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An effective security model with low computational complexity, minimal quality-compromised, and improved security robustness is essential due to the rapid expansion of multimedia data communication through different cloud services. An image steganography model has been proposed for the security of multimedia data in an unreliable cloud environment. This study's main goal is to provide efficient steganography with barely hidden information. A secure data communication model has been developed over a cloud environment using the Adaptive Artificial Bee Colony Algorithm and the Optimum Pixel Adjustment Algorithm Based Image Steganography Method. An adaptive ABC (artificial bee colony) technique is applied to select the optimal pixel positions in the cover image because the goal of this investigation is to increase PSNR. The Optimal Pixel Adjustment approach is used to minimise embedding errors while maintaining the stego image's appearance identical to the cover image after embedding. The MATLAB platform is used to implement the proposed method. The results show that the proposed AABC-based OPA is more efficient across all measures investigated during the embedding and extraction processes. [ABSTRACT FROM AUTHOR]

In today's society, the application of multimedia data is increasing, and the accompanying data security is also an important issue. This paper will analyze the data security of multimedia network based on routing algorithm. The advantages of wireless local area networks (WLAN) and ad hoc networks are combined in wireless mesh networks (WMNs), which have steadily grown to be a reliable method of wireless broadband access. The application of multi RF and multi-channel technology improves the performance of WMN. However, the limitation that includes the number of wireless network interface cards and channels in the network, results in the great challenges ofrouting and channel allocation in multi RF and multi-channel WMN. Based on AODV algorithm, this paper proposes a new cross layer routing algorithm which is JRCA-AODV algorithm. The simulation results demonstrate that the JRCA-AODValgorithm can successfully increase multiRF multi-channel WMN throughput while lowering packet loss rate and network delay. Take the packet rate of 8 Mbps as an example, the network throughput based on JRCA-AODV algorithm is 1.14 times that of MRMC-AODV algorithm, the packet loss rate based on JRCA-AODV algorithm is 14.06% lower than that of MRMC-AODV algorithm, and the network time delay based on JRCA-AODV algorithm is 14.27% lower than that of MRMC-AODV algorithm. At the same time, JRCA-AODV algorithm can effectively reduce the probability of data loss caused by the competitive and shared access mechanism of the channel, improve the success rate of data transmission, thus reduce the probability of data eavesdropping and tampering in the process of repeated transmission, and improve the security of multimedia network data.This will provide a new direction for promoting the idea of multimedia data security. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is the best way to improve the efficiency of spectrum consumption for wireless multimedia communications. Spectrum sensing, which allows legitimate secondary users (SU) to find vacant bands in the spectrum, plays a vital role in CR networks. When cooperative sensing is used in CR networks, spectrum availability must be taken into account. In many ways, the shared cooperative spectrum sensing (CSS) data among SU. The presence of a malicious user (MU) in the system and sending false sensing data can degrade the performance of cooperative CR. The sharp rise in mobile data traffic causes congestion in the licensed band for the transmission of signals. Handling this security issue in real time, on top of spectrum sharing, is a challenge in such networks. In order to manage the spectrum and identify MU, blockchain-based CSS is developed in this article. To gauge the efficiency of the proposed topology, performance metrics like sensitivity, node selection, throughput measurement, and energy efficiency are used. This work suggests a unique, easier-to-use CSS method with MU suppression that outperforms the current one. According to simulation studies, the suggested topology can increase the likelihood of MU detection by roughly 15% when 40% of system users are malicious. [ABSTRACT FROM AUTHOR]

