Reviewerly/exHarmony huggingface dataset:
Optical Character Recognition in Equipment Calibrations: a novel approach in Panama

This work aimed at achieving data acquisition using tools that are available around computer vision. Data were extracted with a camera and saved in a local server. This software is shown as an initiative in digital transformation for the National Metrology Institute.
Question Answering over Knowledge Base with Variational Auto-Encoder

Knowledge Base is a semantic data repos-itory made available over the web. Knowledge bases are multi-relational graphs consisting of entities and relations representing facts about the world. These facts follow a controlled vocabulary that drives the knowledge base, often called ontology. Entities are the graph nodes, while relations are the edges connecting the nodes. While knowledge base attention prolifer-ates, extracting information from these resources is challenging. One of the promising approaches is Question Answering systems. The Question Answering over Knowledge Base(KB-QA) system aims to provide a natural language interface to pose a query to the KB. QA systems use embeddings to map the posed Question to the facts stored in the knowledge bases. QA systems can be effectively improved using large transformer models. These transformer models are computationally expensive, which prevents their use in real-world applications. To solve this, we started our study with the use-case of Knowledge Base Question Answering systems. In KB-QA systems, representing the entities and relationships play a pivotal role in extracting the answer to the posed query. Traditional KB embedding techniques represent entities/relations as vectors, mapping them in different semantic spaces and ignoring the subtle cor-relation. A pre-training language model for encoding the KB facts will preserve the semantic and contextual correlation. While learning linguistic knowledge, these models also store relational knowledge in the training data. These Language Models are often trained on a massive corpus addressing the Out-Of- Vocabulary problem. In this paper, we incorporate a co-embedding model for knowledge base embedding, which learns low-dimensional representations of entities and relations in the same semantic space. We propose a Variational Auto-Encoder, an efficient transformer architecture that represents knowledge base representations as Gaussian distributions to address the issue of neglecting uncertainty. In addition, our method has high quality and interpretability advantages compared with previous methods. Our experimental results show the effectiveness and the superiority of the VAE approach for question-answering systems on knowledge bases over other well-known pre-trained embedding methods.
Objective quality metrics for algorithm evaluation in Computed Tomography

In recent years new algorithms for image reconstruction in computed tomography have been proposed. The aim is to optimize Image Quality (IQ) and reduce the radiation dose applied to patients. This increase makes necessary the evaluation and standardization of objective quality metrics that allow to obtain a measure of existing differences in Hounsfield Unit (HU). This paper compares the performance of fifteen full reference (FR)-IQ metrics with the criteria of specialists. Images obtained from a phantom are modified, emulating the defects caused by factors such as noise, blurring and spatial resolution reduction due to the decrease of the projections used in the reconstruction. To measure the correlation between FR-IQ and the score assigned by the radiologists, Spearman’s nonparametric rank order correlation coefficient and Kendall’s rank order correlation coefficient were used. Cohen’s kappa was used to assess interobserver agreement. The Most Apparent Distortion (MAD), Structural Similarity Index (SSIM), Information Content Weighted SSIM (IW-SSIM), Feature Similarity Index Measure (FSIM), Information Content Weighted - Mean Square Error (IW-MSE), Information Content Weighted PSNR (IW-PSNR), Optimal Scale Selection Structural Similarity Index (OSS-SSIM), Riesz transform and Visual contrast sensitivity-based feature SIMilarity index (RVSIM), and Spectral Residual Based Similarity (SR-SIM) metrics were the best performing $(p\lt 0.001)$. The worst results were obtained by Noise Quality Measure (NQM) $(p=0.051)$ and Weighted Signal to Noise Ratio (WSNR) $(p=0.829)$. Results obtained show that a study using these objective quality metrics could dispense with expert judgment to evaluate the performance of the developed methods.
QuickFaaS: Providing Portability and Interoperability Between FaaS Platforms

Serverless computing hides infrastructure management from developers and runs code on-demand automatically scaled and billed only during code’s execution time. One of the most popular serverless backend service is called Function-as-a-Service (FaaS), in which developers are many times confronted with cloud-specific requirements mainly due to the usage of specific function signatures and unique libraries. In this work, we introduce QuickFaaS, a multi-cloud interoperability desktop tool targeting cloud-agnostic functions development and FaaS deployments. QuickFaaS substantially improves developer’s productivity, flexibility and agility when developing serverless solutions to multiple cloud providers, contributing to vendor lock-in mitigation as well as reducing portability related issues.
A Complementary Result on the Construction of Quadratic Cyclotomic Classes

