Video Data Research Articles

Advancements in Motion Detection Within Video Streams Through the Integration of Optical Flow Estimation and 3D-Convolutional Neural Network Architectures

Motion detection in video streams is important for many applications, such as autonomous navigation, human-computer interaction, and spying. Conventional techniques, which depend on backdrop removal or frame differencing, frequently break down when dealing with intricate motion patterns and occlusions. Current techniques struggle to handle occlusions and correctly capture complex motion patterns. Furthermore, these techniques could be computationally expensive, especially when dealing with big amounts of video data. An integrated strategy combining optical flow estimation with 3D convolutional neural network (3D-CNN) architectures is presented to address these limitations and boost motion detection systems' accuracy and efficiency. The suggested technique is unusual as it combines 3D CNNs with optical flow estimates. Motion vectors representing dynamic scene changes are produced by optical flow estimates, and spatiotemporal characteristics are extracted by 3D CNNs processing together with optical flow information. By using the complementing capabilities of both approaches, the method performs better in motion detection applications such as object tracking, action recognition, and anomaly detection in video streams. The efficiency of the strategy is assessed by experiments carried out on benchmark datasets. The results show that it is more accurate, resistant against occlusions, and computationally efficient than existing approaches. The suggested approach offers a viable way to improve motion detection capabilities for a range of practical uses. The results show a 98% improvement in processing efficiency and motion detection accuracy when compared to baseline methods. More research and advancement in this area are made possible by the attainable way that MATLAB's suggested approach offers to enhance motion detection skills in a variety of real-world applications.    

DEVELOPMENT OF A NOVEL ALGORITHM FOR THE AUTOMATIC DETECTION OF MULTIDIRECTIONAL LOCOMOTION WITHIN TEAM SPORTS

INTRODUCTION & AIMS Field-based sports are characterised by their intermittent nature requiring both, multidirectional locomotion and, sports-specific movements at a range of intensities. Traditionally, athlete-monitoring has focused on quantifying workload based on movement intensity with minimal regard to the direction of locomotion [1]. The aim of this study was to develop and evaluate an algorithm to detect and classify multidirectional movement using signal characteristics from a microtechnology device. METHODS Rugby league referees (n=13) undertook a match-play simulation protocol (i.e., changes in movement speed and locomotion direction) [2], with microtechnology and video data collected across five-trials. Video data was reviewed to identify movement anomalies outside of the simulation protocol for exclusion. From the 100Hz microtechnology data, acceleration measures were used to classify the start and end point of each movement (i.e., backwards, forwards, sideways or other) or marked for exclusion from the algorithm development. The classified sensor data was processed in Python (v3.11), where data were split into training and testing datasets. A Recurrent Neural Network (Long Short-Term Memory) [3] was implemented to develop and validate an algorithm. Model performance was assessed via accuracy, sensitivity, precision and Area Under the Receiver Operating Characteristic Curve (AUC), using the testing dataset. RESULTS The accuracy of the model was 0.973 ± 0.010. Sensitivity and precision of the model varied between movement direction, but was >0.928 and >0.922, respectively. The AUC of the model was 0.988 ± 0.007. CONCLUSION The current study highlights the effectiveness of a microtechnology based algorithm for automatically classifying multidirectional locomotion of various velocities. Practically, such algorithm can be used to inform evidence-based training in relation to multidirectional locomotion. Whilst model performance was very-high, further research should examine the feasibility of applying the algorithm to match-play datasets to enhance athlete-monitoring processes.