Employing differentiable reconstruction to interpret the clustering layers of neural networks presents a potent solution to the interpretability challenges encountered when applying neural networks within the multimedia sector. However, most of the existing approaches proposed for interpretability of machine learning make post hoc interpretations of neural networks. While existing approaches have made progress in post hoc interpretation, they still fall short in providing clear explanations for black-box models. There are two main challenges: the first is how we should design an interpretable neural network using sparse orthogonal non-negative matrix factorization (NMF) instead of performing post hoc interpretation. The second is how to implement sparse orthogonal NMF and perform clustering using the designed neural network. To solve these two problems, we address this issue by designing a New Interpretable Neural Network (NINN) that uses sparse orthogonal NMF as a basis for its architecture. We innovate in three aspects: first, NINN is a transparent neural network model with greater interpretability in the clustering layer. Second, NINN automatically performs gradient-based optimization of the parameters in the program and has the advantage of end-to-end learning. Third, since NINN is a differentiable reformulation of sparse orthogonal NMF, NINN inherits many properties of both NMF and neural networks, such as parallel solving, online clustering, dimensionality reduction, and unsupervised learning. Experimental evidence demonstrates that the NINN outperforms a myriad of conventional clustering algorithms, such as k-means and NMF, as well as some of the latest models, in terms of clustering performance. [ABSTRACT FROM AUTHOR]

Course books are the most popular way to learn foreign languages. However, in the 21st century, there are other ways to learn languages, such as multimedia tools. The use of the Internet, newspapers, radio, or television can be an alternative to traditional language learning methods. The rapid advancement of science and technology, such as multimedia technology, has made it possible to explore this new teaching method more effectively. In reality, multimedia technology plays a vital role in the teaching of English language, particularly in the context of non-native speaking English. It also helps non-native English speakers of language teachers to understand how to use it effectively. The use of multimedia in foreign language teaching has grown steadily, and it has greatly improved the quality of teaching and learning. To effectively teach a foreign language, the traditional method is insufficient. We ought to reconsider the way we teach and accept their impersonal quality as a type of instruction. Thus, we can legitimately use contemporary educational technology to achieve the goal of teaching languages. Nowadays, almost everyone uses multimedia tools to help them with daily tasks. For example, some people get ready to leave the house to the beat of the radio's music, while others can't imagine having breakfast without reading the newspaper and so on. [ABSTRACT FROM AUTHOR]

In recent eras, a large amount of data has been transferred through wireless networks in fifth-generation communication using a two-phase geography greedy forwarding (TPGF) routing algorithm with reconfigurable routing metrics represented by RrTPGF in random duty-cycled wireless multilevel multimedia sensor networks (WMSNs). The proposed method reduces the sleep delay in geographic routing networks. the systematically forwarded node in the geographic routing network to identify the significant neighboring nodes. The proposed algorithm is efficient in identifying the geographical distance of the neighboring node and identifying the sleeping delay of the nodes during the communication process. The proposed algorithm efficiently differentiates the worked node from the unworked node on the basis that it identifies the optimal single routing path with a low sleeping delay time at geographic routing. As per the simulation result, the performance of the proposed method shows better results as compared to the conventional methods when considering the scenario size of 750mm ×450mm and 250 nodes with respect to the average delay of the proposed method, which is reduced to 0.6%, and the average hop counts, which are reduced to 0.56% as compared to the conventional method. [ABSTRACT FROM AUTHOR]

Protecting information from manipulation and theft is a top priority as a result of progress in technology and the infrastructure of the multimedia network in addition to the development of illegal methods of obtaining information. One of the means of protecting and preserving information is to hide it in a digital medium. The motivation for introducing such a system is to enhance the security of confidential data by providing ways to protect the data and reduce attempts to attack it. The proposed system contains several steps summarized as follows: The sender side includes firstly the stage of generating hiding locations randomly depending on the genetic algorithm (GA) to generate rows and seed to generate columns. Secondly, the stage of including data after compressing it by the Huffman method. Data embedding depends on the pixel index as an indicator to choose one of the three bands to hide using LSB. The recipient side extracts the important information hiding in the two last rows which helps to extract the data and convert it into the original text. The proposed system gained efficiency and robustness with the help of genetic and Huffman where genetic chooses the best way for hidding among a set of suggested solution in addition to the randomness it possesses. The role of Huffman reduce data size and thus increase the cover capacity. System efficiency has been measured by PSNR through conducting a number of experiments that included using set of texts with different sizes and two types of standard colored cover images. [ABSTRACT FROM AUTHOR]

