Reviewerly/exHarmony huggingface dataset:
Dual-path deep neural network architecture with explicit features for offline signature recognition

In this work, with the aim of separating the genuine and forgery samples of the signature, we developed a new dual-path architecture using deep neural network and a traditional descriptor for feature extraction toward an automatic offline signature recognition. The proposed approach is an extended version of VGG-16, which is enhanced using our two paths architecture. In the first path, we explore features using a deep convolutional neural network, and in the second path, we discover global features using a traditional heuristic approach. For classical feature extraction, an innovative idea is presented, in which the descriptor is stable for some common changes, such as magnification and epoch, in the signature samples. Our traditional approach extracts global features that are stable with rotation and scaling. The proposed method was analyzed and compared with three well-known databases of CEDAR, UTsig, and GPDS signature images. A dual-patched model architecture is significantly more accurate than the basic model when compared to the basic model. In agreement with the proposed method, the best signature recognition accuracy on the CEDAR database is in the range of 98.04-99.96%, while the best recognition accuracy on the GPDS and UTsig databases is 98.04% and 99.56%, respectively. Furthermore, this technique has been compared with four popular methods such as VGG-S, VGG-M, VGG-16, and LS2Net. The presented approach achieved a recognition rate of 99.96% using a diverse signature database. Experimental results demonstrate that the proposed VGG-16 based signature recognition system is superior over texture-based and deep-learning methods and also outperforms the existing state-of-the-art results in this regard. It is expected that the proposed system will provide fresh acumen to the researchers in developing offline signature verification and recognition systems in other scripts.
Image and Video D’encryption using Gray Pattern based algorithm

In the present era of communication, huge amount of multimedia data is being transmitted and received. Security and privacy of the data are important issues that must be addressed. Therefore, there is need of encryption to prevent breach of multimedia data. In this paper we bring out a new encryption algorithm, efficient for image as well as video. The algorithm mainly focuses on the confusion amongst the image pixels with the shuffle key generated by the gray pattern. Key generated by the gray pattern is observed to be more resistant than other random generated keys. The reason lies in the efficiency of gray pattern to create several pixel permutations. Our algorithm has achieved ideal results for gray scale, color, DICOM, thermal and UV images. The algorithm can be efficiently applied for video encryption. The performance of the proposed algorithm is compared with the existing algorithms by checking the metrics like Histogram, Entropy, Key sensitivity, Number of Pixel Change Rate, Unified Average Change in Intensity and Peak Signal to Noise Ratio. The algorithm has shown good security levels and outperformed existing algorithms. A Graphical User Interface has been designed to showcase all the results and metric which is an added advantage for better analysis of algorithm.
Handwritten Devanagari Word Detection and Localization using Morphological Image Processing

In text analysis systems, text detection and localization techniques are preliminary steps for further text recognition tasks. Finding the specific text information in the image is important as it has many significant real-time applications. The available techniques for text detection and localization work well for printed text, but their performance is not good for handwritten text present in an image. Also, the available modern deep learning techniques require large datasets for training. S0, if small handwritten data images are available, then image processing and an effective morphologybased technique for text detection and localization can be used. In this paper, these techniques are used for handwritten documents written in the Devanagari script.
MDCF_Net: A Multi-dimensional hybrid network for liver and tumor segmentation from CT

The segmentation of the liver and liver tumors is critical in the diagnosis of liver cancer, and the high mortality rate of liver cancer has made it one of the most popular areas for segmentation research. Some deep learning segmentation methods outperformed traditional methods in terms of segmentation results. However, they are unable to obtain satisfactory segmentation results due to blurred original image boundaries, the presence of noise, very small lesion sites, and other factors. In this paper, we propose MDCF_Net, which has dual encoding branches composed of CNN and CnnFormer and can fully utilize multi-dimensional image features. First, it extracts both intra-slice and inter-slice information and improves the accuracy of the network output by symmetrically using multi-dimensional fusion layers. In the meantime, we propose a novel feature map stacking approach that focuses on the correlation of adjacent channels of two feature maps, improving the network's ability to perceive 3D features. Furthermore, the two coding branches collaborate to obtain both texture and edge features, and the network segmentation performance is further improved. Extensive experiments were carried out on the public datasets LiTS to determine the optimal slice thickness for this task. The superiority of the segmentation performance of our proposed MDCF_Net was confirmed by comparison with other leading methods on two public datasets, the LiTS and the 3DIRCADb.
Da <i>Amers</i> di Saint-John Perse Brani e note da una traduzione <i>in fieri</i> del poema

