The goal of this year’s panel is to move beyond debates about AI in the abstract to discussing the concrete costs, benefits, and verification of AI when embedded within academic research. To support this goal, the panel has a new format. First, the audience will be asked to respond to a poll inquiring about the areas of their paper submission where AI was involved. Then, rather than standard opening statements, we asked panelists to present Lightning Evidence. Each individual has strictly 2 minutes and a single slide to present one specific artifact from their research co-produced with AI. A moderated synthesis and audience discussion will follow, and the audience will be repolled at the end of the discussion.

Traditional information retrieval (IR) models, such as keyword-based and vector-based techniques, have long been used in centralized systems. However, the Web’s re-decentralization, with its focus on data ownership and privacy, calls for a re-evaluation of these methods in these settings. While standards for decentralized search enhance privacy to some extent, they also introduce computational overhead, black-box decision-making, and infrastructure complexity. Despite these challenges, traditional IR techniques remain largely unexplored in such environments. This article presents an innovative application of traditional IR models in the decentralized Web by adapting them for Personal Online Data Stores (PODs), where search parties have varying access rights. We explore their role in source selection, document ranking, and result merging, extending them to meet decentralized search demands. Using Solid PODs and a synthetic medical dataset, we evaluate these models in a privacy-sensitive environment. Our findings demonstrate that extended IR methods provide an effective balance of performance, interpretability, and efficiency. These approaches hold strong potential as privacy-preserving alternatives for decentralized search on a re-decentralized Web. Notably, our top-performing model achieved competitive results in top-item retrieval compared to centralized search systems, maintaining high relevance scores under both limited and full data access conditions.

The growing demand for data ownership and privacy is reshaping how information is accessed, managed, integrated, and recommended. Building on the inaugural DESERE workshop at The Web Conference 2024, this second edition advances research on Decentralised Search and Recommendation platforms such as Personal Online Datastores (PODs), where users retain control of their data and explicitly manage permissions. As ecosystems decentralise, traditional information retrieval must be revisited while standards for new techniques and system designs are developed to ensure efficient, accurate, and privacy-preserving search. The Second DESERE workshop at CIKM 2025 focuses on infrastructures and retrieval algorithms for user-controlled data. It convenes a cross-disciplinary community spanning data retrieval, management and integration, semantic technologies, recommendation systems, privacy-aware computing, and search efficiency to explore approaches that prioritize user agency, data ownership, and scalable retrieval across PODs and related architectures. Through paper presentations, panels, and interactive sessions, the workshop will highlight challenges, opportunities, and solutions for privacy-preserving IR. These discussions are especially relevant to domains where user-centric design and data stewardship are critical-such as personal finance, education, and high-stakes areas like criminal justice and health.

Data privacy and sovereignty are open challenges in today’s Web, which the Solid ( https://solidproject.org ) ecosystem aims to meet by providing personal online datastores (pods) where individuals can control access to their data. Solid allows developers to deploy applications with access to data stored in pods, subject to users’ permission. For the decentralised Web to succeed, the problem of search over pods with varying access permissions must be solved. The ESPRESSO framework takes the first step in exploring such a search architecture, enabling large-scale keyword search across Solid pods with varying access rights. This paper provides a comprehensive experimental evaluation of the performance and scalability of decentralised keyword search across pods on the current ESPRESSO prototype. The experiments specifically investigate how controllable experimental parameters influence search performance across a range of decentralised settings. This includes examining the impact of different text dataset sizes (0.5 MB to 50 MB per pod, divided into 1 to 10,000 files), different access control levels (10%, 25%, 50%, or 100% file access), and a range of configurations for Solid servers and pods (from 1 to 100 pods across 1 to 50 servers). The experimental results confirm the feasibility of deploying a decentralised search system to conduct keyword search at scale in a decentralised environment.

Abstract The increasing centralization of the Web raises serious concerns regarding privacy, security, and user autonomy. In response, there has been a renewed interest in the development of secure personal information management systems and a movement towards decentralization. Decentralized personal online data stores (pods) represent a revolutionary example within this movement, built on the W3C’s existing guidelines – an approach exemplified by initiatives such as ( https://solidproject.org ). In the Solid paradigm, individuals store their personal data in pods and have absolute discretion when choosing to grant access to different users and applications. A barrier to the adoption of the pod approach is the predominant reliance on centralized indexes for search functionality in current Web and Web-based systems. This paper introduces the framework, which is designed to facilitate this new paradigm of large-scale searches within personal data stores while respecting the individual pod owners’ data access governance. The current ESPRESSO prototype integrates access control within pod indexes to enhance distributed keyword-based search. ESPRESSO’s unique contribution not only enhances search capabilities on the decentralized Web but also paves the way for future explorations in decentralized search technologies.

