Abstract--Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and available datasets often fail to capture the unique operational conditions and contextual variability of the network environment. Digital twinning provides a potential solution to this problem, as simulators tailored to the current network deployment can generate site-specific data to augment the available training datasets. However, there is a need to develop solutions to bridge the inherent simulation-to-reality (sim-to-real) gap between synthetic and real-world data. This paper reviews recent advances on two complementary strategies: 1) the calibration of digital twins (DTs) through real-world measurements, and 2) the use of sim-to-real gap-aware training strategies to robustly handle residual discrepancies between digital twin-generated and real data. For the latter, we evaluate two conceptually distinct methods that model the sim-to-real gap either at the level of the environment via Bayesian learning or at the level of the training loss via prediction-powered inference. Driven by the continued growth of computing resources and training datasets, artificial intelligence (AI) research is widely considered to be in the scaling era, which is focused on the development of general-purpose models that exhibit emergent capabilities. While this trend has yielded impressive results for many tasks, particularly in the domain of language modeling, it poses unique challenges when applied to engineering domains such as telecommunication networks.

Simulators are useful tools for testing automated driving controllers. Vehicle-in-the-loop (ViL) tests and digital twins (DTs) are widely used simulation technologies to facilitate the smooth deployment of controllers to physical vehicles. However, conventional ViL tests rely on full-size vehicles, requiring large space and high expenses. Also, physical-model-based DT suffers from the reality gap caused by modeling imprecision. This paper develops a comprehensive and practical simulator for testing automated driving controllers enhanced by scaled physical cars and AI-powered DT models. The scaled cars allow for saving space and expenses of simulation tests. The AI-powered DT models ensure superior simulation fidelity. Moreover, the simulator integrates well with off-the-shelf software and control algorithms, making it easy to extend. We use a filtered control benchmark with formal safety guarantees to showcase the capability of the simulator in validating automated driving controllers. Experimental studies are performed to showcase the efficacy of the simulator, implying its great potential in validating control solutions for autonomous vehicles and intelligent traffic.

In intelligent transportation systems (ITSs), incorporating pedestrians and vehicles in-the-loop is crucial for developing realistic and safe traffic management solutions. However, there is falls short of simulating complex real-world ITS scenarios, primarily due to the lack of a digital twin implementation framework for characterizing interactions between pedestrians and vehicles at different locations in different traffic environments. In this article, we propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop. Specifically, SVFDT builds comprehensive pedestrian-vehicle interaction models by leveraging multi-source traffic surveillance videos. Its architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources; (ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions; and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime. We analyze key design requirements and challenges and present core guidelines for SVFDT's system implementation. A testbed evaluation demonstrates its effectiveness in optimizing traffic management. Comparisons with traditional terminal-server frameworks highlight SV-FDT's advantages in mirroring delays, recognition accuracy, and subjective evaluation. Finally, we identify some open challenges and discuss future research directions.

Abstract-- In this paper, we explore the application of the Decision Transformer, a decision-making algorithm based on the Generative Pre-trained Transformer (GPT) architecture, to multi-vehicle coordination at unsignalized intersections. We formulate the coordination problem so as to find the optimal trajectories for multiple vehicles at intersections, modeling it as a sequence prediction task to fully leverage the power of GPTs as a sequence model. Through extensive experiments, we compare our approach to a reservation-based intersection management system. Our results show that the Decision Transformer can outperform the training data in terms of total travel time and can be generalized effectively to various scenarios, including noise-induced velocity variations, continuous interaction environments, and different vehicle numbers and road configurations. Intelligent traffic management has been shown to substantially minimize delays, enhance safety, and improve overall mobility.

The job shop scheduling problem (JSSP) and its solution algorithms have been of enduring interest in both academia and industry for decades. In recent years, machine learning (ML) is playing an increasingly important role in advancing existing and building new heuristic solutions for the JSSP, aiming to find better solutions in shorter computation times. In this paper we build on top of a state-of-the-art deep reinforcement learning (DRL) agent, called Neural Local Search (NLS), which can efficiently and effectively control a large local neighborhood search on the JSSP. In particular, we develop a method for training the decision transformer (DT) algorithm on search trajectories taken by a trained NLS agent to further improve upon the learned decision-making sequences. Our experiments show that the DT successfully learns local search strategies that are different and, in many cases, more effective than those of the NLS agent itself. In terms of the tradeoff between solution quality and acceptable computational time needed for the search, the DT is particularly superior in application scenarios where longer computational times are acceptable. In this case, it makes up for the longer inference times required per search step, which are caused by the larger neural network architecture, through better quality decisions per step. Thereby, the DT achieves state-of-the-art results for solving the JSSP with ML-enhanced search.

