Exploration Of Scenarios Research Articles

Future study of textile industry in Iran using the MICMAC and soft operational research methods

PurposeThis study aims to formulate exploratory scenarios of the textile industry in Iran based on MICMAC and soft operational research methods.Design/methodology/approachIn this study, to formulate plausible scenarios, literature reviews and external experts’ opinions of this field have been gathered through the Delphi approach and uncertainty questionnaires. After the utilization of the most important uncertainties, the textile industry’s plausible scenarios have been mapped with the help of experts through co-thinking workshops. Results show that two factors, including the business atmosphere and membership in World Trade Organization (WTO), play a more important role than the other factors. These two factors were considered for the formulation of the scenario. To formulate plausible scenarios, soft systems methodology, which is a kind of soft operational research methods, is applied.FindingsBased on the results, four scenarios are presented. These scenarios include the Elysium scenario, Hades scenario, Tatarus scenario and Sisyphus scenario. In the Elysium scenario, the business atmosphere has improved and Iran has been granted membership of the WTO. In Hades scenario, Iran has joined the WTO, but due to the government’s weakness and inactivity and key decision-makers, the required preparations have not been made. In the Tatarus scenario, Iran is not a WTO member and the business atmosphere is disastrous. In the Sisyphus scenario, the government takes reasonable actions toward a better business environment.Originality/valueFormulating plausible scenarios of the textile industry is an excellent contribution to the key beneficiaries and actors of this industry so they can present flexible preparation-based programs in the face of circumstances. Future study of the textile industry familiarizes the actors and beneficiaries of this industry with the procedures and the driving forces that influence this industry’s future and it will ascertain various scenarios for the actors of this field.

Assessing the environmental impacts of wind-based hydrogen production in the Netherlands using ex-ante LCA and scenarios analysis

Two electrolysis technologies fed with renewable energy sources are promising for the production of CO2-free hydrogen and enabling the transition to a hydrogen society: Alkaline Electrolyte (AE) and Polymer Electrolyte Membrane (PEM). However, limited information exists on the potential environmental impacts of these promising sustainable innovations when operating on a large-scale. To fill this gap, the performance of AE and PEM systems is compared, using ex-ante Life Cycle Assessment (LCA), technology analysis and exploratory scenarios for which a refined methodology has been developed to study the effects of implementing large-scale sustainable hydrogen production systems. Ex-ante LCA allows modelling the environmental impacts of hydrogen production, exploratory scenario analysis allows modelling possible upscaling effects at potential future states of hydrogen production and use in vehicles in the Netherlands in 2050. A bridging tool for mapping the technological field has been created enabling the combination of quantitative LCAs with qualitative scenarios. This tool also enables diversity for exploring multiple sets of visions. The main results from the paper show, with an exception for the “ozone depletion” impact category, (1) that large-scale AE and PEM systems have similar environmental impacts with variations lower than 7% in all impact categories, (2) that the contribution of the electrolyser is limited to 10% of all impact categories results, and (3) that the origin of the electricity is the largest contributor to the environmental impact contributing to more than 90% in all impact categories, even when renewable energy sources are used. It is concluded that the methodology was applied successfully and provides a solid basis for an ex-ante assessment framework that can be applied to emerging technological systems.

The societal dimension of the automated vehicles transition: Towards a research agenda

Automated vehicles (AVs) represent a socio-technical transition of the mobility system. Thus far, research has focused predominantly on the technical dimension (i.e. technology development and operation, short-term impacts) of this transition. This paper presents a research agenda that contributes in rebalancing the focus from the technical to the societal dimension of the AVs transition. We adopt the social construction of technology approach, suggesting that technology and society are involved in a dialectical process over time that constructs each other and we identify three key dynamics of the societal dimension of AVs transition based on the multi-level perspective of technological transition. After reviewing the scholarly literature, we suggest an expansion of the research focus in three key research areas: societal acceptance, societal implications and governance of AVs to account for the societal dimension of the AVs transition. In the first area, we suggest an expansion of the research focus from consumers' adoption to citizens' acceptance of introduction of AVs. In the second area, we suggest an expansion of the research focus from short-term to long-term societal implications of AVs. In the third area, we suggest a shift from exploratory scenarios to participative anticipatory governance of desirable AVs futures.

