Reviewerly/exHarmony huggingface dataset:
Voting Based CrowdFunding Using Ethereum Blockchain

The Crowdfunding is a type of fundraising for the people in need. Its main objective is to raise funds from investors such that they can provide it to its requesters. The traditional crowdfunding are not trustable as there are third parties involved which cause security breaches. The blockchain technology can help in strengthening the security of the crowdfunding platform as it is transparent and accessible to all, decentralised platform. The most important property of the crowdfunding is the mutual trust between the entities which can be established by using blockchain. This paper majorly focuses on what is crowdfunding, usage of blockchain technology via smart contracts in Ethereum and its advantages through a functional application, its limitations and future scope. It also compares other platforms with the platform developed using Ethereum blockchain and Smart contracts with the traditional as well as the blockchain platforms.
Matching Products with Deep NLP Models

Following the explosive data growth that is presently taking place on the Web, the eCommerce industry has evolved towards enterprises and services that collect product-oriented data from multiple external sources. Since the majority of these sources are usually uncontrolled and independent of each other, they provide their information in a diverse manner, rendering the identification of products a difficult task. The problem becomes particularly challenging by considering the native sparseness and high dimensionality of text, the specific peculiarities of product data, the large data volume and the dynamic nature of the involved applications. Despite the uncontested significance of the problem, there is a lack of works in the relevant literature that confront it by taking into account all the aforementioned challenges. In this paper, we summarize these challenges and provide some useful insights on how they can be effectively tackled. We also present several components of a preliminary system that is being developed to accurately and efficiently identify product entities in diverse data originating from multiple external sources.
About the Choice of Data Balance Method for Neural Network Classification of Electrocardiogram Signals

Diseases of the cardiovascular system are the main cause of death in the world population. Classification of electrocardiogram (ECG) signals is a reliable method for diagnosing cardiac pathologies. The available ECG databases consist of an unequal number of signals from various pathologies. This article analyzes the impact of using class alignment methods on the result of neural network classification of ECG signals. The results demonstrate that the SMOTE GRU algorithm provides high performance in classifying ECG segments, while the BiLSTM ROS algorithm provides high performance in classifying full ECG signals. The Accuracy, Loss, Recall, Precision, F-score values are respectively 70.31% and 77.73%, 0.29 and 0.41, 90.1% and 96.0%, 78.8% and 83.4%, 88.5% and 95.3%.
Unified Face Image Quality Score Based on ISOIEC Quality Components

Faceimage quality assessment is a crucial task in the face enrolment process to obtain high-quality face images in the reference database. Neglecting quality control will adversely impact the accuracy and efficiency of face recognition systems, resulting in an image captured with poor perceptual quality. In this work, we present a holistic combination of 21 component quality measures proposed in “ISO/IEC CD 29794-5” and identify the varying nature of different measures across different datasets. The variance is seen both across capture-related measures and subject-related measures, which can be tedious for validating each component metric by a human observer when judging the quality of the enrolment image. Motivated by this observation, we propose an efficient method of combining quality components into one unified score using a simple supervised learning approach. The proposed approach for predicting face recognition performance based on the obtained unified face image quality assessment (FIQA) score was comprehensively evaluated using three datasets representing diverse quality factors. We further provide an extensive evaluation of the proposed approach using the Error-vs-Discard Characteristic (EDC) and show its applicability using five different FRS. The evaluation indicates promising results of the proposed approach in combining multiple component scores into a unified score for wider application in the context of face image enrolment in FRS.
Energy Sector Digitilisation: A Security Framework Application for Role-Based Access Management

Data security and privacy in the energy sector and especially in buildings' energy management, is an emerging issue which cannot be underestimated, thus, calls for solid and robust solutions. The reliable functioning of energy management data platforms is essential for the efficient operation and performance of buildings, as well as the well-being of occupants. The fast-paced digital advances have aided this direction, but they have also created increased challenges for operation disruption mitigation and immutable services and outcomes. Data obtained from energy management systems frequently comprises highly sensitive and personal information. Consequently, access to such data, whether for reading purposes or for conducting experiments and research, must be strictly restricted and clearly defined. Through security frameworks data protection and information privacy, mitigation of risks and vulnerabilities can be ensured. In this paper, an end-to-end Security Framework is proposed for securing sensitive resources in energy management systems using Keycloak, OAuth2 and UMA 2 protocols as well as an extra security layer on top of the Keycloak, so as to enhance the overall security and avoid immediate interaction with Keycloak, which is the main user storage and access management component. The main concept of this framework is the controlled access of sensitive data and services, such as datasets, databases, functionalities, URLs, APIs, etc., leveraging the OAuth2 access tokens capabilities and the roles of the users. Our proposed architecture ensures that all the functionalities of the involved tools and protocols are properly secured end-to-end. The several existing functionalities of a number of tools and related protocols are properly leveraged and further extended, resulting to a fine-grained architecture with an extra security layer that offers administrative, security and identity access management and orchestration.
Virtual Street Museum - An Augmented Reality Application for the Emergence of the Ancient Topography for the center of Athens

