Prediction of Cervical Lymph Node Metastasis in Clinically Node-Negative T1 and T2 Papillary Thyroid Carcinoma Using Supervised Machine Learning Approach.

Papillary thyroid carcinoma (PTC) is generally considered an indolent cancer. However, patients with cervical lymph node metastasis (LNM) have a higher risk of local recurrence. This study evaluated and compared four machine learning (ML)-based classifiers to predict the presence of cervical LNM in clinically node-negative (cN0) T1 and T2 PTC patients. The algorithm was developed using clinicopathological data from 288 patients who underwent total thyroidectomy and prophylactic central neck dissection, with sentinel lymph node biopsy performed to identify lateral LNM. The final ML classifier was selected based on the highest specificity and the lowest degree of overfitting while maintaining a sensitivity of 95%. Among the models evaluated, the k-Nearest Neighbor (k-NN) classifier was found to be the best fit, with an area under the receiver operating characteristic curve of 0.72, and sensitivity, specificity, positive and negative predictive values, F1 and F2 scores of 98%, 27%, 56%, 93%, 72%, and 85%, respectively. A web application based on a sensitivity-optimized kNN classifier was also created to predict the potential of cervical LNM, allowing users to explore and potentially build upon the model. These findings suggest that ML can improve the prediction of LNM in cN0 T1 and T2 PTC patients, thereby aiding in individual treatment planning.

Security Concerns in MMO Games-Analysis of a Potent Application Layer DDoS Threat.

The application layer in the Internet protocol suite offers a significant degree of freedom regarding the orchestration of distributed denial-of-service attacks due to many different and unstandardized protocols. The primary focus of defending against application-layer distributed denial-of-service attacks has traditionally been Hypertext Transfer Protocols oriented while observing individual users' actions independently from one another. In this paper, we present and analyze a novel application-layer DDoS attack in massively multiplayer online games that utilize the cooperative efforts of the attackers to deplete the server's or players' bandwidth. The attack exploits in-game dependencies between players to cause a massive spike in bandwidth while the attackers' traffic remains legitimate. We introduce a multiplayer-relations graph to model user behavior on a game server. Additionally, we demonstrate the attack's devastating capabilities on an emulated World of Warcraft server. Lastly, we discuss flaws of the existing defense mechanisms and possible approaches for the detection of these attacks using graph theory and multiplayer-relations graphs.

Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programmability.

The academic and professional community has recently started to develop the concept of 6G networks. The scientists have defined key performance indicators and pursued large-scale automation, ambient sensing intelligence, and pervasive artificial intelligence. They put great efforts into implementing new network access and edge computing solutions. However, further progress depends on developing a more flexible core infrastructure according to more complex QoS requirements. Our research aims to provide 5G/6G core flexibility by customizing and optimizing network slices and introducing a higher level of programmability. We bind similar services in a group, manage them as a single slice, and enable a higher level of programmability as a prerequisite for dynamic QoS. The current 5G solutions primarily use predefined queues, so we have developed highly flexible, dynamic queue management software and moved it entirely to the application layer (reducing dependence on the physical network infrastructure). Further, we have emulated a testbed environment as realistically as possible to verify the proposed model capabilities. Obtained results confirm the validity of the proposed dynamic QoS management model for configuring queues' parameters according to the service management requirements. Moreover, the proposed solution can also be applied efficiently to 5G core networks to resolve complex service requirements.

Education in times of crisis: Rapid transition to distance learning.

The COVID-19 crisis is having a significant impact on the quality of life and future of young people; it can also lead to disruption in education. A disruption would pose a severe threat to the entire society in the postcrisis period. Therefore, educational institutions must respond quickly and ensure the continuity of the educational processes. Our research goal has been to develop and implement a model enabling a rapid transition from the traditional to the distance learning model in a state of emergency. Our focus has been on conceiving technical, organizational, and pedagogical changes that educational organizations need to implement to enable different interaction methods, ensure continuity, and provide high-quality education. We have defined and implemented a model, which is described in detail in this paper, thus giving guidelines for a rapid transition to distance learning, which is not restricted to the crisis times only. We have evaluated our approach by monitoring the IT solutions and surveying students and teachers at the School of Computing, Union University of Belgrade. The results indicate the high satisfaction of these participants in the educational processes. They imply the acceptability of prolonged distance learning, if needed, and embrace the hybrid education model for the next generation of students.