Multimedia Content Research Articles

DashReStreamer: Framework for Creation of Impaired Video Clips under Realistic Network Conditions

The continuous rise of multimedia entertainment has led to an increased demand for delivering outstanding user experience of multimedia content. However, modelling user-perceived Quality of Experience (QoE) is a challenging task, resulting in efforts for better understanding and measurement of user-perceived QoE. To evaluate user QoE, subjective quality assessment, where people watch and grade videos, and objective quality assessment in which videos are graded using one or many objective metrics are conducted. While there is a plethora of video databases available for subjective and objective video quality assessment, these videos are artificially infused with various temporal and spatial impairments. Videos being assessed are artificially distorted with startup delay, bitrate changes, and stalls due to rebuffering events. To conduct a more credible quality assessment, a reproduction of original user experiences while watching different types of streams on different types and quality of networks is needed. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open-source framework for re-creating adaptively streamed video in real networks. The framework takes inputs in the form of video logs captured by the client in a non-regulated setting, along with an.mpd file or a YouTube URL. The ultimate result is a video sequence that encompasses all the data extracted from the video log. DashReStreamer also calculates popular video quality metrics like PSNR, SSIM, MS-SSIM and VMAF. Finally, DashReStreamer allows creating impaired video sequences from the popular streaming platform, YouTube. As a demonstration of framework usage we created a database of 332 realistic video clips, based on video logs collected from real mobile and wireless networks. Every video clip is supplemented with bandwidth trace and video logs used in its creation and also with objective metrics calculation reports. In addition to dataset, we performed subjective evaluation of video content, assessing its effect on overall user QoE. We believe that this dataset and framework will allow the research community to better understand the impacts of video QoE dynamics.

Stratospheric platform applications: reduced cognitive load for multimedia and education

The proliferation of computing solutions in future communication networks leads to the occurrence of cognitive visual overstimulation (CVO). CVO occurrence arises in multimedia content editing and provisioning of Science, Technology, Engineering, and Mathematics (STEM) education. The presented research proposes a high-altitude platform-based architecture that enable multimedia content editing related to space themes, and insertion in the edge of space i.e., the stratosphere (space horizon) environment. The use of the proposed architecture in content editing and STEM education contexts reduces the CVO by an average of (22–84.1) % and (13.6–31.7) %, respectively. Investigations show that the proposed architecture reduces the latency by an average of 30.3%, and 49.4% considering individual, and multiple content access, respectively. The use of the proposed architecture also reduces the power consumption associated with multimedia content editing by (8.05–42.9) % on average.

Evaluation of six news aggregator databases: a comparison for researchers and librarians

News aggregator databases offer different content and features, but there is little guidance on how these databases compare. This article compares the coverage scope, availability of 35 U.S. newspaper titles, and features of six news aggregators available to U.S. academic libraries: Newspaper Source Plus (EBSCO), Factiva, News (Gale OneFile), Nexis Uni (LexisNexis), Access World News (NewsBank), and U.S. Newsstream (ProQuest). Factiva and U.S. Newsstream have most of the titles examined, but others offer multimedia content and batch downloads. Librarians and researchers can use this guide to understand the content and features of databases for research projects, consultations, or collection development.

Explainable Artificial Intelligence and Natural Language Processing for Unraveling Deceptive Contents

Deceptive content recognition in social media employing artificial intelligence (AI) includes the use of sophisticated techniques and machine learning (ML) methods to recognize deceptive or wrong data shared on numerous platforms. AI methods analyse textual as well as multimedia content, investigative patterns, linguistic cues, and contextual info to flag latent cases of deception. As a result of the use of natural language processing (NLP) and computer vision (CV), these systems identify subtle nuances, misrepresentation strategies, and anomalies in user-generated content. This active technique permits social media platforms, organizations, and consumers to recognize and diminish the spread of deceptive content, donates to a more reliable online atmosphere, and aids in fighting tasks modelled by misinformation and false news. This study offers a novel sine cosine algorithm with deep learning-based deceptive content detection on social media (SCADL-DCDSM) technique. The SCADL-DCDSM technique incorporates the ensemble learning process with a hyperparameter tuning strategy for classifying the sentiments. Primarily, the SCADL-DCDSM technique pre-processes the input data to change the input data into a valuable format. Moreover, the SCADL-DCDSM algorithm follows the BERT model for the word embedding process. Moreover, the SCADL-DCDSM technique involves an ensemble of three models for sentiment classification such as long short-term memory (LSTM), extreme learning machine (ELM), and attention-based recurrent neural network (ARNN). Finally, SCA can be executed for better hyperparameter choice of the DL models. The SCADL-DCDSM system integrates the explainable artificial intelligence (XAI) system LIME has been employed for a comprehensive explainability and understanding of the black-box process, enhancing correct deceptive content recognition. The simulation result analysis of the SCADL-DCDSM algorithm has been examined on a benchmark database. The simulation outcome illustrated that the SCADL-DCDSM methodology achieves optimum solution than other approaches in terms of different measures.

