Networking Applications Research Articles

Instagram vixens: the racialized sexual scripts of erotic labor online

ABSTRACT Despite growing interest in the ways information and communication technologies shape labor, few studies have accounted for the racialized sexualization of erotic labor. This study examines how Black women exotic dancers deploy racialized sexual scripts in the content they generate on the mobile social networking application Instagram. As erotic influencers, these women engage in multifaceted and multimodal erotic self-presentations both within and beyond the strip club. This article presents data from a digital ethnography of 73 Black female exotic dancers located in the southeastern United States, as well as a content analysis of the digital media files they created on Instagram. The findings suggest four distinct sexual scripts that these Instagram users perform: virtual cover girl, erotic professional, boudoir baddie, and assets model. Findings demonstrate Black women use smartphone and social networking technological features to create presentations of femininity, desirability, and sensuality that simultaneously subvert and adhere to the controlling images that define Western ideologies on race and sexuality. Ultimately, this article argues for the need to account for the social and cultural contexts in which the affordances of networked communication technologies that permeate contemporary erotic labor operate.

Side-channel free quantum digital signature with source monitoring

Abstract Quantum digital signature (QDS) can guarantee the information-theoretical security of a signature with the fundamental laws of quantum physics. However, most current QDS protocols do not take source security into account, leading to an overestimation of the signature rate. In this paper, we propose to utilize Hong-Ou-Mandel interference to characterize the upper-bound of the source imperfections, and further to quantify information leakage from potential side-channels. Additionally, we combine decoy-state methods and finite-size analysis in analyzing the signature rate. Simulation results demonstrate the performance and feasibility of our approach. Our current work can improve the practical security of QDS systems, thereby promoting their further networked applications.

Purcell Enhancement and Spin Spectroscopy of Silicon Vacancy Centers in Silicon Carbide Using an Ultrasmall Mode-Volume Plasmonic Cavity.

Silicon vacancy (VSi) centers in 4H-silicon carbide have emerged as a strong candidate for quantum networking applications due to their robust electronic and optical properties, including a long spin coherence lifetime and bright, stable emission. Here, we report the integration of VSi centers with a plasmonic nanocavity to Purcell enhance the emission, which is critical for scalable quantum networking. Employing a simple fabrication process, we demonstrate plasmonic cavities that support a nanoscale mode volume and exhibit an increase in the spontaneous emission rate with a measured Purcell factor of up to 48. In addition to investigating the optical resonance modes, we demonstrate an improvement in the optical stability of the spin-preserving resonant optical transitions relative to the radiation-limited value. The results highlight the potential of nanophotonic structures for advancing quantum networking technologies and emphasize the importance of optimizing emitter-cavity interactions for efficient quantum photonic applications.

Development of Long-Range and High-Speed Wireless LAN

The development of long-range and high-speed wireless LAN (Local Area Network) technologies has become increasingly important in modern networking applications. We have developed 2.4 GHz wireless LAN units with the longest coverage, which comply with the IEEE 802.11g standard and Indian radio regulations, to re-establish communications temporarily between disaster-devastated areas and hospitals, among other locations. The network deployment is carried out in the college campus, involving two types: a) point-to-point (PtP), and b) point-to-multipoint (PtMP). The throughput within ranges of 500m and 6Km is approximately 100 Mbps, while for 15Km, the achieved throughput was 80 Mbps. Real-world applications and use cases of long-range wireless LAN systems are also highlighted to demonstrate their significance in diverse scenarios, ranging from rural connectivity to urban deployments

Doxycycline post-exposure prophylaxis among men who have sex with men and transgender women in Belgium: awareness, use and antimicrobial resistance concerns in a cross-sectional online survey

ObjectivesWe aimed to assess the awareness, willingness to use and use of doxycycline post-exposure prophylaxis (doxyPEP) among men who have sex with men (MSM) and transgender women (TGW) in Belgium....

