Search Results (145)

Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features—such as throughput, slope, ratio, and moving averages—transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time–frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2–92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments. Full article

The rapid growth in large-scale distributed video conferencing, remote education, and real-time broadcasting poses significant challenges to traditional centralized streaming systems, particularly regarding scalability, cost, and reliability under high concurrency. Centralized approaches often encounter bottlenecks, increased bandwidth expenses, and diminished fault tolerance. This paper proposes a novel decentralized real-time broadcasting system employing a peer-to-peer (P2P) chain topology based on IPv6 networking and the Secure Reliable Transport (SRT) protocol. By exploiting the global addressing capability of IPv6, our solution simplifies direct node interconnections, effectively eliminating complexities associated with Network Address Translation (NAT). Furthermore, we introduce an innovative chain-relay transmission method combined with distributed node management strategies, substantially reducing reliance on central servers and minimizing deployment complexity. Leveraging SRT’s low-latency UDP transmission, packet retransmission, congestion control, and AES-128/256 encryption, the proposed system ensures robust security and high video stream quality across wide-area networks. Additionally, a WebSocket-based real-time fault detection algorithm coupled with a rapid fallback self-healing mechanism is developed, enabling millisecond-level fault detection and swift restoration of disrupted links. Extensive performance evaluations using Video Multi-Resolution Fidelity (VMRF) metrics across geographically diverse and heterogeneous environments confirm significant performance gains. Specifically, our approach achieves substantial improvements in latency, video quality stability, and fault tolerance over existing P2P methods, along with over tenfold enhancements in frame rates compared with conventional RTMP-based solutions, thereby demonstrating its efficacy, scalability, and cost-effectiveness for real-time video streaming applications. Full article

Face-to-face interaction is one of the most natural forms of human communication. Unsurprisingly, Video Conferencing (VC) Applications have experienced a significant rise in demand over the past decade. With the widespread availability of cellular devices equipped with high-resolution cameras, Instant Messaging Video Call Applications (IMVCAs) now constitute a substantial portion of VC communications. Given the multitude of IMVCA options, maintaining a high Quality of Experience (QoE) is critical. While content providers can measure QoE directly through end-to-end connections, Internet Service Providers (ISPs) must infer QoE indirectly from network traffic—a non-trivial task, especially when most traffic is encrypted. In this paper, we analyze a large dataset collected from WhatsApp IMVCA, comprising over 25,000 s of VC sessions. We apply four Machine Learning (ML) algorithms and a Large Multimodal Model (LMM)-based agent, achieving mean errors of 4.61%, 5.36%, and 13.24% for three popular QoE metrics: BRISQUE, PIQE, and FPS, respectively. Full article

Remote support in general is a method that saves time and resources. A relatively new and promising technology for remote support that combines video conferencing and physical mobility is that of telepresence systems. The remote assistant, that is, the user of said technology, gains both presence and maneuverability in the distant location. As telepresence systems vary greatly in their design, the question arises as to whether the design influences the perception of the remote assistant. Unlike pure design studies, the present work focuses not only on the design and evaluation of the telepresence system itself, but especially on its perception during a collaborative task involving a human partner visible through the telepresence system. This paper presents two studies in which participants performed an assembly task under the guidance of a remote assistant. The remote assistant was visible through differently designed telepresence systems that were evaluated in terms of social perception and trustworthiness. Four telepresence systems were evaluated in study 1 (N = 32) and five different systems in study 2 (N = 34). The results indicated that similarly designed systems showed only marginal differences, but a system that was designed to transport additional loads and was therefore less agile and rather bulky was rated significantly less positively regarding competence than the other systems. It is particularly noteworthy that it was not the height of the communication medium that was decisive for the rating, but above all, the agility and mobility of the system. These results provide evidence that the design of a telepresence system can influence the social perception of the remote assistant and therefore has implications for the acceptance and use of telepresence systems. Full article