Research into the ancient topography of the historical center of Athens has produced a rich archive database. Augmented reality technology is also becoming more and more accessible to the public as most mobile devices nowadays can support it. In this publication, the effort to build the augmented reality application “Virtual Street Museum” to highlight the information contained in aforementioned database is analyzed. The application provides a personalized experience for the user by creating a route, in the historical center of Athens, adapted to her personal choices. This route includes points with augmented reality scenes where the user can see the information of the ancient topography in an impressive way. Finally, an attempt to construct a time estimation algorithm for the walking tour was made. This algorithm also adapts to each user by data collected from her use of the application providing a personalized experience.
Real-Time Survivor Detection in UAV Thermal Imagery Based on Deep Learning

In this proposed work, UAV Thermal Imagery technology, which was employed by earlier researchers, to detect the Real-Time Victims, was taken. Due to their great durability, low cost, ease of implementation, and versatility, unmanned aerial vehicles (UAVs) have experienced a considerable surge in use. UAVs can swiftly search the impacted region during a natural catastrophe like earthquakes, floods, etc. to help more survival. One of the main factors to develop a rescue system is Dataset. In the various research papers studied the respective researchers used different algorithms or methods to develop a dataset. Researchers used methods like Reinforcement Learning, Deep Learning, YOLO Algorithms, etc. A comparative analysis of the various techniques was made. Also, the description of the Knowledge distillation process, which was discovered by earlier researchers, is mentioned here. Observation realises that Deep Learning methods are better to design a UAV.
An Ontology-Based Framework for Sports Media Data Interpretation

As sports data become more complex, effective and flexible knowledge management is an important part of the data utilization process. Ontologies are sets of triples used to describe concepts and model contextual information in a domain of interest. In this paper, we present the NBA Multimedia Ontology, an ontology-based framework that provides high-level concepts and relations between events, entities, stats, and multimedia data relevant to a basketball game, covering all the different aspects of such a complex field. To build the conceptual model, we utilize existing models, while developing new ontology schemas for classifying basketball events. Such events represent in-game analysis data and conceptualizing broadcasting information, which can be used effectively by coaching staffs, journalists, medical teams and researchers.
Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition

With the ever-growing complexity of deep learning models for face recognition, it becomes hard to deploy these systems in real life. Researchers have two options: 1) use smaller models; 2) compress their current models. Since the usage of smaller models might lead to concerning biases, compression gains relevance. However, compressing might be also responsible for an increase in the bias of the final model. We investigate the overall performance, the performance on each ethnicity subgroup and the racial bias of a State-of-the-Art quantization approach when used with synthetic and real data. This analysis provides a few more details on potential benefits of performing quantization with synthetic data, for instance, the reduction of biases on the majority of test scenarios. We tested five distinct architectures and three different training datasets. The models were evaluated on a fourth dataset which was collected to infer and compare the performance of face recognition models on different ethnicity.
Advanced Machine Learning Model to Detect Spam on Instagram

In recent years, social media platforms like Facebook, Twitter, and Instagram have become increasingly significant as channels for the diffusion of information. One of these social media platforms that has grown in popularity recently is Instagram. Instagram is a popular network for sharing photos and videos that generate income for celebrities, businesses, and people with large followings. The site could potentially be used for illegal activities because of its enormous profit margin. One of the malicious activities on the Instagram network involves fake accounts. In this study, a hybrid approach that considers both the post's content and the images has been used for identifying Instagram fake accounts. We evaluate content spam with machine learning, and image spam with CNN models. The image dataset has been taken from Image Spam Hunter, and the model was trained using a dataset from Kaggle for categorization based on content. The proposed hybrid model has also been tested on the dataset that was scraped from Instagram. Experimental classification results show that the proposed model has a classification accuracy of 97.1%.
Automatic Validation of ICAO Compliance Regarding Head Coverings: An Inclusive Approach Concerning Religious Circumstances