NEWS PORTAL

Abstract : NewsNow is a comprehensive digital news portal designed to deliver the latest global and local news with unmatched speed and accuracy. The platform aggregates news from a wide range of reputable sources, offering real-time updates on current events, politics, business, technology, sports, entertainment, and more. Featuring a user-friendly interface, NewsNow provides personalized news feeds based on user preferences and reading habits. Advanced filtering options and a robust search function ensure that users can easily find the news that matters most to them. Additionally, the portal includes in-depth analysis, opinion pieces, and multimedia content such as videos and infographics to enhance the reader’s understanding of complex issues. NewsNow is committed to upholding the highest standards of journalism, with a dedicated team of editors fact-checking and curating content to maintain credibility and trustworthiness. The platform also fosters community engagement through interactive features like comments, forums, and social media integration, making NewsNow a dynamic and interactive hub for staying informed in an ever-changing world. Keywords: use Database for data visualization, news portal website.

Identifying integrated pest management resource needs among Virginia Master Gardeners

Abstract Two survey questionnaires distributed to Master Gardener (MG) volunteers in Virginia provide valuable insights into their awareness, understanding, and utilization of integrated pest management (IPM). Survey responses reveal a varied level of familiarity with IPM, with a notable percentage of respondents lacking knowledge of the term and differing perceptions of its focus on non-chemical or chemical pest control methods. While respondents generally recommended IPM solutions more than half the time, factors limiting their decision to do so included comfort level, limited understanding or knowledge, lack of training, lack of client interest, and lack of resources. The survey results highlight an agreement among respondents regarding the adequacy of state Cooperative Extension Service materials in addressing IPM. Pest management guides, websites, and fact sheets were the IPM resources most frequently used and perceived as effective. Respondents expressed a preference for fact sheets as the top resource material to enhance public training programs, followed by other multimedia content. Moreover, the survey results provide insights into the specific pest-related needs of Virginia MGs, revealing their priorities in terms of arthropod, vertebrate pest, plant disease, and weed resource topics. Overall, the data from the surveys offers a comprehensive understanding of MG perspectives on IPM, identifying areas for program improvement and providing guidance for the development of targeted resources to enhance MG knowledge and facilitate more effective public outreach.

A Review on Enhancing Accessibility Through Image and Video Processing: Solutions for Differently Abled Individuals

This paper presents innovative methodologies in image and video processing aimed at augmenting accessibility for differently abled individuals. Central to this research is the development of advanced algorithms that enable enhanced interpretation and interaction with multimedia content, thereby empowering users with sensory impairments. The study introduces a multi-layered framework that integrates adaptive filtering, object recognition, and augmented reality, tailored to the needs of users with visual and auditory challenges. Semantic scene analysis is leveraged to provide descriptive audio annotations for the visually impaired, facilitating a comprehensive understanding of visual data. For individuals with hearing impairments, the system incorporates real-time sign language interpretation within videos, utilizing deep learning techniques. The efficacy of these solutions is measured against conventional accessibility tools, demonstrating significant improvements in user engagement and comprehension. A novel contribution of this research is the application of machine learning to calibrate the system according to individual user profiles, ensuring a personalized and intuitive user experience. The scalability of the proposed system is validated through its implementation across various platforms and content formats. The findings suggest that such technological advancements have the potential to significantly reduce the barriers faced by differently abled individuals in accessing multimedia information.

R2D2-GAN: Robust Dual Discriminator Generative Adversarial Network for Microscopy Hyperspectral Image Super-Resolution.

