Reviewerly/exHarmony huggingface dataset:
Color Adaptive Robust DWT-SVD Watermarking Algorithm and Limitations : Color Space Comparisons

Color image watermarking is a technique for digital data authentication and security. Because of the substantial differences in colour aspects, watermarking a colour image is a time-consuming and challenging problem. Noval algorithm DWT-SVD oriented colour image watermarking based on entropy, adapting colour components is proposed to designed in this paper. The algorithim is expected to improve invisibility, of the colour component with the least amount of information is chosen for watermark insertion. For watermark embedding, the is proposed algorithm takes advantage of dual-level security by employing the SVD of logo and wavelet coefficients. The performance of the proposed technique was also examined for three distinct colour spaces: RGB, YCbCr, and LAB. The comparison of watermarking and logo extraction is used to assess qualitative performance. Peak Signal-to-Noise ratio (PSNR) and Normalize cross Correlation (NC) is used in the parametric analysis under the exposure of the various attacks.
Online Monitoring of Stance from Tweets: The case of Green Pass in Italy

Stance detection on social media has attracted a lot of attention in the last few years, as opinionated posts are an invaluable source of information which can possibly be exploited in dedicated systems. This is especially true in the case of particularly polarizing topics for which there is no clear consensus among population. In this paper, we focus on one of these topics, namely the EU digital COVID certificate (also known as Green Pass), with the objective of uncovering the stance towards it in a specific time period for the Italian Twitter community. To this aim, we first tested some classifiers for determining the most suitable one in terms of performance and complexity for the stance detection problem under consideration. Then, we compared several approaches aimed at counteracting the occurrence of concept drift, i.e., that phenomenon for which the characteristics of the dataset vary over time, possibly resulting in a degradation of classification accuracy. Our experimental analysis suggests that updating the classifier during the stance monitoring campaign is crucial for maintaining a satisfactory level of performance. Finally, we deployed our system to monitor the stance on the topic of Green Pass expressed in tweets published from July to December 2021 and to obtain insights about its evolution.
Innovative use of change detection in large numbers of satellite scenes, with geological applications

The large number of remote-sensing datasets available necessitates the development of efficient methods when assessing change between such data. A series of techniques, optimizing the analysis of change detection, specifically on large remote-sensing dataset collections, is demonstrated. Iterative (online) statistical measures for mean and standard deviation give the ability to gain a measure of change over potentially hundreds of datasets without excessive computing power being needed. From this, the coefficient of variation can be used to provide further insight. Using such measures, seasonal change can be detected on outcrop (as opposed to vegetation), illustrating that change detection can be used to further extend a spectral signature for rocks. Twelve Sentinel-2 scenes over a 3 year period were used in this study. Thematic collection: This article is part of the Remote sensing for site investigations on Earth and other planets collection available at: https://www.lyellcollection.org/cc/remote-sensing-for-site-investigations-on-earth-and-other-planets
Local information interaction enhancement network for person re-identification

Most of existing local-based person re-identification (re-ID) methods extract powerful feature representations from multiple body regions of a pedestrian. However, local-based person re-ID methods employ horizontal or grid slicing to divide pedestrian images, which can easily cause network redundancy and misalignment of pedestrian body parts. Attention mechanisms have been recently introduced to computer vision tasks to strengthen local salient features and suppress irrelevant features simultaneously. However, attention mechanisms overemphasize local salient features, which may lead to potential significant information being missed. To address the above issues, we proposed a local information interaction enhancement network (LIEN) for person re-ID. The network integrates attention mechanism with local-based strategy to achieve mutually facilitating and complementary effects. Specifically, LIEN was composed of three parts: a backbone network, a dual attention module (DAM), and a local information interaction module (LIM). DAM was used to guide the backbone network to extract the local salient information, and LIM was used to mine the potential significant information that DAM ignored by exploring the correlations between different channel-level local features. With the combination of DAM and LIM, LIEN mined the salient features while obtaining more potential effective features. In particular, the way that it extracts local features, LIEN effectively avoided pedestrian misalignment caused by uniformly dividing pedestrian images. We evaluated the performance of LIEN on public person datasets, and experimental results indicated that LIEN achieved a more advanced performance.
QR Technology based ID Proof for a Digitalized World