Supporting treatment adherence for resilience and thriving (START): protocol for a mHealth randomized controlled trial

BackgroundAlthough behavioral interventions show some promise for reducing stimulant use and achieving durable viral suppression in sexual minority men (SMM) with HIV, scalable mHealth applications are needed to optimize their reach and cost-effectiveness.MethodsSupporting Treatment Adherence for Resilience and Thriving (START) is a randomized controlled trial (RCT) testing the efficacy and cost-effectiveness of a mHealth application that integrates evidence-based positive affect regulation skills with self-monitoring of adherence and mood. The primary outcome is detectable HIV viral load (i.e., > 300 copies/mL) from self-collected dried blood spot (DBS) specimens at 6 months. Secondary outcomes include detectable DBS viral load at 12 months, self-reported stimulant use severity, anti-retroviral therapy (ART) adherence, and positive affect over 12 months. A national sample of up to 250 SMM with HIV who screen positive for stimulant use disorder and reporting suboptimal ART adherence is being recruited via social networking applications through April of 2024. After providing informed consent, participants complete a run-in period (i.e., waiting period) including two baseline assessments with self-report measures and a self-collected DBS sample. Those who complete the run-in period are randomized to either the START mHealth application or access to a website with referrals to HIV care and substance use disorder treatment resources. Participants provide DBS samples at baseline, 6, and 12 months to measure HIV viral load as well as complete self-report measures for secondary outcomes at quarterly follow-up assessments over 12 months.DiscussionTo date, we have paid $117,500 to advertise START on social networking applications and reached 1,970 eligible participants ($59.77 per eligible participant). Although we identified this large national sample of potentially eligible SMM with HIV who screen positive for a stimulant use disorder and report suboptimal ART adherence, only one-in-four have enrolled in the RCT. The run-in period has proven to be crucial for maintaining scientific rigor and reproducibility of this RCT, such that only half of consented participants complete the required study enrollment activities and attended a randomization visit. Taken together, findings will guide adequate resource allocation to achieve randomization targets in future mHealth research SMM with HIV who use stimulants.Trial RegistrationThis protocol was registered on clinicaltrials.gov (NCT05140876) on December 2, 2021.

Develop an RS-485 Protocol for Arduino Boards Applied To Networked Real Time Control Systems

The Arduino microprocessor boards such as Mega 2560, UNO R3, Leonardo, Micro, and Nano are simple and low-cost tools for real-time measurement and control applications. These Arduino boards cannot be used in distributed systems because they lack the networking capabilities to transfer data across units. In this study, an RS-485 protocol for Arduino boards that operate in Master-Slave networks was developed. Network operations could be carried out independently on the main thread program, and devices in the network could react quickly to information received. This was made possible by the asynchronous serial communication feature and a high-speed timer provided in Arduino boards. The networks designed in this study were applied to an electric vehicle model with all-wheel drive and all-wheel steering capabilities for supermaneuverability as well as a saltwater intrusion early warning system installed in a river entry. The results showed that highly reliable and stable network operations could be achieved, thus extending the usage of popular Arduino boards for networked real-time applications.

Cybersecurity for tactical 6G networks: Threats, architecture, and intelligence

Edge intelligence, network autonomy, broadband satellite connectivity, and other concepts for private 6G networks are enabling new applications for public safety authorities, e.g., for police and rescue personnel. Enriched situational awareness, group communications with high-quality video, large scale IoT, and remote control of vehicles and robots will become available in any location and situation. We analyze cybersecurity in intelligent tactical bubbles, i.e., in autonomous rapidly deployable mobile networks for public safety operations. Machine learning plays major roles in enabling these networks to be rapidly orchestrated for different operations and in securing these networks from emerging threats, but also in enlarging the threat landscape. We explore applicability of different threat and risk analysis methods for mission-critical networked applications. We present the results of a joint risk prioritization study. We survey security solutions and propose a security architecture, which is founded on the current standardization activities for terrestrial and non-terrestrial 6G and leverages the concepts of machine learning-based security to protect mission-critical assets at the edge of the network.

