Post-hoc 8] for reject, whereincexp(x, y)= c0. AR -10 (left), CIFAR -100 (middle), and ImageNet (right) inalearningtodefersetting, isallowedtodefertoa "specialist " expert.

Context: Modern Systems of Systems (SoSs) increasingly operate in dynamic environments (e.g., smart cities, autonomous vehicles) where runtime composition -- the on-the-fly discovery, integration, and coordination of constituent systems (CSs)--is crucial for adaptability. Despite growing interest, the literature lacks a cohesive synthesis of runtime composition in dynamic SoSs. Objective: This study synthesizes research on runtime composition in dynamic SoSs and identifies core challenges, solution strategies, supporting tools, and evaluation methods. Methods: We conducted a Systematic Literature Review (SLR), screening 1,774 studies published between 2019 and 2024 and selecting 80 primary studies for thematic analysis (TA). Results: Challenges fall into four categories: modeling and analysis, resilient operations, system orchestration, and heterogeneity of CSs. Solutions span seven areas: co-simulation and digital twins, semantic ontologies, integration frameworks, adaptive architectures, middleware, formal methods, and AI-driven resilience. Service-oriented frameworks for composition and integration dominate tooling, while simulation platforms support evaluation. Interoperability across tools, limited cross-toolchain workflows, and the absence of standardized benchmarks remain key gaps. Evaluation approaches include simulation-based, implementation-driven, and human-centered studies, which have been applied in domains such as smart cities, healthcare, defense, and industrial automation. Conclusions: The synthesis reveals tensions, including autonomy versus coordination, the modeling-reality gap, and socio-technical integration. It calls for standardized evaluation metrics, scalable decentralized architectures, and cross-domain frameworks. The analysis aims to guide researchers and practitioners in developing and implementing dynamically composable SoSs.

We present a generic approach to constructing a frequentist CS using Bayesian tools, based on the fact that the ratio of a prior to the posterior at the ground truth is a martingale. We then present Hoeffding-and empirical-Bernstein-type time-uniform CSs and fixed-time confidence intervals for sampling WoR, which improve on previous bounds in the literature and explicitly quantify the benefit of WoR sampling.

This study examines the variability of Chat-GPT machine translation (MT) outputs across six different configurations in four languages,with a focus on creativity in a literary text. We evaluate GPT translations in different text granularity levels, temperature settings and prompting strategies with a Creativity Score formula. We found that prompting ChatGPT with a minimal instruction yields the best creative translations, with "Translate the following text into [TG] creatively" at the temperature of 1.0 outperforming other configurations and DeepL in Spanish, Dutch, and Chinese. Nonetheless, ChatGPT consistently underperforms compared to human translation (HT).

This paper introduces the centroid decision forest (CDF), a novel ensemble learning framework that redefines the splitting strategy and tree building in the ordinary decision trees for high-dimensional classification. The splitting approach in CDF differs from the traditional decision trees in theat the class separability score (CSS) determines the selection of the most discriminative features at each node to construct centroids of the partitions (daughter nodes). The splitting criterion uses the Euclidean distance measurements from each class centroid to achieve a splitting mechanism that is more flexible and robust. Centroids are constructed by computing the mean feature values of the selected features for each class, ensuring a class-representative division of the feature space. This centroid-driven approach enables CDF to capture complex class structures while maintaining interpretability and scalability. To evaluate CDF, 23 high-dimensional datasets are used to assess its performance against different state-of-the-art classifiers through classification accuracy and Cohen's kappa statistic. The experimental results show that CDF outperforms the conventional methods establishing its effectiveness and flexibility for high-dimensional classification problems.