High-resolution microscopy hyperspectral (HS) images can provide highly detailed spatial and spectral information, enabling the identification and analysis of biological tissues at a microscale level. Recently, significant efforts have been devoted to enhancing the resolution of HS images by leveraging high spatial resolution multispectral (MS) images. However, the inherent hardware constraints lead to a significant distribution gap between HS and MS images, posing challenges for image super-resolution within biomedical domains. This discrepancy may arise from various factors, including variations in camera imaging principles (e.g., snapshot and push-broom imaging), shooting positions, and the presence of noise interference. To address these challenges, we introduced a unique unsupervised super-resolution framework named R2D2-GAN. This framework utilizes a generative adversarial network (GAN) to efficiently merge the two data modalities and improve the resolution of microscopy HS images. Traditionally, supervised approaches have relied on intuitive and sensitive loss functions, such as mean squared error (MSE). Our method, trained in a real-world unsupervised setting, benefits from exploiting consistent information across the two modalities. It employs a game-theoretic strategy and dynamic adversarial loss, rather than relying solely on fixed training strategies for reconstruction loss. Furthermore, we have augmented our proposed model with a central consistency regularization (CCR) module, aiming to further enhance the robustness of the R2D2-GAN. Our experimental results show that the proposed method is accurate and robust for super-resolution images. We specifically tested our proposed method on both a real and a synthetic dataset, obtaining promising results in comparison to other state-of-the-art methods. Our code and datasets are accessible through Multimedia Content.

Technology-Enhanced Self-Study Learning Tool: An Approach to Improve Students Skills

Abstract – In the engineering education system, the student's learning styles and abilities to acquire new skill sets have been transformed drastically due to the rising use of technology. The study provides a novel approach to self-learning through the use of technology-enhanced techniques aimed to improve student skills. The tool leverages digital platforms and interactive resources to create an engaging and personalized learning environment. This paper presents an examination of the effectiveness of a technology-enhanced self-study learning tool in enhancing student abilities such as knowledge, use of IT tools, communication, and information sharing across several disciplines. The tool's architecture integrates adaptive learning, multimedia content, and assessment mechanisms that adapt to individual learning styles and pace. This study intends to evaluate the tool's influence on improving students' knowledge, retention, and application of subject matter by examining user interactions and performance data. The methodology used is focused on a particular topic, data collection through the use of all multimedia resources, presentation, posting of videos, peer review, and comments on each task toimprove abilities. With the deployment of technologyenhanced techniques, students' skills were observed to improve; around 8% of students' skills were improved in the internal evaluation. Keywords— Technology enhanced tool; self-learning; learning by doing; enhanced skills; flexible learning framework

Social media interaction and analytics for enhanced educational experiences

In this paper we present an innovative system based on social media and high quality multimedia content delivery designed to enhance the educational experiences of students in cultural centers and museums. The system brings together students that are viewing an educational movie with a panel of experts that may be geographically dispersed, and engages them in an exciting social media experience via which they can interact with each other as well as with the remote experts. We employ appropriate methodologies for monitoring and analysing social network activity in order to extract valuable information from the social network activity and use it to seamlessly annotate in real-time the presented educational movie using anthropocentric semantic extraction based on MPEG-7. In this way, the multimedia content of the presented movie can be easily reviewed by multiple geographically dispersed individuals, and impoved afterwards according to the received feedback, thus maximising the benefit for students, teachers and content producers alike.

Bayesian K-means clustering based quality of experience aware multimedia video streaming

<span>Media streaming is an essential approach for delivering multimedia information from the source distributor to the end-user through the Internet. Along with the development of more number of users and the spread of mobile devices, the availability and diversity of multimedia applications has also increased. Multimedia users primarily prioritize quality of experience (QoE), as they seek to access multimedia content with high availability and enjoy smooth video streaming in the shortest possible time. The impact of video delivery plays a significant role in QoE, which is efficiently made by delivering the content through a specialized content delivery network architecture. In this research, a Bayesian K-means clustering algorithm is proposed for the identification of QoE in multimedia video streaming. In this multimedia video streaming, the Amazon Prime video dataset is utilized for determining the performance of the proposed model. The proposed method is developed from the ‘Patching Up’ the video quality problem (PatchVQ) model, the from patches to pictures (PaQ-2-PiQ) model is utilized for the spatial feature extraction, and 3D ResNet-18 is utilized for temporal feature extraction. The proposed Bayesian K-means achieved a QoE reward function of 5,237.42 and 5841.36 as well as a fairness reward function of 5,841.36 and 8,732.08 at the speed of 1,500 kB/s and 2,000 kB/s respectively.</span>

