Reviewerly/exHarmony huggingface dataset:
Emotion Detection of People Wearing Face Mask

Though many facial emotion recognition models exist, after the Covid-19 pandemic, majority of such algorithms are rendered obsolete as everybody is compelled to wear a facemask to protect themselves against the deadly virus. Face masks can hinder emotion recognition systems, as crucial facial features are not visible in the image. This is because facemasks cover essential parts of the face such as the mouth, nose, and cheeks which play an important role in differentiating between various emotions. This study intends to recognize the emotional states of anger-disgust, neutral, surprise-fear, joy, sadness, of the person in the image with a face mask. In the proposed method, a CNN model is trained using images of people wearing masks. To achieve higher accuracy, the classes in the dataset are combined. Different combinations of clubbing are performed, and results are recorded. Images are taken from FER2013 dataset which consists of a huge number of manually annotated facial images of people.
A Novel Framework for Super-Resolution Reconstruction of Equestrian Videos Considering Compressed Sensing Algorithm

An ideal video super-resolution algorithm not only up-converts the resolution of the original image, but also performs corresponding processing on the color gamut, dynamic range, bit depth, etc. Therefore, keeping the prior information available will be essential, and this paper proposes the novel framework for super-resolution reconstruction of the equestrian videos considering compressed sensing algorithm. The wide application of video super-resolution algorithms and the substantial increase in the number of training videos will help to improve and improve the training model, hence, the system firstly conduct the modelling of down-sampling to understand the factors that influence the image quality. Then, the designed algorithm is discussed in detail, the proposed method combines the optimized wavelet analysis and optical flow method to achieve the better reconstruction performance. Through the comparison testing and select the factors of PSNR and SSIM, the accuracy is tested.
A Novel Segmentation-Registration (SR) based Gesture Recognition Algorithm for Equestrian Images

Obtaining motion features from the RGB video streams for extraction and prediction is a recent research hotspot, this paper then proposes the novel segmentation-registration (SR) based gesture recognition algorithm for equestrian images. At this stage, there are many kinds of open-source human behavior databases with a wide range of sources, but due to the lack of unified standards, no system has been formed, which limits the development of abnormal behavior identification research to a certain extent. Hence, the proposed method first uses the Internet to construct the equestrian image data sets. For the proposed algorithm: 1. The novel segmentation model is designed, the traditional PCNN is modified with integration of nonlinear function ReLU. 2. The novel registration model is designed. The edge of the image is extracted, and then fill the edge image to obtain the outer contour image. The basic idea of the edge extraction method is to use the edge enhancement operator to highlight the local edge in the image 3. The novel gesture recognition algorithm for equestrian images is implemented. Through the experiment on the Top-1 and Top-5, the simulation is conducted. The proposed model has the better accuracy of the estimation.
Two families of few-weight codes over a finite chain ring

Linear codes over finite chain rings meeting the Griesmer bound are particularly interesting since they are closely related to minihyper in projective Hjelmslev spaces. In this paper, motivated by the nice article (Li et al., 2018 [23]), two classes of linear codes with few non-zero Hamming weights over the finite chain ring R=Fq+uFq are constructed, and one of them meets the Griesmer bound over finite commutative local rings. By a Gray map, we obtain that the Gray images of the constructed codes over the ring R are linear codes with two weights over the finite field Fq with q elements, and these codes are optimal with respect to the well-known Griesmer bound under certain conditions. Our results give an affirmative answer to two (related) open questions raised in the article mentioned above. Remarkably, some of the constructed linear codes over R presented in this paper correspond to minihypers in projective Hjelmslev spaces over finite chain rings. Besides, their Gray images can be used to construct secret sharing schemes with nice access structures and other applications thanks to the minimality property that possesses our constructed codes.
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New nonexistence results of generalized bent functions from Z2n to Zm are presented. In particular, we prove that there exists no generalized bent function from Z24 to Zm for every odd m.
Exploring the trade-off between performance and annotation complexity in semantic segmentation