Side-channel analysis (SCA) is a general name for cryptanalytic methods based on side information gathered by measuring and analyzing of various physical parameters. Threshold implementation (TI) is one of the successful countermeasure techniques for some types of SCA. Within this scope, Nikova et al. gave an algorithm on the decomposition of power permutations into quadratic power permutations over finite fields $$\mathbb {F}_{2^n}$$ in [Cryptogr. Commun. 11, 37–384 (2019)]. Later on, Otal and Tekin gave a sufficient way in [Cryptogr. Commun. 13, 837–845 (2021)] to reduce the precomputation cost in the algorithm of Nikova et al. substantially. In this paper, we prove that this sufficient way is also necessary, in other words, the proposed way is an optimal solution. In that way, we provide a complementary result on the construction of quadratic cyclotomic classes.
Integrating Video Production in Early Ages to Promote Motivation for Mathematics and Transversal Competences: Examples from ViduKids Project

Aiming to develop a whole approach education for mathematics in early ages that involve video production by children, in this paper it is described the Erasmus + project ViduKids no. 2020–1-NO01-KA201–076442 aims, pedagogical approach and activities developed. The tasks developed in the scope of the project involve children exploring how to use images and videos to illustrate mathematical concepts, as shapes, numbers, and space. At the same time, children must get familiar about how to use cameras and other devices in order to develop their ideas and projects and learn about videos techniques production as one-shot video or stop-motion. When producing their own videos, children visualize and reflect on mathematical concepts, and at the same time it is expected that they develop transversal competences as communication, cooperation, creativity, and problem solving. Examples of activities developed so far are presented, namely ‘an explorer mission’, ‘a story with mathematical with content’, ‘problem solving with stop motion’, and ‘playing a game and reporting’.
Long-Term Person Reidentification: Challenges and Outlook

Person reidentification, i.e., retrieving a person of interest across several non-overlapping cameras, is a task that is far from trivial. Despite its great commercial value and wide range of applications (e.g., surveillance, intelligent environments, forensics, service robotics, marketing), it remains unsolved, even when the individuals do not change clothes during the recognition period. This paper provides an outlook on long-term person reidentification, an emerging research topic regarding when consecutive acquisitions of an individual can be found apart for days or even months, making such a task even more challenging. A long-term reidentification system using face recognition is presented to emphasize current techniques’ limitations.
Compression Point in Field of Characteristic 3

For some applications, systems and devices, it might be desirable to take as few as possible of bit memory space and still keep the same result. Compression point is a new method that used some arithmetic operation in (ECC) elliptic curve cryptography to reduce memory space. If we take any point on the elliptic curve, we can see that this point is consisting of two coordinates x and y, so with compression point we can compressed this point and keep only one coordinate x or y and one sign bit/trit, requiring only half the space. In this paper, we will show new methods of compression point that can save $$25\%$$ of the space memory in fields of characteristic 3, and compare it with previous result.
Detection of Voice Conversion Spoofing Attacks Using Voiced Speech

Speech consists of voiced and unvoiced segments that differ in their production process and exhibit different characteristics. In this paper, we investigate the spectral differences between bonafide and spoofed speech for voiced and unvoiced speech segments. We observe that the largest spectral differences lie in the 0–4 kHz band of voiced speech. Based on this observation, we propose a low-complexity, pre-processing stage which subsamples voiced frames prior to spoofing detection. The proposed pre-processing stage is applied to two systems, LFCC+GMM and IA/IF+KNN that differ entirely on the features and classifier used for spoofing detection. Our results show improvement with both systems in detection of the ASVspoof 2019 A17 voice conversion attack, which is recognized to have one of the highest spoofing capabilities. We also show improvements in the A18 and A19 voice conversion attacks for the IA/IF+KNN system. The resulting A17 EERs are lower than all reported systems where the A17 spoofing attack is the worst attack except the Capsule Network. Finally, we note that the proposed pre-processing stage reduces the speech date by more than $$4\times $$ due to subsampling and using only voiced frames but at the same time maintaining similar pooled EER as that for the baseline systems, which may be advantageous for resource constrained spoofing detectors.
Virtual Assistants Applications in Education

Due to the rapid development of artificial intelligence, popular Virtual Assistants like Amazon Alexa or Google Assistant, can be applied to a wide variety of business areas. One area in which Virtual Assistants can be very useful is in Education, specially due to the pandemics that is occurring during the last years, as it can provide to students, teachers and staff an alternative administration tool as well as introduce new learning processes in classroom or on online classes. This work reviews and analyses some applications of Virtual Assistants in the education process. The reviewed work relies mainly on three categories: Student engagement with academic life, Education process during lessons and Learning of foreign languages. The presented solutions generally have great potential but the majority are simple proof of concepts and need more development and proper tests to enable retrieving more accurate results.
Adaptive Environment System to Manage Comfort Preferences and Conflicts