Chaotic map-based selective image encryption via diffusion and confusion

Industrial diversification and Technology have a drastic impact on the day-to-day life and economic growth of the country. Sharing multimedia content over the network has drastically increased. The multimedia data can be just social media images or some highly confidential data. There is a high demand for secure image transmission over networks in the age of the Internet and other transmission mediums. Since images are high-volume data, care must be taken to ensure that the security does not become computationally very expensive. To overcome these problems, we propose a method to encrypt the images selectively using a chaos map. Selective encryption is the process of encrypting an image so that only a piece of it is visible while the remainder is encrypted. This decreases the computational cost of the algorithm to a great extent. Selective encryption consists of two parts, public and private. The public part of the image is visible to everyone but the private part is only visible to the authorized user. The chaotic map is used for encrypting the image and providing security to the algorithm. Confusion and diffusion are used for key generation using pixel value substitution and scrambling. We use PSNR, histogram analysis, and correlation coefficient to verify the result.

Development of the Multimodal Handling Interface Based on Google API

Today, Artificial Intelligence is a daily routine, becoming deeply entrenched in our lives. One of the most popular and rapidly advancing technologies is speech recognition, which forms an integral part of the broader concept of multimodal data handling. Multimodal data encompasses voice, audio, and text data, constituting a multifaceted approach to understanding and processing information. This paper presents the development of a multimodal handling interface leveraging Google API technologies. The interface aims to facilitate seamless integration and management of diverse data modalities, including text, audio, and video, within a unified platform. Through the utilization of Google API functionalities, such as natural language processing, speech recognition, and video analysis, the interface offers enhanced capabilities for processing, analysing, and interpreting multimodal data. The paper discusses the design and implementation of the interface, highlighting its features and functionalities. Furthermore, it explores potential applications and future directions for utilizing the interface in various domains, including healthcare, education, and multimedia content creation. Overall, the development of the multimodal handling interface based on Google API represents a significant step towards advancing multimodal data processing and enhancing user experience in interacting with diverse data sources.

Multi-Modal Hashing for Efficient Multimedia Retrieval: A Survey

With the explosive growth of multimedia contents, multimedia retrieval is facing unprecedented challenges on both storage cost and retrieval speed. Hashing technique can project the high-dimensional data into compact binary hash codes. With it, the most time-consuming semantic similarity computation during the multimedia retrieval process can be significantly accelerated with fast Hamming distance computation, and meanwhile the storage cost can be reduced greatly by the binary embedding. In the light of this, multi-modal hashing has recently received considerable attention to support large-scale multimedia retrieval. Different from uni-modal hashing, the multi-modal hashing focuses on modeling the multi-modal semantics and further preserving them into binary hash codes with hash learning. In this paper, we first systematically review the existing learning to hash methods for efficient multimedia retrieval, categorizing them according to the multimedia retrieval tasks, the specific multi-modal semantic modeling techniques, and hash learning strategies. Thereafter, we present the performance comparison results. We ultimately discuss the challenges and potential research directions that may require further investigation in multi-modal hash learning. To facilitate the research on multi-modal hashing, we develop an open-source performance comparison tool at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/BMC-SDNU/Hashing-Retrieval</uri> .

Literary Reading on Paper and Screens: Associations Between Reading Habits and Preferences and Experiencing Meaningfulness

ABSTRACTThe increasing use of digital technologies has implications for reading. Online and on‐screen reading often consist of engaging with multiple, short, multimedia snippets of information, whereas longform reading is in decline. Meta‐analyses have identified a screen inferiority when reading informational texts, but not narrative texts. The mode effect is explained by reference to the Shallowing Hypothesis, postulating that increased screen reading leads to a propensity to skim and scan rather than carefully read, since digital reading material is typically composed of short, decontextualized snippets of multimedia content rather than long, linear, texts. Experiments have found support for the Shallowing Hypothesis when reading expository/informational texts, but the impact of increased habituation to screens on, specifically, literary reading, is largely unknown. It is plausible that shallow modes of reading, prompted by increased screen use, may compromise one's capacity to engage deeply with literary texts and, in turn, negatively affect readers’ motivation and inclination to engage in slower, more reflective, and more effortful reading. This article presents the results from three experiments exploring associations between reading behavior, medium preferences, and the reading of a short literary text on paper versus screen. Although mixed, the results revealed an overall pattern for the role of medium: more frequent reading of short texts on screen predicted less inclination to muster the cognitive persistence required for reading a longer text, and engage in contemplation on the deeper and personally relevant meaning of the literary text. Educational implications of these findings are discussed.

