Characterizing the VPN Ecosystem in the Wild

With the increase of remote working during and after the COVID-19 pandemic, the use of Virtual Private Networks (VPNs) around the world has nearly doubled. Therefore, measuring the traffic and security aspects of the VPN ecosystem is more important now than ever. VPN users rely on the security of VPN solutions, to protect private and corporate communication. Thus a good understanding of the security state of VPN servers is crucial. Moreover, properly detecting and characterizing VPN traffic remains challenging, since some VPN protocols use the same port number as web traffic and port-based traffic classification will not help. In this paper, we aim at detecting and characterizing VPN servers in the wild, which facilitates detecting the VPN traffic. To this end, we perform Internet-wide active measurements to find VPN servers in the wild, and analyze their cryptographic certificates, vulnerabilities, locations, and fingerprints. We find 9.8M VPN servers distributed around the world using OpenVPN, SSTP, PPTP, and IPsec, and analyze their vulnerability. We find SSTP to be the most vulnerable protocol with more than 90% of detected servers being vulnerable to TLS downgrade attacks. Out of all the servers that respond to our VPN probes, 2% also respond to HTTP probes and therefore are classified as Web servers. Finally, we use our list of VPN servers to identify VPN traffic in a large European ISP and observe that 2.6% of all traffic is related to these VPN servers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Are memes selfish? How Internet memes reflect crisis–Covid-19 pandemic in Israel

Purpose: The paper aims to explore, using an analysis of the three components of memes content, form and stance – whether and how the memes offer a broad picture of a specific society during the COVID-19 pandemic crisis. Design/methodology/approach: The author collected, from the two largest Facebook groups in Israel, 25 memes with the largest number of likes in each month, beginning from the month in which awareness of COVID-19 increased significantly, between March 2020 and February 2019. A total of 597 memes were collected. The data were analyzed by a quantitative and qualitative analysis. Findings: Findings indicate that meme culture effectively reflects a society's situation and the challenges it faces. Memes also reflect local cultural icons and effects. Meme contents vary across groups. During a crisis, memes do not function as fertile groups for sharp criticism or calls to take action to resolve society's social ills. Practical implications: Memes may serve as a tool to understand and explore an unfamiliar, foreign culture, its state of mind and its history through meme culture. Social implications: Memes may constitute a platform for relieving stress through light-hearted humor, unaccompanied by a true call to action;that is, "slacktivism” which gives a sense of active participation without involvement in actual activities for change. Originality/value: The study reveals that the Israeli meme culture is not activist and rather focuses on humor to relieve stress. Memes may be used as "bread and circuses” or a means of "slacktivism” that fails to call to genuine activism. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-07-2022-0381. © 2023, Emerald Publishing Limited.

SiCenting+: An Information System for Monitoring the Stunted Growth and Nutritional Status of Children in Pandeglang Regency

Stunted growth is a condition in which toddlers are less than their age or height. As a result, non-optimal nutritional needs were satisfied in the first 1000 days of life. A child suffering from stunting has a slower cognitive and physical development than he should. Stunting also decreases productivity and health. This disease has a risk of degenerative diseases such as diabetes. Stunting in children under five requires special attention because it inhibits physical, mental, and cognitive development effects. Stunting at an early age can increase the risk of death, morbidity, and non-optimal posture in adulthood. Stunting prevention requires behavior change in all intervention targets, especially the primary targets, mothers and toddlers. The number of blockages and malnutrition in Indonesia is expected to increase significantly due to the COVID-19 pandemic. The pandemic has made it more challenging to meet children's nutrition during their growth and development. This pandemic has also prevented monitoring activities for the growth and development of children early in life. The monitoring activities are usually implemented at Integrated Healthcare Centers in villages or in Posyandu. If approximately undetectable through weight measures, body length, and head circumference, children can suffer from chronic malnutrition and become inhibited. Therefore, our $SiCenting+Team$ created a Website about Stunting education for the community in Pandeglang Regency. $SiCenting+is$ an application developed for Mothers, Policymakers, and Posyandu. Extensive data was processed to assist stunting screening for use by cadres with family profiles related to specific nutritional factors and sensitive nutrition. Furthermore, villages can use this data to plan the budget for stunting reduction programs at the village level through village deliberations. The $SiCenting+website$ can be opened via a web browser with the following URL address: https://sicenting.id/ © 2022 IEEE.