Digital Twin Approach Integrates Existing Facilities, Proposed Developments

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 215074, “Digital Twin for Development and Subsurface Integration: A Window Into the Future,” by Thomas K.G. Gan, Elizabeth Sumadh, and Bhaminie Sagram, Shell. The paper has not been peer reviewed. _ The digital twin (DT) approach provides a virtual and digital representation of existing assets, spanning everything from onshore facilities to offshore platforms, pipelines, and subsurface data, all in one location. It has been used for the operator’s flagship Manatee project to develop three stacked reservoirs offshore Trinidad and Tobago. The DT was used to identify design modifications required at this onshore site and visualize proposed offshore assets. It integrates existing facilities and new design data, including site survey, subsurface, and well data to optimize well planning. Introduction The Manatee field lies within the East Coast Marine Area of Trinidad in approximately 92 m of water and consists of stacked reservoirs of clean and thin-bed unconsolidated sands. Production from these wells is expected to be processed through a new offshore platform and transported by the new pipeline to the Beachfield onshore facility. For a feasibility study and concept-selection stages, the DT platform played a crucial factor in visualizing and contextualizing development options and allowed front-end engineering contractors and operator teams to collaborate across multiple countries and inspect the location virtually. All engineering drawings, maps, design documents, and 3D models were powered by the DT platform. Key Challenges Traditionally, digital collaboration is performed through data and model export from different technical applications and results presented in slides and reporting documents. For oil and gas capital projects, typically 3–6 years elapse from feasibility stage to execution, deepening the issues caused by faulty corporate memory retention, staff movement, and project handover from different internal organizations. Manatee project teams were based in various locations in Trinidad, the US, the UK, the Netherlands, and India, thereby increasing the likelihood of error by using multiple sources of data. Furthermore, effective collaboration without a single source of truth of data can be an obstacle. Given that multiple software was used for model building, development-well planning, and satellite imagery, an inability exists to view 2D and 3D imagery of onshore and offshore facilities with the subsea and subsurface in one place. Furthermore, between 2020 and 2022, the COVID-19 pandemic led to imposed restrictions and limitations placed on site visits. In such scenarios, drone images and video data can prove useful to access facilities remotely. These challenges needed to be addressed by access to a centralized platform that collected, stored, and provided virtual representations to ensure continuous monitoring and efficient decision-making.

Інформаційна технологія підтримки прийняття рішень в задачах відеоспостереження та моніторінгу

In the modern world, video surveillance systems are an integral part of security and monitoring measures in various sectors, from public places to industrial facilities. However, with their increasing prevalence, ethical issues arise concerning the use of video data and the impact of these systems on privacy and citizens' rights. In this article, we will delve into some of these issues in detail and propose recommendations for their resolution. It is important to recognize the consequences that video surveillance and monitoring can have on the quality and effectiveness of decisions made by people. This problem proves to be quite relevant in various fields, including manufacturing, engineering, and others. Video surveil-lance and monitoring have become an integral part of the modern world, particularly in the areas of security, transportation, urban planning, and ecology, with the aid of advanced video analysis and data processing technologies. Today's video surveillance systems provide a high level of information support for decision-making, which is why we will explore the key aspects of information technology that support decision-making in video surveillance and monitoring tasks, as well as the prospects and challenges of its application.

Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery.

Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging areas in machine learning and computer vision. However, classical low-rank tensor representations can solely represent multi-dimensional discrete data on meshgrid, which hinders their potential applicability in many scenarios beyond meshgrid. To break this barrier, we propose a low-rank tensor function representation (LRTFR) parameterized by multilayer perceptrons (MLPs), which can continuously represent data beyond meshgrid with powerful representation abilities. Specifically, the suggested tensor function, which maps an arbitrary coordinate to the corresponding value, can continuously represent data in an infinite real space. Parallel to discrete tensors, we develop two fundamental concepts for tensor functions, i.e., the tensor function rank and low-rank tensor function factorization, and utilize MLPs to paramterize factor functions of the tensor function factorization. We theoretically justify that both low-rank and smooth regularizations are harmoniously unified in LRTFR, which leads to high effectiveness and efficiency for data continuous representation. Extensive multi-dimensional data recovery applications arising from image processing (image inpainting and denoising), machine learning (hyperparameter optimization), and computer graphics (point cloud upsampling) substantiate the superiority and versatility of our method as compared with state-of-the-art methods. Especially, the experiments beyond the original meshgrid resolution (hyperparameter optimization) or even beyond meshgrid (point cloud upsampling) validate the favorable performances of our method for continuous representation.