Recently, the increasing frequency of cybersecurity incidents has raised concerns about communication security and personal privacy. In a zero-trust network environment, it is critically important to protect communication content and ensure that it is not intercepted, recorded, or stored without proper authorization. End-to-end encryption (E2EE) is a reliable solution for this purpose. The COVID-19 pandemic has accelerated the adoption of remote work and virtual meetings, making the security of voice conferences a critical issue. This study aims to explore the application of end-to-end encryption technology in voice conferences. We designed and implemented an end-to-end encrypted voice conferencing system based on terminal-side mixing to ensure security while also being applicable in low-bandwidth network environments. The developed system effectively prevented man-in-the-middle attacks and data wiretaps, while maintaining high performance and low latency. It can be used in low-bandwidth scenarios such as satellite networks. The end-to-end encryption technology, when combined with terminal-side voice mixing, significantly enhances the security and usability of voice conferences as a new solution for secure communication in the future. Full article

The COVID-19 pandemic increased the number of video conferences, for example, through online teaching and home office meetings. Even in the medical environment, consultation sessions are now increasingly conducted in the form of video conferencing. This includes sessions between psychotherapists and one or more call participants (individual/group calls). To subsequently document and analyze patient conversations, as well as any other human–human dialog, it is possible to record these video conferences. This allows experts to concentrate better on the conversation during the dialog and to perform analysis afterward. Artificial intelligence (AI) and its machine learning approach, which has already been used extensively for innovations, can provide support for subsequent analyses. Among other things, emotion recognition algorithms can be used to determine dialog participants’ emotions and record them automatically. This can alert experts to any noticeable sections of the conversation during subsequent analysis, thus simplifying the analysis process. As a result, experts can identify the cause of such sections based on emotion sequence data and exchange ideas with other experts within the context of an analysis tool. Full article

Māori wisdom revolutionized the child welfare system through the now manualized Family Group Conferencing method. The global trend of adopting and adapting this culturally grounded child welfare practice has been well documented. However, as this service model is adapted and imported to other countries, so is its legacy of settler colonialism. This qualitative case study applies Settler Colonialism Theory to unpack the settler colonialism embedded in the process of adopting an adapted Indigenist family engagement program in Taiwan. Research findings indicate that cultural adaptation reproduces settler colonialism. To implement family engagement within a paternalistic CPS system, program implementers struggled between authoritative decision making and building meaningful state–family partnerships. Although adhering to a model that ostensibly involves family decision making may ease settler anxiety among program implementers, settler colonialism remains the elephant in the room. It frequently undergirds the cultural adaptation process. Liberatory social work practice calls for unpacking settler anxiety, systems of power, and cultural imperialism embedded in program implementation. Full article

Background/Objectives: Little is known about preschool-age children’s food concepts among diverse populations. Grounded in the Theory of Mind and Naïve Biology, the primary aim of this study was to describe Black and Hispanic preschool-age children’s food concepts. A secondary aim was to determine the feasibility of collecting data from preschool-age children via a video conferencing platform. Methods: Preliminary qualitative descriptive study. A purposive sample of nine 4- to 6-year-old children ($\overline{\mathrm{x}}$ age = 4.9; Black, n = 7; Hispanic, n = 2), mostly female (n = 7) participated. Children generated two free lists: foods they think of, and foods they eat, reported mouthfeel of 16 foods, and performed a constrained card sort with rationale. Results: All children were able to use the video conference platform. Foods that Black and Hispanic children frequently listed as thought of ($\overline{\mathrm{x}}$ = 6.75) included chicken, rice, carrots, and apples; those frequently listed as foods they eat ($\overline{\mathrm{x}}$ = 8.33) included pancakes and grapes. Black and Hispanic children used various lexicon such as warm, soft, crunchy, and “ouchy” to describe mouthfeel. All preschool-age children sorted foods into piles (range 4–20 piles). Younger children used discrete labels to categorize foods and created many piles while older children used broader labels and created fewer piles. Conclusions: This is the first study to add to the literature about Black and Hispanic preschool-age children’s food concepts before receiving formal education about nutrition. Additionally, we highlight the novel and successful use of ethnographic techniques via internet video conferencing. Subtle differences in their experiential knowledge about food reflect culturally salient qualities that are critical to consider when developing interventions to promote healthy eating behavior. Full article