An End-to-End Knowledge Graph Based Question Answering Approach for COVID-19

Question Answering based on Knowledge Graph (KG) has emerged as a popular research area in general domain. However, few works focus on the COVID-19 kg-based question answering, which is very valuable for biomedical domain. In addition, existing question answering methods rely on knowledge embedding models to represent knowledge (i.e., entities and questions), but the relations between entities are neglected. In this paper, we construct a COVID-19 knowledge graph and propose an end-to-end knowledge graph question answering approach that can utilize relation information to improve the performance. Experimental result shows that the effectiveness of our approach on the COVID-19 knowledge graph question answering. Our code and data are available at https://github.com/CHNcreater/COVID-19-KGQA. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Characterizing the VPN Ecosystem in the Wild

With the increase of remote working during and after the COVID-19 pandemic, the use of Virtual Private Networks (VPNs) around the world has nearly doubled. Therefore, measuring the traffic and security aspects of the VPN ecosystem is more important now than ever. VPN users rely on the security of VPN solutions, to protect private and corporate communication. Thus a good understanding of the security state of VPN servers is crucial. Moreover, properly detecting and characterizing VPN traffic remains challenging, since some VPN protocols use the same port number as web traffic and port-based traffic classification will not help. In this paper, we aim at detecting and characterizing VPN servers in the wild, which facilitates detecting the VPN traffic. To this end, we perform Internet-wide active measurements to find VPN servers in the wild, and analyze their cryptographic certificates, vulnerabilities, locations, and fingerprints. We find 9.8M VPN servers distributed around the world using OpenVPN, SSTP, PPTP, and IPsec, and analyze their vulnerability. We find SSTP to be the most vulnerable protocol with more than 90% of detected servers being vulnerable to TLS downgrade attacks. Out of all the servers that respond to our VPN probes, 2% also respond to HTTP probes and therefore are classified as Web servers. Finally, we use our list of VPN servers to identify VPN traffic in a large European ISP and observe that 2.6% of all traffic is related to these VPN servers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Mining the Health Information Needs of COVID-19 Patients Based on Social Q&A Community

COVID-19 has become a global pandemic, and COVID-19 patients are in a medical dilemma with no effective treatment and no effective drugs. The questions and answers in the social Q&A community can reveal the characteristics and evolution rules of the health information needs of COVID-19 patients. Using the Q&A data in Baidu Zhidao (https://zhidao.baidu.com/ ) as the research object, using the web crawlers to capture the data, automatic topic recognition on the acquired data by constructing an LDA topic model, exploring the content of COVID-19 patients' health information needs, and revealing the change rule of Q&A publication volume and health information need topics from the time dimension. Combining statistical information such as the number of answers, the number of likes, and the level of respondents, cluster analysis is used to reveal the changing rules of social characteristics and health information need topics. By analyzing the Q&A data on COVID-19 patients in Baidu Zhidao, it is found that the topic distribution of health information needs topic is relatively concentrated. Moreover, the number of Q&A and the types of health information needs to be changed in different development periods. There are differences in social characteristics that correspond to different topics of health information needs. Through in-depth analysis of the characteristics of health information needs of COVID-19 patients in the social Q&A community, on the one hand, it is beneficial for COVID-19 patients to obtain the required health information content timely. On the other hand, it is beneficial to optimize the community information display mechanism and improve the organization of information resources. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

What are people asking about COVID-19? A question classification dataset

We present COVID-Q, a set of 1,690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question clusters. The most common questions in our dataset asked about transmission, prevention, and societal effects of COVID, and we found that many questions that appeared in multiple sources were not answered by any FAQ websites of reputable organizations such as the CDC and FDA. We post our dataset publicly at https://github.com/JerryWei03/COVID-Q. For classifying questions into 15 categories, a BERT baseline scored 58.1% accuracy when trained on 20 examples per category, and for a question clustering task, a BERT + triplet loss baseline achieved 49.5% accuracy. We hope COVID-Q can help either for direct use in developing applied systems or as a domain-specific resource for model evaluation. © ACL 2020.All right reserved.