Image encryption based on chaotic maps is an important method for ensuring the secure communication of digital multimedia on the Internet. To improve the encryption performance and security of image encryption systems, a new image encryption algorithm is proposed that employs a compound chaotic map and random cyclic shift. First, a new hybrid chaotic system is designed by coupling logistic, ICMIC, Tent, and Chebyshev (HLITC) maps. Comparison tests with previous chaotic maps in terms of chaotic trajectory, Lyapunov exponent, and approximate entropy illustrate that the new hybrid chaotic map has better chaotic performance. Then, the proposed HLITC chaotic system and spiral transformation are used to develop a new chaotic image encryption scheme using the double permutation strategy. The new HLITC chaotic system is used to generate key sequences used in the image scrambling and diffusion stages. The spiral transformation controlled by the chaotic sequence is used to scramble the pixels of the plaintext image, while the XOR operation based on a chaotic map is used for pixel diffusion. Extensive experiments on statistical analysis, key sensitivity, and key space analysis were conducted. Experimental results show that the proposed encryption scheme has good robustness against brute-force attacks, statistical attacks, and differential attacks and is more effective than many existing chaotic image encryption algorithms. [ABSTRACT FROM AUTHOR]

Recognizing facial expressions plays a crucial role in various multimedia applications, such as human–computer interactions and the functioning of autonomous vehicles. This paper introduces a hybrid feature extraction network model to bolster the discriminative capacity of emotional features for multimedia applications. The proposed model comprises a convolutional neural network (CNN) and deep belief network (DBN) series. First, a spatial CNN network processed static facial images, followed by a temporal CNN network. The CNNs were fine-tuned based on facial expression recognition (FER) datasets. A deep belief network (DBN) model was then applied to integrate the segment-level spatial and temporal features. Deep fusion networks were jointly used to learn spatiotemporal features for discrimination purposes. Due to its generalization capabilities, we used a multi-class support vector machine classifier to classify the seven basic emotions in the proposed model. The proposed model exhibited 98.14% recognition performance for the JaFFE database, 95.29% for the KDEF database, and 98.86% for the RaFD database. It is shown that the proposed method is effective for all three databases, compared with the previous schemes for JAFFE, KDEF, and RaFD databases. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The user datagram protocol is the most widely used protocol for streaming multimedia data over the Internet. Like other protocols at the network and transport layers, it uses the Internet Checksum (IC) to detect channel errors. The consequence of this is that each corrupted packet is dropped, which can degrade the quality of service perceived by a user. In this paper, the authors present a solution to this problem based on integer codes capable of correcting single and double adjacent errors. The presented codes are a generalization of the IC, which makes them easy to implement in software. [ABSTRACT FROM AUTHOR]

Traditional satellite remote sensing data acquisition, processing, and application models can no longer meet the demands of populism and real-time applications. In order to meet the real-time intelligent service demand for space-based information in the era of Internet of Things, it is of strategic significance to construct a positioning, navigation, timing, remote sensing, communication (PNTRC) integrative space-based information real-time service system that is system-connected, spatio-temporally fused, and service-oriented. This system supports users of all kinds to obtain information, high-precision positioning and timing, and multimedia communication services anytime and anywhere. Wuhan University has deployed the Luojia series of scientific experimental satellites to verify the integrated intelligent remote sensing satellite technology. In order to further promote the construction of the PNTRC system in China, Wuhan University, in cooperation with the Yantai Municipal Government in Shandong Province, has proposed the Oriental Smart Eye intelligent remote sensing satellite constellation plan. This plan aims to achieve innovative service mode transitions in satellite remote sensing, spanning government, industries, and the general public, by utilizing key technologies such as integrated positioning, navigation, timing, remote sensing, communication, satellite constellation network observation, on-orbit processing, and artificial intelligence. It aims to open up the blue ocean market of commercial remote sensing. [ABSTRACT FROM AUTHOR]

