Reviewerly/exHarmony huggingface dataset:
A Combined Noisy Borehole Image Log Segmentation Method

Borehole Image Logs are a valuable tool for studying and characterizing reservoirs. However, the quality of these images is often compromised by various types of noise due to the environment in which they are acquired. Additionally, extracting the relevant information from these images can be a challenging task, as the existing noise can impede data segmentation even after improvement. Therefore, this paper aims to investigate the best combined strategy for enhancing these images using Equalization and Retinex methods, as well as segmenting them using threshold and quantization techniques to achieve better extraction of regions of interest. By analyzing the source of the noise, we determined that the Multi-Scale Retinex with Color Preservation and Color Image Quantization is an effective approach for this task.
Cross-lingual Text-to-Speech with Prosody Embedding

The research presented in the paper handles the problem of multilingual text-to-speech, particularly its capability of synthesis of speech when the appropriate combination of desired properties (speaker, language, speaking style) is missing from the training corpus. The model proposed in the paper achieves cross-lingual speech synthesis through the use of neural network embeddings, applied not only to speaker and speaking style IDs, but also to context-dependent phonemes and a range of prosodic events, including accents and phrase breaks. This allows the model to efficiently capture relationships between phones and prosodic events in different languages, and consequently to synthesize speech in the voice of a person who has never spoken the target language or used a target style. The proposed model was trained on speech corpora of American English and Serbo-Croatian. A range of experiments including subjective evaluation of synthesis was carried out to establish both the quality of synthesis in different scenarios and under different conditions, as well as the similarity of speaker voices between cross-lingual and original language scenario.
Capturing and Processing Sign Animations to a Portuguese Sign Language 3D Avatar

Usually, the deaf community faces enormous difficulties in communicating with the hearing community. Unfortunately, there are few systems available to the public to assist in the communication between the deaf community and the hearing community. Some countries, including Portugal, are trying to develop systems to mitigate this problem. Thus, in this paper, it is proposed a system that captures signs, using motion capture systems and produces the needed animations, represented by a 3D Avatar, so that a deaf user may understand them. This first implementation consists of a component, receiving text in Glosses format, from the Portuguese Sign Language, getting the corresponding animations from a database, and animating a 3D Avatar with these animations. The work being carried out is part of a larger system, that includes a user interface and another component that translates Portuguese Spoken Language to Portuguese Sign Language, through a Neural Network. The system aims to assist deaf people in communicating in all scenarios of everyday life. The first results show great potential for the deaf community to be able to use the proposed system.
Image Enhancement using Recursive Anisotropic and Stationary Wavelet Transform

Image enhancement is a useful application to provide more explicit visual interpretation for the human eye, and a better understanding of digital imagery which is particularly useful in satellite images captured from remote sensing devices. In this correspondence, a recursive image enhancement technique is proposed; combining anisotropic and stationary wavelet transform. The estimated low-frequency and high-frequency subbands are interpolated and added recursively, to generate a new enhanced image with sharpened edge details. The proposed system is evaluated with famous wavelet families namely Haar, Daubechies-4, Symlet-4, and Coiflet-4; on randomly selected satellite images and noisy images. Subjective and quantitative methods are used to evaluate the proposed system.
A Feature-based Approach for the Recognition of Image Quality Degradation in Automotive Applications

Cameras play a crucial role in modern driver assistance systems and are an essential part of the sensor technology for automated driving. The quality of images captured by in-vehicle cameras highly influences the performance of visual perception systems. This paper presents a feature-based algorithm to detect certain effects that can degrade image quality in automotive applications. The algorithm is based on an intelligent selection of significant features. Due to the small number of features, the algorithm performs well even with small data sets. Experiments with different data sets show that the algorithm can detect soiling adhering to camera lenses and classify different types of image degradation.
A Translation System from European Portuguese to Portuguese Sign Language

The deaf community is usually isolated from the rest of the hearing community, and this problem is found in many countries, including Portugal, where only about 1% of the population know Portuguese Sign Language. There are still no suitable applications to help them and, therefore, in this work, a system is proposed that translates European Portuguese into Portuguese Sign Language, showing the translation to an end user through a 3D Avatar. The system is divided into two components, the first being the textual translation system and the second the translations’ animated representation, using a 3D Avatar. The former uses a hybrid solution of rule-based and neural approaches. The latter uses a motion capture system to represent the signs’ gestures and creates a visual interface to animate the translation through an avatar. The implementation consists in a system receiving text in Portuguese, converting it into Portuguese Sign language, getting the corresponding animations from a database and animating a 3D Avatar with these animations. The system aims to assist deaf people communicating in all scenarios of the everyday life. The first results obtained are very promising for the added value of the system under development.
Prediction of Hospital Readmission using Federated Learning

