Reviewerly/exHarmony huggingface dataset:
A practical approach to validate the authenticity of identity documents

There is a need for a system capable of determining the authenticity of identity documents in a simple way due to the increase over the years of crimes such as fraud and identity theft, this in the specific case of Costa Rica; the reason for this situation is that many processes and transactions can be carried out virtually, which opens the door for this type of crime to take place. For the above purpose, the present work consists of the research, design, and development of a prototype system to determine whether an identification document is authentic. This authenticity estimator consists of a composition of algorithms applied to images, such as filters and transformations; in order to perform a process called Optical Character Recognition (OCR), which consists of the recognition of symbols or characters from a given image. This proposal uses classical computer vision techniques, although it is important to mention that machine learning techniques can also be used for this purpose. Nevertheless, with this approach, 80 percent of accuracy was obtained in the recognition of identity documents.
Video Game's Mechanics Generation and Balancing

. This article is a continuation of the idea about developing a gaming prototype generator from natural language text. Game balance theme, which mentioned in previous authors' papers, fully opens up from theoretical and practice standpoints. Many expert opinions and definitions were analyzed to understand the identity of that problem. As a result, a full understanding of the problem was formed and challenges were listed, which exists in this direction. The purpose of the research is automation of game-designers routine in the prototyping stage. For this, the problem of generation of game balance from text documents is solved, so a number of scientific papers are considered, which offer algorithms, optimizing and automating approaches and computer games balance. The functionality of the dynamic balance editor Machinations is presented in detail, and the principle of operations is illustrated. To check the availability of approaches in the overall collection of the prototype generator work, a number of experiments were provided. They prove effective work with exhaustive diagrams and saves important development team resources. In addition, a particular problem of formalization and visualizing connection between gameplay and plot was solved, which is justified by the context dependence of the game balance. In conclusion, plans for the further development of a full-featured tool for the game scriptwriters and designers are given. As a conclusion, we state that automatic correction of the game balance is possible as well as its generation based on text.
The Flag Median and FlagIRLS

Finding prototypes (e.g., mean and median) for a dataset is central to a number of common machine learning algorithms. Subspaces have been shown to provide useful, robust representations for datasets of images, videos and more. Since subspaces correspond to points on a Grassmann manifold, one is led to consider the idea of a subspace prototype for a Grassmann-valued dataset. While a number of different subspace prototypes have been described, the calculation of some of these prototypes has proven to be computationally expensive while other prototypes are affected by outliers and produce highly imperfect clustering on noisy data. This work proposes a new subspace prototype, the flag median, and introduces the FlagIRLS algorithm for its calculation. We provide evidence that the flag median is robust to outliers and can be used effectively in algorithms like Linde-Buzo-Grey (LBG) to produce improved clusterings on Grassmannians. Numerical experiments include a synthetic dataset, the MNIST handwritten digits dataset, the Mind's Eye video dataset and the UCF YouTube action dataset. The flag median is compared the other leading algorithms for computing prototypes on the Grassmannian, namely, the l2-median and to the flag mean. We find that using FlagIRLS to compute the flag median converges in 4 iterations on a synthetic dataset. We also see that Grassmannian LBG with a codebook size of 20 and using the flag median produces at least a 10% improvement in cluster purity over Grassmannian LBG using the flag mean or l2-median on the Mind's Eye dataset.
Surveying Neural Network Validation through analysis and classification premised on varied standpoints

The future of artificial intelligence is not only limited to the technology itself. It also includes the way we live with this new tech, and how it will shape our future. AI is a powerful tool that can help us make better decisions about everything from healthcare to education. However, it also has its downsides like privacy concerns and bias in algorithms. In this paper, the author dives into the problems associated with the anomalies in the behaviors of machine learning models. They review the different methods in literature to identify such behaviors and prevent them. The author then attempts to verify the authenticity of these methods through three different perspectives. This paper surveys these works with the goal of culling out verification methods capable of addressing major challenges.
Picture and Picture Learning Model in Improving Speaking Skills Elementary School Students