The virtual reference system--real-time kinematic (VRS--RTK) method can be used for the precise position monitoring of construction equipment, thus facilitating automation in the construction industry. However, the use of the VRS--RTK method is limited to construction sites where monitoring multiple construction equipment requires constant access to long-term evolution (LTE) data communication, which poses a cost issue and limits connections in accordance with the VRS--RTK service server capacity. To address these challenges, the broadcast real-time kinematic (B'RTK) method was developed. In this study, we aim to evaluate the B'RTK method as a new network RTK positioning service for construction automation in terms of RTK positioning performance and positioning accuracy in various global navigation satellite system (GNSS) reception environments by comparing it with various RTK methods. To assess B'RTK positioning accuracy, simultaneous observation was carried out using post-kinematic, single RTK, and VRS methods for each GNSS signal reception environment, and the accuracy of B'RTK positioning was evaluated by comparing the post-kinematic positioning results with other kinematic positioning results. The B'RTK positioning accuracy in actual construction sites is similar or superior to other kinematic positioning methods, including the VRS--RKT method. Therefore, the B'RTK method may be highly useful for the real-time monitoring of construction equipment and construction automation. [ABSTRACT FROM AUTHOR]

Due to COVID-19, online education has become an important tool for teachers to teach students. Also, teachers depend on a multimedia teaching system (platform) to finish online education. However, interacted images from a multimedia teaching system may suffer from noise. To address this issue, we propose a lightweight image denoising network (LIDNet) for multimedia teaching systems. A parallel network can be used to mine complementary information. To achieve an adaptive CNN, an omni-dimensional dynamic convolution fused into an upper network can automatically adjust parameters to achieve a robust CNN, according to different input noisy images. That also enlarges the difference in network architecture, which can improve the denoising effect. To refine obtained structural information, a serial network is set behind a parallel network. To extract more salient information, an adaptively parametric rectifier linear unit composed of an attention mechanism and a ReLU is used into LIDNet. Experiments show that our proposed method is effective in image denoising, which can also provide assistance for multimedia teaching systems. [ABSTRACT FROM AUTHOR]

In today's rapidly evolving digital landscape, interactive communication networks play a pivotal role in facilitating real-time interactions among users. One of the critical challenges in these networks is ensuring the seamless delivery of multimedia content that caters to the diverse needs and preferences of individual users. This research endeavors to address this challenge by introducing a novel approach, where it places user satisfaction at its core, leveraging adaptive streaming techniques to dynamically adjust multimedia content delivery. By considering parameters such as network conditions, device capabilities, and user preferences, it optimizes the streaming experience in real-time. A key innovation lies in the integration of Shannon-Fano coding principles and genetic algorithms. Shannon-Fano coding enhances data compression efficiency, reducing bandwidth consumption, while genetic algorithms fine-tune the adaptive streaming parameters for each user. Our experimentation and evaluations demonstrate the effectiveness of this approach, showcasing improved multimedia streaming quality, reduced latency, and efficient bandwidth utilization. The synergy of user-centricity, adaptive streaming, Shannon-Fano coding, and genetic algorithms presents a promising avenue for enhancing multimedia communication in interactive networks. [ABSTRACT FROM AUTHOR]

Accurate throughput predictions can significantly improve the quality of experience (QoE), where QoE denotes a network's capacity to provide satisfactory service. By increasing the results of good throughput predictions, the best strategy can be planned for managing data transmission networks with the aim of better and faster data transmission, thereby increasing QoE. Consequently, this paper investigates how to predict the throughput of wireless sensor networks utilizing multimedia data. First, we conducted a comparative analysis of relevant prior research on the topic of throughput prediction in Multimedia Internet of Things (Multimedia IoT). We developed a throughput prediction framework for wireless sensor networks based on what we learned from these studies using machine learning. The Throughput Prediction Framework identifies historical throughput data and employs these traits to predict throughput. In the final phase, multiple camera nodes and local servers are utilized to test a framework for throughput prediction. Our analysis demonstrates that WSN-IoT predictions are quite precise. For a 1-second time breakdown, the average absolute percentage error for all investigated scenarios ranges from 1 to 8 percent. [ABSTRACT FROM AUTHOR]

