Abstract. A significant source of human pressure on the environment originates from human-wildlife encounters during recreational activities in natural spaces. These events are difficult to study given their short duration, as well as the sparsity and granularity of concurrently tracked human and wildlife data. Wildlife often perceives and reacts to human presence over distances greater than GNSS uncertainty. Thus, we propose a new encounter detection method that incorporates a broader range of human disturbance as opposed to addressing incompleteness, enabling us to identify where and when human-wildlife encounters are most likely to occur, and thus offering a more ecologically realistic assessment of encounter risk. The method was applied on a pilot study in the Bauges massif in the French Alps, leveraging semantically enhanced chamois and human trajectories. A spatio-temporal analysis of human-wildlife encounter results is presented to demonstrate how the method can support ecologists or stakeholders in gaining deeper understanding of wildlife behavior or in taking actions to mitigate human impacts. Reproducibility review available at: https://doi.org/10.17605/OSF.IO/DZT8C

Exploratory data visualization tools help users visually build a better understanding from a dataset they do not fully know yet. To ensure tools are useful, authors often conduct user evaluations.However, practices are not formalized nor standardized, and every author makes their own protocol. This paper aims to explore data visualization authors’ practices in evaluating their tools. We conducted a systematic literature review and, when relevant, reported it with the guidance of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement.In January 2025, we sought 32 recent and influential dataviz papers from Google Scholar and Semantic Scholar. These reports are about exploratory data visualization; they all present a new tool and include a user evaluation. Drawing from the user-centered design field, we identified 56 dimensions about those reports and synthesized our findings by categorizing each aspect of the protocols (main protocols, data they produced, participant management). From these insights, we discussed best practices, open science, and biases (social desirability bias, non-probability sampling, Hawthorne effect).This study provides an overview and a categorization of user-centered testing notions. It provides general knowledge to help authors form informed opinions when building user-centered protocols for data visualization. However, this study does not provide comprehensive guidelines in how to create protocols, as we are not aware of any empirical data that helps compare protocols to one another.

Temporal semantic sequences—chronologically ordered elements enriched with semantic content—arise across domains such as medicine, sociology, and transportation. Their varied temporal descriptions and rich semantic content make them hard to model, and no existing framework jointly addresses their high-level conceptual and low-level computational facets. We introduce a dual-faceted framework that decouples user-oriented conceptual reasoning from computational execution. It comprises a multidimensional model, a flat model, and transformation rules that unify sequences with explicit or implicit temporal information. It accommodates diverse temporal formats, supports comparative analyses, and enables cross-domain applications, as illustrated with an example.

Cycling practice is quickly increasing around the world, giving rise to the development of devoted infrastructure to protect its users and offer them a more enjoyable ride. Mobility infrastructure is represented in geographical databases, but these databases are often centered on car and pedestrian mobility. This causes some data quality problems like the lack of completeness or freshness. Volunteered geographical information (VGI) is affected by this kind of problem with a variable extent relying on the contributors' wish and skills. Research on VGI evolution for a network mainly focuses on the main usage of a road section, ignoring secondary information related to other road users of a specific section. This paper contains two contributions. To model the evolution, we define a multiplex graph where each layer represents a snapshot. It is implemented with an infrastructure class based on how cyclists perceive an infrastructure. We also present two complementary VGI road network evolution methods with a usage-centric approach on cycling. These approaches are adaptable for any usage of the network and are based on the multiplex graph. The first approach is based on the road sections, analyzing the evolution of each section individually. The second approach is based on randomly generated starting/ending points. These methods are illustrated in the Centre-Val de Loire region with OpenStreetMap.

Temporal semantic sequences-temporally ordered elements with meaningful labels-are widely used in fields such as mobility, medicine, and e-learning. However, analyzing and comparing these sequences is challenging due to length variations, semantic richness, and temporal dynamics. Existing similarity measures are limited to sequences of identical lengths or fail to capture both semantic and temporal aspects simultaneously. We propose the Relative Fuzzy Temporal Hamming (RFTH) similarity measure, which leverages fuzzy logic to adapt to sequences of varying lengths. It not only captures semantic and temporal similarity but also guarantees robust performance with interesting properties. A running example illustrates its applicability and advantages. This measure promises interesting usages for analyzing human mobility, patient records, weather data, and many other applications that require robust sequence comparison.

