Reviewerly/exHarmony huggingface dataset:
Kannada Word Detection in Heterogeneous Scene Images

Text extraction from scene images has started gaining a lot of traction in recent years in the computer vision field as its applications is manifold. One of its sub-categories is scene text detection. Factors like complex backgrounds, curvature, orientation, image quality, and various font styles and sizes makes it a difficult task. Moreover, building a single language scene text detector in a multilingual setting, especially Indian scripts, is more challenging in contrast to a general text detector. In this Work We attempt the task of Kannada text localization as Well as Word detection in natural scenes, Which is underexplored in comparison to other Well-known Indian languages. To achieve this, We fine tune a You Only Look Once (YOLOv4) based object detection model on our Kannada scene text images dataset collected. Experiments on local boards and sign images captured on smartphone, showed a mean average precision of 93.5% and per image average detection speed of 30 milli-seconds; pointing towards real time use and integration With mobile applications.
Optical Character Recognition for Handwritten Telugu Text

Optical Character Recognition (OCR) is a process of digitalization of scanned documents. OCR for Telugu language is challenging due to intricacy of characters with almost similar structure. As Telugu has spaces between the characters in a word, character level training decreases the complexity in training dataset and enhances the character recognition rates. The major confronts for developing Telugu OCR especially for handwritten text is character level segmentation which depends of a person’s handwritten style. To deal with Unicode training problem that arises for most of Indic scripts, we have converted label to a string during training and after recognition they are inverse mapped. For feature extraction we have used ResNet-50, which captures the small variances in the character there by increasing the recognition rates. The character level recognition rates of 93.5% are achieved and word level recognition rates of 84% are achieved.
A real-time e-commerce accessories recommender system by coupling deep learning and histogram features

Online shopping is a form of electronic commerce that enables customers to purchase products directly from the seller over the internet using a web browser or a mobile application. With the growing number of research papers on Deep Learning (DL)-based recommendations, DL models have proven to be effective in Recommender systems. Hence in this paper, relevant product accessories recommendation is proposed using deep learning and histogram features. The proposed recommendation describes the novel way of content-based filtering which divides the work into two phases. In phase 1, the implemented approach determines the color and category of the product. In phase 2, the recommendation system retrieves the matching accessories for the chosen product. For phase 1, Decision Tree, Linear Regression, Logistic Regression, Naïve Bayes, Long Short-Term Memory (LSTM), and a novel method of Stack dense Recurrent Neural Network (RNN) along with the color histogram is applied to identify the category and color of the product. It is justified that the proposed technique of stack dense RNN achieves 98% accuracy. In phase 2, from the Machine Learning (ML) perspective, Random Forest (RF) is employed for the recommendation. The RF model finds the matching accessories for the chosen product and retrieves the top relevant items.
Performance Analysis of IIC techniques for Brain MR-images

This paper aims to solve the issue of intensity inhomogeneity in brain MR-images. Intensity inhomogeneity causes the development of false pixel intensities within an MRimage making its diagnosis difficult. Such images could even lead to the development of incorrect diagnosis. The issue of intensity inhomogeneity also causes a major issue in image segmentation. Although many standard image denoising methods exist and are used for such correction purposes, these are not all as focused and specific to this issue. In this paper, an attempt to develop a comparative study of a few commonly used standard methods and a newly suggested deep learning solution for this problem is made. All methods given in this paper are compared in the same platform using the same performance metrics to get a fair idea of which methods yield the best results. In this paper, python programming language is used to compare the methods. The methods included in this study are InhomoNET, BM3D, WNNM, NLM Algorithm, FFT denoising, Bayes denoising and VisuShrink denoising. Data used for all methods was obtained from the simulated brain MRI data available on BrainWeb. In addition to this, InhomoNET is also provided with some real brain MRI data obtained from Kaggle databases for its training since it is a deep learning solution. Each method is compared using the performance metrics SSIM, MSSSIM, and PSNR. It is found that the methods BM3D and NLM by far obtain the best results.
Underwater Image Restoration Using Color Correction and Empirical Mode Decomposition

Underwater imaging plays a significant role in discovering assets in the ocean. Underwater images are not visible due to the presence of noise and artifacts in the sea. The author proposed a technique to resolve this problem. In this article, the degraded underwater image applies to empirical mode decomposition after color correction to enhance the image. The suggested approach is compared to the existing state-of-the-art method. The picture has been taken from the UIEB data set for qualitative and quantitative analysis. Compared to traditional procedures, the suggested method produces superior enhanced images with improved visual quality.
Handwritten OCR for word in Indic Language using Deep Networks