To increase the interpretability and prediction accuracy of the Machine Learning (ML) models, visualization of ML models is a key part of the ML process. Decision Trees (DTs) are essential in machine learning (ML) because they are used to understand many black box ML models including Deep Learning models. In this research, two new methods for creation and enhancement with complete visualizing Decision Trees as understandable models are suggested. These methods use two versions of General Line Coordinates (GLC): Bended Coordinates (BC) and Shifted Paired Coordinates (SPC). The Bended Coordinates are a set of line coordinates, where each coordinate is bended in a threshold point of the respective DT node. In SPC, each n-D point is visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed graph. These new methods expand and complement the capabilities of existing methods to visualize DT models more completely. These capabilities allow us to observe and analyze: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) sensitivity of each split threshold in the DT nodes, and (5) density of cases in parts of the n-D space. These features are critical for DT models' performance evaluation and improvement by domain experts and end users as they help to prevent overgeneralization and overfitting of the models. The advantages of this methodology are illustrated in the case studies on benchmark real-world datasets. The paper also demonstrates how to generalize them for decision tree visualizations in different General Line Coordinates.

Recent research has shown that Machine Learning/Deep Learning (ML/DL) models are particularly vulnerable to adversarial perturbations, which are small changes made to the input data in order to fool a machine learning classifier. The Digital Twin, which is typically described as consisting of a physical entity, a virtual counterpart, and the data connections in between, is increasingly being investigated as a means of improving the performance of physical entities by leveraging computational techniques, which are enabled by the virtual counterpart. This paper explores the susceptibility of Digital Twin (DT), a virtual model designed to accurately reflect a physical object using ML/DL classifiers that operate as Cyber Physical Systems (CPS), to adversarial attacks. As a proof of concept, we first formulate a DT of a vehicular system using a deep neural network architecture and then utilize it to launch an adversarial attack. We attack the DT model by perturbing the input to the trained model and show how easily the model can be broken with white-box attacks.

Another of the new machine learning algorithms in Oracle 21c Database is called XGBoost. Most people will have come across this algorithm due to its recent popularity with winners of Kaggle competitions and other similar events. XGBoost is an open source software library providing a gradient boosting framework in most of the commonly used data science, machine learning and software development languages. It has it's origins back in 2014, but the first official academic publication on the algorithm was published in 2016 by Tianqi Chen and Carlos Guestrin, from the University of Washington. The algorithm builds upon the previous work on Decision Trees, Bagging, Random Forest, Boosting and Gradient Boosting.

The adoption of the Prospective Payment System (PPS) in the UK National Health Service (NHS) has led to the creation of patient groups called Health Resource Groups (HRG). HRGs aim to identify groups of clinically similar patients that share similar resource usage for reimbursement purposes. These groups are predominantly identified based on expert advice, with homogeneity checked using the length of stay (LOS). However, for complex patients such as those encountered in burn care, LOS is not a perfect proxy of resource usage, leading to incomplete homogeneity checks. To improve homogeneity in resource usage and severity, we propose a data-driven model and the inclusion of patient-level costing. We investigate whether a data-driven approach that considers additional measures of resource usage can lead to a more comprehensive model. In particular, a cost-sensitive decision tree model is adopted to identify features of importance and rules that allow for a focused segmentation on resource usage (LOS and patient-level cost) and clinical similarity (severity of burn). The proposed approach identified groups with increased homogeneity compared to the current HRG groups, allowing for a more equitable reimbursement of hospital care costs if adopted.

The birth of massive open online courses (MOOCs) has had an undeniable effect on how teaching is being delivered. It seems that traditional in class teaching is becoming less popular with the young generation, the generation that wants to choose when, where and at what pace they are learning. As such, many universities are moving towards taking their courses, at least partially, online. However, online courses, although very appealing to the younger generation of learners, come at a cost. For example, the dropout rate of such courses is higher than that of more traditional ones, and the reduced in person interaction with the teachers results in less timely guidance and intervention from the educators. Machine learning (ML) based approaches have shown phenomenal successes in other domains. The existing stigma that applying ML based techniques requires a large amount of data seems to be a bottleneck when dealing with small scale courses with limited amounts of produced data. In this study, we show not only that the data collected from an online learning management system could be well utilized in order to predict students overall performance but also that it could be used to propose timely intervention strategies to boost the students performance level. The results of this study indicate that effective intervention strategies could be suggested as early as the middle of the course to change the course of students progress for the better. We also present an assistive pedagogical tool based on the outcome of this study, to assist in identifying challenging students and in suggesting early intervention strategies.