Political ideology and differences in seeking COVID-19 information on the internet: examining the comprehensive model of information seeking

Purpose: Guided by the Comprehensive Model of Information Seeking (CMIS), this article identifies significant predictors that impact individuals seeking COVID-19 information. People with different political ideologies read contradictory information about the COVID-19 pandemic. However, how political ideology may affect COVID-19 information seeking remains unclear. This study explores the major information channels for individuals with different political ideologies to seek COVID-19 information. It further examines how political ideologies influence CMIS's effectiveness in predicting online health information-seeking. Design/methodology/approach: This study collected 394 completed survey responses from adults living in the United States after the 2020 lockdown. ANOVA analyses revealed the differences in salience, beliefs, information carrier characteristics, utilities and information-seeking actions between Liberals and Conservatives. Regression analyses discovered variables that predict Liberals' and Conservatives' online health information seeking. Findings: Results suggest that the internet is the top channel for COVID-19 information seeking. Compared to Conservatives, Liberals report more COVID-19 information-seeking actions. Liberals also express stronger salience, perceive higher trustworthiness of online COVID-19 information, are more likely to think of seeking online COVID-19 information as useful and helpful and report more substantial efficacy to mitigate the risk. Most CMIS variables predict Liberals' information seeking;however, only salience significantly predicts Conservatives' information seeking. Originality/value: This article indicates that CMIS should include political ideology to refine its prediction of information seeking. These findings offer practical implications for designing health messages, enhancing information distribution and reducing the public's uncertainty. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2022-0436. © 2023, Emerald Publishing Limited.

Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text

Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure. In addition, the recent COVID-19 pandemic fueled a surge in the use of video conferencing tools. Since videos take up considerable bandwidth ( ∼ 100 Kbps to a few Mbps), improved video compression can have a substantial impact on network performance for live and pre-recorded content, providing broader access to multimedia content worldwide. We present a novel video compression pipeline, called Txt2Vid, which dramatically reduces data transmission rates by compressing webcam videos ('talking-head videos') to a text transcript. The text is transmitted and decoded into a realistic reconstruction of the original video using recent advances in deep learning based voice cloning and lip syncing models. Our generative pipeline achieves two to three orders of magnitude reduction in the bitrate as compared to the standard audio-video codecs (encoders-decoders), while maintaining equivalent Quality-of-Experience based on a subjective evaluation by users ( n=242 ) in an online study. The Txt2Vid framework opens up the potential for creating novel applications such as enabling audio-video communication during poor internet connectivity, or in remote terrains with limited bandwidth. The code for this work is available at https://github.com/tpulkit/txt2vid.git. © 1983-2012 IEEE.

Software Configurable Hardware-Based Remote Laboratory System

Working and learning remotely has been a rising and steadily increasing phenomenon. Of particular interest is the conducting of laboratory experiments over remote facilities. The ever-evolving electronic systems application requirements and the dynamic operating environments have necessitated the need for configurable systems. Internet of Things (IoT) is now driving the industrial revolution 4.0 and hence learning remotely is inevitable. In the same vein, configurable remote laboratories are an important aspect of the electronic engineering revolution. Configurability enables laboratory platforms to run with multiple sets of hardware and to design systems capable of handling future developments and plugins, bringing more support to students performing laboratory work during the Covid-19 era. This is a study to develop a relatively inexpensive, configurable, and cloud-based remote laboratory platform for electronic engineering students. The laboratory platform provides real-time interaction with two myRIO devices. The devices are used as the instructor's hardware experimental setup and are in the faculty laboratory and functioning as IoT nodes. LabVIEW myRIO toolkit was used to design the finite state machines which contain the lab applications. Each device consists of a tri-state finite state machine with each state being a laboratory session. Six basic electronic engineering experiments were used, and students can access them from any internet-connected device through common browsers. The system can be configured to have more myRIO devices and more experiments per device. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

SunFA - An open-source application for behavior analysis in online video-conferencing

Video-conferencing applications are becoming increasingly popular, especially with the remote working trend after COVID-19. The benefits of meeting online cannot be denied;however, this is still quite limited. In particular, it is essential to monitor and analyze participants' behavior. In this paper, we proposed SunFA - an open-source participants analysis tool for video-conferencing based on face analysis and virtual camera technology. The advantage of our system is that it is compatible with almost available video conferencing applications, such as Google Meet, Skype, Microsoft Teams, Zoom, Slack, etc. Furthermore, we packaged this software as a desktop application for Windows operating system to make it easy to install. The memory usage and execution time evaluation ensure the real-time and resource-saving of a video-conferencing application. We open-source our entire source code and solutions at https://github.com/sun-asterisk-research/sun-fa © 2022 IEEE.