Today’s world has been impacted by Covid-19 widely, and is moving towards a digitalized universe rapidly, wherein the future technologies that can play an important role in reviving the world cannot be forgotten. Nowadays, the use of Quick Response (QR) codes play an efficient role in a human’s life, and id cards are always carried by a person since they are frequently required in his everyday life. The official documents being issued in the name of the person, or the records of his visit at various official places had to be kept handy for anytime use. So with this arising situation, a solution has been proposed serving two main vital goals: firstly, the person will be able to access all required documents only by scanning a single id card where the documents are being stored in a compressed format and represented in an augmented manner, and secondly, the person can easily maintain his visiting records with the help of a Unique Identity(UID) number provided to the users individually while registering. Thus, this paper attempts to promote the use of id digitalization in this futuristic world of technology.
Research on Brand Image Evaluation Method Based on Consumer Sentiment Analysis

Brand image assessment is a key step to reasonably quantify the value of a brand and has far-reaching significance for improving the competitiveness of an enterprise. With the rapid development of Internet technology, traditional questionnaires can no longer meet the current needs of brand image assessment. In this environment, the huge amount of fragmented consumer topic data provides a rich data resource and new research ideas for brand image assessment. Therefore, a brand image assessment method based on consumer sentiment analysis is proposed. First, a topic-based brand image cognitive label extraction method is proposed by setting language rules, aggregation rules, and ranking rules according to the characteristics of online topic data. Then, the fusion of cognitive labels and deep features is performed by fusing the deep features extracted from word vectors. Finally, a supervised learning support vector machine is selected as the sentiment classification model. The experimental results show that based on the obtained important cognitive labels, enterprises are able to better understand the unique attributes that consumers have for the brand; the feature fusion approach is better evaluated and can accurately reflect consumers’ views on brand image and quantified as brand score.
MiniOpenLab

Education for sustainable development and STEM education are two major EU priorities. Both should be addressed from an early age. At school, children must be motivated to learn maths and science and to imagine working in these fields, and to learn about sustainability and develop attitudes and behaviours that are in line with the UN's SD Goals. Over the past years, children have taken interest in SD and in some cases. By contrast, STEM is still regarded as difficult and unattractive by many children. Thus, it may be beneficial to couple both these fields. The project MiniOpenLabs proposes to set-up and test a different methodology with a higher prevalence of experiential learning and relying on the collaboration between science and technology organisations, enterprises, and civil society to ensure relevant and meaningful engagement of all societal actors with science and increase the uptake of science studies, citizen science initiatives and science-based careers, employability, and competitiveness.
KLASIFIKASI PENYAKIT MATA MENGGUNAKAN CNN

The eye is one of the organs of the human senses, namely the sense of sight. Eyes has an important function in capturing visual information that is used for daily activities. Eye health is important because vision can't be replaced by anything.Previously, a doctor made a diagnosis of an eye disease using retinal fundus images. But it takes expertise and a long time. Therefore, a classification system was made using the Convolutional Neural Network (CNN). The CNN network is used to recognize the visual pattern of image pixels with minimal preprocessing.The variables used during testing are data and batch size for the CNN training process. The data variables consist of 50 images from each class which are reproduced using mirroring with a total of 1,000 images; 50 images from each class reproduced using rotation totaling 2,000 images; and 275 normal images, 55 diabetic images, 250 glaucoma images, 250 cataract images, and 170 hypertension images totaling 1,000 images. Batch size variables used were 25 and 32. After all models were tested, it was concluded that the model trained using 1,600 images and 32 batch size gave the best results, namely loss: 0.1228 and accuracy: 0.9100.
Relationship aware context adaptive deep learning for image parsing

The formation of deep learning architectures is challenged in several aspects, among the major and fundamental steps to develop an effective image parsing network is feature selection. In addition, the exploration of context information in such frameworks is also of prime importance. In this research, a novel architecture that utilizes distinctive feature selection algorithm, and the context adaptive information is proposed. The feature selection algorithm defines the idea of exploring relationship aware information to minimize the similarity among features and select an affluent and optimum set of feature representations. The efficacy of proposed framework is analyzed using several benchmark datasets including Stanford Background, CamVid and MSRC v2. The proposed framework achieves 93.8%, 91.8% and 96.1% global pixel segmentation accuracy on the benchmark datasets respectively. Furthermore, we present a comprehensive comparative analysis with state-of-the-art techniques in the literature. The analysis reveals meaningful refinements in terms of segmentation accuracy.
Research on Multilevel Chaotic Image Encryption Algorithm Based on Optical Processing Technology

