Design and Implementation of the Service-Aware Traffic Engineering (SATE) in the LISP Software- DefinedWireless Network (LISP-SDWN)

Software Defined Wireless Networks (SDWN) have been considered to have a feasible architecture that enables the fast deployment of new services and solutions in response to the explosion in the number of users and network traffic. Currently, the telecommunications sector is ensuring flexibility in network management and configuration. However, fluctuations in traffic are still beyond the control of SDWN providers. This paper suggests ways to manage fluctuations of traffic with service type. We propose the design of a service-aware network management service that achieves the maximum network utilization among heterogeneous Radio Access Networks (RANs) as a form of Traffic Engineering (TE). In this paper, we implement and test the Service-Aware Traffic Engineering (SATE) that distributes the network traffic to RANs according to the service type of traffic in the network layer. The traffic shift latency (e.g., from an LTE RAN to a Wi-Fi RAN) is considered as a performance metric that does not affect the end-to-end latency of some network applications (e.g., VoIP), and it is 3.51ms from our testbed. Therefore, it might not affect the end-to-end latency of the VoIP application in the telecommunications. SATE is implemented using OpenDaylight (ODL) and Ingress/Egress Tunneling Routers (xTRs) running on Vector Packet Processing (VPP)

MadFed : enhancing federated learning with marginal-data model fusion

As the demand for intelligent applications at the network edge grows, so does the need for effective federated learning (FL) techniques. However, FL often relies on non-identically and non-independently distributed local datasets across end devices, which could result in considerable performance degradation. Prior solutions, such as model-driven approaches based on knowledge distillation, meta-learning, and transfer learning, have provided some reprieve. However, their performance suffers under heterogeneous local datasets and highly skewed data distributions. To address these challenges, this study introduces the MArginal Data fusion FEDerated Learning (MadFed) approach, a groundbreaking fusion of model- and data-driven methodologies. By utilizing marginal data, MadFed mitigates data distribution skewness, improves the maximum achievable accuracy, and reduces communication costs. Furthermore, the study demonstrates that the fusion of marginal data can significantly improve performance even with minimal data entries, such as a single entry. For instance, it provides up to a 15.4% accuracy increase and 70.4% communication cost savings when combined with established model-driven methodologies. Conversely, relying solely on these model-driven methodologies can result in poor performance, especially with highly skewed datasets. Significantly, MadFed extends its effectiveness across various FL algorithms and offers a unique method to augment label sets of end devices, thereby enhancing the utility and applicability of federated learning in real-world scenarios. The proposed approach is not only efficient but also adaptable and versatile, promising broader application and potential for widespread adoption in the field

An ICN-Based Data Marketplace Model Based on a Game Theoretic Approach Using Quality-Data Discovery and Profit Optimization

In the age of data and machine learning, massive amounts of data produced throughout our society can be rapidly delivered to various applications through a broad spectrum of cloud services. However, the spectrum of applications has vastly different data quality requirements and Willingness-To-Pay(WTP), creating a general and complex problem matching consumer quality requirements and budgets with providers’ data quality and price. This paper proposes the Information-Centric Networking(ICN)-based data marketplace to foster quality-data trading service to address the challenge above. We embed a WTP mechanism into an ICN-based data broker service running on cloud computing; therefore, a data consumer can request its desired data with a data name and quality requirement. By specifying nominal WTPs, data consumers can acquire data of the desired quality at the range of maximum nominal WTP. At the same time, a data broker can offer data of a suitable quality based on the profit-optimized price and the proposed service quality using ground-truth accuracy trained by data. We demonstrate that the data broker’s profit can be almost doubled by using the optimal data size and budget determined by considering the one-leader-multiple-followers Stackelberg game. These results show that a value-added data brokering service can profitably facilitate data trading

COVID-19-Associated Lung Lesion Detection by Annotating Medical Image with Semi Self-Supervised Technique

Diagnosing COVID-19 infection through the classification of chest images using machine learning techniques faces many controversial problems owing to the intrinsic nature of medical image data and classification architectures. The detection of lesions caused by COVID-19 in the human lung with properties such as location, size, and distribution is more practical and meaningful to medical workers for severity assessment, progress monitoring, and treatment, thus improving patients’ recovery. We proposed a COVID-19-associated lung lesion detector based on an object detection architecture. It correctly learns disease-relevant features by focusing on lung lesion annotation data of medical images. An annotated COVID-19 image dataset is currently nonexistent. We designed our semi-self-supervised method, which can extract knowledge from available annotated pneumonia image data and guide a novice in annotating lesions on COVID-19 images in the absence of a medical specialist. We prepared a sufficient dataset with nearly 8000 lung lesion annotations to train our deep learning model. We comprehensively evaluated our model on a test dataset with nearly 1500 annotations. The results demonstrated that the COVID-19 images annotated by our method significantly enhanced the model’s accuracy by as much as 1.68 times, and our model competes with commercialized solutions. Finally, all experimental data from multiple sources with different annotation data formats are standardized into a unified COCO format and publicly available to the research community to accelerate research on the detection of COVID-19 using deep learning