People living with Parkinson's disease engage in self-tracking as part of their health self-management. Whilst health technologies designed for this group have primarily focused on improving the clinical assessments of the disease, less attention has been given to how people with Parkinson's use technology to track and manage their disease in their everyday experience. We report on a qualitative study in which we systematically analysed posts from an online health community (OHC) comprising people with Parkinson's (PwP). Our findings show that PwP track a diversity of information and use a wide range of digital and non-digital tools, informed by temporal and structured practices. Using an existing framework of sensemaking for chronic disease self-management, we also identify new ways in which PwP engage in sensemaking, alongside a set of new challenges that are particular to the character of this chronic disease. We relate our findings to technologies for self-tracking offering design implications.

PDkit is an open source software toolkit supporting the collaborative development of novel methods of digital assessment for Parkinson's Disease, using symptom measurements captured continuously by wearables (passive monitoring) or by high-use-frequency smartphone apps (active monitoring). The goal of the toolkit is to help address the current lack of algorithmic and model transparency in this area by facilitating open sharing of standardised methods that allow the comparison of results across multiple centres and hardware variations. PDkit adopts the information-processing pipeline abstraction incorporating stages for data ingestion, quality of information augmentation, feature extraction, biomarker estimation and finally, scoring using standard clinical scales. Additionally, a dataflow programming framework is provided to support high performance computations. The practical use of PDkit is demonstrated in the context of the CUSSP clinical trial in the UK. The toolkit is implemented in the python programming language, the de facto standard for modern data science applications, and is widely available under the MIT license.

The various activities that take place within an observed environment such as a university campus, determine to a large extent, the kind of social interactions exhibited by the users in such environments. Using a big data set of wifi-traces, we attempt to understand the rules that governs these social interactions. We discovered that there are at least two types of social interactions within a university campus: formal such as attending a class and informal such as meeting friends at the cafeteria for coffee. Each of these two types of social interactions is tightly associated with a specific set of locations within the university campus. We also discovered that users tend to restrict their social interactions to a small set of geographical locations, where users revisited the same location to socialise with the same social group. Also, irrespective of the type of the social interactions, users tend to restrict their revisits to geographically nearby locations and only revisit locations that are further afield when they are in the company of their social group. These findings are based on the social groups detected by a new scalable density-based clustering method applied to a large data set of mobile users wifi traces. The results of the large experiments carried out in this research demonstrate how the proposed algorithm can noninvasively detect social groups on the basis of the activity performed at the selected location.

The cloudUPDRS app is a Class I medical device, namely an active transient non-invasive instrument, certified by the Medicines and Healthcare products Regulatory Agency in the UK for the clinical assessment of the motor symptoms of Parkinson's Disease. The app follows closely the Unified Parkinson's Disease Rating Scale which is the most commonly used protocol in the clinical study of PD; can be used by patients and their carers at home or in the community; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors. This paper discusses how the cloudUPDRS system addresses two key challenges towards meeting essential consistency and efficiency requirements, namely: (i) How to ensure high-quality data collection especially considering the unsupervised nature of the test, in particular, how to achieve firm user adherence to the prescribed movements; and (ii) How to reduce test duration from approximately 25 minutes typically required by an experienced patient, to below 4 minutes, a threshold identified as critical to obtain significant improvements in clinical compliance. To address the former, we combine a bespoke design of the user experience tailored so as to constrain context, with a deep learning approach used to identify failures to follow the movement protocol while at the same time limiting false positives to avoid unnecessary repetition. We address the latter by developing a machine learning approach to personalise assessments by selecting those elements of the UPDRS protocol that most closely match individual symptom profiles and thus offer the highest inferential power hence closely estimating the patent's overall UPRDS score.

No abstract available.

Cost effectiveness is critical to the success of immunisation programmes in developing countries. Being able to continuously, flexibly and accurately monitor the vaccine supply chain can help reduce the cost of operating it, and savings can be applied to deliver further resources to those that need

A warm welcome to the Twelfth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom 2014). We are pleased to introduce the technical program of the conference which this year includes 25 papers representing high-quality research conducted over a broad spectrum of topics related to pervasive computing.

Wireless sensor networks (WSN) are one of the enabling technologies for the Internet of Things. Due to the restricted capability of sensor, the performance of WSN is sensitive to interference from nearby wireless devices utilizing the same band. The channel hopping technique is adopted by IEEE 802.15.4e standard to mitigate such adverse effect. In this paper we propose the Adaptive Time-slotted Channel Hopping (A-TSCH) framework which augments standard IEEE 802.15.4e channel hopping with mechanisms for learning the spectral condition and blacklisting. Complete design and implementation are provided, followed by analysis of experimental results. We discover that A-TSCH improves the packet delivery in the presence of interference from co-located devices, leading to substantially enhanced reliability of WSN based on IEEE 802.15.4e.

A core ingredient of Smart Cities is the use of emergency services both as a lens through which to monitor their ever-changing state and as a rapid response mechanism to the needs of their population. Emergency response units in particular employ diverse ubiquitous computing technologies for sensing, resilient communication, and dispatch and depend on extensive command and control infrastructure that links into the healthcare and transportation systems. In the case of ambulance services in particular, command and control centres collate medical incident, vehicle position and status data to build a realtime picture of the City. Taking the London Ambulance Service (LAS) as our case study we develop a simulation framework and introduce an enhanced routing and dispatch method that combines concurrent assignment and redeployment of units in a single algorithm. We provide evidence that our unified proactive relocation and dispatch model produces significant improvements in measured performance in terms of meeting citizen needs.