This study was structured with the aim of improving the speaking skills of fourth grade students of elementary school by using the picture and picture learning model. This type of research is Classroom Action Research (CAR), where the teacher is the implementer of learning while the researcher is the observer. The design of this study uses the Kemmis and Mc. Taggart. The subjects of this study were fourth grade students of elementary school which consisted of 20 students. The object of research is students' speaking skills. Data collection techniques using tests and observations. Data were analyzed descriptively and presented in tabular form. The results showed an increase in students' speaking skills, the average value of speaking skills in the first cycle of the first meeting was 64.95 and the second meeting was 68.3. In the second cycle, the average value of speaking skills for the first meeting was 72.35 and the second meeting was 76.9. This means that learning in cycle II has met the indicators of success, so it can be concluded that improving speaking skills using the picture and picture learning model can improve the speaking skills of fourth grade elementary school students.
Jointly Linear Embedding via <i>L</i><sub>2,1</sub>-Norm for Dimensionality Reduction

In this study, we present a novel dimensionality reduction method called Jointly Linear Embedding (JLE). Unlike previous methods such as Neighborhood Preserving Embedding (NPE), in which local neighborhood information is preserved during the dimensionality reduction procedure, JLE aims to preserve the sparse reconstructive relationship of the original data by taking the merits of jointly sparse learning based on L 2,1 -norm. In the framework of JLE, the sparse weight matrix can reflect the intrinsic geometric structures of the original data and inherit the ability of discriminant analysis. By solving the L 2,1 -norm regularized objective function, the optimal projections, which are invariant to rotations and robust to outliers, can be obtained by JLE. Extensive experiments on visual recognition and fabric defect classification datasets demonstrate the superiority of the proposed L 2,1 -norm regularized learning method compared with the state-of-the-art methods.
Robust Handwritten Digit Recognition System using Hybrid Artificial Neural Network on FPGA

Handwritten digit recognition (HDR) deals with variety of sources which includes papers, photographs, touch screens to recognize them into different categories. Deep Learning and Machine Learning algorithms are usually difficult to implement on FPGA because of their requirements in terms of power. In this work, handwritten number recognition for FPGAs method is employed using ANN networks. The proposed ANN’s correctness was determined by verification and comparison with numerous ANN networks, and the recognition rate is recorded to be 99.38%. The Xilinx Zybo is used to replicate the network. The utilization of FPGA resources was computed for various activation functions like ReLU, sigmoid size-5, sigmoid size-10 and the accuracy is retained. The parameters like power consumption and timing comparison are observed. The developed IP block occupies less of the total area and results in resource-saving and effective and making it appropriate for additional embedded applications.
Real-Time Classification of Disaster Images from Social Media with a Self-Supervised Learning Framework

Many studies have shown that disaster images posted on social media by users can be helpful to establish situational awareness and support disaster management. As such, it is important to develop a real-time framework to automatically classify disaster related images on social media from not informative ones. Unfortunately, most of the existing methods are based on supervised learning which requires manual data labeling. Meanwhile, some studies adopt unsupervised learning classification, which avoids manual labeling, but results in lower accuracy. To fill the research gap, this paper built upon MoCo model and developed a self-supervised learning classification framework. Using social media images of seven real disaster events as case studies, the results demonstrate that the proposed method without the manual labeling, can reach relatively high accuracy for disaster image classification by comparing with the state-of-the-art supervised learning and unsupervised learning models.
A Reconfigurable Approach for Deconvolutional Network Acceleration with Fast Algorithm

Recently, deconvolutional neural network (DeCNN) has attracted widespread attention in various applications. The deconvolution (DeConv), as the main operation in DeCNN, has become the bottleneck of acceleration, due to its high computational complexity. Previous works have introduced fast algorithms such as the cascaded fast FIR algorithm (CFFA) and the Winograd algorithm to reduce the computational complexity of DeConv for the applications on mobile devices. Since these fast algorithms need different computing parameters to accelerate various operations, directly applying these methods to process DeCNNs with different kernels usually causes limited flexibility. To address this problem, we propose a reconfigurable scheme based on the fast transformation algorithm (FTA) to accelerate multiple types of DeConvs, minimizing the hardware overhead for reconfigurability. Based on this scheme, a reconfigurable hardware architecture is developed to support several types of DeConvs. In addition, an adaptive dataflow is proposed to handle different convolutional layers. The presented design can support several types of operations and achieve up to 222.54 GOPS under 210 MHz on the Intel Arria 10SX FPGA platform, which shows our design can obtain better flexibility and computational efficiency compared with prior arts.
Depth Estimation with Sonar-based Correction for Low-cost Underwater Drone