The world has taken a drastic look towards scalar mobile wireless multi media sensor networks (MWMSN) and almost all the nodes (IOT devices) are connected via the internet. The amount of image, video and data transmission has increased significantly. For battery-powered smart gadgets like smartphones and tablets, energy efficiency is a constant issue. To fully utilise the advantages of the multiple-input-multiple-output technology, MWMSN heavily relies on cooperative communication. Since each node in a cooperative mobile multimedia sensor network is mobile, energy consumption and routing present significant issues. Since each node in the network is mobile, energy consumption and routing pose significant problems for cooperative mobile multimedia sensor networks. To address these challenges optimal cooperative relay selection (OCRS) technique was proposed by utilizing velocity, distance, and connection quality factors. The suggested method bases its determination of the link quality for each pair of nodes on the signal strength and distance values. The highest quality of service (QoS) rating, which is determined to ensure route stability, reliability, and durability, is chosen as the relay nodes. Further, we have proposed an energy efficient cooperative multi media transmission (EECMT) to achieve a better compression rate with lower energy consumption. This approach utilizes the auto-correction code feature present in the QR code to further improve the performance of MWMSN. The simulation result shows that the proposed approach gives better performance and saves the energy up to 39% (approximately) compared to the efficient cooperative image transmission (ECIT), energy efficient relay node placement (EERP) and energy aware cooperative image transmission (EACIT) approaches. [ABSTRACT FROM AUTHOR]

Many claims have been made about the learning benefits of communicating strategies in multimedia picture-plus-text formats, rather than monomedia text-only formats. However, there is little theorization and empirical evidence to support these claims. Drawing upon cognitive load theory to develop learning-related hypotheses, this manuscript reports on a multicountry experiment that tests the effects of different modes of strategy communication on student learning. The results show the learning benefits to students of multimedia presentations of strategy and suggests how strategy professors should further encourage students to draw strategies in class. [ABSTRACT FROM AUTHOR]

The demand for good quality satellite signals in earth-space communication multimedia services has continuously increased due to the expansion of service providers for various telecommunication companies and internet network providers such as MTN, ETISALAT, GLOBACOM, and AIRTEL. Therefore, a radio engineer needs continuous information on the statistics of rain-induced attenuation to overcome the degradation of satellite signals due to heavy rain downpours. In the present study, an algorithm for the future prediction of rain attenuation based on the Feed Forward Neural Networks (FFNN) prediction model is presented. The seasonal assessment of rain rate and rain attenuation is also investigated for five stations (Okitipupa, Ikole-Ekiti, Ile-Ife, Ogbomosho, and Sagamu) covering the Southwestern region of Nigeria using the Global Precipitation Mission (GPM) satellite observations data. The study revealed that the intensive season exhibited the highest rainfall regime compared to the pre-monsoon, cessation, and dry seasons, which implies maximum rain attenuation during the intensive period of the year. The results of this study would be helpful for radio engineers to plan future link budgets for the provision of adequate signals for link availability. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) has revolutionized the way we interact with technology, with the proliferation of interconnected devices leading to an increase in the volume of data transmitted over the network. One of the key applications of IoT is in multimedia communication, where real-time audio and video data is transmitted over the network. However, the diverse nature of applications and the sheer volume of data in IoT networks can lead to network congestion, latency, and variable quality of service (QoS) at the internet side, resulting in a degradation of the overall QoS for multimedia traffic. In this paper, we propose a cross-layer multimedia optimization solution for multi-point to point IoT networks that incorporates service differentiation and bandwidth reservation techniques to improve the QoS of multimedia traffic. We evaluate the performance of our proposed solution using simulations and compare it with existing solutions. Our results show that our proposed solution can significantly improve the QoS of multimedia traffic in IoT networks, even during periods of high network congestion or variable QoS at the internet side. [ABSTRACT FROM AUTHOR]