This special issue explores the state of the art across the RFID technology landscape from hardware, to systems, as well as applications and support for innovative business models.

Radio-frequency identification (RFID) has a great number of unfulfilled prospects. Part of the problem until now has been the value proposition behind the technology-it has been marketed as a replacement technique for the barcode when the reality is that it has far greater capability than simply non-line-of-sight identification, towards decision making in strategic management and reengineered business processes. The vision of the internet of things (IOT) has not eventuated but a world in which every object you can see around you carries the possibility of being connected to the internet is still within the realm of possibility. However incremental innovations may see RFID being sold as a service (much like photocopiers are maintained today) than a suite of technologies within a system that are sold as individual or bundled packaged components. This paper outlines the vision for such a product service system, what kinds of smart applications we are likely to see in the future as a result, and the importance of data management capabilities in planetary-scale systems.

Moving human-computer interaction off the desktop and into our cities requires new approaches to understanding people and technologies in the built environment. We approach the city as a system, with human, physical and digital components and behaviours. In creating effective and usable urban pervasive computing systems, we need to take into account the patterns of movement and encounter amongst people, locations, and mobile and fixed devices in the city. Advances in mobile and wireless communications have enabled us to detect and record the presence and movement of devices through cities. This article makes a number of methodological and empirical contributions. We present a toolkit of algorithms and visualization techniques that we have developed to model and make sense of spatial and temporal patterns of mobility, presence, and encounter. Applying this toolkit, we provide an analysis of urban Bluetooth data based on a longitudinal dataset containing millions of records associated with more than 70000 unique devices in the city of Bath, UK. Through a novel application of established complex network analysis techniques, we demonstrate a significant finding on the relationship between temporal factors and network structure. Finally, we suggest how our understanding and exploitation of these data may begin to inform the design and use of urban pervasive systems.

We perceive just a tiny proportion of the phenomena that surround us everyday. Sensory Threads is a technologically mediated collective sensing expedition which allows people to explore imperceptible phenomena in the world around them. We present a description of the technical system and reflect on our design decisions. Key questions we are concerned with centre around how to represent imperceptible phenomena in real time both inside a building, and outside, and how to engender a sense of collective engagement. We also consider the value created through the multi-disciplinary work involved in constructing such an experience.

We present a novel approach for the on-line detection of complex events in wireless sensor networks. Complex events are sets of data points that correspond to unusual patterns that can not be detected using threshold-based techniques. Our method uses an efficient implementation of SAX, a mature data mining algorithm, that transforms a stream of readings into a symbolic representation. Complex event detection is then performed via four alternative modes: (a.) multiple pattern detection using a suffix array, (b.) distance-based comparison, (c.) unknown pattern detection, and (d.) probabilistic detection. The method allows users to specify complex events as patterns or to search for interesting changes without supplying any information. The appropriateness of the approach has been verified by applying it to four sensor data sets. In addition, we have developed an efficient implementation for the TinyOS operating system, and further validated our assertions by collecting and analyzing data in real-time.

Drawing on the 'representation' TUI framework [21], this paper reports a study that investigated the concept of 'representation location' and its effect on interaction and learning. A reacTIVision-based tangible interface was designed and developed to support children learning about the behaviour of light. Children aged eleven years worked with the environment in groups of three. Findings suggest that different representation locations lend themselves to different levels of abstraction and engender different forms and levels of activity, particularly with respect to speed of dynamics and differences in group awareness. Furthermore, the studies illustrated interaction effects according to different physical correspondence metaphors used, particularly with respect to combining familiar physical objects with digital--based table-top representation. The implications of these findings for learning are discussed.

The ASSIST project aims to facilitate cervical cancer research by integrating medical records containing both phenotypic and genotypic data, and residing in different medical centres or hospitals. The goal of ASSIST is to enable the evaluation of medical hypotheses and the conduct of association studies in an intuitive manner, thereby allowing medical researchers to identify risk factors that can then be used at the point of care to identify women who are at high risk of developing cervical cancer. This paper presents the current status of the ASSIST medical knowledgebase. In particular, we discuss the challenges faced in constructing the ASSIST integrated resource and in enabling query processing through a domain ontology, and the solutions provided using the AutoMed heterogeneous data integration system. We focus on data cleansing issues, on data integration issues related to integrating relational medical data sources into an independent domain ontology and also on query processing. Of particular interest is the challenge of providing an easily maintainable integrated resource in a setting where the data sources and the domain ontology are developed independently and are therefore both highly likely to evolve over time.

The last few years have witnessed a flurry of research in the field of query processing for networks of sensors and actuators. It is widely accepted that query processing is the method of choice for acquiring data from a sensor field. Although query processing offers a very good computational model for a variety of applications such as environmental monitoring, it is a poor match for application scenarios where a timely response to an event is required by the system. With this in mind, we propose a mature database technology, namely active rules, that provides a natural computational paradigm for sensor network applications that require reactive behavior, such as rapid forest fire response and security management.