Applications of neural networking in Eyring-Powell nanofluid dynamics on a rotating surface in a porous medium

One of the fundamental aspects of solving difficult and nonlinear mathematical ideas is the use of Artificial Neural Networks due to their exceptional efficiency in handling such problems. In many complex fields such as computational fluid system, biological computation, and biotechnology, a distinct computing structure is provided by Artificial Neural Networks, which is extremely valuable. The main purpose of this article is to dig out the abilities of the Levenberg-Marquardt technique using back-propagation artificial neural networks regarding the fluid mechanics and the heat transport assessment in nanoparticles. This interdisciplinary field explores heat and mass transfer through objects and fluids, and their impacts on temperature as well as concentration distributions. With the help of mathematical modelling and numerical solution methodologies, researchers can simulate and analyze these processes. The present analysis communicates the Eyring-Powell fluid flow caused by a rotating disk placed in the horizontal direction. The fluid flow over a rotating disk through non-linear partial differential equations is modeled. After converting the partial differential equations to ordinary ones, they are tackled numerically through shooting technique. The Levenberg-Marquardt algorithm using back-propagation artificial neural networks technique is used with reference datasets, having 70 % training, 15 % testing, and 15 % to validation. The method is validated with the help of mean squared error, error histogram and comprehensive regression analysis. These figures show the accuracy of the proposed method for solving nonlinear problems. Flow features such as velocity, temperature as well as the concentration profiles are exemplified quantitatively and have been graphically discussed. Velocity of the fluid decreases for porosity and increases for fluid parameter while temperature increases for thermophoresis and Brownian motion parameters. Consistency is shown by getting a minimum absolute error approaching zero, showing the strength of the proposed approach.

The Use of a Spanish-Translated PrEP Stigma Scale among the Latino Sample of the UNITE Cohort Study.

Pre-Exposure Prophylaxis (PrEP) related stigma is linked to inadequate PrEP uptake, yet there are no validated scales to test this association among Spanish-speaking LSMM. The current study examined if the Spanish-translated PrEP Stigma Scale (PSS) was psychometrically appropriate for implementing in Spanish language dominant Latino/e/x Sexual Minority Men (SMM). Recruitment was conducted using geosocial networking applications, social media sites, and e-mail blasts (N=3,049). First, we utilized Item Response Theory (IRT) modeling to evaluate the reliability of the PSS items and the latent construct across both language groups (nEnglish = 2844 and nSpanish = 205). Subsequently, we applied the PSS scale in a theoretical application by examining its association with key steps in the PrEP uptake cascade (i.e., perceived PrEP candidacy, PrEP willingness, PrEP intentions, and having spoken to provider about PrEP) stratified by language. Results of the IRT analyses provided evidence that the translated version of the PSS was appropriate for use among this sample. Further, among English respondents, PrEP stigma was negatively associated with perceived PrEP candidacy (B=-0.30, p=<.001), PrEP willingness (B=-0.46, p=<.001), and PrEP intentions (B=-0.23, p=.003). PrEP stigma, among Spanish respondents, was not significantly associated with any of the PrEP cascade steps. This study demonstrated that the PSS scale performs adequately for both English and Spanish-speaking Latino SMM. However, researchers and health professionals alike should pay close attention to the nuanced effects in U.S. based English and Spanish language samples as PrEP stigma may impact the PrEP cascade for one language sample and not the other.