Blockchain technologies have emerged to serve as a trust basis for the monitoring and execution of business processes, particularly business process choreographies. However, dealing with changes in smart contract-enabled business processes remains an open issue. For any required modification to an existing smart contract (SC), a new version of the SC with a new address is deployed on the blockchain and stored in a contract registry. Moreover, in a choreography, a change in a partner process might affect the processes of other partners, and thus, must be propagated to partners affected by the change. In this paper, we propose an approach overcoming the limitations of SCs and allowing for the change management of blockchain-enabled declarative business process choreographies modeled as DCR graphs. Our approach allows a partner in a running blockchain-based DCR choreography instance to change its private process. A change impacting other partners is propagated to their processes in a decentralized manner using a SC. The change propagation mechanism ensures the compatibility checks between public processes of the partners and the consistency between the private and public processes of one partner. We demonstrate the approach's feasibility through an implemented prototype and its effectiveness via a set of evaluation tests.

The big volume of candidate Web services and their differences make it hard for developers to discover a set of appropriate ones for mashup creation. Thus, recommending suitable services is a vital problem. Service recommendation methods should not only meet the functional needs of users but also consider contextual features like application domain and service performances to provide more personalized recommendations. In this paper, we propose an attention-based deep learning model for service recommendation. It makes service recommendation based on service characteristics and user feed-backs. Specifically, we build a service network, which learns to intelligently discover services with two attention mechanisms - a functional attention mechanism that takes tags as functional prior to mine the function-related features of services and mashups, and a non-functional attention mechanism that considers service qualities to guide the selection of the most appropriate ones and improves user satisfaction. Experiments are carried out on a real-world web API dataset crawled from ProgrammeableWeb.com.

Abstract. Cycling practice has been constantly increasing for several years and the COVID crisis has just accelerated the process. Indeed, more and more municipalities have developed new cycle paths to facilitate cycling. Considering this increasing interest for cycling, it makes sense to study how this recent evolution is reflected in the underlying representation of the cycling network in the geographic databases. Main studies analysing the evolution of the road network focus on the motor vehicle network in the major cities of the world. These studies do not seem applicable to cycling network specially to some low population density areas or even to smaller cities. This paper analyses the changes in the cycling network through OSM data from a data freshness perspective. These changes can be either updates from changes in the real-world network or upgrades to the network. To these end, we propose a method using a Monte Carlo simulation (MCS) to analyse the frequency of changes in cycling routes in several areas with different population density, all in the Loire Valley region in France. We also define the cycling network, which is a very complex concept and we explain how it is represented in OSM data and suffers from different data quality issues. Results show that the number of changes across time are similar in areas having a similar population density, while being lower in low population density areas. These phenomena is higher in the cycling network compared to other networks.

Nowadays, we live in an interconnected ecosystem of heterogeneous services which are available anytime and anywhere. Although these services can be used individually, their combined usage provides end-users with real added value. Nevertheless, existing mashup solutions are not fully targeted to end-users. Indeed, these solutions require technical knowledge to deal with the technological heterogeneity in which they are offered. The key limitation of current mashup solutions is their general-purpose nature, which privileges expressive power over intuitiveness. As the reason for mashups was the desire of end-users to build applications responding to their situational needs, we propose a domain-specific mashup approach that "speaks the language of the user." The article presents (1) a domain-specific visual language that allows end-users to easily build mashup-based compositions, (2) a mashup schema which is a gateway between service implementations and end-users, providing a high-level description in order to hide technological issues when including services in composition; and (3) a configuration module that executes the travel mashup query and generates a personalized trip planning. The results of a user study confirm that domain-specific mashup tools lower the entry barrier to mashup development.

Blockchain technologies have emerged to serve as a trust basis for the monitoring and execution of business processes, particularly business process choreographies. However, dealing with changes in smart contract-enabled business processes remains an open issue. For any required modification to an existing smart contract (SC), a new version of the SC with a new address is deployed on the blockchain and stored in a contract registry. Moreover, in a choreography, a change in a partner process might affect the processes of other partners. Thus, the change effect must be propagated to partners of the choreography affected by the change. In this paper, we propose a new approach overcoming the limitations of SCs and allowing for the change management of blockchain-enabled declarative business process choreographies modeled as DCR graphs. Our approach allows a partner in a running blockchain-based DCR choreography instance to change its private DCR process. A change impacting other partners is propagated to their affected processes using a SC. The change propagation mechanism ensures the compatibility checks between public DCR processes of the partners. We demonstrate the approach's feasibility through an implemented prototype.