Wearable devices have the ability to generate vast amounts of data that can be put to use in a multitude of applications, particularly in the field of e-health. However, the potential invasion of privacy that comes with utilizing personal data collected by these devices cannot be overlooked. Federated Learning (FL) is a promising solution to this issue that allows models to be trained in a decentralized manner while keeping user data on their own devices. This approach effectively minimizes the risk of privacy breaches and has the potential to be employed in a variety of applications where the protection of user data is of utmost importance. This paper focuses on the use of FL in predicting hospital readmission in 130-US diabetes hospitals for a data set collected over an 9-year period. The results suggest that FL can achieve comparable performance while maintaining privacy and diversity of data. This is an essential aspect of FL, as it enables continuous real-time learning without compromising privacy.
Identification During Verification Of Diplomas In The Blockchain System

Blockchain technology is encountering many challenges during its application in the field of education, especially in higher education institutions. Above all, it can be used for the process of generating and verification of various academic documents, including diplomas and various certificates. Apart from the creation of several prototypes and pilot projects, recent research shows that there is no standard platform based on blockchain that has managed to show good results and is applicable in any higher education institution. Through the paper, we try to clarify the challenges of blockchain application in higher education institutions and we will propose a method for electronic generation through the blockchain system of academic documents. For faster and easier verification, we propose the method by generating a QR code during the generation of academic documents. Higher education institutions should cooperate with each other to reach a standard platform based on blockchain for the generation and verification of academic documents, as the only possibility to prevent various forgeries.
Automatic quantitative and morphometric analysis of muscle fibers

In this work, we propose an automated solution to support researchers of histological sciences labs on the analysis of muscle fibers microscopic images. For this purpose, we developed a dataset carefully curated composed of semi-seriated muscle samples. The manual annotation of these images is laborious and tiring, and the results may become compromised as the human gets tired. Thus, we propose a framework to address this problem which starts with a preprocessing step, aiming to mitigate discrepancies regarding the color variation frequently found in this type of image, followed by the use of Mask R-CNN, to automatically perform muscle fibers segmentation and also to extract the smallest diameter of these fibers, which is an important measure for further analysis. Experiments showed that there is no statistically significant difference between the results obtained manually and those obtained using our proposal. The dataset used in the experiments is also a contribution of this work.
Piracy detection in online soccer streaming with video content inspection: an application to the Portuguese market

This study presents the earlier steps in the detection of pirate soccer streams using computer vision. The goal is to provide broadcasters, advertisers, and sports organizations with a powerful tool to protect their intellectual property and revenue. The focus of this study is the Portuguese market, therefore the majority of work relates to the country’s major soccer competitions and TV broadcasters. The study employs a DenseNet201 frame classifier and YOLOv5 object detector models trained with transfer learning using a dataset of manually annotated images extracted from publicly available sources. The study demonstrates that shot classification and broadcaster logo detection are highly effective in identifying visual elements in pirate streams, allowing the detection of unauthorized use of copyrighted content. The results show that the proposed method can be powerful solution for the growing problem of pirate soccer streams, achieving high accuracy in real-time applications, enabling broadcasters to take immediate action to protect their content and revenue.
Impact of Metaverse and Advanced Technology on the Enterprises

The current study sought to understand and explore the field of technological advancements in these modern times, especially after the covid-19 surge, from the perspective of Business and Management. The development of science and technology will define a new meaning and application of the management and administration of the firm. Due to modernization in technology and automation of work in managerial work, the processes of businesses become easy to handle, maintain and control. Technologies have made different elements of business such as manufacturing, communication, purchase and sales, marketing, trading and advertising became easier than before. With the present technology, machines are able to process the functions and give the desired outputs after feeding the respective commands in that machine. Technology also aids the productivity of the employees, to work better, easier and with convenience. With the betterment of AI (Artificial Intelligence), businesses are able to provide satisfaction to the customer, giving response at time, reviewing, analyzing their issues, etc., which saves their overhead costs and expands the capabilities of the firms. With many upcoming advancements like MR (Mixed Reality), Metaverse, IOT (Internet of Things), Robotic Process Automation, Quantum Computing and many others, it will surely impact the management work in the work in both positive and negative ways. This study focuses to cover both the impacts and make the future even better, majorly comprising of the Metaverse and upcoming digitalization and technological evolutions and the rise of new tech-gen.
Integrating Multiple Public Datasets for Human Activity Recognition using Machine Learning