Multi-Antenna Coded Caching for Location-Dependent Content Delivery

Human-computer interaction continuously evolves towards a genuinely immersive experience, submerging users in a three-dimensional (3D) virtual world. A realistic, immersive experience necessitates a highly reliable and agile wireless connection to support immense data transmission. Yet, there are abundant but underutilized memory resources available at the devices which can be harnessed as supplementary assets to reduce the excessive burden on the wireless medium. What is more, the use of Coded Caching (CC) techniques enables the cumulative cache memory of users in the network to be used as an additional communication resource. To this end, a location-dependent multi-antenna CC-based content delivery scheme tailored specifically for wireless extended reality applications is proposed in this paper. First, a novel memory allocation process is developed, enabling an appropriate trade-off between local and global caching gains. In this regard, the local caching gain is maximized when the memory is mostly dedicated to locations with poor connectivity conditions (absolute fairness). In contrast, the global caching gain is maximized when the memory is uniformly allocated among all the locations. As a result of the memory allocation process, unequal fractions of location-dependent multimedia content are cached by each user. Given the asymmetric cache placement, a novel algorithm is proposed to create suitable codewords for each user during the subsequent delivery phase, which simultaneously achieves a global and local caching gain. The proposed delivery scheme also combines global caching and spatial multiplexing gains using a weighted max-min multicast beamformer design with multi-rate modulation. Numerical experiments and mathematical analysis demonstrate significant performance gains, in terms of the 95-percentile expected delivery time, compared to unicast and multicast scenarios where either the local or global caching gain is maximized.

Design and Implementation of Fuzzy Logic-Based Key Exchange Protocol in Medical Image Cryptographic Protection Scheme

Images may be protected from hackers and attackers with the use of steganography. The rapid expansion of the internet has led to the widespread distribution of vast quantities of multimedia content, including photos, movies, and audio files, via various online platforms. To ensure the safety of sensitive information while it is in transit and upon receipt, a high degree of security is required. During the patient scanning procedure, hospitals and scan centers save many pictures of patients on personal computers. Protection from strangers who may see the patients' scanned photos would be necessary for this. Therefore, scan centers and hospitals all over the globe rely heavily on medical image security. The proposed technique includes Fuzzy Logic-Based Key Exchange Protocol in Medical Image Cryptographic Protection Scheme. To provide the utmost protection for the medical pictures, the cover image incorporates the secret image. At the outset, we standardize the cover and hidden photos. The cover image for this thesis might be a picture of nature or a benchmark; the hidden image, on the other hand, is a medical image in grayscale or binary format. After that, the normalized picture is processed using DWT. The hidden picture is embedded into the cover image using a fuzzy-based edge-related steganography approach, which uses these altered coefficients. To get the stego image, the embedded picture is normalized in the reverse direction. Additionally, this study suggests DT-CWT transform based picture security. Part one of the suggested approach to picture security is image steganography, and part two is picture cryptography. Module 1 uses the DT-CWT transform to fuse the coefficients of the cover picture with the hidden image. After that, the steganography picture is subjected to module 2, which is based on the IE calculation. Analysis of experimental data for the suggested picture security approach revealed improved outcomes for encrypted communication.