A large number of Indian documents are handwritten and India is a diverse nation with many languages. These handwritten documents contain important historical and cultural information which needs to be preserved by converting to digital format. The major problem is everyone has unique handwriting with different styles of writing. To address this problem, we have trained Handwritten Optical Character Recognition (HOCR) in eight Indian languages i.e. Bangla, Gujarati, Gurumukhi, Hindi, Kannada, Odia, Telugu, and Urdu. The datasets IIIT-HW-Dev and IIIT-HW-Telugu refer to a Devanagari dataset and a Telugu dataset respectively. The IIITINDIC-HW-WORDS consists of 872K handwritten words written in 8 Indic scripts by 135 writers. Devanagari and Telugu datasets are comprised of 95K and 120K handwritten words respectively. Tamil and Malayalam languages are excluded due to issues in the IIIT-INDIC-HW-WORDS dataset. The paper describes how the CNN-Transformer architecture leverages visual and textual features to perform OCR tasks in different languages. The model takes word images as input then CNN generates visual features, and feeds them to the transformer decoder for text generation. An encoder ResNet <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</inf> and a decoder from a transformer have been used for all eight languages to evaluate the performance of this architecture. This architecture performed best in Kannada with just a 1.5% character error rate.
A Keyframe Extraction Using HDBSCAN With Particle Swarm Optimization

Currently video summarization is an emerging research field. Video summarization which is also termed as Keyframe extraction provides an abstract view of the original video. It aims in finding the important events and objects in a video to give an overview of the original video. This research work focuses on fimding the keyframes of the video through the proposed Keyframe Extraction using Particle Swarm optimization. Two features namely color histogram and histogram of Local Binary Pattern are extracted and clustered using Hierarchical Density-Based Spatial Clustering of Applications with Noise. The parameters of clustering are optimized using Particle Swarm optimization. The experimental results show that the proposed method gives better results when compared to the existing methods.
FPGA Implementation Of a High Throughput Low Power Advanced Encryption Standard (AES-128) Cipher

Ensuring secure transmission and storage of digital information is critical for any organization to function properly. To address this issue, encryption algorithms are commonly used. Advanced Encryption Standard (AES) has been globally adopted as the mainstay cipher algorithm for securing transmission networks and storage devices due to its easy implementation and compatibility with both hardware and software applications. Another reason for its widespread popularity is its uncompromisable nature against existing brute force attacks, making it practically unbreakable on existing computing power. AES implementation for battery operated devices requires an algorithm with low power consumption and high-speed encryption/decryption of digital data. This paper proposes an FPGA implementation of a high throughput parallel pipelined 128-bit AES algorithm with a low power key expansion mechanism for iterative stages. A 128-bit symmetric key has been used for undertaking 10 rounds of transformations. All the encryption and decryption transformations are simulated using iterative design methodology in order to minimize hardware consumption. Xilinx Artix-7 FPGA device is used for hardware evaluation and Verilog HDL for programming. Simulation and synthesis task has been performed on Xilinx Vivado v2021.1 IDE. The results exhibit high-rate encryption of 68 Gb/s and low energy consumption of 7 pJ/bit.
Performance Improvement of Water Body Segmentation by DeeplabV3+Using Two Dimensional Variational Mode Decomposition

Monitoring changes in surface water bodies and other earth surface features uses remote sensing data. The DeeplabV3+ network is an encoder decoder based deep neural network that is widely used to segment images with good precision. To improve the predictions made by the DeeplabV3+ model, a novel technique based on the two-dimensional variational mode decomposition (2D-VMD) is proposed in the present work. Sentinel 2A/B images dataset from Kaggle is used for this study. The images and their corresponding annotations are also available. The masks were obtained using the index known as Normalized Water Difference Index (NDWI). From 2841 images, we cropped 100 x100 subsets resulting in 1,50,204 images. The proposed method is found to be effective in improving the predictions made by DeeplabV3+ model. With respect to the images considered for the study, the average F1 score increased from 0.33 to 0.47. The average Jaccard score increased from 0.21 to 0.34.
Color Attenuation Prior Based Single Image Dehazing

In today’s technologies, digital images of the real world play a crucial role. Any mode of transportation can employ digital imagery to control traffic. Clearly stated Images are essential for traffic monitoring, but our environment is full of dust particles, fog, and other disturbances depending on the weather. Haze is the aggregate name for these disturbances that scatter ambient light and subsequently affect the clarity of the images that are taken. The images that we take often have haze, thus we refer to them as hazy images.The collected images must have the haze removed in order to be monitored. Dehazing, often known as the process of removing haze, can be carried out in a variety of methods. In this research, we employ the Haze lines Prior method of estimating atmospheric light along with the Color Attenuation Prior method for dehazing. The databases O-haze, I-haze,FRIDA and some hazy real world images without ground truth images are used to validate the proposed method. By analyzing PSNR and SSIM values on a pair of ground truth and hazy images, the algorithm’s effectiveness is evaluated.The performance metrics of the suggested algorithm produced outcomes that were superior to those of the existing methods.
Semantic Communications enabled Intelligent Wireless Networks