Image semantic segmentation, a fundamental computer vision task, performs the pixel-wise classification of an image seeking to group pixels that share some semantic content. One of the main issues in semantic segmentation is the creation of fully annotated datasets where each image has one label per pixel. These annotations are highly time-consuming and, the more the labelling increases, the higher the percentage of human-entered errors grows. Segmentation methods based on less supervision can reduce both labelling time and noisy labels. However, when dealing with real-world applications, it is far from trivial to establish a method that minimizes labelling time while maximizing performance. Our main contribution is to present the first comprehensive study of state-of-the-art methods based on different levels of supervision. Image processing baselines, unsupervised, weakly supervised and supervised approaches have been evaluated. We aim to guide anyone approaching a new real-world use case by providing a trade-off between performance and supervision complexity on datasets from different domains, such as street scenes (Camvid), microscopy (MetalDAM), satellite (FloodNet) and medical images (NuCLS). Our experimental results suggest that: (i) unsupervised and weak learning perform well on majority classes, which helps to speed up labelling; (ii) weakly supervised can outperform fully supervised methods on minority classes; (iii) not all weak learning methods are robust to the nature of the dataset, especially those based on image-level annotations; and (iv) among all weakly supervised methods, point-based are the best-performing ones, even competing with fully supervised methods. The code is available at https://github.com/martafdezmAM/lessen_supervision.
Securing web documents by using piggybacked framework based on Newton's forward interpolation method

The modern telecommunication system that transfers data over the web requires a secure channel so that integrity, authentication, and confidentiality of data are ensured. To achieve this, the SSL/TLS protocol of the transport layer provides end-to-end security to transfer data without any intrusion. But this security fails due to vulnerabilities reported in the last few years on SSL/TLS not ignorable which makes the data communication process vulnerable. The handshake protocol used by SSL/TLS decides a common session key to share the message among the communicating nodes. The common key remains the same in a session to encrypt entire data payload. If a hacker breaks into the session and accesses the key, then the entire session would be compromised. Therefore, there is a need for a mechanism that ensures the proper key exchanges so that the messages transferred during a session uses different keys for encryption and decryption process. This is achieved by implementing the key exchange mechanism using Newton's interpolation method which is used to generate a polynomial function that derives the key for the ongoing communications. The paper represents the result of the research conducted and also recommends a few changes to the existing key exchange mechanism.
Block-segmentation vectors for arousal prediction using semi-supervised learning

To handle emotional expressions in computer applications, Russell’s circumplex model has been useful for representing emotions according to valence and arousal. In SentiWordNet, the level of valence is automatically assigned to a large number of synsets (groups of synonyms in WordNet) using semi-supervised learning. However, when assigning the level of arousal, the existing method proposed for SentiWordNet reduces the accuracy of sentiment prediction. In this paper, we propose a block-segmentation vector for predicting the arousal levels of many synsets from a small number of labeled words using semi-supervised learning. We analyze the distribution of arousal and non-arousal words in a corpus of sentences by comparing it with the distribution of valence words. We address the problem that arousal level prediction fails when arousal and non-arousal words are mixed together in some sentences. To capture the features of such arousal and non-arousal words, we generate word vectors based on inverted indexes by block IDs, where the corpus is divided into blocks in the flow of sentences. In the evaluation experiment, we show that the results of arousal prediction with the block-segmentation vectors using semi-supervised learning outperform the results of the previous methods in SentiWordNet and SocialSent.
Analyzing the potential of active learning for document image classification

Abstract Deep learning has been extensively researched in the field of document analysis and has shown excellent performance across a wide range of document-related tasks. As a result, a great deal of emphasis is now being placed on its practical deployment and integration into modern industrial document processing pipelines. It is well known, however, that deep learning models are data-hungry and often require huge volumes of annotated data in order to achieve competitive performances. And since data annotation is a costly and labor-intensive process, it remains one of the major hurdles to their practical deployment. This study investigates the possibility of using active learning to reduce the costs of data annotation in the context of document image classification, which is one of the core components of modern document processing pipelines. The results of this study demonstrate that by utilizing active learning (AL), deep document classification models can achieve competitive performances to the models trained on fully annotated datasets and, in some cases, even surpass them by annotating only 15–40% of the total training dataset. Furthermore, this study demonstrates that modern AL strategies significantly outperform random querying, and in many cases achieve comparable performance to the models trained on fully annotated datasets even in the presence of practical deployment issues such as data imbalance, and annotation noise, and thus, offer tremendous benefits in real-world deployment of deep document classification models. The code to reproduce our experiments is publicly available at https://github.com/saifullah3396/doc_al .
Contrastive adversarial learning in text classification tasks

