Digital orthodontics increasingly depends on accurate 3D dental models, yet conventional intraoral scanning remains inaccessible in remote tele-orthodontics, which typically relies on sparse smartphone imagery. While 3D Gaussian Splatting (3DGS) shows promise for novel view synthesis, its application to the standard clinical triad of unposed anterior and bilateral buccal photographs is challenging. The limitations of sparse-view photometric supervision, combined with large baselines, inconsistent illumination, and specular surfaces, can destabilize simultaneous pose and geometry estimation, often inducing frequency bias and over-smoothed reconstructions that lose critical diagnostic details. To address these issues, we propose Dental3R, a pose-free, graph-guided pipeline for robust, highfidelity reconstruction from sparse intraoral photographs. Our method first constructs a Geometry-Aware Pairing Strategy (GAPS) to select a compact subgraph of high-value image pairs, improving correspondence matching, stabilizing geometry initialization, and reducing memory usage. Leveraging on the recovered poses and point cloud, we train the 3DGS model with a wavelet-regularized objective. By enforcing band-limited fidelity via a discrete wavelet transform, our approach preserves fine enamel boundaries and interproximal edges while suppressing high-frequency artifacts. We validate our approach on a largescale dataset of 950 clinical cases and an additional video-based test set of 195 cases, demonstrating that Dental3R effectively handles sparse, unposed inputs and achieves superior novel-view synthesis quality over state-of-the-art methods.

Reliable 3D-2D alignment between intraoral scan (IOS) models and lateral cephalometric radiographs is critical for orthodontic diagnosis, yet conventional intensity-driven registration methods struggle under real clinical conditions, where cephalograms exhibit magnification, distortion, low-contrast dental crowns, and acquisition-dependent variation. These factors hinder the stability of appearance-based metrics and often lead to convergence failures or anatomically implausible alignments. To address these limitations, we propose DentalSCR, named for its silhouette-to-contour registration scheme, a pose-stable and contour-guided framework for accurate and interpretable alignment that achieves state-of-the-art performance. Our method constructs a U-Midline Dental Axis (UMDA) to establish a unified cross-arch anatomical coordinate system, stabilizing initialization and standardizing projection geometry across cases. Using this reference frame, we generate radiograph-like projections via a surface-based DRR (Digitally Reconstructed Radiograph) formulation with coronal-axis perspective and Gaussian splatting, which preserves clinically accurate magnification and emphasizes external silhouettes. Registration is formulated as a 2D similarity transform optimized with a symmetric bidirectional Chamfer distance under a hierarchical coarse-to-fine schedule, enabling both large capture range and subpixel-level contour agreement. We evaluate DentalSCR on 34 expert-annotated cases. Results demonstrate substantial reductions in landmark error, particularly at posterior teeth, tighter lower-jaw dispersion, and low Chamfer and controlled Hausdorff distances. These findings indicate that DentalSCR robustly handles real-world cephalograms and delivers high-fidelity, clinically inspectable 3D-2D alignment, consistently outperforming baselines and establishing a new state-of-the-art.

This paper proposes LLM-GA, a novel framework that integrates large language models (LLMs) with genetic algorithms (GA) for automated trading strategy generation. The system architecture comprises three synergistic modules: 1) a signal generator extracting technical, fundamental, and sentiment indicators; 2) an LLM-enhanced GA core that initializes seed strategies and performs semantically-aware crossover/mutation operations; and 3) an execution module forming a closed-loop adaptive system. Unlike traditional GA that randomly combines signals, our approach leverages LLMs’ financial reasoning capability to maintain logical consistency during strategy evolution. Experiments based on historical data of the Chinese stock market in the past five years (2020-2024) show that, LLM-GA achieves superior risk-adjusted returns (Annualized Excess Return (AER)=12.3%, Maximum Drawdown (MDD)=35.2%) compared to baseline methods including vanilla GA, PSO, and ensemble learning. Ablation studies reveal that LLM-guided initialization improves starting strategy quality by 215%, while semantic crossover reduces invalid strategies by 83.5%. Despite performance gaps against RL methods (2-3% lower AER), our method provides unique advantages in strategy interpretability and diversity, addressing critical limitations in black-box approaches like reinforcement learning. The work establishes a new paradigm for human-AI collaborative quantitative strategy development.

The allocation of multiple types of resources fairly and efficiently has become a substantial concern in state-of-the-art computing systems. Accordingly, the rapid growth of cloud computing has highlighted the importance of resource management as a complicated and NP-hard problem. Unlike traditional frameworks, in modern data centers, incoming jobs pose demand profiles, including diverse sets of resources such as CPU, memory, and bandwidth across multiple servers. Accordingly, the fair distribution of resources, respecting such heterogeneity appears to be a challenging issue. Furthermore, the efficient use of resources as well as fairness, establish trade-off that renders a higher degree of satisfaction for both users and providers. Dominant Resource Fairness (DRF) has been introduced as an initial attempt to address fair resource allocation in multi-resource cloud computing infrastructures. Dozens of approaches have been proposed to overcome existing shortcomings associated with DRF. Although all those developments have satisfied several desirable fairness features, there are still substantial gaps. Firstly, it is not clear how to measure the fair allocation of resources among users. Secondly, no particular trade-off considers non-dominant resources in allocation decisions. Thirdly, those allocations are not intuitively fair as some users are not able to maximize their allocations. In particular, the recent approaches have not considered the aggregate resource demands concerning dominant and non-dominant resources across multiple servers. These issues lead to an uneven allocation of resources over numerous servers which is an obstacle against utility maximization for some users with dominant resources. Correspondingly, in this paper, a resource allocation algorithm called H-FFMRA is proposed to distribute resources with fairness across servers and users, considering dominant and non-dominant resources. The experiments show that H-FFMRA achieves approximately %20 improvements on fairness as well as full utilization of resources compared to DRF in multi-server settings.