Data narration is the activity of crafting narratives supported by facts extracted from data analysis, using interactive visualizations. It allows the transmission of findings in the data, by visual means, in order to facilitate their reception by a target audience. Despite its recognized utility in public health, data narratives are typically limited to the transmission of treatment recommendations to educate the general public. This paper describes the crafting of a data narrative about tuberculosis pandemic in Gabon, intended to an audience of health professionals and authorities. Specifically, we describe and illustrate all phases of the crafting process, combining best practices in data and epidemic intelligence.

Personalized travel mashups aim to assist users in making decision i.e what places to visit. To facilitate human decisions with credible suggestions, these systems should have the ability to generate corresponding explanations while making recommendations. Knowledge graphs (KG), which contain rich and comprehensive information among items are widely used to enable this. By reasoning over a KG in a nodeby-node manner, the connectivity between items can be discovered as paths that serve as an explanation to enhance the interpretability of recommendations. However, existing methods failed to utilize the information of collective-level POI sequences. The individual-level cannot represent more holistic semantic features and cannot express complete transition patterns. To this end, we propose knowledge-aware approach for explainable travel mashup that joints the multi-granularity representation and the attention mechanism to capture the sequential dependencies at collective-level POI on different granularities. Specifically, we encode a diversity of semantic relations and connectivity patterns into a travel knowledge graph. Then, we employ a recurrent network architecture to exploit the semantics of paths entities pair, which are fused into explainable recommendation using attentive graph. Extensive validation on a real-world datasets shows the effectiveness of the proposed approach.

While personal coach applications are now ubiquitous, personal lifelong pathway co-construction, although present in a wide range of situations (e-learning, healthcare, return to employment, home-care of elders, etc.) is largely overlooked. Modeling generic lifelong pathway co-construction is feasible since these situations share many properties. In this paper, we contribute with a model for the co-construction of lifelong pathways, backed by a literature survey of semantic trajectories, and applicable in a wide range of situations. The model supports the interactions between the two main actors, the advisee and the advisor, in the co-construction, and include the key specific aspects of lifelong pathway: the advisee’s long-term goal, the evaluation of the advisee’s multi-dimensional (educational, professional, financial, medical, etc.) trajectory, the guiding of the advisee via advices that influence the stages of the pathway. To illustrate the importance of this model, we detail its use in a guidance system, to help the advisor’s work in guiding the advisee towards their goal.

Cet article s’inscrit dans le champ de recherche des pratiques de mobilites quotidiennes enfantines etudiees a travers des traces numeriques issues de capteurs (geolocalisation, audiovisuels) et enrichies d’informations semantiques et contextuelles. Nous montrons comment la trace constitue une notion partagee entre les differentes disciplines (geographie, informatique, psychologie environnementale, sociologie) engagees dans la recherche Mobi'kids qui vise a comprendre les conditions d’evolution des mobilites quotidiennes d’enfants. Nous proposons d’abord un cadre conceptuel autour de la trace pour definir de facon interdisciplinaire les mobilites et rapports a l’espace des enfants et leur famille. La trace comme entite observable est ensuite utilisee pour definir et mettre en oeuvre un corpus original de : traces brutes, traces geolocalisees sequencees, traces enrichies semantiquement et narratives, traces sensibles. Enfin, la trace comme concept analytique est organisee et transformee a differents niveaux d’echelles par des operations de correction et de categorisation dans l'optique d’une exploitation interdisciplinaire des donnees.

The study of semantic aspects of human behavior is an hot topic. Most of the time, semantic sequences describe these complex behaviors. Indeed, sequences include several information as type of human activities or places. To study these complex data, we need to define new similarity measures and select appropriate clustering processes. This article proposes a semantic similarity measure, based on ontologies, which manages complex semantic elements with different levels of detail and incertitude. An application of this approach from the domain of touristic mobility shows the interest of this process.

The study of temporal sequences is a main topic in different domains, especially for human mobility mining. This article defines the Fuzzy Temporal Hamming (FTH) distance between temporal sequences. This new measure generalises the Hamming distance and improves it by introducing a fuzzy time-window. This fuzzy approach tolerates temporal distortions as shifting and permutations. Moreover, the time computation of FTH is competitive with other Optimal Matching methods used for temporal sequences comparison. To validate this approach, we cluster data from a real Time-Use Survey and we compare the results obtained with other methods.