Sources of Sound Exposure in Pediatric Critical Care.

Sound levels in the pediatric intensive care unit (PICU) are often above recommended levels, but few researchers have identified the sound sources contributing to high levels. To identify sources of PICU sound exposure. This was a secondary analysis of continuous bedside video and dosimeter data (n = 220.7 hours). A reliable coding scheme developed to identify sound sources in the adult ICU was modified for pediatrics. Proportions of sound sources were compared between times of high (≥45 dB) and low (<45 dB) sound, during day (7 AM to 6:59 PM) and night (7 PM to 6:59 AM) shifts, and during sound peaks (≥70 dB). Overall, family vocalizations (38% of observation time, n = 83.9 hours), clinician vocalizations (32%, n = 70.6 hours), and child nonverbal vocalizations (29.4%, n = 64.9 hours) were the main human sound sources. Media sounds (57.7%, n = 127.3 hours), general activity (40.7%, n = 89.8 hours), and medical equipment (31.3%, n = 69.1 hours) were the main environmental sound sources. Media sounds occurred in more than half of video hours. Child nonverbal (71.6%, n = 10.2 hours) and family vocalizations (63.2%, n = 9 hours) were highly prevalent during sound peaks. General activity (32.1%, n = 33.2 hours), clinician vocalizations (22.5%, n = 23.3 hours), and medical equipment sounds (20.6, n = 21.3 hours) were prevalent during night shifts. Clinicians should partner with families to limit nighttime PICU noise pollution. Large-scale studies using this reliable coding scheme are needed to understand the PICU sound environment.

Exploration of factors affecting webcam-based automated gaze coding.

Online experiments have been transforming the field of behavioral research, enabling researchers to increase sample sizes, access diverse populations, lower the costs of data collection, and promote reproducibility. The field of developmental psychology increasingly exploits such online testing approaches. Since infants cannot give explicit behavioral responses, one key outcome measure is infants' gaze behavior. In the absence of automated eyetrackers in participants' homes, automatic gaze classification from webcam data would make it possible to avoid painstaking manual coding. However, the lack of a controlled experimental environment may lead to various noise factors impeding automatic face detection or gaze classification. We created an adult webcam dataset that systematically reproduced noise factors from infant webcam studies which might affect automated gaze coding accuracy. We varied participants' left-right offset, distance to the camera, facial rotation, and the direction of the lighting source. Running two state-of-the-art classification algorithms (iCatcher+ and OWLET) revealed that facial detection performance was particularly affected by the lighting source, while gaze coding accuracy was consistently affected by the distance to the camera and lighting source. Morphing participants' faces to be unidentifiable did not generally affect the results, suggesting facial anonymization could be used when making online video data publicly available, for purposes of further study and transparency. Our findings will guide improving study design for infant and adult participants during online experiments. Moreover, training algorithms using our dataset will allow researchers to improve robustness and allow developmental psychologists to leverage online testing more efficiently.

Video anomaly detection guided by clustering learning

With the fuzzy boundary between normal and abnormal video data, which cannot be well distinguished by most methods, anomaly detection in video requires better characterization of the data. First we give a convolution-enhanced self-attentive video auto-encoder based on the U-Net architecture, which can extract richer image features. Secondly we design a dual-scale feature clustering structure for this encoder, which simultaneously compresses the channel and spatial structure features of the image to represent the features to obtain good coding characteristics and expand the boundary between normal and abnormal data. We also verify that our approach is equivalent to a class of auto-encoders for memory-guided learning. Finally, in the reconstruction task, since video auto-encoders are capable of triggering temporal time leakage phenomena that can lead to network performance degradation, we propose an anomaly score computation paradigm for video auto-encoders that utilizes the average frame anomaly score of a video clip to compute the first frame anomaly score in that video clip.Extensive experiments on three benchmark datasets show that our method outperforms most existing methods on large datasets with complex patterns. The code will be published at the following link: Anomaly-detection-guided-by-clustering-learning