Symposium 5: Networked Learning: Classroom Experiences at AUT

This short paper describes a proposed case study which aims at exploring to what extent Networked Learning enhances classroom experiences at the authors’ institution and how the authors’ institution could adopt networked learning in producing Great Graduates, the authors' institution mission. The case study is significant with the re-definition of Networked Learning within an institutional context. The study employs an interpretivist research approach (Erickson, 1998) with a combination of direct observation and interview-like methods to understand the experiences of students and teaching academics in on-campus Networked Learning situations among all 15 study options at the authors' institution. Participants will include volunteered students and academics from various disciplines. The literature review indicates that the adoption of Networked Learning in classroom boosts students’ self-efficacy for social networking and professional development, enhances student-centred experiences with a community focus (e.g., social presence) and provides opportunities for students to further develop their critical thinking. The changing landscape of higher education, especially in this post global pandemic era, warrants the development of a case study approach within a particular context in order to further investigate the adoption of Networked Learning in enhancing teaching and learning processes. The findings will gain insights into achieving the strategic goal of student-directed learning at the authors' institution and could be applicable to higher education in general in order to improve teaching and learning experiences further. In short, it is an iterative process of developing one’s own Networked Learning application through the affordance of the Networked Learning principles and the practical accomplishment of teaching and learning practices at the course as well as the programme level. Limitations and possible future study are being identified from this proposal.

The effect of aggressive group norms on young adults’ conformity behavior in WhatsApp chats: a vignette-based experiment

Social networking and messaging applications, such as WhatsApp, have become an essential social environment for adolescents and young adults (AYA). While facilitating connectivity, they also bear hazards, including cyber-aggression. This study investigates the impact of (aggressive) group norms on AYA's propensity to expect cyberaggressive behaviors within different group chats. Based on a vignette scenario, realistically simulated WhatsApp group chats enabled scrutinizing, if and how exemplary reactions (funny, aggressive, friendly) of group members influence AYA's conformity to cyber-aggression (N = 500, aged 16 to 29). Additionally, we examined the effect of chat group type—close friends versus fellow students—on the anticipation of aggressive reactions. Sociodemographic, social, and developmental-psychological factors were evaluated for potential effects. Multilevel logistic regression analyses indicated that aggressive group norms significantly predict cyber-aggression anticipation, while no effect of chat group type was observed. Controlling for the size and vivacity of participant’s friend group, gender, age, and educational status were significant predictors: males, younger participants, and non-university students expected higher levels of cyber-aggression conformity. This study underlines the importance of group dynamics on perceptions of cyber-aggression and hints at individual risk factors for AYA's digital communication behavior.

Electronic Visualization Laboratory's 50th Anniversary Retrospective: Look to the Future, Build on the Past

Abstract September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL's introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual-reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations—pillars of VR. But EVL's culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person's expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL's past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans.

Towards Distributed Flow Scheduling in IEEE 802.1Qbv Time-Sensitive Networks

Flow scheduling plays a pivotal role in enabling Time-Sensitive Networking (TSN) applications. Current flow scheduling mainly adopts a centralized scheme, posing challenges in adapting to dynamic network conditions and scaling up for larger networks. To address these challenges, we first thoroughly analyze the flow scheduling problem and find the inherent locality nature of time scheduling tasks. Leveraging this insight, we introduce the first distributed framework for IEEE 802.1Qbv TSN flow scheduling. In this framework, we further propose a multi-agent flow scheduling method by designing Deep Reinforcement Learning (DRL)-based route and time agents for route and time planning tasks. The time agents are deployed on field devices to schedule flows in a distributed way. Evaluations in dynamic scenarios validate the effectiveness and scalability of our proposed method. It enhances the scheduling success rate by 20.31% compared to state-of-the-art methods and achieves substantial cost savings, reducing transmission costs by 410× in large-scale networks. Additionally, we validate our approach on edge devices and a TSN testbed, highlighting its lightweight nature and ease of deployment.