Managing comfort preferences conflicts of the different users and locals on an Internet of Things (IoT) adaptive system is a actual problem. This paper, proposes a protocol and hierarchical rules to develop a multi-agent system to achieve an Adaptive Environment System that supports interaction between persons and physical spaces, where spaces smartly adapt to their preferences in a transparent way. And also a set of security customization’s to secure the actuators and users on space, that has been developed using a multi agent system architecture with different features to achieve a solution that supports the proposed objectives. Supported by a base architecture to achieve the full system implementation.
StegIm: Image in Image Steganography

Today, data security is a significant problem in data communication. Sometimes we do not realize how susceptible we are to cyberattacks and the theft of critical information, including everything, from social network passwords to complete identities. Any system, no matter how sophisticated it is, is vulnerable and susceptible to attack. Steganography is a technique for hiding secret information by adding it in a non-secret file. Also, it can be used in combination with encryption to further conceal or safeguard data. Therefore, in this paper, we present the StegIm model for hiding, retrieving, and detecting images using variants of LSB Encoding Steganography. We present the implementation of the model with different algorithms for hiding the image in another image (shape steganography algorithm and standard delimiter-based steganography algorithm) and give comparison analysis of used algorithms. To further strengthen the security, we implemented image encryption for this type of image steganography and analyzed the improvements, benefits, advantages, and disadvantages of this model in each phase of hiding/retrieving. Also, StegIm can detect hidden data in given images with the help of machine learning methods. This model will strive to reach the highest level of confidentiality, authenticity, and integrity of the user’s confidential and sensitive data.
Data Visualization Guide for Smart City Technologies

Smart city technologies collect and analyze urban data from which they generate visualizations that allow authorities to monitor the city and make more efficient decisions based on evidence. However, visually representing the data by itself does not reveal the visual patterns of interest; each type of visualization has its strengths and limitations that will make it suitable for some purposes and unsuitable for others. In this study, we propose a guide for selecting data visualization techniques to support people who wish to visualize and take advantage of the data available in a city. The guide was developed based on the fundamentals of visualization and considering the data visualization needs for city management. Using the guide will help analysts select effective visualization techniques, thus helping to create communication channels between data and people.
A Toolkit for Re-Mar to Enhance Classroom Ocean Literacy

Developing environmental concerns about the ocean at all ages underlies ensuring a better and healthier planet. To cope with that, there are several international initiatives to promote ocean literacy. However, there is an instigation lack in the introduction process of the sea theme in school education. A domain repository with marine learning objects could mitigate this gap by providing educational material that can be used in classroom activities or even for self-learning. So, to be successful, the repository has to be attractive to users and motivate them to become ocean literate. Artificial Intelligence technologies can play a relevant role, providing assisted services, such as learning object publication and search. The Re-Mar repository employs some technologies (defined as a toolkit), such as ontology and thesaurus, natural language processing, and a multi-agent system. The integration of these technologies provides a rich environment to promote ocean literacy.
Teacher Training in the Fields of STEAM: From Physical to Digital Tools

The twenty-first century presents fundamental challenges for education systems focused on skills development. With this requirement, the training of teachers is changing little by little. Where the learning of the knowledge to be taught seeks to articulate itself in different modalities. Teachers can use various resources, from manipulative materials to technological resources, which can make the class more attractive and contribute to the student’s interest in the content being worked on, thus building knowledge. The Poly-Universe in Teacher Training Education (PUNTE) project aims to develop innovative and transdisciplinary pedagogical methods that are mainly related to a revolutionary educational tool called Poly-Universe. This work aims to describe and analyse the material of the PUNTE project, which allows the development of a new methodology for visual mathematics education in the physical and digital aspects to improve the quality of students’ learning. Based on classroom observations and workshops for prospective teachers, the results have shown that this tool can be used in a science, technology, engineering, art, and mathematics (STEAM) context.
One-Against-All Halfplane Dichotomies

Given M vectors in N-dimensional attribute space, it is much easier to find M hyperplanes that separate each of the vectors from all the others than to solve M arbitrary linear dichotomies with approximately equal class memberships. An explanation of the rapid growth with M and N of the number of separable one-against-all linear halfplane dichotomies is proposed in terms of convex polyhedra in a hyperspherical shell. The counterintuitive surge is illustrated by averaged results on pseudo-random integer arrays obtained by Linear Programming and Neural Networks. Although the initial motivation arose from seemingly arbitrary rankings of scientists and universities, this project is not directed at any application.
Review of Handwriting Analysis for Predicting Personality Traits