Underwater drones are expected to survey in areas where human beings cannot go directly. To achieve automation of underwater drone operation, high-performance sensing technology is highly required. Especially, depth estimation plays an important role to confirm the relative position of the target objects and the underwater drone. However, there is still no efficiently trained deep learning model for underwater depth estimation to estimate the depth of underwater objects. In this paper, an underwater depth estimation model and depth correction method for depth maps using sonar is proposed. By enhancing the training dataset, an underwater depth estimation model is generated. Moreover, the generated depth map can be updated using the corrected depth of the target object catched by the assistant sonar system. Simulation results show that the proposed method performs highly accurate depth estimation for underwater images.
An Integrated Learning Platform Focusing on STEAM and Collaborative Modules for Macau

In Macau, the effectiveness of traditional classroom learning is questioned as the problem is discovered by the changes in technology advances, social media, and the varieties of learning methods. Learning experiences, interests, discoveries, and creativity development are considered essential to academic performance, and the interactive collaboration between parents and children in the learning processes becomes crucial. This article presents the work's design and preliminary analysis, explaining the characteristics of an integrated learning platform and its design for a multicultural, multilingual learning environment. The “Fries Lab” is a learning platform currently being developed that is compatible with various digital devices and aims to create a collaborative learning experience based on blended learning approaches, children's psychology, STEAM educational approaches, gamification, user-centred design, and interface design principles, in which parents, educators, and children collaborate and learn in real-time.
DecisionVis: A Method of Multi-attribute Correlation Visual Analysis Decision

In the process of fluorine material research and development, the traditional "manual" analysis method can no longer meet the needs of large and complex high-dimensional data analysis. This paper proposes DecisionVis, a multi-attribute correlation visual analysis decision method based on the data of multi-attribute influencing factors of fluorine chemical products, which supports the user to design and implement a multi-attribute correlation visual analysis decision system to explore the correlation between multi-attribute influencing factors of fluorine chemical products through the visual analysis of high-dimensional data and the visual analysis results of market price uncertainty using the 3D parallel coordinate technology with the premise of node connection. The system is designed and implemented to explore the correlation relation-ship between the factors influencing fluorine chemical products. Finally, case studies and interviews with experts from fluorochemical companies demonstrate the effectiveness and ease of use of this method for visual analysis of multi-attribute correlations of fluorochemical products.
Contrastive Prototypical Network with Wasserstein Confidence Penalty

Unsupervised few-shot learning aims to learn the inductive bias from unlabeled dataset for solving the novel few-shot tasks. The existing unsupervised few-shot learning models and the contrastive learning models follow a unified paradigm. Therefore, we conduct empirical study under this paradigm and find that pairwise contrast, meta losses and large batch size are the important design factors. This results in our CPN (Contrastive Prototypical Network) model, which combines the prototypical loss with pairwise contrast and outperforms the existing models from this paradigm with modestly large batch size. Furthermore, the one-hot prediction target in CPN could lead to learning the sample-specific information. To this end, we propose Wasserstein Confidence Penalty which can impose appropriate penalty on overconfident predictions based on the semantic relationships among pseudo classes. Our full model, CPNWCP (Contrastive Prototypical Network with Wasserstein Confidence Penalty), achieves state-of-the-art performance on miniImageNet and tieredImageNet under unsupervised setting. Our code is available at https://github.com/Haoqing-Wang/CPNWCP .
PhishSpy – A Phishing Detection Tool and Defensive Approaches

The art to trick the victim into believing the fake scenarios as legitimate with the intention of getting the target to either download malware or take over personal information. Phishing has become the ultimate fashionable cybercrime among cybercriminals. The impact of phishing is adverse and can lead to unfavorable scenarios, indeed cybercrime. Since these attacks are increasing exponentially and cause huge damage and financial losses, the detection of phishing is of great importance and has also become an area of great interest. In this paper we will discuss a few new tactics for detecting this phishing technique. However, there are already contrasting explanations in many papers, but phishing is very active and in action with new masks that were just discovered in 2022, and for detecting them, there is no algorithm or approach yet. This document describes the most frequent phishing tactics as well as the PhishSpy algorithmic (heuristics-based) tool created to detect phishing, which can discover phishing URLs and provide a suspect score as an output to the user. The PhishSpy algorithmic program features a catch rate of 95%. This paper discusses the methodology used to create this algorithmic program, as well as the implementation details and testing results.
The Correlation between E-Banking Services Quality and Customers’ Satisfaction During COVID-19 Pandemic: A Case Study