A multilevel chaotic image encryption solution based on optical processing technology is proposed to solve the difficulties of poor security and slow picture processing performance in the present avatar encryption method. The security key is created by collecting and categorising the features of multilevel chaotic pictures combined with optical processing technology. As a result, the original picture data are crystal clear and accurate. The increase in image processing security will help in simplifying and speeding the multilevel chaotic picture enrcryption process. As a result of these findings, researchers discovered that the multilevel chaotic picture encryption method based on optical processing technology is more secure in the application process, can perform image encryption faster, and improves the efficiency and accuracy of image processing overall.
DsetGenS: An Automated Technique for Building Dataset From Speech with respect to Gujarati-English

The computer has seen significant evolution in recent years, with applications in variety of disciplines like Machine Learning, Deep Learning etc. Machine Translation (MT) technology has advanced significantly as a subfield, with many methodologies and techniques. The number of individuals using the internet has risen tremendously. Most documents are written in English since that is the most extensively used language on the internet. If a user’s first dialect is Gujarati, he or she will naturally prefer to access the information in Gujarati whenever possible. Even though there are already various MT systems and tools that support Indian languages; however, the translation’s quality is mediocre and might be improved. As observed, when models are trained on limited quantities of parallel data, their performance declines. Learned models often have limited performance (inaccurate translations and feature scores) along with low coverage (high out-of-vocabulary rates). Furthermore, the researchers were driven to present novel methodologies and solutions that would automatically construct Datasets for MT due to the increased demand for effective technologies to process and translate information from/to Gujarati Language. Our objective of generating a Gujarati-English dataset has been met in two ways. We have already introduced GEDset, that is automatic Dataset Builder for Machine Translation System with Specific Reference to Gujarati-English [1]. Here, in this paper we are proposing a model to automatically build Gujarati-English dataset from audio that is available in Gujarati Language through Speech Processing.
A novel framework for automatic caption and audio generation

In recent times, with the advancements of Artificial Intelligence, multidomain problems of Computer vision (CV) and Natural language processing (NLP) such as Image caption generation, audio generation has piqued the attention of researchers all across the globe due to its application in medical, business and technology. Image caption generating entails automatically generating text according to the contents available in the image. In this paper we present a novel caption generation and audio generation framework. We use Deep Neural Networks like Convolutional Neural Network (CNN), Long short-term memory (LSTM) and transfer learning techniques to perform this task. The model has two stages: 1] Generate captions for any given image 2] Then gTTS (google text to speech) generator is used to generate audio for the generated captions. This framework is extremely beneficial to visually impaired people since it allows them to comprehend visuals. The Flickr8K dataset was used to train and test the model. A total of 6000 photos were utilised to train the model, with an additional 1000 images used for validation and testing.
Health Record Management Using Blockchain Technology

The world is progressing, but it needs healthy people to succeed, and health records are a measure of human health across time. When health records are stored in a single system, data breaches might occur. A data breach will cost healthcare organizations $ 380 per record, according to estimates. Incidents reported to the federal government in 2021 resulted in the compromising of more than 40 million medical records this year. As a result, we switched to a record-keeping center, Which has not made a great deal of a difference. Because the patient has no control over the data, there is a high chance of it being mishandled. As a result, we require a patient-centered strategy that is entirely decentralized, capable of detecting data theft, preventing data tampering, and granting patients access control. Blockchain technology is the most effective way to solve all problems and meet all needs. As a decentralized and distributed ledger, blockchain has the potential to impact billing, medical research, identity theft, record sharing, and data crimes in the future. In the department of health, Smart contracts help to clarify things more particularly. On the Blockchain, invocation, record creation, and validation will all take place. This article mainly focuses on the maintenance of the patients-focused architecture of health records with the use of blockchain, so here smart contracts can be used to modify or be newly added in the future to increase data interchange potential.
Evaluating the FixMatch Semi-Supervised Algorithm for Unbalanced Image Data

In recent years there has been a major rise in interest in the field of semi-supervised deep learning with newer techniques being proposed which rapidly push the state-of-the-art. Most techniques, however, use balanced benchmarking datasets such as CIFAR and SVHN and therefore do not translate into real life applications, many of which involve highly imbalanced datasets. An investigation was conducted into the performance of the FixMatch algorithm when trained on unbalanced benchmarking datasets and a real world dataset. Three different entropy-based distributions of imbalance, with the proportion of labeled samples varied from 80% to 40%, were applied and compared to a baseline which was computed on uniformly balanced data. An increase in error rate is noted for the imbalanced datasets with larger errors seen in cases where there are a greater number of minority classes. Indeed, the distribution containing the most minority classes showed the maximum drop in performance with a mean error rate increase of 12.67% compared to the uniform baseline.
TEMPPO: Twin Entropy Maximized Proximal Policy Optimization