Excerpts from a translation in progress of Amers by Saint-John Perse. Notes on the translation of Amers . Notes on the poetics of Saint-John Perse. Notes on the theory of language.
Studying the Use of Phonetic Language Script in a Limited Training Environment for Text-to-Speech

This study looks into a variety of characteristics of text-to-speech (TTS) model training and transfer learning with a phonetic language script. When compared to training using a non-phonetic script, training with a phonetic language script appears to speed up the process. In both Hindi and Indian English evaluation data, transfer learning with phonetic writing (in Devanagari script) leads to remarkable improvements in the number of pronunciation errors (87.5% and 16.67% relative). As a result, a case is made for publishing models pre-trained on phonetic language transcripts rather than the models that have been pretrained on non-phonetic transcripts for the purpose of transfer learning. Going further, a mix-lingual training approach is also explored to solve the problem of transforming code-switched text to speech. It is emphasized that even with commercial text-to-speech solutions, the pronunciation of Indian proper nouns contained in English text is a difficulty, which has been addressed in the current framework.
Image flare removal using Deep Convolutional Generative Adversarial Networks

In this paper, a novel approach is proposed for removing flare artefacts from images using a two-stage method. Flare is a phenomenon that occurs when a strong source of light hits the lens of the camera. The converging light rays are scattered due to imperfections in the lens, creating a glare or halo effect in the image. This degrades the image quality and can make it challenging to extract useful information from the image, making it problematic for industries such as autonomous driving and medical robotics that rely heavily on image processing algorithms. To solve this problem, we utilize a flare removal model based on a conditional generative network. This model is trained on a dataset created using computer vision techniques. The dataset creation process involves scraping internet images of different scenes under various lighting conditions without the presence of flare. These images are then pre-processed to extract the flare-corrupted images, which serve as input for the flare removal model, which produces flare-free output. However, in order to make the model more generalizable and able to handle real-world data, we incorporate the flare removal module in a self-supervised setting along with a flare addition module. This allows us to fine-tune the flare removal model using real images and improve its performance. The flare removal module achieves an SSIM score of 0.916, which was previously 0.887. Our approach can be used in a variety of applications, such as autonomous driving and camera photo optimization, and has the potential to be further improved by incorporating a flare mask detector module.
Experimental results from a proof-of-concept method for accurate volumetric estimation of hematoma in asymmetrically formed objects comprising solid materials as impediments.

Hemorrhage, also known as internal bleeding, is a potentially fatal disorder requiring immediate medical attention based mainly on its seriousness. Medical imaging can qualitatively identify the extent of hemorrhage, but it is usually complicated to measure the amount of posthemorrhagic blood deposited inside the organs. Based on an imaging dataset, this study focuses on estimating blood volume with precision. A proof-of-concept (POC) phase has been designed for it. In Digital Imaging and Communications in Medicine (DICOM) format, images of liquid-filled vessels with irregular forms and hard, solid impediments inside them were obtained from computed tomography (CT) scans. These containers store various concentrations of aqueous iodine solution for our contrast CT scans. The estimated results of two novel volume quantification using slicing approaches and algorithms with minimum user input were designed, and they were contrasted with measurements of the liquid present.
Indian Sign Language Interpretation using Convolutional Neural Networks

Sign language is the most common means of communication among the speech- and hearing-impaired. Just like all other languages, tools are being developed for interlanguage translation from sign language to text; however, the complexity of the sign language makes it difficult to create a computer vision model. The focus of this study is to tackle the problem with a vision-based approach using deep learning. In this work, an 8-layer Convolutional Neural Network (CNN) aimed at sign language recognition has been proposed. The model extracts feature from input images using convolutional filters before passing them to dense layers. A validation accuracy of 99.34% has been achieved on a self-created dataset comprising of 52 classes. The model has also been tested on a publicly available dataset to evaluate its efficacy.
Solving Combined Field Integral Equations with Physics-informed Residual Learning

This study applies physics-informed residual learning to compute electromagnetic scattering by perfect electric conductors (PECs). The formulation is based on the combined field integral equation. The graph neural network is used for its advantage of computing based on unstructured data. This allows machine learning schemes to handle triangular meshes. The physics-informed residual learning framework has the advantage of integrating information from both physics and data. Therefore, it provides a good generalization ability. After carefully preparing the dataset and finely training the network parameters, we can model electromagnetic scattering by various PEC targets with a relative error lower than 0.035. This study verifies the feasibility of further enhancing computation speed in electromagnetic modeling by combining data and physics.
La Bibbia in bresciano di Achille Platto: da Caino al Calvario