Real-Time Violence Detection in Surveillance Videos Using Deep Learning Approach

Abstract: In order to tackle the ongoing problem of keeping fruits fresh, this research presents an automated freshness detection. The ever-growing presence of surveillance cameras has undoubtedly enhanced public safety. However, manually reviewing vast quantities of video footage for violence detection remains a tedious and error-prone task. This paper proposes a deep learningbased approach for anomaly detection in surveillance videos, with a specific focus on identifying violent activities. Traditional surveillance methods often lack the sophistication to distinguish between normal behaviour and potentially threatening actions. Deep learning offers a significant advantage by automating the identification of deviations from established behavioural patterns in video data. This automation enables real-time analysis of footage, potentially signifying the occurrence of violent incidents and allowing for a swifter response from security personnel. This research delves into the application of deep learning models for violence detection in surveillance footage. We begin by discussing the limitations inherent in conventional methods, such as motion detection and manual review, which struggle to capture the nuances of human behaviour. Subsequently, we explore the advantages that deep learning-based approaches bring to the table, including their ability to learn complex patterns from large datasets and identify subtle anomalies that might escape human observation.

Playing to learn? Analyzing participants' framing of competition and professional conduct in maritime simulations

This study scrutinizes how a simulated scenario framed as a competition is discussed in a post-simulation debriefing, where students reflect on game-like play in the context of professional education. This study draws on Erwin Goffman's (1974/86) analytical concept of framing. In particular, the analysis focuses on the instructor's and students' authentic conversations during a debriefing session and explores how these conversations open up negotiations of professional and playful learning. The research design is ethnographically informed and based on observations, field notes, and 34 h of video data from a navigation course in which 35 bachelor's students from a Norwegian university participated. Our analytical findings reveal that the activity undergoes continuous negotiation between two framings: professional and competitive. In professional framing, students are held accountable for adhering to professional rules, regulations, and norms of “good seamanship.” In the competition framing, students were competing to win the race. Moreover, as the competition mode intensified, students prioritized winning the race over adhering to the rules and regulations of the profession. Consequently, the findings illustrate how a competition framing within a professional education context has engaged the students, letting them demonstrate their knowledge and skills in a playful manner. Furthermore, the findings emphasize the need for an additional exploration of the opportunities and challenges of competition on professional decision making and ethical conduct in settings where simulation-based learning is utilized for training, particularly in domains that train students in high safety standards.

Extended repair in SGD-mediated conversation involving speakers with dysarthria

Background: The ability to engage successfully in repair is central to accomplishing shared conversational activities. Based on two case studies, this paper describes one strategy individuals with dysarthria who use speech generating devices (SGD) may engage in to manage extended repair in multiparty conversation. Method: Conversation Analysis was applied to video data of two extended repair interactions involving an aided speaker and two interlocutors, one familiar, the other less familiar. Results: Aided speakers took an agentive role in the organization of repair when dyadic repair with the less familiar conversant was unsuccessful by selecting the more familiar partners to participate in repair. Aided speakers capitalized on the partner shift by immediately switching to use of idiosyncratic communication modalities and by making reference to past shared experiences relevant to the repair. Conclusion/Discussion: The use of this strategy warrants further exploration as a means for managing extended repair in SGD-mediated conversation. 

Gait Features in Different Environments Contributing to Participation in Outdoor Activities in Old Age (GaitAge): Protocol for an Observational Cross-Sectional Study.

