep learning solutions are targeted to be deployed in mobile devices. This makes the need for development of lightweight models all the more imminent. Another solution is to optimize and prune regular deep learning models. In this paper, we tackle the problem of CNN model pruning with the help of Self-Similarity Matrix (SSM) computed from the 2D CNN filters. We propose two novel algorithms to rank and prune redundant filters which contribute similar activation maps to the output. One of the key features of our method is that there is no need of finetuning after training the model. Both the training and pruning process is completed simultaneously. We benchmark our method on two of the most popular CNN models - ResNet and VGG and record their performance on the CIFAR-10 dataset.
Share-GAN: A Novel Shared Task Training in Generative Adversarial Networks for Data Hiding

This manuscript proposes a novel training process for a pre-existing architecture of GANs to enable task-sharing or multi-tasking of sub-modules. We explore the application of data hiding to analyse the model’s performance. Share-GAN consists of an embedder network (to encode secret messages into a cover), a U-Net autoencoder (that consists of encoder and decoder). The embedder’s encoder network is custom trained to act as an extractor network (to extract the hidden message from the encoded image). The multi-tasking of the embedder’s encoder is, to our knowledge, never explored prior to this work. The encoded image is subjected to multiple attacks to analyse the noise sensitivity of the model. The proposed method shows inherent robustness towards attacks like Gaussian blurring, rotation, noise, and cropping. However, the model can be trained on any possible attacks to reduce noise sensitivity further. In this manuscript, we considered images as both messages and containers. However, the method can be extended to any combination of multi-media data.
Combining Deep-Learned and Hand-Crafted Features for Segmentation, Classification and Counting of Colon Nuclei in H &amp;E Stained Histology Images

Colon nuclei detection within Haematoxylin & Eosin (H &E) stained histology images is important to mitigate abnormalities or diseases like colon cancer in its early stages. Therefore, the objective of the proposed work is to perform the colon nuclei segmentation, classification, and counting of the nuclei or cellular composition. This paper presents a hybrid deep learning model that combines deep-learned features obtained from the ResNet50-based model with the handcrafted features. The proposed work uses the horizontal and vertical net (HoVer-Net) as baseline model presented by the CoNIC2022 challenge team and modified it to incorporate handcrafted features obtained using two feature descriptors such as local binary patterns and the histogram of oriented gradients. The proposed model is trained and validated using the CoNIC2022 dataset. The proposed model shows a significant improvement over the baseline HoVer-Net model in segmentation and classification as well as nuclei counting tasks. The proposed work demonstrates the usefulness of combining deep features with the handcrafted features in the colon nuclei identification task.
PELATIHAN PEMBUATAN WEBSITE TOKO ONLINE DAN LANDING PAGE MENGGUNAKAN CANVA DI SMK TRITECH INDONESIA

Community service is the Tri Dharma of Higher Education which has now become an obligation and is an agenda for routine activities every semester as a responsibility and thought to the community, especially the community in the field of education. When viewed also, the current curriculum sharpens students to think creatively, skillfully, independently, and to race on technology. To improve the performance of education in the future, information systems and information technology are needed, which not only function as a means of support, but as the main weapon to support the success of the world of education so that they can compete in the global world. One of them is the field of graphic design. If we think that image design can only be done by a graphic designer, now we can handle that freely using the Canva application, which is an online design program that provides various visual content such as websites, landing pages, presentations, resumes, posters, pamphlets, brochures, graphics, infographics, banners, bookmarks, bulletins, and so on. Through training for Tritech Indonesia Vocational School students, it is hoped that they can use the Canva application to create an online store website and its landing page correctly as a support in the teaching and learning process.
Innovative teaching technologies in higher educational institutions

The article provides a theoretical analysis and generalization of the data of scientific publications on the process of technologization of education at the university, improving the quality of education, rethinking the relationship between the subjects of education, optimizing material and technical costs. Various views of scientists on the definition of the concept of pedagogical technology are given. Shows the differences between teaching technologies and interactive teaching methods, ways to improve and measure the quality of teaching based on the introduction of the educational technology “Three-dimensional methodical teaching system (TMTS)” into the educational process. Criteria for assessing student achievement (level of competence) based on the qualimetric approach are described. Effective ways of solving such topical issues as: tracking the trajectory of a student's development through monitoring their educational achievements within the studied disciplines, identifying gifted students and creating conditions for their independent development are recommended.
Feature Fusion and Multi-Stream CNNs for ScaleAdaptive Multimodal Sign Language Recognition

Sign language can be defined as a visual-spatial language used by deaf and hard-of-hearing individuals for everyday communication. However, the use of sign language is often limited by the lack of technology capable of recognizing and interpreting it. Our research aims to create a method for recognizing static sign language, a crucial aspect of sign language communication. We propose using multi-stream Convolutional Neural Networks (CNNs), incorporating local scale awareness and multimodality, to recognize static Hand signs more effectively. The local scale awareness aspect is achieved through spatial pyramidal pooling (SPP), which allows CNN to obtain and merge features from different scales. The multimodal aspect is achieved using Color (RGB), depth information, and extracted image features as these modalities provide complementary information for recognizing SL. We also introduce a method for feature extraction based on the fusion of extracted features from the Gabor Filter and the Local Binary Pattern (LBP) method. The multi-stream CNN architecture is used to effectively combine the information from multiple sources, whereas the proposed feature fusion method provides extensive details about image features, resulting in improved recognition accuracy. We achieved a high accuracy rate in recognizing individual signs and our approach has the potential to improve accessibility for hard-of-hearing communities.
Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches

Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general mass.The primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches.
SignBot: An Automated Robot with Efficient Net Model for Signature Creation

Because of their ease of use and one-of-a-kind character, handwritten signatures are one of the first forms of biometric verification and remain one of the most popular methods utilized in administrative and financial organizations. Authentication mechanisms have also progressed alongside the development of technology. These days, handwritten signatures are used extensively for a wide variety of reasons, including those that require individual validation in various forms, it is exceedingly difficult for the authorized person to sign a large number of papers at once or to sign when he is not physically present at the location. In this research, we presented a SignBot, which will draw the person's signature when verification from the appropriate authorities has been completed. In addition to this, the detection of the signature is performed with the use of an EfficientNet Model (ENM). Comparison and analysis were performed on the acquired human and robot sketching trajectories. Our model provided a greater performance compared to the previous model when it was evaluated alongside the model that was already in use.