Poetry in Brescian dialect has flourished in recent decades. Among the authors, Achille Platto stands out for his originality. In the various poems collected in the volume Bibbiù Platto moves from a light-hearted rewriting of the Old Testament in a peasant key to more complex tones, partly redeeming the figure of Cain and arriving at a tragic register that is never rhetorical in the Passion, comparable to the venerable Passiù in the fourteenth century Brescian vernacular or to the expressionistic sculpture of Beniamino Simoni’s Via crucis camuna . The essay follows this path, which will lead to the dramatic masterpiece of Aqua trobia (1998), the poet's sorrowful and vibrant protest against an absent or indifferent God.
Development of Hybrid Image Caption Generation Method using Deep Learning

The image captioning is a process of generating descriptive sentence for a given image in human understandable language and such sentence is known as caption of the image. The automatic image caption generated is a result of deep analysis performed on the image which involves detecting objects in the image as well as the relationship between them. The generated caption should be meaningful and related to the context of the image. Image captioning techniques are the most researched area. It involves expertise of computer vision (CV), natural language processing (NLP) and artificial intelligence (AI). This paper proposes a novel hybrid approach for higher accuracy of image captioning. A detailed review of traditional methods and methods based on deep learning developed for image captioning are analyzed. The different method has significantly different Bilingual Evaluation Understudy (BLEU) scores on similar images. Thus, the hybrid approach is developed to get high BLEU score for each input images. The data set generation, implementation of hybrid approach, and the challenges along with the future work are discussed.
Development of an modified Ant Colony Optimization algorithm for solving path planning problems of a robot system

In the modern world of high-speed technologies, where every operation needs to be performed instantaneously and more efficiently, scientists and engineers have created a Bioinspired algorithm to solve the problems encountered in realworld activities. The Ant Colony optimisation (ACO) algorithm is one such solution that assists in solving the problems of robot path planning. In this work-in-progress article, we propose a new way of using the ACO algorithm which ensures solving the problems encountered in traditional ACO algorithms. This algorithm was tested on two environments to examine output efficiency and computational output time. The results show that the proposed ACO algorithm is completely efficient in small-scale environments and remarkably better results were observed on testing it in the bigger-scale environment. The evaluations prove that the ACO algorithm for path planning can provide rapid path planning with acceptable results and for future development can be integrated with the robot system to test it in any real-world scenarios by increasing the number of ants.
Dante e il dialetto veneziano

This paper focuses on the points of Dante Alighieri’s works where Venice is evoked, or where Dante uses terms of Venetian origin or pertinence. A short inventory of these occurrences is provided, and the most interesting words (they are in particular nautical terms like ‘arsenal’, ‘artemon’, ‘burchio’, ‘impegolar’, ‘palmizar’, ‘pegola scaula’, ‘terzariol’) are presented through their respective articles written in the frame of the lexicographical project VEV – Vocabolario storico-etimologico del veneziano .
Ancient Indian Script Transliteration Using GANs

This study aims to investigate the application of Generative Adversarial Networks (GANs) for transliterating ancient Indian script characters into present day devanagari script characters. The ancient scripts Nandinagari and Sharda are known for their ornate and complex forms, making it a challenge to read and understand for modern readers. The goal of this research is to use GANs to train a model that can convert ancient Indian script images like Nandinagari and Sharda to present day Devanagari script images, which can be useful for preserving and digitizing ancient texts, and making them more accessible to modern readers. The study will evaluate the ability of Generative Adversarial Networks to accurately recognize and transliterate ancient script characters, and will provide insights into the potential of this approach for preserving cultural heritage and digitizing and transliterating historical texts to modern scripts.
Test-based and metric-based evaluation of code generation models for practical question answering

We performed a comparative analysis of code generation model performance with evaluation using common NLP metrics in comparison to a test-based evaluation. The investigation was performed in the context of question answering with code (test-to-code problem) and was aimed at applicability checking both ways for generated code evaluation in a fully automatic manner. We used CodeGen and GPTNeo pretrained models applied to a problem of question answering using Stack Overflow-based corpus (APIzation). For test-based evaluation, industrial test-generation solutions (Machinet, UTBot) were used for providing automatically generated tests. The analysis showed that the performance evaluation based solely on NLP metrics or on tests provides a rather limited assessment of generated code quality. We see the evidence that predictions with both high and low NLP metrics exist that pass and don't pass tests. With the early results of our empirical study being discussed in this paper, we believe that the combination of both approaches may increase possible ways for building, evaluating, and training code generation models.
Amended feeding for Deep Knowledge Tracing