Constraint Acquisition (CA) aims to widen the use of constraint programming by assisting users in the modeling process. However, most CA methods suffer from a significant drawback: they learn a single set of individual constraints for a specific problem instance, but cannot generalize these constraints to the parameterized constraint specifications of the problem. In this paper, we address this limitation by proposing GenCon, a novel approach to learn parameterized constraint models capable of modeling varying instances of the same problem. To achieve this generalization, we make use of statistical learning techniques at the level of individual constraints. Specifically, we propose to train a classifier to predict, for any possible constraint and parameterization, whether the constraint belongs to the problem. We then show how, for some classes of classifiers, we can extract decision rules to construct interpretable constraint specifications. This enables the generation of ground constraints for any parameter instantiation. Additionally, we present a generate-and-test approach that can be used with any classifier, to generate the ground constraints on the fly. Our empirical results demonstrate that our approach achieves high accuracy and is robust to noise in the input instances.

Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from the graph used for testing. This work delves into Graph Domain Adaptation (GDA) to address the unique complexities of distribution shifts over graph data, where interconnected data points experience shifts in features, labels, and in particular, connecting patterns. We propose a novel, theoretically principled method, Pairwise Alignment (Pair-Align) to counter graph structure shift by mitigating conditional structure shift (CSS) and label shift (LS). Pair-Align uses edge weights to recalibrate the influence among neighboring nodes to handle CSS and adjusts the classification loss with label weights to handle LS. Our method demonstrates superior performance in real-world applications, including node classification with region shift in social networks, and the pileup mitigation task in particle colliding experiments. For the first application, we also curate the largest dataset by far for GDA studies. Our method shows strong performance in synthetic and other existing benchmark datasets.

Arm has confirmed that it will be offering its next-gen Arm compute platform, called Arm CSS for Client, at Android smartphones. Executives also mentioned that they could be used for PCs as well. The announcement follows an earlier report that indicated that Arm might expand its traditional business model. Arm has traditionally sold CPU designs, not silicon, to partners like Qualcomm. Those companies have the freedom to adjust Arm's designs -- depending upon their license agreement -- and then ask foundries like TSMC to actually manufacture the chip.

Due to the limited driving range, inadequate charging facilities, and time-consuming recharging, the process of finding an optimal charging route for electric vehicles (EVs) differs from that of other vehicle types. The time and location of EV charging during a trip impact not only the individual EV's travel time but also the travel time of other EVs, due to the queuing that may arise at the charging station(s). This issue is at large seen as a significant constraint for uplifting EV sales in many countries. In this study, we present a novel Electric Vehicle Route Planning problem, which involves finding the fastest route with recharging for an EV routing request. We model the problem as a new graph problem and present that the problem is NP-hard. We propose a novel two-phase algorithm to traverse the graph to find the best possible charging route for each EV. We also introduce the notion of `influence factor' to propose heuristics to find the best possible route for an EV with the minimum travel time that avoids using charging stations and time to recharge at those stations which can lead to better travel time for other EVs. The results show that our method can decrease total travel time of the EVs by 50\% in comparison with the state-of-the-art on a real dataset, where the benefit of our approach is more significant as the number of EVs on the road increases.

The reconstruction of Configuration Space (CS) from a limited number of samples plays a vital role in expediting motion planning for random tree algorithms. Traditionally, the evaluation of CS reconstruction is performed through collision checking. However, employing the collision checker as an evaluation measure can be misleading. In particular, a collision checker may exhibit high accuracy even when only a subset of the original CS is reconstructed, limiting the motion planner's ability to find paths comparable to those in the original CS. Additionally, a significant challenge arises when dealing with high-dimensional CSs, as it becomes increasingly difficult, if not impossible, to perform qualitative evaluations when working in dimensions higher than three. In this paper, we introduce a novel approach for representing high-dimensional CSs of manipulator robots in a 2D format. Specifically, we leverage the kinematic chain of manipulator robots and the human ability to perceive colors based on hue. This allows us to construct a visualization comprising a series of pairs of 2D projections. We showcase the efficacy of our method in representing a 7-degree-of-freedom CS of a manipulator robot in a 2D projection. This representation provides qualitative insights into the joint boundaries of the robot and the collision state combinations. From a quantitative perspective, we show that the proposed representation not only captures accuracy but also furnishes additional information, enhancing our ability to compare two different high-dimensional CSs during the deployment phase, beyond what is usually offered by the collision checker. The source code is publicly available on our repository.