Mobile agents are essential for automating tasks in complex and dynamic mobile environments. As foundation models evolve, the demands for agents that can adapt in real-time and process multimodal data have grown. This survey provides a comprehensive review of mobile agent technologies, focusing on recent advancements that enhance real-time adaptability and multimodal interaction. Recent evaluation benchmarks have been developed better to capture the static and interactive environments of mobile tasks, offering more accurate assessments of agents' performance. We then categorize these advancements into two main approaches: prompt-based methods, which utilize large language models (LLMs) for instruction-based task execution, and training-based methods, which fine-tune multimodal models for mobile-specific applications. Additionally, we explore complementary technologies that augment agent performance. By discussing key challenges and outlining future research directions, this survey offers valuable insights for advancing mobile agent technologies. A comprehensive resource list is available at https://github.com/aialt/awesome-mobile-agents

Modern mobile devices are able to provide context-aware and personalized services to the users, by leveraging on their sensing capabilities to infer the activity and situation in which a person is currently involved. Current solutions for context-recognition rely on annotated data and experts’ knowledge to predict the user context. In addition, their prediction ability is strongly limited to the set of situations considered during the model training or definition. However, in a mobile environment, the user context continuously evolves, and it cannot be merely restricted to a set of predefined classes. To overcome these limitations, we propose COMPASS, a novel unsupervised and online clustering algorithm aimed at identifying the user context in mobile environments based on the stream of high-dimensional data generated by smartphone sensors. COMPASScan distinguish an arbitrary number of user’s contexts from the sensors’ data, without defining a priori the collection of expected situations. This key feature makes it a general-purpose solution to provide context-aware features to mobile devices, supporting a broad set of applications. Experimental results on 18 synthetic and 2 real-world datasets show that COMPASS correctly identifies the user context from the sensors’ data stream, and outperforms the state-of-the-art solutions in terms of both clusters configuration and purity. Eventually, we evaluate its performances in terms of execution time and the results show that COMPASS can process 1000 high-dimensional samples in less than 20 seconds, while the reference solutions require about 60 minutes to evaluate the entire dataset. Keywords: Context-awareness, Unsupervised Machine Learning, Online Clustering, Mobile Computing

This article is part of our February 2019 series about mobile. Smartphones and other mobile technologies have revolutionized the way people connect with friends, business associates, and the larger world. They've transformed everything from agriculture and energy production to retailing and zoology. It's also safe to say we now live in a world where mobile apps rule and wireless connectivity is viewed as a utility. "The mobile experience is front and center for every business," said Aaron Tenbuuren, experience design lead at Intrepid Pursuits, a design firm that's part of Accenture.

User surveys for Quality of Experience (QoE) are a critical source of information. In addition to the common "star rating" used to estimate Mean Opinion Score (MOS), more detailed survey questions (problem tokens) about specific areas provide valuable insight into the factors impacting QoE. This paper explores two aspects of the problem token questionnaire design. First, we study the bias introduced by fixed question order, and second, we study the challenge of selecting a subset of questions to keep the token set small. Based on 900,000 calls gathered using a randomized controlled experiment from a live system, we find that the order bias can be significantly reduced by randomizing the display order of tokens. The difference in response rate varies based on token position and display design. It is worth noting that the users respond to the randomized-order variant at levels that are comparable to the fixed-order variant. The effective selection of a subset of token questions is achieved by extracting tokens that provide the highest information gain over user ratings. This selection is known to be in the class of NP-hard problems. We apply a well-known greedy submodular maximization method on our dataset to capture 94% of the information using just 30% of the questions.

Oslo, Norway - Latest release of Puzzel cloud contact centre solution provides new bot options including its own Chat bot, multi-channel capabilities and introduces enhanced measures to protect customer data Puzzel has announced new functionality in the latest release of its cloud-based contact centre solution, designed to extend the system's multi-channel capabilities and help organisations to meet important changes in EU data protection legislation. Users are now able to integrate third party or Puzzel's own Chat bots directly into their core contact centre solution to improve first contacts with customers and save valuable live agent time. Furthermore, Puzzel has made several adjustments to its platform in preparation for the advent of General Data Protection Regulation (GDPR) in May this year. Christian Thorsrud, Product Manager at Puzzel commented, "Chat bots and GDPR are hot topics in the contact centre world today. On the one hand, innovations based on Artificial Intelligence such as Chat bots are creating new opportunities to expand and improve customer interactions and Puzzel's latest release is designed to make them a reality. On the other hand, the imminent arrival of GDPR is putting pressure on contact centres to review how they collect and store their own and third party data. The latest version of our cloud-based software brings renewed assurance that contact centres can rely on Puzzel to provide them with a secure and auditable framework to help meet critical new legislative requirements."

In this paper, we build a social search engine named Glaucus for location-based queries. They compose a significant portion of mobile searches, thus becoming more popular with the prevalence of mobile devices. However, most of existing social search engines are not designed for location-based queries and thus often produce poor-quality results for such queries. Glaucus is inherently designed to support location-based queries. It collects the check-in information, which pinpoints the places where each user visited, from location-based social networking services such as Foursquare. Then, it calculates the expertise of each user for a query by using our new probabilistic model called the location aspect model . We conducted two types of evaluation to prove the effectiveness of our engine. The results showed that Glaucus selected the users supported by stronger evidence for the required expertise than existing social search engines. In addition, the answers from the experts selected by Glaucus were highly rated by our human judges in terms of answer satisfaction.