Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
Artificial intelligence is increasingly being used in all branches of the media system and has transformed the way specialists in this field work in recent years. Currently, applications of artificial intelligence are used across a range of processes involved in the production, editing, distribution, and consumption of media content. These include technologies such as generative chatbots, automated transcription, writing, translation, and editing tools, as well as applications for image and video creation. All of these types of applications have taken over a significant portion of the traditional activities carried out by media professionals. From a technological point of view, these uses primarily rely on machine learning, natural language processing, and computer vision techniques, complemented by generative models that automatically analyze, generate, and interpret text, sound, and images. Although these technologies contribute to increased efficiency, faster work, and reduced operating costs, they also pose significant risks, particularly regarding the spread of false information. From a theoretical perspective, artificial intelligence goes beyond the status of a technological tool, being conceptualized as a communicational actor that actively intervenes in the generation, structuring, and circulation of messages, influencing the relationships between producers, content, and audiences in the current media environment....
The detection of sensitive content in online videos is a key challenge for ensuring digital safety and effective content moderation. This work proposes the Multimodal Audiovisual Attention (MAV-Att), a multimodal deep learning framework that jointly exploits audio and visual cues to improve detection accuracy. The model was evaluated on the LSPD dataset, comprising 52,427 video segments of 20 s each, with optimized keyframe extraction. MAV-Att consists of dual audio and image branches enhanced by attention mechanisms to capture both temporal and cross-modal dependencies. Trained using a joint optimisation loss, the system achieved F1-scores of 94.9% on segments and 94.5% on entire videos, surpassing previous state-of-the-art models by 6.75%....
Background: Dental caries and poor oral hygiene remain major public health problems among school-aged children, particularly in low- and middle-income countries. Teachers play a strategic role in delivering sustainable school-based oral health education; however, their effectiveness depends on appropriate pedagogical training. Objective: This study aimed to evaluate the effectiveness of a multimedia-assisted microteaching intervention for elementary school teachers in improving students’ oral health knowledge, attitudes, practices, and oral hygiene status. Methods: A mixed-methods sequential explanatory design was employed. Quantitative data were collected from 582 students and their teachers across three groups: multimedia-enhanced microteaching, multimedia-only training, and a control group. Outcomes were assessed using Knowledge–Attitude–Practice (KAP) questionnaires, the Oral Hygiene Index–Simplified (OHI-S), and the Decayed, Missing, and Filled Teeth (DMFT) index before and after a two-month implementation period. Non-parametric statistical tests were applied. Qualitative data were obtained through focus group discussions with teachers and were analyzed thematically. Results: Students in the multimedia-enhanced microteaching group demonstrated greater improvements in KAP scores and OHI-S values compared with the multimedia-only and control groups (p < 0.05). Qualitative findings indicated increased teacher confidence, improved classroom engagement, and better integration of oral health education into daily lessons. Changes in DMFT values were interpreted descriptively due to the short follow-up period. Conclusions: Multimedia-assisted microteaching appears to be a promising approach for strengthening teacher-led oral health education and improving short-term behavioral and hygiene outcomes among elementary school children. Further longitudinal studies are needed to assess long-term clinical effects....
Deepfakes, synthetic multimedia files generated by artificial intelligence, are drastically undermining digital credibility. Their ability to manipulate our perception of reality has created a new and complex battleground for disinformation, posing a critical threat to non-English-speaking audio with distinctive accents. Consequently, the objective of this study is to determine the human capacity to detect deepfake audio in Spanish with a Paraguayan accent through an experiment conducted with an Android application called ReFake (developed specifically for this research). In this experiment, 450 participants, aged 16–72, evaluated 10 audio samples of up to 15 s each, classifying them as authentic (belonging to Paraguayan journalists) or fake (generated with ElevenLabs). The findings suggests that human ear is more accurate than artificial intelligence (AI) at detecting vocal ‘naturalness’. This ability is influenced by generational age and educational level, with younger people and those with postgraduate degrees demonstrating greater performance. Conversely, gender and nationality do not influence detection, although the high prosodic quality of deepfakes still leads to errors in human judgment. Given these results, it is crucial to adapt and develop new strategies for a secure and resilient online ecosystem....
With the increasing number of end users that are using multimedia services, demand for access network high-bitrate systems with sufficient quality of services is also increasing. However, this might not always be ensured by telecom operators, as they must optimize networks according to the Quality of Service (QoS) and multimedia data transmission. In this work, we tested Gigabit Passive Optical Network (GPON) performance with the help of various tools (iPerf, RFC 6349 or ITU-T Y.1564). The Grafana software v7.3.3 tool is used to monitor data streams. Measurements were made to limit the downstream bitrate of up to 20 end users at 1 Gbit/s, 500 Mbit/s, 300 Mbit/s and 100 Mbit/s. Based on repeated measurements, an aggregation curve was modelled, indicating the available bitrate with respect to the network load....
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