This action-research-based study aims to investigate the effect of digital stories on students' English micro-skills of reading by designing a series of seven digitized stories based on the seven themes covered in English for Palestine, fourth grade. A digital story is a newly recognized technique in teaching that is believed to improve students' learning and make it more real and meaningful. It is a technological facility that allows storytellers to accompany their scratch stories with different sorts of multimedia such as video clips, recorded audio, computer generated text and music so that they can be played on learners' computers and mobiles. The specific objectives of this research are to identify students' English micro-skills of reading, to illustrate how reading micro-skills-based digital stories can be designed, to illustrate how these stories can be used as a medium to develop English micro-skills of reading and to evaluate the outcome of this technique on these skills. This research study adopted a descriptive analytical approach using a mixed-methods design. The study employed two primary data collection instruments: pre/posttests and reading assessment sessions, and three secondary ones: field notes, comprehension worksheets, and parental reports. The sample consisted of 84 children from UNRWA Biet Lahia Elementary School in North Gaza. The researcher examined the effect of digital stories on students' English reading micro-skills in the experimental group while maintaining the traditional teaching techniques with the control one. The findings revealed that digital stories can largely enhance students' English micro-skills of English. Additionally, it promotes students' motivation, critical thinking skills, selfconfidence and enthusiasm. The researcher recommended further studies in this area with adequate preparation and planning during the whole process of action research. [ABSTRACT FROM AUTHOR]

The experimental research described in this manuscript proposes a complete network system for distributed multimedia acquisition by mobile remote nodes, streaming to a central unit, and centralized real-time processing of the collected signals. Particular attention is placed on the hardware structure of the system and on the research of the best network performances for an efficient and secure streaming. Specifically, these acoustic and video sensors, microphone arrays and video cameras respectively, can be employed in any robotic vehicles and systems, both mobile and fixed. The main objective is to intercept unidentified sources, like any kind of vehicles or robotic vehicles, drones, or people whose identity is not a-priory known whose instantaneous location and trajectory are also unknown. The proposed multimedia network infrastructure is analysed and studied in terms of efficiency and robustness, and experiments are conducted on the field to validate it. The hardware and software components of the system were developed using suitable technologies and multimedia transmission protocols to meet the requirements and constraints of computation performance, energy efficiency, and data transmission security. [ABSTRACT FROM AUTHOR]

Internet technology has revolutionized the landscape of communication technologies in the modern era. However, because the internet is open to the public, communication security cannot be guaranteed. As a result, data concealment approaches have been developed to ensure confidential information sharing. Various methods have emerged to achieve the goal of secure data communication via multimedia documents. This study proposes a method, which is both adaptable and imperceptible, for concealing a secret text in a color image. From an adaptivity perspective, image corners are detected using the Harris corner detection algorithm and utilized as anchor points for picking the optimal hiding regions of interest using Bezier curve interpolation. On the other hand, because human vision is less sensitive to aberrations in edge regions, imperceptibility is guaranteed by utilizing curves that cross through these regions. Experiments indicate that utilizing gradient selective Bezier curves for secret text concealment can keep the imperceptibility even when the payload capacity is increased. [ABSTRACT FROM AUTHOR]

With the advent of cloud computing and social multimedia communication, more and more social images are being collected on social media platforms, such as Facebook, TikTok, Flirk, and YouTube. The amount of social images produced and disseminated is rapidly increasing. Meanwhile, cloud computing-assisted social media platforms have made social image dissemination more and more efficient. There exists an unstoppable trend of fake/unauthorized social image dissemination. The growth of social image sharing underscores potential security risks for illegal use, such as image forgery, malicious copying, piracy exposure, plagiarism, and misappropriation. Therefore, secure social image dissemination has become urgent and critical on social media platforms. The authors propose a secure scheme for social image dissemination on social media platforms. The main objective is to make a map between the tree structure Haar (TSH) transform and the hierarchical community structure of a social network. First, perform the TSH transform on a social image using social network analysis (SNA). Second, all users in a social media platform are coded using SNA. Third, watermarking and encryption are performed in a compressed domain for protecting social image dissemination. Finally, the encrypted and watermarked contents are delivered to users via a hybrid multicast–unicast scheme. The use of encryption along with watermarking can provide double protection for social image dissemination. The theory analysis and experimental results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Anthropomorphism is defined as attributing human traits and emotions to non-human entities. In the field of emotional design in multimedia learning, anthropomorphising essential learning elements has been associated with promoting positive learning experiences. Although it has been widely used for educational purposes, there are still limitations when considering different contexts, learning variables, and non-invasive measurement. Therefore, in this study, we investigated how anthropomorphising affects and associates with learning based on three perspectives: learning outcomes, epistemic emotions, and gaze behaviour. The findings indicate that anthropomorphism did not directly affect learning achievement, perceived satisfaction, and effort or when moderated by the need for cognition. However, anthropomorphism reduced the effect of perceived negative epistemic emotions, namely Bored and Anxiety. Additionally, a comparative correlation analysis indicated that anthropomorphism significantly reduced the perception of negative epistemic emotions for learning achievement (Confused and Frustrated) and effort (Frustrated). The gaze behaviour analysis revealed that anthropomorphism only influenced the initial view and not the number of views or dwell time. However, dwell time reflected partiality towards anthropomorphised elements showing negative emotions. The results implicate design and research considerations for future studies. [ABSTRACT FROM AUTHOR]

