Los NOOC para el desarrollo de competencias digitales y formación virtual: una revisión sistemática de la literatura

Desde el año 2016, los NOOC han aparecido como una alternativa para la formación continua en diferentes áreas temáticas. Se trata de una herramienta que brinda tanto a estudiantes como a docentes la oportunidad de adquirir competencias y conocimientos mediante un formato creativo, innovador y flexible. En búsqueda de examinar y recuperar evidencias empíricas sobre el uso de los NOOC para la capacitación y formación virtual se realizó una revisión sistemática. Se extrajeron cinco estudios en función de criterios de selección determinados, las bases de datos consultadas fueron: Google Académico, SciELO, ScienceDirect, Redalyc, Scopus y la Web of Science (WoS), el periodo seleccionado para la búsqueda de la información fue de 6 años, de 2017 a 2022. Dentro de los principales resultados obtenidos se observa que la metodología utilizada en los documentos analizados, destaca el tipo de investigación cuantitativa y mixta. En relación con los instrumentos de validación encontrados en las distintas investigaciones, se utilizaron encuestas iniciales (pre-test) y finales (post-test). Finalmente, los artículos analizados arrojan evidencia empírica y científica que demuestra que los cursos de menor duración tienen menores tasas de deserción y que los NOOC funcionan como estrategia efectiva para el aprendizaje de conocimientos y nuevas competencias

Kalibracija Kinect V2 sustava s više kamera

In this paper, we propose a method to easily calibrate multiple Kinect V2 sensors. It requires the cameras to simultaneously observe a 1D object shown at different orientations (three at least) or a 2D object for at least one acquisition. This is possible due to the built-in coordinate mapping capabilities of the Kinect. Our method follows five steps: image acquisition, pre-calibration, point cloud matching, intrinsic parameters initialization, and final calibration. We modeled radial and distortion parameters of all the cameras, obtaining a root mean square re-projection error of 0.2 pixels on the depth cameras and 0.4 pixels on the color cameras. To validate the calibration results we performed point cloud fusion with color and 3D reconstruction using the depth and color information from four Kinect sensors.U ovom je radu predložena metoda za jednostavnu kalibraciju proizvoljnog broja senzora Kinect V2. Izvodi se istovremenim snimanjem objekta s više kamera. Jednodimenzionalan objekt potrebno je snimiti s najmanje 3 različite orijentacije, a dvodimenzionalan s najmanje jedne orijentacije. Istovremeno snimanje s više kamera moguće je zahvaljujući integriranom mapiranju koordinata u Kinect sustavu. Predložena metoda izvodi se u pet koraka: akvizicija slike, pred-kalibracija, usklađivanje oblaka točaka, inicijalizacija intrinzičnih parametara i konačna kalibracija. U radu su modelirani radijalni i distorzijski parametri svih kamera, pri čemu se ostvaruje korijen srednje kvadratične pogreške ponovne projekcije iznosa 0:2 piksela na kamerama dubine i 0:4 piksela na kamerama u boji. Za validaciju rezultata kalibracije provedena je fuzija oblaka točaka s rekonstrukcijom trodimenzionalnog objekta i boje korištenjem informacije o dubini i boji s četiri Kinect senzora

Active Learning Strategies in Computer Science Education: A Systematic Review

The main purpose of this study is to examine the implementation of active methodologies in the teaching–learning process in computer science. To achieve this objective, a systematic review using the PRISMA method was performed; the search for articles was conducted through the Scopus and Web of Science databases and the scientific search engine Google Scholar. By establishing inclusion and exclusion criteria, 15 research papers were selected addressing the use of various active methodologies which have had a positive impact on students’ learning processes. Among the principal active methodologies highlighted are problem-based learning, flipped classrooms, and gamification. The results of the review show how active methodologies promote significant learning, in addition to fostering more outstanding commitment, participation, and motivation on the students’ part. It was observed that active methodologies contribute to the development of fundamental cognitive and socio-emotional skills for their professional growth

Three-Dimensional Reconstruction of Indoor and Outdoor Environments Using a Stereo Catadioptric System

In this work, we present a panoramic 3D stereo reconstruction system composed of two catadioptric cameras. Each one consists of a CCD camera and a parabolic convex mirror that allows the acquisition of catadioptric images. We describe the calibration approach and propose the improvement of existing deep feature matching methods with epipolar constraints. We show that the improved matching algorithm covers more of the scene than classic feature detectors, yielding broader and denser reconstructions for outdoor environments. Our system can also generate accurate measurements in the wild without large amounts of data used in deep learning-based systems. We demonstrate the system’s feasibility and effectiveness as a practical stereo sensor with real experiments in indoor and outdoor environments