Notice of Violation of IEEE Publication Principles: Fault Detection Using Industrial Video Information

Notice of Violation of IEEE Publication Principles Fault Detection Using Industrial Video Information by Y. Zhang, Z. Wang, Y. Fu and X. Li, in the IEEE Transactions on Industrial Electronics, vol. PP, no. 99 After careful and considered review of the content of this paper and associated multimedia content by a duly constituted expert committee, this work has been found to be in violation of IEEE's Publication Principles. This paper and associated multimedia files contain fraudulent data and images. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper and refrain from future references. In this study, a new data-modelling algorithm, referred to as relaxed independent component analysis, is proposed for the purpose of industrial video process monitoring. The contributions are as follows. (1) Physical variables and image data are unified in a framework to propose fault detection method of industrial process for the first time. (2) We find that independent components are sensitive to extract features of image and physical variables. In original KICA method, KPCA+ICA are used to extract nonlinear independent components. Since ICA is linear method, the original method only extracts semi-nonlinear independent components. In our method, the complete nonlinear independent components are extracted using the independence criterion. (3) A process monitoring approach of relaxed independent component analysis is presented. When applied to the monitoring of an electrical fused magnesia furnace, it can effectively improve the accuracy of forecasting the non-Gaussian process and reduce the ratio of false alarms compared to kernel principle component analysis and traditional kernel independent component analysis.

Robust Image Watermarking using DWT, DCT, and PSO with CNN-Based Attack Evaluation

Digital Content Protection is one of the most significant research areas that lies at the intersection of cyber-security and multimedia processing. Protect multimedia content from copyright violation, unauthorized use, replication, and online content theft is needed. Digital Image Watermarking is utilized to preserve the copyright of different digital images from forgery. Various techniques had been developed in this regard with two main issues, the method robustness and the resistance against various types of attacks like, Salt and Pepper noise, filtering and blurring. Current Digital watermarking techniques may reduce the quality of the original digital media content if it is not robust. The purpose of this research is to create a robust image watermarking technique against different attack types such as salt and pepper noise and Gaussian noise, ensuring the image content is protected. Specifically, this study proposed a new mechanism for Image watermarking based on combining Discrete Wavelet Transform (DWT), and Discrete Cosine Transform (DCT). Additionally, Particle Swarm Optimization (PSO) was applied to perform the optimization for both the embedding and extraction processes. At the final stage we assess the proposed approach against some types of attacks such as Additive White Gaussian Noise (AWGN). The Denoising Convolutional Neural Network, (DnCNN) was used to evaluate the mechanism against AWGN. For testing, we utilized measures such as Peak Signal-to-Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). The experimental results of implementing our proposed method for embedding and extracting watermarks across various host image sizes were encouraging, achieving a PSNR ratio of 0.998 and an NCC of 1 in the absence of attacks. Additionally, our evaluation revealed that a specific type of denoising attack, while damaging the watermark (though not completely), actually improved the image quality. It is also important to highlight that our findings surpassed those reported in existing literature, with the PSNR and NCC values serving as evidence of this superior performance.

"Self-Designed Simulation-Based Laparoscopic Training Program for Urology Residents: Results After 6 Years of Experience".

Learning laparoscopy (LAP) is challenging and requires different skills to conventional open surgery. There is a recognized need for a standardized laparoscopic training framework within urology to overcome these difficulties and to shift learning curve from patient to skills laboratory. Simulation-based training has been widely commented, but implementation in real day practice is lacking. We present our "LAP training program for residents". Between 2017 and 2022, 11 residents participated in our self-designed program: Theoretical: (Moodle platform) basic knowledge and multimedia content for initiation into LAP. Evaluated through online exam. Practical: exercises for LAP skills acquisition were proposed and encouraged residents' practice in a box trainer available and experimental surgery sessions on a porcine model. On-site E-BLUS (European Basic Laparoscopic Urologic Skills) examination was performed annually. Feedback was obtained through an anonymous online survey. All residents positively evaluated the program. Theoretical: 82% passed the online exam. The most valued topics: LAP in special clinical situations, complications, instruments, and configuration of the operating room (OR). Practical: all residents increased dry-lab box practices. A total of 23 experimental surgical sessions were carried out. For 64%, simulation in the experimental OR was a necessary complement to achieve laparoscopic skills and allowed them to feel more confident. Forty-five percent considered it essential to improve their surgical technique. E-BLUS evaluation was valued as a means to achieve dexterity and safer surgery by 90%. Reduction in time and errors were observed through time, although only 2 passed the E-BLUS. Our program for learning LAP includes the acquisition of knowledge, training of basic skills and surgical technique in a safe environment, as well as an objective evaluation. Encouraged practice of basic skills and surgical technique simulation and improved objective evaluation. It is structured, reproducible, systematic and has been positively valued, although it requires commitment for success.