Aim: The aim of this study was to identify the effectiveness of Family Group Conferencing (FGC), a decision-making model that is not only family-centered but also takes the form of a family-driven or social network, and to consider the challenges to FGC implementation. Methods: A scoping review was conducted using the Arksey and O’Malley framework. A systematic search was conducted of such electronic databases as PsycInfo, CINAHL, Google Scholar, and Web of Science. Criteria were set utilizing the search terms “family group conferencing” or “family group conference”, with the search refined to studies published between January 2015 and July 2020. The data extracted by the review team were inductively analyzed, and the findings were classified into categories. Results: This review included a total of 26 studies. The categories underscoring the effectiveness of FGC included “sense of ownership”, “restoring belongingness”, “reduction of coercion”, and “learning platform”. Categories presenting challenges to FGC implementation included “severe situations of main actor”, “severe situations of the family”, “the complex role of the FGC coordinator”, and “the cost-ineffectiveness of FGC”. Conclusions: The effectiveness in the capacity of decision-makers was determined by the interaction between the main actor and social network of the FGC, with the challenges to FGC reducing the likelihood of completing the FGC process. It will be necessary therefore to identify the skills and qualifications of FGC coordinators, who must take into account group dynamics, so as to enable the main actor and their social network to develop a positive reciprocal interaction. Full article

Nazarbayev University School of Medicine selected the University of Pittsburgh School of Medicine to guide their curricular development. University of Pittsburgh faculty members teaching in the medical school were asked to help develop the curriculum in Nazarbayev. Some were asked to travel to Nazarbayev University to provide mentoring. Realizing that this would be a new activity, we wanted to investigate the perceived motivations, rewards, and barriers to participation. We conducted open-ended interviews of University of Pittsburgh faculty members, who were asked to participate in a project about motivations for accepting or rejecting the offer. We asked those accepting about the benefits and negatives. Nineteen faculty members agreed to 30 min interviews, which were digitally recorded and transcribed. All interviews were coded. Participating faculty members felt that reviewing their courses improved them. Most noted increased altruism and felt improved as educators. Some felt angst in providing their curricula. Several felt that traveling was challenging, but video conferencing technologies facilitated communication. Interviewees desired tangible rewards. This study highlights faculty perceptions of international curricular development. Faculty members felt that rewards included an improved native curriculum and personal and professional enrichment. Time constraints and distance were the main challenges and the primary reason others declined. The faculty perceived multiple benefits from this curricular development and collaboration. More transparency regarding expectations and the degree of assistance Nazarbayev University needed may have assuaged these fears. Full article

The growing popularity of online learning brings with it inherent challenges that must be addressed, particularly in enhancing teaching effectiveness. Artificial intelligence (AI) offers potential solutions by identifying learning gaps and providing targeted improvements. However, to ensure their reliability and effectiveness in educational contexts, AI models must be rigorously evaluated. This study aimed to evaluate the performance and reliability of an AI model designed to identify the characteristics and indicators of engaging teaching videos. The research employed a design-based approach, incorporating statistical analysis to evaluate the AI model’s accuracy by comparing its assessments with expert evaluations of teaching videos. Multiple metrics were employed, including Cohen’s Kappa, Bland–Altman analysis, the Intraclass Correlation Coefficient (ICC), and Pearson/Spearman correlation coefficients, to compare the AI model’s results with those of the experts. The findings indicated low agreement between the AI model’s assessments and those of the experts. Cohen’s Kappa values were low, suggesting minimal categorical agreement. Bland–Altman analysis showed moderate variability with substantial differences in results, and both Pearson and Spearman correlations revealed weak relationships, with values close to zero. The ICC indicated moderate reliability in quantitative measurements. Overall, these results suggest that the AI model requires continuous updates to improve its accuracy and effectiveness. Future work should focus on expanding the dataset and utilise continual learning methods to enhance the model’s ability to learn from new data and improve its performance over time. Full article