The current wireless technology research focuses on developing a ubiquitous virtual human society. This will result in provisioning advanced use cases/applications requiring enormous bandwidth for which research community envisions employing Tera-Hertz (THz) frequency. Such a trend is mainly due to pushing for efforts to reproduce the transmitted data at the receiver. However, numerous undesired effects occur at THz range resulting in major performance bottlenecks primarily due to limited resources. As an alternate, semantic communications, which focus on the context/meaning of the transmitted data, has received considerable recent research attention since it can aid in reducing the bandwidth requirements. This article presents a semantic communications enabled source and channel coding model for the intelligent wireless networks. The simulation results demonstrate that the proposed semantic model achieves better performance compared to the existing syntactic schemes.
A vision-based litter detection and classification using SSD MobileNetv2

Municipal solid litter generation has steadily increased significantly in recent years. To prevent waste from collecting in the environment, it is beneficial to clean up wastes and rubbish using autonomous cleaning equipment such as unmanned surface vehicles. Cleaning efficiency requires a high-accuracy and reliable object detection system. Many machine learning techniques are explored so far to produce litter recognition systems. There are few studies on the effectiveness of cuttingedge deep learning object detection algorithms in the domain of litter localization and detection. We propose a detection and classification module for five different waste objects in this research. The dataset is created by gathering photos from various sources and under various environmental conditions at our Institute. In order to enhance the quantity of data, data augmentation techniques are used. Image processing and feature extraction steps are applied to enhance the performance of the proposed model. The SSD MobileNetv2 model is employed for litter recognition which detects and identifies distinct contaminants and hazardous waste materials with a mean Average Precision (mAP) of 0.84. The proposed module aids in environmental cleanup by efficiently detecting the waste objects present in the surrounding environment.
A New GAN-based data augmentation method for Handling Class Imbalance in Credit Card Fraud detection

One of the most common cybercrimes that people encounter is credit card fraud. Systems for identifying fraudulent transactions that are based on intelligent machine learning are particularly successful in real-world situations. Nevertheless, when creating these systems, machine learning algorithms face the issue of imbalanced data or an unbalanced distribution of classes. Because of this, balancing the dataset becomes a crucial sub-task. A review of cutting-edge methods highlights the necessity for a thorough assessment of class imbalance management techniques in order to create a smart and effective system to identify fraudulent transactions. The goal of the current study is to compare several strategies for dealing with class imbalance. Therefore, the present study compares the performance of our novel K-CGAN method with SMOTE, B-SMOTE, and ADASYN in terms of Recall, Fl-score, Accuracy, and Precision. The result shows that novel K-CGANs generated high quality test dataset and performs better as compared to other resampling techniques.
Distributed variational Bayesian adaptive filtering for randomly delayed measurements and unknown noise statistics in multi-sensor networked systems

To deal with randomly delayed measurements and unknown noise statistics in multi-sensor networked systems, a distributed variational Bayesian adaptive cubature Kalman filtering algorithm (DVB_ACKF) is presented in this paper. To compensate for the effects induced by randomly delayed measurements, a refined measurement reorganization method is designed via a two-stage estimation strategy. Specifically, we first estimate the time delay step. The measurements are then rearranged based on the estimated results from the first stage. Then, a novel noise estimator with the sliding-window and variational Bayesian method is developed to estimate the mean and covariance of the unknown noise. The network topology attribute and measurement accuracy have been combined to eliminate the distributed fusion error. The tuning parameter and forgetting factor as well as their bounds are analyzed. Furthermore, numerical simulations are carried out to verify the superior performance of the developed algorithm.
Effective method for making Chinese word vector dynamic

Word vector is an important tool for natural language processing (NLP) tasks such as text classification. However, existing static language models such as Word2vec cannot solve the polysemy problem, leading to a decline in text classification performance. To solve this problem, this paper proposes a method for making Chinese word vector dynamic (MCWVD). The part of speech (POS) is used to solve the ambiguity problem caused by different POS. The POS structure graph is constructed and the syntactic structure information of POS features is extracted by GCN (Graph Convolutional Network). POS vector and word vector are concatenated into PW (POS-Word) vector. Parametric matrix is added to improve the fusion effect of POS and word features. Multilayer attention is used to distinguish the importance of different features and further update the vector expression of word vectors about the current context. Experiments on Chinese datasets THUCNews and SogouNews show that MCWVD effectively improves the accuracy of text classification and achieves better performance than CoVe (Context Vectors) and ELMo (Embeddings from Language Models). MCWVD also achieves similar performance to BERT and GPT-1 (Generative Pre-Training), but with a much lower computational cost and only 4% of BERT parameters.
Low-rank and global-representation-key-based attention for graph transformer