User Experience Design for Automatic Credibility Assessment of News Content About COVID-19

The increasingly rapid spread of information about COVID-19 on the web calls for automatic measures of credibility assessment [18]. If large parts of the population are expected to act responsibly during a pandemic, they need information that can be trusted [20]. In that context, we model the credibility of texts using 25 linguistic phenomena, such as spelling, sentiment and lexical diversity. We integrate these measures in a graphical interface and present two empirical studies to evaluate its usability for credibility assessment on COVID-19 news. Raw data for the studies, including all questions and responses, has been made available to the public using an open license: https://github.com/konstantinschulz/credible-covid-ux. The user interface prominently features three sub-scores and an aggregation for a quick overview. Besides, metadata about the concept, authorship and infrastructure of the underlying algorithm is provided explicitly. Our working definition of credibility is operationalized through the terms of trustworthiness, understandability, transparency, and relevance. Each of them builds on well-established scientific notions [41, 65, 68] and is explained orally or through Likert scales. In a moderated qualitative interview with six participants, we introduce information transparency for news about COVID-19 as the general goal of a prototypical platform, accessible through an interface in the form of a wireframe [43]. The participants' answers are transcribed in excerpts. Then, we triangulate inductive and deductive coding methods [19] to analyze their content. As a result, we identify rating scale, sub-criteria and algorithm authorship as important predictors of the usability. In a subsequent quantitative online survey, we present a questionnaire with wireframes to 50 crowdworkers. The question formats include Likert scales, multiple choice and open-ended types. This way, we aim to strike a balance between the known strengths and weaknesses of open vs. closed questions [11]. The answers reveal a conflict between transparency and conciseness in the interface design: Users tend to ask for more information, but do not necessarily make explicit use of it when given. This discrepancy is influenced by capacity constraints of the human working memory [38]. Moreover, a perceived hierarchy of metadata becomes apparent: the authorship of a news text is more important than the authorship of the algorithm used to assess its credibility. From the first to the second study, we notice an improved usability of the aggregated credibility score's scale. That change is due to the conceptual introduction before seeing the actual interface, as well as the simplified binary indicators with direct visual support. Sub-scores need to be handled similarly if they are supposed to contribute meaningfully to the overall credibility assessment. By integrating detailed information about the employed algorithm, we are able to dissipate the users' doubts about its anonymity and possible hidden agendas. However, the overall transparency can only be increased if other more important factors, like the source of the news article, are provided as well. Knowledge about this interaction enables software designers to build useful prototypes with a strong focus on the most important elements of credibility: source of text and algorithm, as well as distribution and composition of algorithm. All in all, the understandability of our interface was rated as acceptable (78% of responses being neutral or positive), while transparency (70%) and relevance (72%) still lag behind. This discrepancy is closely related to the missing article metadata and more meaningful visually supported explanations of credibility sub-scores. The insights from our studies lead to a better understanding of the amount, sequence and relation of information that needs to be provided in interfaces for credibility assessment. In particular, our integration of software metadata contributes to the more holistic notion of credibility [47, 72] that has become popular in recent years Besides, it paves the way for a more thoroughly informed interaction between humans and machine-generated assessments, anticipating the users' doubts and concerns [39] in early stages of the software design process [37]. Finally, we make suggestions for future research, such as proactively documenting credibility-related metadata for Natural Language Processing and Language Technology services and establishing an explicit hierarchical taxonomy of usability predictors for automatic credibility assessment. © 2022, Springer Nature Switzerland AG.