Outdoor artificial light at night, obesity, and sleep health: Cross-sectional analysis in the KoGES study

<p>Obesity is a common disorder with many complications. Although chronodisruption plays a role in obesity, few epidemiological studies have investigated the association between artificial light at night (ALAN) and obesity. Since sleep health is related to both obesity and ALAN, we investigated the association between outdoor ALAN and obesity after adjusting for sleep health. We also investigated the association between outdoor ALAN and sleep health. This cross-sectional survey included 8526 adults, 39–70 years of age, who participated in the Korean Genome and Epidemiology Study. Outdoor ALAN data were obtained from satellite images provided by the US Defense Meteorological Satellite Program. We obtained individual data regarding outdoor ALAN; body mass index; depression; and sleep health including sleep duration, mid-sleep time, and insomnia; and other demographic data including age, sex, educational level, type of residential building, monthly household income, alcohol consumption, smoking status and consumption of caffeine or alcohol before sleep. A logistic regression model was used to investigate the association between outdoor ALAN and obesity. The prevalence of obesity differed significantly according to sex (women 47% versus men 39%, <i>p</i> < 0.001) and outdoor ALAN (high 55% versus low 40%, <i>p</i> < 0.001). Univariate logistic regression analysis revealed a significant association between high outdoor ALAN and obesity (odds ratio [OR] 1.24, 95% confidence interval [CI] 1.14–1.35, <i>p</i> < 0.001). Furthermore, multivariate logistic regression analyses showed that high outdoor ALAN was significantly associated with obesity after adjusting for age and sex (OR 1.25, 95% CI 1.14–1.37, <i>p</i> < 0.001) and even after controlling for various other confounding factors including age, sex, educational level, type of residential building, monthly household income, alcohol consumption, smoking, consumption of caffeine or alcohol before sleep, delayed sleep pattern, short sleep duration and habitual snoring (OR 1.20, 95% CI 1.06–1.36, <i>p</i> = 0.003). The findings of our study provide epidemiological evidence that outdoor ALAN is significantly related to obesity.</p

Bright light exposure before bedtime impairs response inhibition the following morning: a non-randomized crossover study

<p><b>Introduction</b>: Bright light exposure in the late evening can affect cognitive function the following morning either by changing the biological clock and/or disturbing sleep, but the evidence for this effect is scarce, and the underlying mechanism remains unknown. In this study, we first aimed to evaluate the effect of bright light exposure before bedtime on frontal lobe activity the following morning using near-infrared spectroscopy (NIRS) during a Go/NoGo task. Second, we aimed to evaluate the effects of bright light exposure before bedtime on polysomnographic measures and on a frontal lobe function test the following morning.</p> <p><b>Methods</b>: Twenty healthy, young males (mean age, 25.5 years) were recruited between September 2013 and August 2014. They were first exposed to control light (150 lux) before bedtime (from 20:00 h to 24:00 h) for 2 days and then to bright light (1,000 lux) before bedtime for an additional 5 days. We performed polysomnography (PSG) on the final night of each light exposure period (on nights 2 and night 7) and performed NIRS, which measures the concentrations of oxygenated and deoxygenated hemoglobin (OxyHb and DeoxyHb, respectively), coupled with a Go/NoGo task the following morning (between 09:30 h and 11:30 h). The participants also completed frontal lobe function tests the following morning.</p> <p><b>Results</b>: NIRS showed decreased hemodynamic activity (lower OxyHb and a tendency toward higher DeoxyHb concentration) in the right frontal lobe during the NoGo block after 1000-lux light exposure compared with that during the NoGo block after 150-lux light exposure. The commission error rate (ER) during the Go/NoGo task was higher after 1000-lux light exposure than that during the Go/NoGo task after 150-lux light exposure (1.24 ± 1.09 vs. 0.6 ± 0.69, <i>P </i>= 0.002), suggesting a reduced inhibitory response.</p> <p><b>Conclusion</b>: This study shows that exposure to bright light before bedtime for 5 days impairs right frontal lobe activation and response inhibition the following morning.</p