This study proposes the integration of multiple publicly available datasets for Human Activity Recognition (HAR) to create a unified dataset, which was used to train and test different machine learning algorithms, including J48, kNN, LR, MLP, NB, RF, and SVM. The performance of these algorithms was evaluated in terms of accuracy and F-score, and the results showed that RF achieved the best performance, with an accuracy and F-score of 0.969. The study also compared the results with previous studies on individual datasets and showed that the proposed approach has promising results for HAR.
An Augmented Reality Application with EasyAR for Spatial Mapping in Museums

The present time has seen advances in the use of augmented reality technologies, making its use commonplace. Nowadays, it is common to apply digital transformation to the environment of museums. This article discusses a solution for creating artificial reality environments using the EasyAR software development kit. The solution was created as a response to a real case study for the Foz Côa Museum, in Portugal. The museum seeks to improve the user experience of its visitors, by using a new technology. The technology is capable to perform the spatial mapping of areas, populating the created maps with content and sharing these environments between multiple devices. Although the app was developed to be used in museums there is no restrictions to use it in any other area, where the creation of an artificial reality environment would be deemed advantageous.
Slot Lost in Translation? Not Anymore: A Machine Translation Model for Virtual Assistants with Type-Independent Slot Transfer

In this article, we present a machine translation model adapted to the domain of intelligent virtual assistants (IVA) that can be used to translate training and evaluation resources. Our model is capable of transferring natural language understanding (NLU) slots between source and target language. Slots can be annotated with a simple XML-like format and no specific types are expected, which makes our model task-independent. We evaluate the quality of translations with BLEU metric and slot transfer using Fl-score on the dataset prepared for the purpose of this model creation as well as the WMT dataset. We report an improvement of 17.21 BLEU in the IVAs domain when compared to the baseline M2M100 model and slot F1 of 65.47% for sentences with multiple slot types and 87.54% for sentences with single slot type. To analyze the quality of our model, we trained NLU models from translated training resources and compared them with models trained from the original data. Results indicate that our model is particularly good for the translation of intent detection models. We released an English-to-Polish translation model, and tools for model training and evaluation. We also created a corpus for the community to foster further research on multilingual IVA.
Real–Time Vehicle Detection Based on Wavelet Decomposition and CNN

This paper presents a new approach for real-time vehicle detection and classification using a fully convolutional neural network (FCNN) with wavelet transform as a preprocessing technique. Real-time prediction is crucial for efficient traffic control, as it enables timely decision-making and response. The proposed approach addresses this issue by using stacked images obtained from wavelet transform as input to the FCNN, resulting in faster inference time and lower number of FLOPs compared to traditional CNN-based methods. Additionally, the proposed method shows promising results in terms of accuracy, especially with some variations of the base model. Overall, this approach represents a significant step forward in the development of real-time object detection systems.
Improving Ear Recognition with Super-resolution

Super-resolution (SR) techniques are advanced image enhancement methods that increase the resolution of an image, proving to be valuable in various applications such as enhancing photographs and boosting person recognition performance. In this study, we focus on Single Image Super Resolution of ear images, a super-resolution approach that generates missing information from a single input image. Early super-resolution attempts relied on interpolation, but these methods had limited success. The advent of deep neural networks has led to significant improvements in SR performance. We assess the performance of the Enhanced Deep Residual Network (EDSR) and Shifted Windows Transformer Network (SwinIR) for ear image super-resolution and extend the study to recognition experiments using super-resolution images. Utilizing the UERC dataset, which contains 16,665 ear images with varying sizes, shapes, and orientations, we trained four models: two on EDSR and two on SwinIR networks, with scaling factors of two and four, respectively. Recognition experiments were employed to evaluate the two distinct model designs. The EDSR model demonstrates superior performance in terms of Rank-1 recognition. Recognition experiments using super-resolution images were conducted using original images and bicubic interpolated images as a baseline. A regular ResNet was used to showcase applicability of super-resolution for ear recognition in virtually any ear recognition model.
Parameterization of Educational Activities in an Educational Institution of Higher Education