Lifetime Reduction of Single Germanium-Vacancy Centers in Diamond via a Tunable Open Microcavity

Coupling between a single quantum emitter and an optical cavity presents a key capability for future quantum networking applications. Here, we explore interactions between individual germanium-vacancy (GeV) defects in diamond and an open microcavity at cryogenic temperatures. Exploiting the tunability of our microcavity system to characterize and select emitters, we observe a Purcell-effect-induced lifetime reduction of up to 4.5±0.3 and extract coherent-coupling rates up to 360±20 MHz. Our results indicate that the GeV defect has favorable optical properties for cavity coupling, with a quantum efficiency of at least 0.34±0.05 and likely much higher. Published by the American Physical Society 2024

A Face Detection System: A Comprehensive Survey

The domain of facial detection has witnessed significant advancement, mostly attributed to the progress in machine learning and deep learning methodologies. These enhancements have resulted in a fundamental change in the field. Face detection algorithms are extensively utilized in several domains, such as security, surveillance, and social networking applications. This survey has thoroughly investigated various methodologies for facial identification, with a focus on the challenges, applications, and evolution from traditional to deep learning-based approaches. Furthermore, it provides important insights into commonly used datasets for facial detection, including a comprehensive examination of their unique characteristics. The study is structured sequentially, commencing with an introduction to the assessment of significant previous research and the challenges faced by face detection algorithms. Subsequently, the investigation delves into different practical uses, delineates methods for identifying, and scrutinizes frequently employed data sets. The transition from traditional classifiers to deep learning models in face identification technology represents a significant improvement in addressing the complexities of face detection in various environments. Despite the obstacles that now exist, the continuous advancements in deep learning offer promising solutions that enhance the accuracy and efficiency of face identification systems in a wide range of applications.

Software-defined optical networking applications enabled by programmable integrated photonics

Data center networks are experiencing unprecedented exponential growth, mostly driven by the continuous computing demands in machine learning and artificial intelligence algorithms. Within this realm, optical networking offers numerous advantages, including low latency, energy efficiency, and bandwidth transparency, positioning it as a compelling alternative to its electronic counterparts. In this work, we showcase a range of software-defined optical networking applications deployed on a general-purpose programmable integrated photonic processor. Leveraging graph-based theory, we experimentally demonstrate dynamic optical interconnects, circuit switching, and multicasting on the same photonic platform, yielding remarkable results in terms of crosstalk and reconfiguration speed. Our approach harnesses the benefits of reconfigurability and reliability, paving the way for a new generation of high-performance optical devices tailored for data center and computing clusters.

Fully Wireless Coherent Distributed Phased Array System for Networked Radar Applications

Fully Wireless Coherent Distributed Phased Array System for Networked Radar Applications

ESTADO DEL ARTE, CHECK!

TikTok is a social networking application that has attracted great attention from the public, companies and, more recently, academia. Therefore, this article aims to present the scientific production carried out on the platform in Brazil between 2019 and 2023. The research analyzed 66 works, focusing on the titles, abstracts and keywords of the publications. From this analysis it is expected to demonstrate the development and main themes of scientific production related to the Chinese application in the Brazilian context.

Implementation and optimization of Burg’s method for real-time packet loss concealment in networked music performance applications

AbstractIn networked music performance (NMP) applications, which entail real-time audio streaming over the Internet, strict latency requirements are needed to ensure a realistic interaction between geographically dispersed musicians. Thus, NMP applications typically leverage uncompressed audio and unreliable transport protocols to avoid unnecessary processing and re-transmission delays. Given that no guarantee on packet delivery is offered, NMP applications must deal with late/lost audio packets to mitigate the impact of the resulting audio artifacts on the quality of the playback audio stream. This paper explores an audio packet loss concealment (PLC) technique based on autoregressive (AR) models. In particular, it investigates the algorithmic implementation of Burg’s method and the parameters configuration that offers the best trade-off between prediction error and computational time requirements. The purpose is to find the most suitable solution capable of running on a Raspberry Pi 4B within the real-time audio boundaries imposed by NMP applications. Additionally, we analyze the computational time required to fit the model and predict future samples by considering six implementations and various compilation flags. Results confirm that AR models can predict future audio samples more accurately than traditional PLC approaches, which consist of filling audio gaps with silence or repeating the last received audio segment. Furthermore, results demonstrate the effectiveness of the proposed solution in meeting the strict latency requirements when deployed on a Raspberry Pi 4B.