The ability to walk is a key issue for independent old age. Optimizing older peoples' opportunities for an autonomous and active life and reducing health disparities requires a better understanding of how to support independent mobility in older people. With increasing age, changes in gait parameters such as step length and cadence are common and have been shown to increase the risk of mobility decline. However, gait assessments are typically based on laboratory measures, even though walking in a laboratory environment may be significantly different from walking in outdoor environments. This project will study alterations in biomechanical features of gait by comparing walking on a treadmill in a laboratory, level outdoor, and hilly outdoor environments. In addition, we will study the possible contribution of changes in gait between these environments to outdoor mobility among older people. Participants of the study were recruited through senior organizations of Central Finland and the University of the Third Age, Jyväskylä. Inclusion criteria were community-dwelling, aged 70 years and older, able to walk at least 1 km without assistive devices, able to communicate, and living in central Finland. Exclusion criteria were the use of mobility devices, severe sensory deficit (vision and hearing), memory impairment (Mini-Mental State Examination ≤23), and neurological conditions (eg, stroke, Parkinson disease, and multiple sclerosis). The study protocol included 2 research visits. First, indoor measurements were conducted, including interviews (participation, health, and demographics), physical performance tests (short physical performance battery and Timed Up and Go), and motion analysis on a treadmill in the laboratory (3D Vicon and next-generation inertial measurement units [NGIMUs]). Second, outdoor walking tests were conducted, including walking on level (sports track) and hilly (uphill and downhill) terrain, while movement was monitored via NGIMUs, pressure insoles, heart rate, and video data. A total of 40 people (n=26, 65% women; mean age 76.3, SD 5.45 years) met the inclusion criteria and took part in the study. Data collection took place between May and September 2022. The first result is expected to be published in the spring of 2024. This multidisciplinary study will provide new scientific knowledge about how gait biomechanics are altered in varied environments, and how this influences opportunities to participate in outdoor activities for older people. RR1-10.2196/52898.

Utilizing data-driven algorithms for blind modal parameter identification of structures from output-only video measurements

In structural dynamics and vibration analysis, modal identification plays a pivotal role in understanding and characterizing the dynamic behavior of complex systems. Traditional modal analysis techniques heavily rely on accurate sensor placement and knowledge of the excitation forces, which may not always be feasible or practical in real-world scenarios. Recently, non-contact video-based modal analysis methods for structures with arbitrary complexity using computer vision techniques have gained much importance. But these methods need to know ahead of time where the structure's natural frequency ranges are, and they use steerable pyramids, which are complicated multi-scale image decomposition filters. To address these issues, a blind modal identification technique is suggested to extract the modal parameters (modal frequency and mode shapes) from the recorded structural vibration video signal. The proposed algorithm for unsupervised machine learning is the Non-Negative Matrix Factorization (NNMF) method, which is combined with the Generalized Complexity Pursuit (GCP) method for blind source separation. The NNMF algorithm is directly applied to the raw pixel-time series made from the video data to get the temporal components, which are then separated using GCP to find the individual modal frequencies and mode shapes. The above algorithm is first validated on a numerical model and then implemented on laboratory scale models (cantilever, single-degree, and multi-degree frame models). Finally, the proposed methodology is implemented on real-world recorded structural vibration videos to determine its noise sensitivity, such as the Tacoma Narrows bridge and the London Millennium footbridge. The modal parameters extracted are compared with those from the available literature for validation. The estimation errors obtained from all the validations are well below 1%, which makes the technique quite suitable and reliable for structural vibration monitoring by identifying and reproducing complex vibration modes.

Video anomaly detection: A systematic review of issues and prospects

The increase in the deployment of surveillance camera in outdoor and indoor settings have resulted in a growing demand for intelligent systems that can accurately detect and recognize human actions as well as other entities of interest within the captured video data. Although, human action recognition is a well-established topic in computer vision, abnormal behaviour detection has recently received increased research attention. Several abnormal behaviour detection systems have been proposed over the years to ensure human safety. However, only a few comprehensive and systematic reviews report on the current state and future direction of video anomaly detection(VAD) research. This present effort aims to contribute a systematic and detailed review of current research and advances in the detection of anomalous actions and entities in videos. The review focuses on studies published between 2003 and 2023. During the literature selection process, 530 scholarly articles were identified and evaluated to showcase prevalent research trends, techniques, datasets, and frameworks within the realm of VAD This review aims to offer a comprehensive understanding of key areas of focus among researchers, provide resources for commonly used public datasets for evaluation and experimentation, and examine advancements and integration of network design to accommodate the needs for handling multimedia information. To sum up, this study highlights various potential opportunities and obstacles about the VAD domain.VAD has gained significant interest in recent years due to its potential applications in various domains such as security, surveillance, traffic monitoring, medical imaging among others. Areas of further research include, Anomaly detection in real-time, multi-camera anomaly detection, privacy preserving in VAD to mention a few. The study revealed some key trends and challenges in VAD that can guide future research direction.