Knowledge Tracing is the process of tracking and monitoring a learner’s knowledge of a particular subject over time to identify gaps in knowledge and adjust instruction accordingly. In this paper, we present a simple idea showing another insight into how to approach this problem to get different outcomes. This work is focused on the data that is fed into Deep Knowledge Tracing (DKT) model. We propose to add a pre-trained layer between the input and the hidden layer. The introduced layer is designed to produce a better representation of the questions that are fed to the model. That representation should conserve the correlation between the questions. That information provides a hint to the DKT model to enhance prediction accuracy. The experiments show that it is possible to extract the correlation between the exercises from the sequence of answers a learner gave to a random sample of the exercises.
Comparative Analysis of Spoken Telugu digits using MFCC and LPCC via Hidden Markov Models

The problem of speech recognition deals with interpreting the message conveyed in a spoken word or sentence. It has much significance in various automated tasks such as controlling home appliances, mobile applications, robotic operations, etc. As the domain of Internet of Things (IoT) evolves, there is much scope to assist people with special needs in various ways in their day today routines. The main stakeholders for this type of applications include senior citizens, specially-abled people, and illiterates of different age groups. In IoT environment, speechenabled supportive systems has got a great significance with various challenges. The systems that deal with spoken languages with a tiny data sets for a specific problem wm be a challenging task. This paper studies spoken digit recognition problem using Hidden Markov Models with three different features. The paper discusses the methods and obtained results.
Using deep learning to analyse behaviour in video surveillances

The use of video survemance has become crucial in, addressing the growing concerns of crime and terrorism in the world, leading to an increased need for automated data processing. This study focuses on the application of Deep Learning (DL) models to extract data from video sequences captured by police cameras. Specifically, it explores the use of DL models for crowd behavior analysis and anomaly detection. The study examines publicly available datasets and cutting-edge studies to classify abnormal behaviors accurately. The practical aspect of the study involves developing a 3D anomaly detection system based on a convolutional feature extractor that combines the strengths of 2D CNNs and RNNs. The proposed feature extractor’s complexity and ability to preserve temporal information improve the quality of data extraction, leading to better classification results than the original model, supporting our hypothesis. Overall, this study demonstrates the effectiveness of DL models in addressing the challenges of anomaly detection in crowds and highlights the potential for future research in this area.
Investigation of Deep Learning Models for Vehicle Damage Classification

This paper presents a study of Deep Learning models of convolution neural networks (CNN) applied to vehicle damage classification (VDC). Number of real-world domains may benefit from the proposed solution such as: car rental, auto dealerships, auto insurance businesses, etc. DL has significant advantages over conventional machine learning (ML) models. The primary advantage of DL models is their ability to learn and extract features automatically as opposed to hand-crafting them as in ML models. The study used MatLab as the development and testing environment. A CNN based architecture was constructed which comprised typical DL layers of: raw image input, convolution, activation, pooling, flattening and fully-connected layers. The study used real world images collected from online sources to conduct the experimental work to validate the proposed model. The results showed that the overall average accuracy of all tested models was 91.8% and the best model produced an impressive accuracy of 99.4%. Furthermore, confusion matrix metrics were used to further validate the best performing model and all metrics such as accuracy, precision, sensitivity, specificity were reliable.
Cyberbullying and Suicide Ideation Detection via Hybrid Machine Learning Model

Increase in Cyberbullying and suicide ideas has led a great need in its ideation detection to prevent it. Since the detection of it is even difficult for human and hence it is much more complicated in terms of automatic detection. Many past efforts in this field were tedious, few lexicon-based methods were functional but later turned out to be scope-limiting. The current requirement emphasizes on the use of Machine learning models for different Natural Language Processing problems. Use of these technology gives the quicker and accurate way in detection. In this paper SVM, Random-forest, BDRNN GRU AND BDRNN LSTM are used as convolutional networks. The model uses pre-trained glove embedding layer and hybrid BiGRU model that gives the accuracy of 92.70% and F-score of 0.9431 for cyberbullying detection and accuracy of 97% for suicide ideation. Based on these results the performance of this model is tested by the analysis of four parameters which are Recall, Precision, $\mathrm{F}_{\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{e}}$ and Accuracy.