The Fifth International Conference on Bioscience and Biotechnology, 2024 (ICBB-2024), held in Godawari, Nepal, from 21 to 24 April 2024, aimed to address planetary health challenges within the local context of Nepal while fostering global dialogue. Bringing together 240 participants from 10 countries, including 20 international invited speakers, the conference sought to explore the intersections of human health, environmental sustainability, and societal well-being. Jointly organized by the Research Institute for Bioscience and Biotechnology (RIBB), the University of Nepal Development Board (UoN-DB), Vrije Universiteit Amsterdam (VUA), and the Himalayan Environment Research Institute (HERI) and co-organized by Phutung Research Institute (PRI), Kathmandu Research Institute for Biological Sciences (KRIBS), Engage Nepal with Science (ENwS), and Kathmandu Center for Research and Education (KCRE), the conference represented a collaboration of 15 institutions and companies. With attendees from diverse backgrounds—academia, research institutes, private companies, NGOs, and government organizations—the conference featured a robust program of keynotes, workshops, panel discussions, focus group discussions, and oral and poster presentations. Thematic focuses included sessions on Air and Water, Climate Change, Ecology, Evolutionary and Environmental Sciences, and Global Health. A major highlight was the recognition of Nepal’s rich biodiversity and its vulnerability to the impacts of climate change. The event drew inspiration from the European Planetary Health Hub, convening and exploring sustainable, locally relevant solutions to global planetary health issues. Outcomes of the conference included new research collaborations, an enhanced focus on interdisciplinary approaches to biodiversity conservation, and a deeper understanding of how indigenous knowledge can play a crucial role in environmental sustainability. Additionally, ICBB-2024 set a precedent for eco-friendly conferencing practices by emphasizing sustainability throughout the event. In conclusion, ICBB-2024 succeeded in fostering meaningful dialogue and collaboration, inspiring both local and global actions to address pressing planetary health challenges. The event underscored the importance of integrating science, policy, and traditional knowledge in the pursuit of sustainable solutions for planetary health. Full article

Background: Alberta Cancer Exercise (ACE) is a hybrid effectiveness–implementation study evaluating a cancer-specific community-based exercise program across urban sites in Alberta, Canada. The purpose of this paper is to describe the baseline characteristics of participants. Methods: Adults with any type and stage of cancer, who were undergoing cancer treatment or up to three years post treatment completion, were eligible. ACE was delivered in person at 18 sites across 7 cities in Alberta, with video conferencing introduced during the COVID-19 pandemic. Participants took part in 60 min of mild-to-moderate intensity exercise twice weekly for a 12-week period and were encouraged to increase overall physical activity. Results: From January 2017 to February 2023, 2570 individuals enrolled. Participants were a mean age of 57.8 years, 71.3% were female, 45.4% had breast cancer, and 49.4% were undergoing cancer treatment. At baseline, only 22.4% of participants self-reported meeting recommended physical activity levels, 66.0% were overweight/obese, and 71.4% reported one or more comorbidities. Most participants were below normative levels for the six-minute walk and 30 s sit-to-stand tests, and 75.9% reported fatigue. Conclusion: Participants were largely inactive, unfit, and symptomatic. ACE attracted more females and individuals with breast cancer but was otherwise representative of the Alberta cancer population. Full article

The COVID-19 pandemic accelerated the shift to digital education as universities across the world rapidly adopted virtual classrooms for remote learning. Ensuring continuous student engagement in virtual environments remains one of the key challenges. This paper discusses how AI and data analytics are being applied to education, particularly the ways in which technologies such as biometrics and facial recognition can be used to improve student engagement in online and hybrid learning environments. This paper tries to revisit the dynamics of engagement across virtual platforms by comparing traditional learning models and digital learning models and showing the gaps that exist. This study reviewed six widely used video conferencing tools and their effectiveness in fostering engagement in virtual classrooms. The research goes on to investigate cross-cultural tech adoption in education—how regions and educational systems respond to these emerging technologies. Against this background of the challenges identified, a new application, “CONNECT”, is proposed in this paper that can integrate AI-driven features on face recognition and speech-to-text and attendance monitoring to enable real-time authentication and tracking of engagement. This study also provides an overview of the theoretical models of digital, hybrid, and blended learning and provides actionable recommendations for future research and innovation in cross-cultural online education. Full article

Traffic encryption is vital for internet security but complicates analytical applications like video delivery optimization or quality of experience (QoE) estimation, which often rely on clear text data. While many models address the problem of QoE prediction in video streaming, the video conferencing (VC) domain remains underexplored despite rising demand for these applications. Existing models often provide low-resolution predictions, categorizing QoE into broad classes such as “high” or “low”, rather than providing precise, continuous predictions. Moreover, most models focus on clear-text rather than encrypted traffic. This paper addresses these challenges by analyzing a large dataset of Zoom sessions and training five classical machine learning (ML) models and two custom deep neural networks (DNNs) to predict three QoE indicators: frames per second (FPS), resolution (R), and the naturalness image quality evaluator (NIQE). The models achieve mean error rates of 8.27%, 7.56%, and 2.08% for FPS, R, and NIQE, respectively, using a 10-fold cross-validation technique. This approach advances QoE assessment for encrypted traffic in VC applications. Full article