Motion Vector-Based Self-Attention for Real-Time Human Activity Recognition in Compressed Videos: The MVViT Approach

Herein, a novel methodology is proposed for real-time recognition of human activity in a compressed domain of videos based on motion vectors and self-attention mechanism using vision transformers, and it is termed as motion vectors and vision transformers (MVViT). The videos in MPEG-4 and H.264 compression formats are considered for this study. Any video source without any prior setup could be considered by adopting the proposed method to the corresponding video codecs and camera settings. Existing algorithms for recognition of human action in a compressed video have some limitations in this regard, such as (i) requirement of keyframes at a fixed interval, (ii) usage of P frames only, and (iii) normally support single codec only. These limitations are overcome in the proposed method by using arbitrary keyframe intervals, using both P and B frames, and supporting MPEG-4 as well as H.264 codecs. The experimentation is carried out using the benchmark datasets, namely, UCF101, HMDB51, and THUMOS14, and the recognition accuracy in a compressed domain is found to be comparable to that observed in raw video data but at reduced cost of computation. The proposed MVViT method has outperformed other recent methods in terms of a lesser (61.0%) number of parameters and (63.7%) Giga Floating Point Operations Per Second (GFLOPS), while significantly improving accuracy by 0.8%, 5.9% and 16.6% for UCF101, HMDB51 and THUMOS14, respectively. Also, it is observed that the speed is increased by 8% in case of UCF101 when compared to the highest speed reported in the literature on the same dataset. The ablation study of the proposed method has been done using MVViT variants for different codecs and the performance analysis is done in comparison with the state-of-the-art network models.

Object Detection Using CNN

Object detection system using Convolutional Neural Network(CNN) that can accurately identify and classify objects in videos. The purpose of object detection using CNN to enhance technology such as security cameras, smart devices by enabling them to identify and understand objects in videos. Object detection using CNN is a fascinating filed in computer vision. Detection can be difficult since there are all kinds of variations in orientation, lighting, background that can result in completely different videos of the very same object. Now with the advance of deep learning and neural network, we can finally tackle such problems without coming up with various heuristics real-time. The project “Object detection using CNN for video streaming” detects objects efficiently based on CNN algorithm and apply the algorithm on image or video data. In this project, we develop a technique to identify an object considering the deep learning pre-trained model MobileNet for Single Shot Multi-Box Detector (SSD). This algorithm is used for real-time detection and for webcam, which detects the objects in a video stream. Therefore, we use an object detection module that can detect what is in the video stream. In order to implement the module, we combine the MobileNet and the SSD framework for a fast and efficient deep learning-based method of object detection. The main purpose of our research is to elaborate the accuracy of an object detection method SSD and the importance of pre-trained deep learning model MobileNet. The experimental results show that the Average Precision (AP) of the algorithm to detect different classes as car, person and chair is 99.76%, 97.76% and 71.07%, respectively. The main objective of our project is to make clear the object detecting accuracy. The existing methods are Region Based Convolutional Neural Network(R-CNN) and You Only Look Once(YOLO).R-CNN could not pushed real time speed though its system is updated and new versions of it are deployed and YOLO network is popular but YOLO is to struggle to detect objects grouped close together, especially smaller ones. To avoid the drawbacks of these methods we proposed this model which included single shot multi-box detector (SSD), this algorithm is used for real time detection and Mobile-Net architecture.