Purpose of the study. The formulation of a program of educational work in universities is one of the most important in the framework of the formation of the personality of a graduate. However, the rules for the formation of such a program are framework and are based on the state strategy of educational activities, as well as in accordance with the profile of the educational institution itself. Such an «open» state of the formation of programs, of course, gives advantages in the freedom to choose the trajectories of educational work with students; however, it also leaves an imprint on the possibility of fully considering the interests of all aspects of the educational process. It is important to form a vision of how, what parameters will consider the opinion of not only the leadership of the educational institution, the teaching staff, but also the students themselves, as well as their parents. Materials and methods. The basis for the study was the materials of the regulatory framework and theoretical articles in the field of educational activities. Emphasis was placed on a systematic analysis of the relationship of educational practices with an eye on the value orientations of today’s youth. In terms of developing proposals, the method of an online questionnaire survey of students, parents and legal guardians, teaching staff, faculty management, as well as representatives of departments in whose competence are issues of educational activities was used. Results. Maps for assessing the parameters of educational work have been developed in the context of spiritual, moral, and socio-axiological parameters. Based on the analysis, it was possible to structure both the strengths of the educational practice of the university, as well as weaknesses that are important to pay attention to. As a result, the approach described in the article allows an educational institution, when forming its own program of educating students, determining the development trajectories of each student, to understand what problems and misunderstandings may arise in the system plan, and to determine for themselves what individual parameters of educational contexts are important to focus on. Conclusion. The author is working on the systematization of the practices of the educational process through the systemic decomposition of the individual components of the programs of educational work with students at the university. Questions that require further research – what should be the indicative indexes and tools for measuring them, as well as the frequency of such work. Therefore, for example, it draws attention to what metrics will allow assessing the degree of development of internal feelings among students as will, justice, faith in goodness, etc.
Evaluating Quality of Visual Explanations of Deep Learning Models for Vision Tasks

Explainable artificial intelligence (XAI) has gained considerable attention in recent years as it aims to help humans better understand machine learning decisions, making complex black-box systems more trustworthy. Visual explanation algorithms have been designed to generate heatmaps highlighting image regions that a deep neural network focuses on to make decisions. While convolutional neural network (CNN) models typically follow similar processing operations for feature encoding, the emergence of vision transformer (ViT) has introduced a new approach to machine vision decision-making. Therefore, an important question is which architecture provides more human-understandable explanations. This paper examines the explain-ability of deep architectures, including CNN and ViT models under different vision tasks. To this end, we first performed a subjective experiment asking humans to highlight the key visual features in images that helped them to make decisions in two different vision tasks. Next, using the human-annotated images, ground-truth heatmaps were generated that were compared against heatmaps generated by explanation methods for the deep architectures. Moreover, perturbation tests were performed for objective evaluation of the deep models' explanation heatmaps. According to the results, the explanations generated from ViT are deemed more trustworthy than those produced by other CNNs, and as the features of the input image are more dispersed, the advantage of the model becomes more evident.
Data Collection Framework for End-to-End Radio and Transport Network Quality Monitoring

Estimating Quality of Experience (QoE) and Quality of Service (QoS) metrics is crucial for delay-sensitive use cases, and it relies on the available information in an operator's network. However, for low-latency traffic types that use unreliable and best-effort transport solutions for data transfer, obtaining these metrics only based on transport network probing – and ignoring the radio characteristics – is challenging. While end-to-end QoS metrics can be obtained by correlating per session radio and core data, predicting QoE is more difficult and requires the use of machine learning (ML) models. Nevertheless, training these models demands a vast amount of diverse measured reference metric data, which can be hard to acquire. In this study, we propose a delay-critical service-focused data collection framework that automatizes the measurements of networked services under various synthetic network degradation and jointly monitors radio and transport metrics, e.g., radio quality, throughput, latency, and user plane traffic. Our proposed framework runs on general-purpose laptops, eliminating the need for any specialized or expensive hardware. Furthermore, our tool provides a significant amount of data and meta-labels in an easy and automated way, which can be used to train future ML models to estimate QoS and QoE for the examined service. This demo paper demonstrates our proposed data collection framework, with synthetic transport and radio degradation, on a video conferencing use case and provides insight into how the main disturbance types influence the service streams.