In recent years, contrastive learning has been very successful in unsupervised tasks of representation learning and has received a lot of attention in supervised tasks. In supervised tasks, the discrete nature of natural language makes the construction of sample pairs difficult and the models are poorly robust to adversarial samples, so it remains a challenge to make contrastive learning effective for text classification tasks and to guarantee the robustness of the models. This paper presents a contrastive adversarial learning framework built using data augmentation with labeled insertion data. Specifically,By adding perturbation to the word-embedding matrix, adversarial samples are generated as positive examples of contrastive learning, and external semantic information is introduced to construct negative examples. Contrastive learning is used to improve the sensitivity and generalization ability of the model, and adversarial training is used to improve robustness, thereby improving the classification accuracy. In addition, the momentum contrast from unsupervised tasks is also introduced into the text classification task to increase the number of sample pairs. Experimental results on several datasets show that the proposed approach outperforms the baseline comparison approach, and in addition some experiments are conducted to verify the effectiveness of the proposed framework under low-resource conditions.
Classification of hundreds of classes: A case study in a bank internal control department

The main function of the internal control department of a bank is to inspect the banking operations to see if they are performed in accordance with the regulations and bank policies. To accomplish this, they pick up a number of operations that are selected randomly or by some rule and, inspect those operations according to some predetermined check lists. If they find any discrepancies where the number of such discrepancies are in the magnitude of several hundreds, they inform the corresponding department (usually bank branches) and ask them for a correction (if it can be done) or an explanation. In this study, we take up a real-life project carried out under our supervisory where the aim was to develop a set of predictive models that would highlight which operations of the credit department are more likely to bear some problems. This multi-classification problem was very challenging since the number of classes were enormous and some class values were observed only a few times. After providing a detailed description of the problem we attacked, we describe the detailed discussions which in the end made us to develop six different models. For the modeling, we used the logistic regression algorithm as it was preferred by our partner bank. We show that these models have Gini values of 51 per cent on the average which is quite satisfactory as compared to sector practices. We also show that the average lift of the models is 3.32 if the inspectors were to inspect as many credits as the number of actual problematic credits.
Adaptive boosting model for breast cancer prediction

The aggressive form of cancer commonly in breast cells is breast cancer. The highly aggressive form of cancer is frequently created in breast cells. The need for the predictive model to accurately measure the prognosis prediction of breast cancer in the earlier stage is highly recommended. This development of methods for protecting people from fatal diseases by the researchers from the different disciplines who are all working altogether. An accurate breast cancer prognosis prediction is made by using a good predictive model to assist Medical Internet of Things (mIoT). Various advantages such as cancer detection in an earlier stage, medical expenses related to treatment, and having unwanted treatment gives the accurate prediction attains spare patients. Existing models lie on the uni-modal data such as chosen gene expression to predict the model’s design. Few learning-based predictive models are used in the proposed method to improve breast cancer prognosis prediction from the current data sets. Most of the peculiar benefits of the suggested method rely on the model’s architecture. Here, a novel adaptive boosting model (a-BM) is used to measure the loss function of every individual and intends to reduce the error rate. Various performances metrics are used to evaluate the predictive performance, which provides the model gives a good outcome rather than the previous techniques.
PFGAN: Fast transformers for image synthesis

Recently, the Transformers have shown great potential in computer vision tasks, such as classification detection, segmentation, and image synthesis, etc. The success of Transformers has been long attributed to the attention-based token mixer. However, the computational complexity of the attention-based token mixer module is quadratic to the number of tokens to be mixed. Therefore, the attention-based token mixer module requires more parameters and will cause a very large amount of computation. As far as image synthesis task is concerned, the attention-based token mixer module increases the computation amount of generative adversarial networks (GANs) based on Transformers. To address this problem, we propose the PFGAN method. The motivation is based on our observation that the computational complexity of pooling is linear to the sequence length, without any other learnable parameters. Based on this observation, we use pooling rather than self-attention as the token mixer. Experimental results on CelebA, CIFAR-10 and LSUN datasets demonstrate that our proposed method has fewer parameters and fewer computational complexity.
Evaluation of image segmentation and multi class object recognition algorithm based on machine learning