Modeling and Performance Analysis of a Notification-Based Method for Processing Video Queries on the Fly

With the rapid growth of video data, the search for content and events in videos is becoming increasingly relevant, and many challenges arise. Various approaches have been proposed to deal with many issues. However, many open questions are still related to computational cost and latency, especially for real-time applications. Considering the need for new and efficient solutions, the so-called NOP (Notification Oriented Paradigm) could be a suitable alternative. NOP introduced a new way of thinking and developing software in which small collaborative entities perform fact execution and logical decision processing based on precise notifications. Following these concepts and practical tools, this paper proposes a new querying processing method based on NOP, focusing on search and matching in a continuous flow context. Experiments on a labeled dataset demonstrated the suitability of the proposed method for low-latency processing with polynomial complexity. The results are better than the state of the art, which works at exponential cost.

Multi-View Inconsistency Analysis for Video Object-Level Splicing Localization

In the digital era, the widespread use of video content has led to the rapid development of video editing technologies. However, it has also raised concerns about the authenticity and integrity of multimedia content. Video splicing forgery has emerged as a challenging and deceptive technique used to create fake video objects, potentially for malicious purposes such as deception, defamation, and fraud. Therefore, the detection of video splicing forgery has become critically important. Nevertheless, due to the complexity of video data and a lack of relevant datasets, research on video splicing forgery detection remains relatively limited. This paper introduces a novel method for detecting video object splicing forgery, which enhances detection performance by deeply exploring inconsistent features between different source videos. We incorporate various feature types, including edge luminance, texture, and video quality information, and utilize a joint learning approach with Convolutional Neural Network (CNN) and Vision Transformer (ViT) models. Experimental results demonstrate that our method excels in detecting video object splicing forgery, offering promising prospects for further advancements in this field.

World futures through RT’s eyes: multimodal dataset and interdisciplinary methodology

There is a need to develop new interdisciplinary approaches suitable for a more complete analysis of multimodal data. Such approaches need to go beyond case studies and leverage technology to allow for statistically valid analysis of the data. Our study addresses this need by engaging with the research question of how humans communicate about the future for persuasive and manipulative purposes, and how they do this multimodally. It introduces a new methodology for computer-assisted multimodal analysis of video data. The study also introduces the resulting dataset, featuring annotations for speech (textual and acoustic modalities) and gesticulation and corporal behaviour (visual modality). To analyse and annotate the data and develop the methodology, the study engages with 23 26-min episodes of the show ‘SophieCo Visionaries’, broadcast by RT (formerly ‘Russia Today’).

Edge based content delivery scheme to ensure quality video streaming for online course material access in smart learning

In today’s Higher Education System (HES), Smart Learning (SL), also known as Intelligent Learning (IL) or Adaptive Learning (AL), plays an increasingly vital role. No longer is the traditional, one-size-fits-all method of education suitable for filling the several demands of students. Using SL technologies powered by Artificial Intelligence (AI) and Machine Learning (ML) algorithms can potentially revolutionize the HES. An emerging area of study, edge-based SL helps use Edge Computing (EC) to provide learners with instantaneous, specialized, and context-aware learning. Internet of Things (IoT) devices are becoming increasingly well-liked, and data is proliferating. Using video data as a primary source of learning content and delivering it via EC infrastructure is what is referred to as “Video Streaming (VS)” in Edge-Based Learning (EBL). By examining the importance of providing mobile video clients with a high-quality visual experience—especially considering that video streaming (VS) traffic makes up a significant amount of mobile network traffic—the research gap is filled. The proposed Content Delivery Scheme (CDS), which is based on long short-term memory, is intended to improve security and privacy protocols, accelerate network service response times, and increase application intelligence. The project intends to close the current gap in edge-based Smart Learning (SL) technologies, namely in the distribution of video material for adaptive learning in higher education, by concentrating on these elements. Given that VS traffic forms a considerable portion of mobile network traffic, this paper aims to investigate the significance of delivering a performing visual experience to mobile video clients. Fast network service response, enhanced application intelligence, and enhanced security and privacy are all made possible by the proposed LSTM-based Content Delivery Scheme (CDS). The proposed approach attains minimal stall time of 2347 ms, which outperforms the existing techniques.