Single-Stage Real-Time Face Mask Detection

With the battle against COVID-19 entering a more intense stage against the new Omicron variant, the study of face mask detection technologies has become highly regarded in the research community. While there were many works published on this matter, we still noticed three research gaps that our contributions could possibly suffice. Firstly, despite the introduction of various mask detectors over the last two years, most of them were constructed following the two-stage approach and are inappropriate for usage in real-time applications The second gap is how the currently available datasets could not support the detectors in identifying correct, incorrect and no mask-wearing efficiently without the need for data pre-processing. The third and final gap concerns the costly expenses required as the other detector models were embedded into microcomputers such as Arduino and Raspberry Pi. In this paper, we will first propose a modified YOLO-based model that was explicitly designed to resolve the real-time face mask detection problem;during the process, we have updated the collected datasets and thus will also make them publicly available so that other similar experiments could benefit from;lastly, the proposed model is then implemented onto our custom web application for real-time face mask detection. Our resulted model was shown to exceed its baseline on the revised dataset, and its performance when applied to the application was satisfactory with insignificant inference time. Code available at: https://bitbucket.org/indigoYoshimaru/facemask-web © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text

Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure. In addition, the recent COVID-19 pandemic fueled a surge in the use of video conferencing tools. Since videos take up considerable bandwidth (~100 Kbps to a few Mbps), improved video compression can have a substantial impact on network performance for live and pre-recorded content, providing broader access to multimedia content worldwide. We present a novel video compression pipeline, called Txt2Vid, which dramatically reduces data transmission rates by compressing webcam videos (“talking-head videos”) to a text transcript. The text is transmitted and decoded into a realistic reconstruction of the original video using recent advances in deep learning based voice cloning and lip syncing models. Our generative pipeline achieves two to three orders of magnitude reduction in the bitrate as compared to the standard audio-video codecs (encoders-decoders), while maintaining equivalent Quality-of-Experience based on a subjective evaluation by users (n = 242) in an online study. The Txt2Vid framework opens up the potential for creating novel applications such as enabling audio-video communication during poor internet connectivity, or in remote terrains with limited bandwidth. The code for this work is available at https://github.com/tpulkit/txt2vid.git. IEEE

Digital Contact Tracing Solutions: Promises, Pitfalls and Challenges

The COVID-19 pandemic has caused many countries to deploy novel digital contact tracing (DCT) systems to boost the efficiency of manual tracing of infection chains. In this paper, we systematically analyze DCT solutions and categorize them based on their design approaches and architectures. We analyze them with regard to effectiveness, security, privacy and ethical aspects and compare prominent solutions based on these requirements. In particular, we discuss shortcomings of the Google and Apple Exposure Notification API (GAEN) that is currently widely adopted all over the world. We find that the security and privacy of GAEN has considerable deficiencies as it can be compromised by severe large-scale attacks. We also discuss other proposed approaches for contact tracing, including our proposal <sc>TraceCORONA</sc>, that are based on Diffie-Hellman (DH) key exchange and aim at tackling shortcomings of existing solutions. Our extensive analysis shows that <sc>TraceCORONA</sc> fulfills the above security requirements better than deployed state-of-the-art approaches. We have implemented <sc>TraceCORONA</sc> and its beta test version has been used by more than 2000 users without any major functional problems<uri>https://tracecorona.net/download-tracecorona/</uri>, demonstrating that there are no technical reasons requiring to make compromises with regard to the requirements of DCT approaches. IEEE

Software Configurable Hardware-Based Remote Laboratory System

Working and learning remotely has been a rising and steadily increasing phenomenon. Of particular interest is the conducting of laboratory experiments over remote facilities. The ever-evolving electronic systems application requirements and the dynamic operating environments have necessitated the need for configurable systems. Internet of Things (IoT) is now driving the industrial revolution 4.0 and hence learning remotely is inevitable. In the same vein, configurable remote laboratories are an important aspect of the electronic engineering revolution. Configurability enables laboratory platforms to run with multiple sets of hardware and to design systems capable of handling future developments and plugins, bringing more support to students performing laboratory work during the Covid-19 era. This is a study to develop a relatively inexpensive, configurable, and cloud-based remote laboratory platform for electronic engineering students. The laboratory platform provides real-time interaction with two myRIO devices. The devices are used as the instructor’s hardware experimental setup and are in the faculty laboratory and functioning as IoT nodes. LabVIEW myRIO toolkit was used to design the finite state machines which contain the lab applications. Each device consists of a tri-state finite state machine with each state being a laboratory session. Six basic electronic engineering experiments were used, and students can access them from any internet-connected device through common browsers. The system can be configured to have more myRIO devices and more experiments per device. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Hereditary thrombotic thrombocytopenic purpura and COVID-19: Impacts of vaccination and infection in this rare disease.