Most research on document analysis is carried out through human intervention, such as manually counting the parameters of document analysis as the input of neural networks. Document analysis via computer vision methods is a relatively less explored area of research. In this paper, we investigated the literature on document analysis in recent years, and summarized its development process and the commonly used research methods, discussed their advantages and disadvantages. Meanwhile, we put forward the general research ideas and steps for document analysis, highlight the limitations of existing processes and the challenges typically faced when designing such systems, provide potential, feasible solutions, and point out the direction of further research, which give guidance to novice researchers and have reference value for subsequent researchers. Our experiments verify that our method is feasible and can be used as a substitute for specific application scenarios without professional handwriting experts.
A New Preprocessing Method for Measuring Image Visual Quality Robust to Rotation and Spatial Shifts

Measuring the visual quality of an image is an extremely important task in computer vision. In this paper, we perform 2D fast Fourier transform (FFT) to both test and reference images and take the logarithm of their spectra. We convert both log spectra images to polar coordinate system from cartesian coordinate system and use FFT to extract features that are invariant to translation and rotation. We apply the existing structural similarity (SSIM) index to the two invariant feature images, where no extra inverse transform is needed. Experimental results show that our proposed preprocessing method, when combined with the mean SSIM (MSSIM), performs better than the standard MSSIM significantly in terms of visual quality scores even when no distortions are introduced to the images in the LIVE Image Quality Assessment Database Release 2. In addition, when images are distorted by small spatial shifts and rotations, our new preprocessing step combined with MSSIM still performs better than the standard MSSIM.
Automatic Extraction of Verbal Phrasemes in the Electrical Energy Field with NooJ

The abundant presence of prefabricated sequences attracts the attention of linguists. Phraseological units are often problematic for foreign learners both receptively and productively. In addition, they think that the number of phraseological units is greater in FOS (French for specific purposes) than in general French. In the field of electrical energy, the abundant presence of phrasemes [verb + nominal group or not + preposition + nominal group] pose many problems for non-native engineer-learners. The modeling is done based on the observation of our corpus and dictionaries in electricity. By using three grammars (rational grammar, algebraic grammar and contextual grammar.), NOOJ allows us to extract 1389 phrase sequences for teaching. Also, a disambiguation can be set up to reject useless phrasemes by using the + EXCLUDE operator. At the end, for the selection of phrases for teaching, three criteria (frequency, fixation and pragmatic criterion) seem relevant to us for the identification or classification of phrases from the professional field for foreign learners.
Self-supervised Out-of-Distribution Detection with Dynamic Latent Scale GAN

Dynamic latent scale GAN is a learning-based GAN inversion method with maximum likelihood estimation. In this paper, we propose a self-supervised out-of-distribution detection method using the encoder of dynamic latent scale GAN. When the dynamic latent scale GAN converges, since the entropy of the scaled latent random variable is optimal to represent in-distribution data, in-distribution data are densely mapped to latent codes with high likelihood. Therefore, out-of-distribution data can only be mapped to latent codes with low likelihood. Also, since the latent random variable of GAN is i.i.d. random variable, it is easy to calculate the log-likelihood of predicted latent code. These characteristics of dynamic latent scale GAN enable the log-likelihood of the predicted latent code to be used for out-of-distribution detection. The proposed method does not require a pre-trained model, mutual information of in-distribution data, and additional hyperparameters for prediction. The proposed method showed better out-of-distribution detection performance than the previous state-of-art method.
Realization of Autoencoders by Kernel Methods

An autoencoder is a neural network to realize an identity mapping with hidden layers of a relatively small number of nodes. However, the role of the hidden layers is not clear because they are automatically determined through the learning process. We propose to realize autoencoders by a set of linear combinations of kernels instead of neural networks. In this framework, the roles of the encoder and/or decoder, are explicitly determined by a user. We show that it is possible to replace almost every type of autoencoders realized by neural networks with this approach. We compare the pros and cons of this kernel approach and the neural network approach.
Retargeted Regression Methods for Multi-label Learning

In Multi-Label Classification, utilizing label relationship is a key to improve classification accuracy. Label Space Dimension Reduction or Classifier Chains utilizes the relationship explicitly however those utilization are still limited. In this paper, we propose Retargeted Regression methods for Multi-Label classification by extending Retargeted Linear Least Squares originally proposed for Multi-Class Classification. Retargeted methods not only learn classifiers but also modify targets with margin constraints. Since in Multi-Label Classification, an instance may have more than one label, large margin constraints between all pairs of positive labels and negative labels are introduced. This enables to utilize the label relationship with taking ranking of labels for each instance into consideration. We also propose a simple heuristic to determine a threshold parameter for each instance to earn zero-one classification. On nine benchmark datasets, the proposed method outperformed conventional methods in the sense of instance-wise ranking. In best cases, classification accuracy was improved at $$7\%$$ on AUC metric.