The quality of the provided services is vital to the existence and to the profitability of any organization, regardless its business type or size. In general, providing and maintaining high levels of quality would increase the organizations’ ability to meet, and sometimes to exceed, the customers’ needs and requirements, and hence obtaining their satisfaction. This in turn would boost the reputation, revenue, and sales. Currently, at the times of COVID-19 crisis, a significant surge in the usage of different electronic means has been witnessed. One of the industries, which its customers’ has shifted to utilizing e-channels, is the banking sector. Therefore, it became challenging for all banks across the world to keep a pace with the rapid technological development and to deliver banking services through various digital channels, while at the same time ensuring the delivery of high quality services. In this research, we examined the quality of the e-banking services during the pandemic and how it affected customers’ satisfaction. The study was performed and carried out based on the feedback got from Jordanian banks’ customers. The results showed that reliability, efficiency, security, privacy, website/app design, and usability of the provided e-banking services, have a significantly positive influence on the overall customers’ satisfaction.
Association Rules and Networks: A Method of Analysis with Unidirectional Relations Networks

Billions of commercial transactions are conducted daily. By analyzing items that constantly appear in the shopping carts of their clients, companies extract relevant information to maximize their profit with sales of products that are frequently sold together and to reduce their loss by eliminating items that rarely boost the sale of other products. The analysis of shopping carts is a line of research that investigates commercial transactions with the objective of extracting information that can be financially and commercially useful for the business. \textit{Apriori} is an algorithm popularly used in the analysis of shopping carts. By analyzing a set of commercial transactions, it finds combinations of frequent items and organize them in a structure called association rule. However, the number of association rules created by \textit{apriori} is often so large that the process of manual analysis becomes onerous and impractical. To reduce the effort of the analysis, some algorithms reorganize the association rules and the items in networks. Even though networks enable a data structure with smaller dimension if compared to the sets of association rules that are usually created, the combinations of higher frequency items stand out numerically and overshadow combinations of relevant lower frequency ones. In this article we present a new method for the analysis and organization of association rules created by the algorithm \textit{apriori}. Our method transforms the association rules into unidirectional relations networks and evaluate, based on the metrics of \textit{confidence}, \textit{support}, and interest, which sets of items generate more disproportionate relations in the networks. Our method, by ignoring 'evident' relations between items (i.e., pairs of items in the network with the same weight in incoming and outgoing arcs), evaluates the relations that are overshadowed by equivalent higher frequency relations in other methods. The results obtained with our method evidence the extent that the relations between the items in the networks are disproportionate and how such differences identify the items with higher and lower interdependency. These disproportionate relations are relevant, for example, to show hierarchies with the items, to stablish the minimum limits of comparison and to identify items that are extremely dependent on other items.
Research on visual recognition technology of Damaged navigation mark based on improved Efficientnet

Recognizing the structural states of different navigation marks from camera images is an important field for efficient navigation marks maintenance. This paper focuses on the classification of structural damaged navigation marks image, and uses the navigation marks image in the middle and lower reaches of the Yangtze River to construct a navigation marks image dataset with structural damage. An improved Efficientnet is proposed for processing classification of structural damaged navigation marks image. In the improved EfficientNet-b0, Fused-MBConv is used to speed up the training. And the SE module is retained to improve the extraction of subtle features. Experimental results based on a dataset with 7894 images of damaged navigation marks and 34695 images of normal navigation marks show that the improved EfficientNet-b0 has an accuracy about 95% to classify damaged navigation marks, which had a faster training speed than the original EfficientNet-b0.
Enhanced Feature Aggregation for Deep Neural Network Based Speaker Embedding

This paper proposes a new feature aggregation mechanism for deep neural network based speaker embedding for text-independent speaker verification. In speaker verification models, frame-level features are fed into the pooling layer or the feature aggregation component to obtain fixed-length utterance-level features. Our method utilizes the correlation between frame-level features such that dependencies between speaker discriminative information are represented with weights and produces weighted mean features with fixed-length as output. Our pooling mechanism is applied to the ECAPA-TDNN baseline architecture. In comparison to the Attentive Statistics Pooling applied to the same baseline, training on VoxCeleb1-dev dataset and an evaluation on the VoxCeleb1-test dataset shows that it reduces equal error rate (EER) by 7.32% and minimum normalized detection cost function (MinDCF10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ) by 7.34%.
CHATBOT @RIBS_KARKAS_BOT IMPLEMENTS THE FUNCTIONS OF THE EXPERT SYSTEM

The article presents the results of integrating the chatbot @ribs_karkas_bot with an expert system for organizing online consultation. A description of the architecture and implementation of the chatbot messenger Telegram in an expert system based on the