Introduction: Severe COVID-19 is associated with an important increase of von Willebrand factor and mild lowering of ADAMTS13 activity that may, in the presence of a strong inflammatory reaction, increase the risk of acute thrombotic thrombocytopenic purpura (TTP). Although acute episodes of immune-mediated TTP associated with COVID-19 or SARS-CoV-2 vaccination have been reported, data about clinical evolution of hereditary TTP (hTTP) during the pandemic are scarce. Method: We conducted a survey among adult patients of the International Hereditary TTP Registry about SARS-CoV-2 vaccination, COVID-19, and occurrence of acute hTTP episodes. Results: Of 122 adult hTTP patients invited to participate, 86 (70.5%) responded. Sixty-five had been vaccinated (75.6%), of which 14 had received in addition a booster, resulting in 139 individual vaccine shots. Although vaccinations in patients on plasma prophylaxis were done within 1 week of the last plasma infusion, all 23 patients treated with plasma on demand were vaccinated without prior plasma infusions. One patient on uninterrupted weekly plasma infusions presented within 3 days from his second vaccination with neurological symptoms and computed tomography scan 9 days later showed subacute ischemic/hemorrhagic frontal lobe infarction. A second male patient developed acute myocarditis after his second dose of mRNA-1273 vaccine. Twelve (14%) patients had COVID-19, associated with an acute hTTP episode in three of them: one patient had a transient ischemic attack, one a stroke, and a pregnant woman was hospitalized to intensify plasma treatment. Discussion: The risk of an acute episode triggered by COVID-19 seems higher than following vaccination in hTTP patients, who can be safely vaccinated against SARS-CoV-2.

Intelligent Sampling of Anterior Human Nasal Swabs using a Collaborative Robotic Arm

Advanced robotics does not always have to be associated with Industry 4.0, but can also be applied, for example, in the Smart Hospital concept. Developments in this field have been driven by the coronavirus disease (COVID-19), and any improvement in the work of medical staff is welcome. In this paper, an experimental robotic platform was designed and implemented whose main function is the swabbing samples from the nasal vestibule. The robotic platform represents a complete integration of software and hardware, where the operator has access to a web-based application and can control a number of functions. The increased safety and collaborative approach cannot be overlooked. The result of this work is a functional prototype of the robotic platform that can be further extended, for example, by using alternative technologies, extending patient safety, or clinical tests and studies. Code is available at https://github.com/ Steigner/ Robo_ Medicinae_ I. © 2022, Brno University of Technology. All rights reserved.

MoS2 Quantum Dots Based MSM Deep UV Photodetector

This letter reports facile hydrothermally synthesized colloidal MoS₂ quantum dots (QDs) based inexpensive solution processed metal-semiconductor-metal (MSM) photodetector (PD) with device structure Au/MoS₂ QDs/Au on Si/SiO₂ substrate to observe the ~275 nm deep UV radiation used in SARS-CoV-2 virus inactivation. The ligand exchange technique has been used thereby, reducing inter QDs distance for better film quality. Under deep UV exposure (275 nm), the fabricated MSM PD displays good responsivity (343.53 mA/W), external quantum efficiency (EQE) (154.9 %), and detectivity (2.51 ×1011Jones) values. The time response analysis under deep UV irradiation ~275 nm demonstrates adequate rise time (81.17 ms) and fall time (79.58 ms). IEEE

Server-Side Versus Client-Side Synchronization for Watch Together Applications Using CMAF Low Latency

Watch together is an application that was widely deployed during the COVID-19 global health crisis. Early results show a much longer viewing time when the feature is activated. The synchronization between the A/V streamed content, combined with the need to have a low, end-to-end latency compatible with the user interactions through integrated social media apps, is challenging, especially when watch together is deployed on all devices. The legacy approach used by existing watch together applications is to rely on a master user that will drive the other members of the group using an overlay control protocol to make sure that all the players will synchronize their playback. This article proposes a scheme that uses the built-in synchronization provided in Common Media Application Format (CMAF) low latency for both Dynamic Adaptive Streaming over HTTP (DASH) and HTTP Live Streaming (HLS), enabling a subsecond time delay between clients in the same geographical location. © 2002 Society of Motion Picture and Television Engineers, Inc.