With the proliferation of the internet, big data continues to grow exponentially, and video has become the largest source. Video big data introduces many technological challenges, including compression, storage, transmission, analysis, and recognition. The increase in the number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. The integration between Semantic link Networks and multimedia resources provides a new prospect for organizing them with their semantics. The tags and surrounding texts of multimedia resources are used to measure their semantic association. Two evaluation methods including clustering and retrieval are performed to measure the semantic relatedness between images accurately and robustly. A Fuzzy Rule-Based Model for Semantic Content Extraction is designed which performs classification with fuzzy rules. The features extracted are trained with the neural network where each network contains several layers among them each layer of neurons is dedicated to measuring the weight towards different semantic events. Each neuron measures its weight according to different features like shape, size, direction, speed, and other features. The object is identified by subtracting the background features and trained to detect based on the features like size, shape, and direction. The weight measurement is performed according to the fuzzy rules and based on the weight measures. These frameworks enhance the video analytics feature and help in video surveillance systems with better accuracy and precision. [ABSTRACT FROM AUTHOR]

Copyright of Ciencias Administrativas is the property of Universidad Nacional de La Plata and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Black feminist scholarship provokes us to reimagine archives in creative and speculative ways and echoes everyday Black communities' deep investment in memory that rejects the idea of African people as a people without history. I enter this discourse through Umi's Archive is an interdisciplinary and multimedia research project that draws on my family archive to engage everyday Black women's thought to investigate key questions of archives and power. In this article, I describe my experience curating, preserving, and presenting the archive and how that became a process of de-disciplining myself, turning to intimacy and mourning to learn from Black women by searching in my Umi's archive. [ABSTRACT FROM AUTHOR]

Copyright of Trabajos y Comunicaciones is the property of Universidad Nacional de La Plata and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Historia y Comunicación Social is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the explosive growth of information on the web, users face difficulties finding their desired information. There is a need to manage and cluster data efficiently. Although there are various multimedia database systems available for retrieval, most of the methods are not efficient enough. Artificial Intelligence (AI) is increasingly shaping media production and consumption, particularly in the field of video blogs (vlogs). This paper explores the intersection of AI and media, focusing on its implications for freedom of speech and media, content creation, video recommendations, content moderation, and content personalization. The objective is to extract relevant videos from a multimedia database, evaluate the performance of AI algorithms in processing video data, and demonstrate the effectiveness of these algorithms in enhancing the quality and accessibility of video blogs. [ABSTRACT FROM AUTHOR]

Image hiding secures information security in multimedia communication. Existing deep image hiding methods usually process the secret and cover information at first, and then fuse such entire processed information. This complete and rough fusion pipeline, however, severely hinders the quality improvement of the stego and revealed secret images. This paper proposes a deep image hiding architecture, named Deep Adaptive Hiding Network (DAH-Net), to gradually extract and fuse the necessary secret and cover information at the frequency and the depth (layer) extents. Specifically, we propose the Attentive Frequency Extraction method for the DAH-Net to adaptively extract the necessary secret and cover information at the frequency level. The Gradual Depth Extraction method is further proposed for the DAH-Net to gradually extract and fuse the attentive frequency secret and cover information at the depth (layer) level of the deep image hiding network. Extensive experiment results demonstrate the proposed DAH-Net is more universal and achieves state-of-the-art performances in image hiding, watermarking, and photographic steganography. [ABSTRACT FROM AUTHOR]