Task scheduling in cloud computing is considered as a significant issue that has attracted much attention over the last decade. In cloud environments, users expose considerable interest in submitting tasks on multiple Resource types. Subsequently, finding an optimal and most efficient server to host users' tasks seems a fundamental concern. Several attempts have suggested various algorithms, employing Swarm optimization and heuristics methods to solve the scheduling issues associated with cloud in a multi-resource perspective. However, these approaches have not considered the equalization of dominant resources on each specific resource type. This substantial gap leads to unfair allocation, SLA degradation and resource contention. To deal with this problem, in this paper we propose a novel task scheduling mechanism called MRFS. MRFS employs Lagrangian multipliers to locate tasks in suitable servers with respect to the number of dominant resources and maximum resource availability. To evaluate MRFS, we conduct time-series experiments in the cloudsim driven by randomly generated workloads. The results show that MRFS maximizes per-user utility function by %15-20 in FFMRA compared to FFMRA in absence of MRFS. Furthermore, the mathematical proofs confirm that the sharingincentive, and Pareto-efficiency properties are improved under MRFS.

Digital motivation refers to the use of software-based solutions to change, enhance, or maintain people’s attitude and behaviour towards specific tasks, policies, and regulations. Gamification, persuasive technology, and entertainment computing are example strands of such a paradigm. Digital motivation has unique properties which necessitate careful consideration of its analysis design methods. This stems from the strong human factor involvement, and if it is not implemented effectively, it can result in digital motivation being perceived negatively or leading to reduced motivation. The emerging literature on the topic includes approaches for creating digital motivation solutions. However, their primary focus is on specifying its operation, for example, the design of feedback, rewards and levels. In this paper, we propose a novel modelling language which enables capturing digital motivation as an integral part of the organisational and social structure of a business, captured via goal models. We also demonstrate how modelling of motivational techniques at this level, the goal level, enables a more powerful analysis that informs the introduction, design and management of digital motivation. Finally, we evaluate the language and its analysis using different perspectives and quality measures and report the results.

Procrastination refers to a voluntary postponement that prevents people from performing their tasks and can hurt productivity and wellbeing. Procrastination might occur due to a lack of motivation to perform tasks or due to the low self-control that people might have over their time and task management. Social Networking Sites (hereafter SNS) are designed to enable their users to engage in online interaction for different purposes such as increasing popularity or exploring information. SNS embed influence and persuasion techniques to attract users which can make them a medium for procrastination where some users fail to maintain a desirable level of self-control over their usage. However, we argue that advances in persuasive technology and gamification techniques can be utilised to augment SMS and help users to regain self-control over their procrastination. Implementing these techniques correctly means that users can still enjoy accessing SNS while maintaining a desirable level of control over their procrastination. Building these anti-procrastination tools, however, is a challenging design activity due to their potential of triggering negative side-effects such as reactance and workarounds, and affecting the overall user experience. In this paper, we conduct user studies, consisting of an exploratory stage using focus groups, diary study and interviews and followed by a design stage based mainly on co-design sessions. Our studies' participants self-declared having a problematic degree of procrastination on SNS, to explore procrastination countermeasure techniques that can augment the future designs of SNS and how best to apply them.

Social Network Sites (SNSs) are meant to facilitate interaction between people. The design of SNSs employs persuasive techniques with the aim of enhancing the user experience but also increasing interaction and user retention.Examples include the personalisation of content, temporarily available feeds, and notification and alert features. Socialness is now being embedded in new paradigms such as the Internet of Things and cyber-physical systems where devices can link people to each other and increase relatedness and group creation. One of the phenomena associated with such persuasion techniques is the experience of Fear of Missing Out (FoMO). FoMO typically refers to the preoccupation of SNS users with being deprived of interaction while offline. The salience, mood modification and conflict typically experienced as part of FoMO, are symptoms of digital addiction (DA). Despite recognition of the widespread experience of FoMO, existing research focuses on user psychology to interpret it. The contribution of SNS design in triggering FoMO remains largely unexplored. In this paper, we conduct a multi-stage qualitative research including interviews, a diary study and three focus group sessions to explore the relationship between SNS features and FoMO. Our findings demonstrate how the different SNS features act as persuasion triggers for certain kinds of FoMO. Also, we suggest features that could be introduced to social network sites to allow individuals to manage FoMO and identify the principles and challenges associated with engineering them.