Abstract This paper mainly updates the image segmentation and multi-target recognition algorithm through the support vector machine algorithm in ML, and verifies the reliability of the new proposed algorithm through simulation experiments, and determines that the ML-based image segmentation and multi-target recognition algorithm can have stronger processing capability in image recognition and processing. Finally, the performance of traditional image segmentation and multi class object recognition algorithms and the image segmentation and multi class object recognition algorithms proposed in this paper were compared through experiments. The results showed that the performance of the ML based image segmentation and multi class object recognition algorithms proposed in this paper has increased by 29.1% on average in all aspects.
Review Paper on an Android Application for Farmers to Solution of their Problems and for Selling Products

The Virtual Fruits Market app provides a platform for Indian farmers to directly connect with end users and supply their produce with a fair and consistent price. This will ensure that farmers receive a better profit for their produce, while end users can benefit from lower prices and higher quality products from the farmers themselves. Additionally, the app also provides access to experts to provide solutions to the farmers, thus allowing them to make better informed decisions and maximize their profits. The app also ensures that the solutions provided are unique and of the highest quality. The Virtual Fruits Market app is therefore a beneficial tool that provides a win-win situation for both farmers and end users
Building a Trustworthy News Ecosystem using Blockchain

This paper introduces a highly innovative and revolutionary decentralised application that is built using cutting-edge blockchain technology. The application is designed to provide a trustworthy and transparent news sharing platform that is decentralised in nature. The main objective of our platform is to mitigate the spread of fake news that has become rampant in recent times, especially through traditional news sharing platforms. Our platform is designed to allow users to curate and share news in a highly efficient and transparent manner. We have implemented various features that allow our users to verify the authenticity of the news articles being shared on our platform. One of our most important features is the ability to trace the source of origin of the articles being shared, which promotes enhanced transparency and accuracy in news reporting. Our strategies and the workings of our application are elaborated in great detail in this paper. We demonstrate how our platform ultimately achieves verified news to be disclosed to everyone and reduces the risk of fake news being spread across the community. Our decentralised news sharing platform is a crucial step towards promoting transparency and accuracy in news reporting, and restoring faith in the media. We believe that our platform has the potential to revolutionize the way news is shared and consumed, and we are excited to present it to the world. With our platform, we hope to make a significant contribution towards promoting trust, transparency, and accuracy in news reporting, which are essential values in any democratic society.
“Who Said That?” Applying the Situation Awareness Global Assessment Technique to Social Telepresence

As with all remotely controlled robots, successful teleoperation of social and telepresence robots relies greatly on operator situation awareness; however, existing situation awareness measurements, most being originally created for military purposes, are not adapted to the context of social interaction. We propose an objective technique for telepresence evaluation based on the widely accepted Situation Awareness Global Assessment Technique, adjusted to suit social contexts. This was trialled in a between-subjects participant study ( n = 56) comparing the effect of mono and spatial (binaural) audio feedback on operator situation awareness during robot teleoperation in a simulated social telepresence scenario. Subjective data were also recorded, including questions adapted from Witmer and Singer’s Presence Questionnaire, as well as qualitative feedback from participants. No significant differences in situation awareness measurements were detected; however, correlations observed between measures call for further research. This study and its findings are a potential starting point for the development of social situation awareness assessment techniques, which can inform future social and telepresence robot design decisions.
Towards Safe Visual Navigation of a Wheelchair Using Landmark Detection

This article presents a method for extracting high-level semantic information through successful landmark detection using 2D RGB images. In particular, the focus is placed on the presence of particular labels (open path, humans, staircase, doorways, obstacles) in the encountered scene, which can be a fundamental source of information enhancing scene understanding and paving the path towards the safe navigation of the mobile unit. Experiments are conducted using a manual wheelchair to gather image instances from four indoor academic environments consisting of multiple labels. Afterwards, the fine-tuning of a pretrained vision transformer (ViT) is conducted, and the performance is evaluated through an ablation study versus well-established state-of-the-art deep architectures for image classification such as ResNet. Results show that the fine-tuned ViT outperforms all other deep convolutional architectures while achieving satisfactory levels of generalization.