This paper contributes with a dataset and an algorithm that automatically verifies the compliance with the ICAO requirements related to the use of head coverings on facial images used on machine-readable travel documents. All the methods found in the literature ignore that some coverings might be accepted because of religious or cultural reasons, and basically only look for the presence of hats/caps. Our approach specifically includes the religious cases and distinguishes the head coverings that might be considered compliant. We built a dataset composed by facial images of 500 identities to accommodate these type of accessories. That data was used to fine-tune and train a classification model based on the YOLOv8 framework and we achieved state of the art results with an accuracy of 99.1 % and EER of 5.7%.
Greek Political Speech Classification Using BERT

A few years ago, Transformer architecture was proposed for Natural Language Processing (NLP) tasks, having state-of-the-art performance and improved efficiency. As a consequence, Transfer Learning (TL) started flourishing, and various new language models like BERT took advantage of Transformer structure. At first, BERT was trained for the English language, but today a Greek version of BERT, GREEK-BERT, is available. The purpose of this paper is to propose a language model that exploits GREEK-BERT for text classification among Greek political parties. In order to accomplish that, recent speeches from all Greek political parties were gathered in our dataset. As expected, fine-tuning GREEK-BERT for multi-class and binary classifications among all the political parties has had satisfactory performance. Both types of models achieve differentiation between the speeches of variant political parties, but binary models surpass marginally the multi-class models. All the models seem to have a lower classification weighted Fl score in political parties with similar ideologies.
A Survey on Video Steganography Based on the Techniques and Evaluation Metrics

Video steganography is used in a wide range of security applications. The hiding capacity, imperceptibility, and robustness of any steganographic method ultimately determine its effectiveness. many video steganography methods have been presented in the last decade, however there aren't enough survey articles in the literature that cover all of them. This paper provides a review of video steganography through examination and analysis of several approaches and evaluations of their effectiveness. This study also aims to give a qualitative analysis of several video steganography techniques by emphasizing their characteristics. Furthermore, many quality metrics for evaluating various steganography techniques have been presented. Following that, this report offered several future directions that the research community should consider and improve on existing video steganographic techniques. The primary goal of this paper is to assist new researchers in grasping the fundamental principles of this study area in order to begin their research in this topic.
Efficient Solution for NoSQL Database Security in Blockchain - Based Applications

In recent years many applications have moved to blockchain-based services due to the increased popularity of blockchain technology. The implementation of this technology in numerous domain applications, such as finance, health management, land record registration, etc. As a result, data security and privacy are essential requirements of the Blockchain platform for protecting users' sensitive data and code. In this paper, a database security framework is proposed specifically tailored for Blockchain-based solutions, including projects based on Hyperledger. Furthermore, this framework detects vulnerabilities in MongoDB and CouchDB and suggests remedial actions. The solution identifies missing patches, exposed Config API, unauthorized changes, misconfigured privileges, and more.
Comparative Study of Recurrent and Dense Neural Networks for Classifying Maritime Terms

Despite its importance, the extraction of domain-specific terms has not been sufficiently studied. This paper proposes an automated approach for semi-supervised term extraction from Greek-language legal documents related to the shipping/maritime industry. The approach employs a deep learning scheme based on a machine learning model that uses several linguistic features and word embeddings, and is trained using two different kinds of ground truth: the size of words in characters and the use of freely available nautical dictionaries. In addition, the approach was able to extract a large number of domain-specific terms that were not included in existing dictionaries. The results show that the proposed approach outperforms human annotation when a semi-supervised method is used for ground truth. These results suggest that the proposed approach could be a useful tool for term extraction from legal documents in the shipping/maritime industry, and could potentially be adapted for other domains and languages as well.
Boosting Cross-Quality Face Verification Using Blind Face Restoration