Transformer architectures have been applied to graph-specific data such as protein structure and shopper lists, and they perform accurately on graph/node classification and prediction tasks. Researchers have proved that the attention matrix in Transformers has low-rank properties, and the self-attention plays a scoring role in the aggregation function of the Transformers. However, it can not solve the issues such as heterophily and over-smoothing. The low-rank properties and the limitations of Transformers inspire this work to propose a Global Representation (GR) based attention mechanism to alleviate the two heterophily and over-smoothing issues. First, this GR-based model integrates geometric information of the nodes of interest that conveys the structural properties of the graph. Unlike a typical Transformer where a node feature forms a Key, we propose to use GR to construct the Key, which discovers the relation between the nodes and the structural representation of the graph. Next, we present various compositions of GR emanating from nodes of interest and α-hop neighbors. Then, we explore this attention property with an extensive experimental test to assess the performance and the possible direction of improvements for future works. Additionally, we provide mathematical proof showing the efficient feature update in our proposed method. Finally, we verify and validate the performance of the model on eight benchmark datasets that show the effectiveness of the proposed method.
Tracking and handling behavioral biases in active learning frameworks

The computational capabilities of AI engines integrated with human knowledge and experience can help create intelligent human-in-the-loop (HITL) decision systems. Such synergistic frameworks improve the decision-making process and make AI-enabled systems more robust, accurate and smarter. In safety-critical applications that require a certain level of human supervision, human and AI engine errors can be costly. However, modeling human behavior in collaborative human-AI decision setup is not straightforward. Humans use cognitive mechanisms and decision heuristics to process information and make decisions under uncertainty. This paper, for the first time, presents a systematic framework for modeling, tracking and adaptation of behavioral biases in a collaborative decision environment within an "Active Learning" context. The proposed framework is validated using experiments conducted on a real-world pancreatic cancer dataset. We consider five different learning scenarios based on different grades of human experts and compare the performance of bias-aware decision models with naive models. It is observed that bias-aware models improve the classification accuracy of decision models by upto 16%.
LL-CSFormer: A Novel Image Denoiser for Intensified CMOS Sensing Images under a Low Light Environment

Intensified complementary metal-oxide semiconductor (ICMOS) sensors can capture images under extremely low-light conditions (≤0.01 lux illumination), but the results exhibit spatially clustered noise that seriously damages the structural information. Existing image-denoising methods mainly focus on simulated noise and real noise from normal CMOS sensors, which can easily mistake the ICMOS noise for the latent image texture. To solve this problem, we propose a low-light cross-scale transformer (LL-CSFormer) that adopts multi-scale and multi-range learning to better distinguish between the noise and signal in ICMOS sensing images. For multi-scale aspects, the proposed LL-CSFormer designs parallel multi-scale streams and ensures information exchange across different scales to maintain high-resolution spatial information and low-resolution contextual information. For multi-range learning, the network contains both convolutions and transformer blocks, which are able to extract noise-wise local features and signal-wise global features. To enable this, we establish a novel ICMOS image dataset of still noisy bursts under different illumination levels. We also designed a two-stream noise-to-noise training strategy for interactive learning and data augmentation. Experiments were conducted on our proposed ICMOS image dataset, and the results demonstrate that our method is able to effectively remove ICMOS image noise compared with other image-denoising methods using objective and subjective metrics.
IRelNet: An Improved Relation Network for Few-Shot Radar Emitter Identification

In future electronic warfare (EW), there will be many unmanned aerial vehicles (UAVs) equipped with electronic support measure (ESM) systems, which will often encounter the challenge of radar emitter identification (REI) with few labeled samples. To address this issue, we propose a novel deep learning network, IRelNet, which could be easily embedded in the computer system of a UAV. This network was designed with channel attention, spatial attention and skip-connect features, and meta-learning technology was applied to solve the REI problem. IRelNet was trained using simulated radar emitter signals and can effectively extract the essential features of samples in a new task, allowing it to accurately predict the class of the emitter to be identified. Furthermore, this work provides a detailed description of how IRelNet embedded in a UAV was applied in the EW scene and verified its effectiveness via experiments. When the signal-to-noise ratio (SNR) was 4 dB, IRelNet achieved an identification accuracy of greater than 90% on the samples in the test task.