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking

Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks

Automatic sign language recognition is a challenging task in machine learning and computer vision. Most works have focused on recognizing sign language using hand gestures only. However, body motion and facial gestures play an essential role in sign language interaction. Taking this into account, we introduce an automatic sign language recognition system based on multiple gestures, including hands, body, and face. We used a depth camera (OAK-D) to obtain the 3D coordinates of the motions and recurrent neural networks for classification. We compare multiple model architectures based on recurrent networks such as Long Short-Term Memories (LSTM) and Gated Recurrent Units (GRU) and develop a noise-robust approach. For this work, we collected a dataset of 3000 samples from 30 different signs of the Mexican Sign Language (MSL) containing features coordinates from the face, body, and hands in 3D spatial coordinates. After extensive evaluation and ablation studies, our best model obtained an accuracy of 97% on clean test data and 90% on highly noisy data

Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks

Automatic sign language recognition is a challenging task in machine learning and computer vision. Most works have focused on recognizing sign language using hand gestures only. However, body motion and facial gestures play an essential role in sign language interaction. Taking this into account, we introduce an automatic sign language recognition system based on multiple gestures, including hands, body, and face. We used a depth camera (OAK-D) to obtain the 3D coordinates of the motions and recurrent neural networks for classification. We compare multiple model architectures based on recurrent networks such as Long Short-Term Memories (LSTM) and Gated Recurrent Units (GRU) and develop a noise-robust approach. For this work, we collected a dataset of 3000 samples from 30 different signs of the Mexican Sign Language (MSL) containing features coordinates from the face, body, and hands in 3D spatial coordinates. After extensive evaluation and ablation studies, our best model obtained an accuracy of 97% on clean test data and 90% on highly noisy data

Automatic Translation between Mixtec to Spanish Languages Using Neural Networks

This paper introduces a novel method for collecting and translating texts from the Mixtec to the Spanish language. The method comprises four primary steps. First, we collected a Mixtec–Spanish corpus that includes 4568 sentences from educational and religious domain texts. To enhance the parallel corpus, we generate synthetic data with GPT-3.5. Second, we cleaned the data with a semi-automatic approach followed by preprocessing and tokenization. In preprocessing, we removed stop words, duplicated sentences, special characters, and numbers and converted them to lowercase. Third, we performed semi-automatic alignment to find the correspondence of Mixtec–Spanish sentences to generate sentence-level aligned texts necessary for translation. Finally, we trained automatic translation models based on recurrent neural networks, bidirectional recurrent neural networks, and Transformers. Our system achieved a BLEU score of 95.66 for Mixtec-to-Spanish translation and 99.87 for Spanish-to-Mixtec translation. We also obtained a translation edit rate (TER) of 0.5 for Spanish-to-Mixtec and a TER of 16.5 for Mixtec-to-Spanish. Our research stands out as a pioneering effort in the field of automatic Mixtec-to-Spanish translation in Mexico, filling a gap identified in the current literature

Logical Execution Time and Time-Division Multiple Access in Multicore Embedded Systems: A Case Study

The automotive industry has recently adopted multicore processors and microcontrollers to meet the requirements of new features, such as autonomous driving, and comply with the latest safety standards. However, inter-core communication poses a challenge in ensuring real-time requirements such as time determinism and low latencies. Concurrent access to shared buffers makes predicting the flow of data difficult, leading to decreased algorithm performance. This study explores the integration of Logical Execution Time (LET) and Time-Division Multiple Access (TDMA) models in multicore embedded systems to address the challenges in inter-core communication by synchronizing read/write operations across different cores, significantly reducing latency variability and improving system predictability and consistency. Experimental results demonstrate that this integrated approach eliminates data loss and maintains fixed operation rates, achieving a consistent latency of 11 ms. The LET-TDMA method reduces latency variability to approximately 1 ms, maintaining a maximum delay of 1.002 ms and a minimum delay of 1.001 ms, compared to the variability in the LET-only method, which ranged from 3.2846 ms to 8.9257 ms for different configurations

A Clustering and PL/SQL-Based Method for Assessing MLP-Kmeans Modeling

With new high-performance server technology in data centers and bunkers, optimizing search engines to process time and resource consumption efficiently is necessary. The database query system, upheld by the standard SQL language, has maintained the same functional design since the advent of PL/SQL. This situation is caused by recent research focused on computer resource management, encryption, and security rather than improving data mining based on AI tools, machine learning (ML), and artificial neural networks (ANNs). This work presents a projected methodology integrating a multilayer perceptron (MLP) with Kmeans. This methodology is compared with traditional PL/SQL tools and aims to improve the database response time while outlining future advantages for ML and Kmeans in data processing. We propose a new corollary: hk→H=SSE(C),wherek>0and∃X, executed on application software querying data collections with more than 306 thousand records. This study produced a comparative table between PL/SQL and MLP-Kmeans based on three hypotheses: line query, group query, and total query. The results show that line query increased to 9 ms, group query increased from 88 to 2460 ms, and total query from 13 to 279 ms. Testing one methodology against the other not only shows the incremental fatigue and time consumption that training brings to database query but also that the complexity of the use of a neural network is capable of producing more precision results than the simple use of PL/SQL instructions, and this will be more important in the future for domain-specific problems