In recent years, various Blind Face Restoration (BFR) techniques were developed. These techniques transform low quality faces suffering from multiple degradations to more realistic and natural face images with high perceptual quality. However, it is crucial for the task of face verification to not only enhance the perceptual quality of the low quality images but also to improve the biometric utility face quality metrics. Furthermore, preserving the valuable identity information is of great importance. In this paper, we investigate the impact of applying three state-of-the-art blind face restoration techniques namely, GFP-GAN, GPEN and SGPN on the performance of face verification system under very challenging environment characterized by very low quality images. Extensive experimental results on the recently proposed cross-quality LFW database using three state-of-the-art deep face recognition models demonstrate the effectiveness of GFP-GAN in boosting significantly the face verification accuracy.
A Code-Based Multiple Watermarking Scheme for the Preservation of Authenticity of Forensic Images

In this work we present watermarking technique for the preservation of authenticity property of forensic images. The proposed technique has its basis on the utilization of watermarks by the embedding of Self-Inverting Permutations by their 2D representation in the frequency domain of forensic images. We propose the embedding of multiple integers through a code-sequence structure, where the robustness and the imperceptibility of the proposed technique has been attested and particular settings on their application e.g. the lower their imperceptibility the higher the fragility of a watermark is, cooperate on the development of the watermarking technique that can be utilized for the originality of the image. The effectiveness of proposed technique is validated throughout an experimental evaluation deploying various attacks, while the achieved results prove the potentials of the proposed model in the preservation of forensic Image authenticity.
Overview on Deep learning aided channel Equalizer Techniques

Data transmission over channels has significant distortions because of the inter-symbol interference that interferes with the dispersive nature of the channel. The performance of wireless network systems depends on channel equalization. In 5G networks, deep learning (DL) has made significant strides toward enhancing communication dependability and reducing computational complexity. Deep learning has shown that it can improve system performance in 5G and future networks while reducing computing complexity. The main topic of this review research is deep learning-aided channel equalization. This study compares several equalization approaches and examines channel equalization methods using classical and deep learning techniques. The comparative research has allowed us to identify the best deep learning models for Channel equalization as well as the previous and present methods used to achieve it. Overall, in various circumstances, the suggested work provides high-speed data transmission across a trustworthy communication channel with the least BER.
Face Image De-identification Based on Feature Embedding for Privacy Protection

With the expansion of social networking services, a large number of face images have been disclosed on the Internet. Since face recognition makes it easy to collect face images of specific persons, the collected face images can be used to attack face recognition systems, such as spoofing attacks. Face image de-identification, which makes face recognition difficult without changing the appearance of the face image, is necessary for disclosing face images safely on the Internet. In this paper, we propose a face image de-identification method by embedding facial features of another person into a face image. The proposed method uses a convolutional neural network to generate a face image that can be recognized as that of another person while preserving the appearance of the face image. Through a set of experiments using a public face image dataset, we demonstrate that the proposed method preserves the appearance of face images and has high de-identification performance against unknown face recognition models compared to conventional methods.
Annotation for computational argumentation analysis: Issues and perspectives

Abstract Argumentation has long been studied in a number of disciplines, including several branches of linguistics. In recent years, computational processing of argumentation has been added to the list, reflecting a general interest from the field of natural language processing (NLP) in building natural language understanding systems for increasingly intricate language phenomena. Computational argumentation analysis – referred to as argumentation mining in the NLP literature – requires large amounts of real‐world text with manually analyzed argumentation. This process is known as annotation in the NLP literature and such annotated datasets are used both as “gold standards” for assessing the quality of NLP applications and as training data for the machine learning algorithms underlying most state of the art approaches to NLP. Argumentation annotation turns out to be complex, both because argumentation can be complex in itself and because it does not come across as a unitary phenomenon in the literature. In this survey we review how argumentation has been studied in other fields, how it has been annotated in NLP and what has been achieved so far. We conclude with describing some important current and future issues to be resolved.
Statistical Methods for Testing Equity of False Non Match Rates Across Multiple Demographic Groups

Biometric recognition is used for a variety of applications including authentication, identity proofing, and border security. One recent focus of research and development has been methods to ensure fairness across demographic groups and metrics to evaluate fairness. However, there has been little work in this area incorporating statistical variation. This is important because differences among groups can be found by chance when no difference is present or may be due to an actual difference in system performance. We extend previous work to consider when individuals are members of one or more demographics (age, gender, race). Our methodology is meant to be more comprehendable by a non-technical audience and uses a robust bootstrap approach for estimation of variation in false non-match rates. After presenting our methodology, we present a simulation study and we apply our approach to MORPH-II data.