In recent years the Deep Reinforcement learning has been progressing so fast and more and more complicated reinforcement learning environments have been examined with new deep reinforcement algorithms. The game of football is a complicated game with a sparse reward environment. ActorCritic methods are gaining more popularity, but these methods have two important challenges. One challenge is the overestimation error and another challenge is how to explore more effectively in the big environments with sparse rewards. As to tackle the overestimation error, this paper proposes to use twin critics and as for the exploration, this paper proposes a new way to use the entropy in the objective function and cost function. This new method name is TEMPPO and it is based on the Proximal Policy optimization algorithm. In this paper, the results of the TEMPPO are tested on the Google Research Football environment.
SaveLives - A Real-Time Threat Detection System

These days CCTV cameras are everywhere - malls, schools, shops, public places etc. But still people are able to commit crimes and run away because the police or the authorities are notified very late and are thus unable to do anything. Guns and knives are the most common weapons used in committing crimes today. We propose SaveLives - a system which can detect guns and knives from video footage in real time and thus notify the authorities instantly, so that they can come at the right moment and intervene. Unlike today's surveillance systems, it doesn't require human supervision. Our system also eliminates false positives by cross checking against registered faces like that of policemen or security guard. After the system detects a gun or a knife, Twilio API is triggered to alert the concerned authorities about a possible terrorist attack or a threat to public safety, so that timely action can be taken. This paper presents a system for detection of guns and knives based on Yolov4. We achieved Mean Average Precision (mAP) score of 90.35% and Average Intersection Over Union (IOU) score of 73.68%. Our proposed framework helps to stop crimes and reduce the damage done by criminals, thus saving lives.
Product Aspect Ranking Using Multi-Criteria Decision Making

Online product reviews often mention several aspects of the product. Reviews with multiple aspects are sometimes problematic because some of the aspects mentioned are of little or no relevance to either consumers or providers. Hence, it is important to identify relevant aspects of a product by ranking them in the order of their importance. With that, this chapter introduces a new criterion known as Aspect Relevancy in the process of ranking aspects. The study also incorporates multi-criteria decision-making (MCDM) to recognize vital aspects retrieved from the consumers reviews of products and services. In ranking the selected aspects, the subjective technique for order of preference by similarity to ideal solution (TOPSIS) is employed. The experimental results using Bing Liu and SemEval 2016 Task 5 datasets have demonstrated positive outcome of the proposed approach when compared with two baseline approaches in terms of NDCG@k ranking measure.
Integration and Optimization of British and American Literature Information Resources in the Distributed Cloud Computing Environment

One of the most effective approaches to improve resource usage efficiency and degree of resource collecting is to integrate resources. Many studies on the integration of information resources are also available. The search engines are the most well-known. At the same time, this article intends to optimize the integration of British and American literature information resources by employing distributed cloud computing, based on the needs of British and American literature. This research develops a model for the dispersed nature of cloud computing. It optimizes the method by fitting the mathematical model of transmission cost and latency. This article analyzes the weaknesses of the current British and American literature information resource integration and optimizes them for the integration of British and American literature resources. The Random algorithm has the longest delay, according to the results of this paper's experiments (maximum user weighted distance). The algorithms NPA-PDP and BWF have longer delays than the algorithm Opt. The percentage decline varies between 0.17 percent and 1.11 percent for different algorithms. It demonstrates that the algorithm presented in this work can be used to integrate and maximize information resources from English and American literature.
Detection of Microcalcifications on Mammograms

Breast cancer is a common disease of women. The number of new cases diagnosed in Algeria is increasingly high and it is the first cause of cancer related deaths for women. The microcalcifications are considered the primary sign of breast cancer. The early detection of these allows doctors to take the necessary measures for the treatment of this pathology. Medical imaging has made possible enormous progress in the field of diagnosis and provides an important contribution to the care of patients. This article proposes an approach for the segmentation and detection of microcalcifications in mammographic images based on wavelets, K- means, and the windows of Parzen, in order to detect the presence of breast cancer as early as possible and to avoid radical treatment such as the removal of the breast.