Arda Yüksel, Gabriel Thiem, Susanne Walter, Patrick Felka, Gabriela Alves Werb, Ivan Habernal. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

The digital divide remains one of the pressing challenges, as it persists in both developed and developing regions. Recent events that required a sudden and radical shift from offline to online have brought the digital divide issue sharply into focus. Prior research has investigated how differences in mobile ICT availability and use relate to individuals’ income opportunities or lack thereof. In this research, we take a new perspective on digital divides. We show that besides the commonly investigated digital divide in economic and opportunity-related terms, the unavailability of mobile ICT can give rise to a digital divide regarding public safety. In detail, we leverage publicly available spatial crime data and the random occurrence of mobile ICT outages to investigate the link between ICT use and safety for distinctly wealthy population sub-groups. Based on the results of a difference-in- differences analysis, we find that the effects of mobile ICT discontinuations are non- uniform across socioeconomic groups with socioeconomically disadvantaged areas suffering disproportionally higher increases in property crimes than their wealthier counterparts. Overall, our research supports the idea of mobile ICT infrastructure as a public utility by showing the importance of fail-safe mobile ICT infrastructures for all, but particularly for socioeconomically disadvantaged areas.

Multi-access edge computing (MEC) refers to deploying computation resources, known as cloudlets or edge servers, near the edge of the mobile network. Services like augmented reality (AR) benefit from MEC by service placement, which refers to installing service-specific software and allocating resources on cloudlets. Service placement in MEC improves service quality and potentially reduces costs compared to centralized cloud computing approaches. The main stakeholders in MEC are infrastructure providers (IPs), who manage the MEC infrastructure, and service providers (SPs), who offer services to users. Both have unique technical and economic perspectives, such as resource demands, resource availability, and costs. Information asymmetries exist as only IPs have access to information about their resources, and only SPs have information about service usage and resource demands. This work addresses challenges of service placement in MEC from a multi-stakeholder, techno-economic perspective. We introduce a model including the stakeholders’ technical and economic goals and information asymmetries. To solve this problem efficiently, we propose a multi-stakeholder bargaining mechanism, termed Nash Backward Induction with Linear Equilibrium Strategies (NBI-LES). In a case study with 544 users and 16 SPs, we achieve$\text{79}{\%}$of the optimal reduction in traffic given by a centralized optimal service placement strategy.

The statistics department of the Deutsche Bundesbank offers high-quality data to analysts, researchers, and the public to assess the financial system and support policymaking. However, company master data, which is crucial for analyses at the microeconomic level, originates from several sources and may contain gaps, inconsistencies, and a lack of standardization. Accurate information, such as the sector classification and regional location of companies are essential for meaningful analyses and valid conclusions. Incorrect sector classification can often be recognized with the help of additional data, such as satellite images, cartographic data, and company reports. For example, such data can help determine whether a company operates in the manufacturing, agriculture, or energy supply sector. However, performing these manual checks to ensure data consistency and reliability is costly. This project aims to automate these data quality checks using multimodal LLMs. By proposing a system architecture for combining and processing geospatial data from images, texts, and digital twin data within a multimodal fact-checking approach, we aim to demonstrate the potential of multimodal fact-checking to enhance data quality and integrity. Furthermore, this paper highlights the integration challenges and discusses the potential for generating multimodal, data-driven insights.

Mobile edge computing based on cloudlets is an emerging paradigm to improve service quality by bringing computation and storage facilities closer to end users and reducing operating cost for infrastructure providers (IPs) and service providers (SPs). To maximize their individual benefits, IP and SP have to reach an agreement about placing and executing services on particular cloudlets. We show that a Nash Bargaining Solution (NBS) yields the optimal solution with respect to social cost and fairness if IP and SP have complete information about the parameters of their mutual cost functions. However, IP and SP might not be willing or able to share all information due to business secrets or technical limitations. Therefore, we present a novel iterative bargaining approach without complete mutual information to achieve substantial cost reductions for both IP and SP. Furthermore, we investigate how different degrees of information sharing impact social cost and fairness of the different approaches. Our evaluation based on the mobile augmented reality game Ingress shows that our approach achieves up to about 82% of the cost reduction that the NBS achieves and a cost reduction of up to 147% compared to traditional Take-it-or-Leave-it approaches, despite incomplete information.

Multipath TCP (MPTCP) promises improvements in Quality of Service through connection bundling. This leads to the belief that it will inevitably improve the Quality of Experience (QoE), especially, for mobile applications running on top. The networking and transport layer improvements stem from bundling multiple paths, e.g., WiFi and LTE, as well as increasing the connection reliability through redundancy. For example, a smartphone running an application over WiFi may switch to the cellular network without service interruption upon user movement that gets the device out of the WiFi range, thus avoiding outage events. However, the impact of MPTCP on QoE for different applications has not yet been fully understood.

Augmented Reality (AR) is an approach to enrich the real world with additional information. It allows users to interact with virtual objects that are linked to locations in the real world. In the area of mobile computer games, AR is quite demanding for managing resources in communication networks, since in-game points of interest typically lead to high network loads, whereas network utilization is otherwise below the average. Predicting the number of in-game actions of mobile AR games can help to scale the game back-end reasonably without over- or under-provisioning of resources. Context information like weather or available Wi-Fi access points can play a key role in estimating or predicting the number of in-game actions. In this paper, we analyze a comprehensive dataset that contains players' actions of one of the most popular mobile AR games, Ingress. The dataset entails more than 23.9 million player actions over a period of 17 months, as well as additional contextual information. Our analysis shows a highly significant relationship between context factors and in-game actions, which explains large parts of users' behavior in terms of when, where and how often they play the game. By combining four different context factors (i.e., time, user, physical and computing context), our analysis can explain up to 84.44% of the variation in the number of in-game actions.