Robust Feature Extraction for Geochemical Anomaly Recognition Using a Stacked Convolutional Denoising Autoencoder

Deep neural networks perform very well in learning high-level representations in support of multivariate geochemical anomaly recognition. Geochemical exploration data typically contain a proportion of large variations and missing values, which motivated us to construct a network architecture optimized to deal with these data. Our approach adopted a stacked convolutional denoising autoencoder (SCDAE) to extract robust features and decreased the level of sensitivity to partially corrupted data, that is, input data that are partially missing. SCDAE parameters, which include the network depth, number of convolution layers, number of convolution kernels, and convolution kernel size, were optimized using trial-and-error experiments. The optimal SCDAE architecture was then used to recognize multivariate geochemical anomalies related to mineralization in a case study in southwestern Fujian Province, based on the differences in the reconstruction errors between sample populations. The spatial distribution of high reconstruction errors in the anomaly map was closely related to most known Fe deposits, indicating the effectiveness of the SCDAE at recognizing geochemical anomalies related to Fe mineralization. A comparative study between the SCDAE and a stacked convolutional autoencoder (SCAE) with different corruption levels showed that the SCDAE exhibited reduced sensitivity to stochastic disturbances with different corruption proportions, and had an enhanced ability to recognize geochemical anomalies varying in a reasonable range. The robustness of the SCDAE makes it applicable to a wide variety of geochemical exploration scenarios, particularly in areas with incomplete or missing data.

Joint and constrained inversion of magnetic and gravity data: A case history from the McArthur River area, Canada

Magnetic and gravity data are used in the early stages of exploration for uranium deposits in the Athabasca Basin of Canada, just as for many other mineral exploration scenarios. Uranium mineralization in the Athabasca Basin is located where faults in the basement intersect the unconformity between the basement and the overlying sandstones. The gravity and magnetic data are dominated by signatures from the basement and an overburden of glacial sediments. The gravity and magnetic data are effective at mapping the basement geology. Any subtle gravity signal from the mineralization related to the formation of the uranium deposits is masked by the signal from the variable-thickness overburden. Three-dimensional joint inversion of gravity and magnetic data, first without and then with constraints, is evaluated as a means of better determining the structure of the three main lithologies (overburden, sandstones, and basement) in the Athabasca Basin. A significant amount of physical property information is available for the main rock units (and overburden), which makes the use of the compositional approach to joint inversion appropriate. For the joint inversion, the fuzzy c-means clustering method is used. Results from representative synthetic examples indicate that the joint inversions can construct the overburden and basement structures better than the independent inversions of gravity and magnetic data. Furthermore, constrained joint inversion allows delineation of all three major layers in the area. The same inversion strategies then are applied to real airborne gravity and magnetic data from the McArthur River area in the eastern Athabasca Basin. The results we obtain demonstrate the capabilities of joint inversion for real-life situations.

Electroseismic and seismoelectric responses at irregular interfaces: Possible application to reservoir exploration

Seismoelectric and electroseismic interface responses resulting from the electrokinetic effect are useful to study the properties of subsurface medium. In this article, we investigate the characteristics of these interfacial signals generated at irregular subsurface interfaces considering hydrocarbon exploration scenarios. Adopting several typical models, the electroseismic and seismoelectric wavefields are calculated using the finite-difference frequency domain method. Besides the well-known electroseismic and seismoelectric signals created at a flat interface, the scattered seismic wave and scattered electromagnetic (EM) wave can also be generated by EM and seismic sources at subsurface scattering points, respectively. When an electric source is applied for excitation, the waveforms recorded by horizontally- or vertically-aligned receiver array indicate that electroseismic interface responses nearly do not change with the source locations and can directly delineate the shapes and morphologies of the corresponding interfaces. Simulations of seismoelectric wavefield show that both the interface seismoelectric responses and scattered EM waves display as flat events in the electric record. It is not easy to distinguish them if we do not know the realistic underground structure. Based on simulations, the electroseismic interface responses seem more promising than the seismoelectric interface responses for imaging the subsurface interfaces.

Future Socio-Political Scenarios for Aquatic Resources in Europe: A Common Framework Based on Shared-Socioeconomic-Pathways (SSPs)

It has proven extremely challenging for researchers to predict with confidence how human societies might develop in the future, yet managers and industries need to make projections in order to test adaptation and mitigation strategies designed to build resilience to long-term shocks. This paper introduces exploratory scenarios with a particular focus on European aquaculture and fisheries and describes how these scenarios were designed. Short-, medium- and long-term developments in socio-political drivers may be just as important in determining profits, revenues and prospects in the aquaculture and fisheries industries as physical drivers such as long-term climate change. Four socio-political-economic futures were developed, based partly on the IPCC SRES (Special Report on Emissions Scenarios) framework and partly on the newer system of Shared Socio-economic Pathways (SSPs). ‘Off the shelf’ narrative material as well as quantitative outputs were ‘borrowed’ from earlier frameworks but supplemented with material generated through in-depth stakeholder workshops involving industry and policy makers. Workshop participants were tasked to outline how they thought their sector might look under the four future worlds and, in particular, to make use of the PESTEL conceptual framework (Political, Economic, Social, Technological, Environmental, and Legal) as an aide memoire to help define the scope of each scenario. This work was carried out under the auspices of the EU Horizon 2020 project CERES (Climate change and European aquatic RESources), and for each ‘CERES scenario’ (World Markets, National Enterprise, Global Sustainability and Local Stewardship), additional quantitative outputs were generated, including projections of future fuel and fish prices, using the MAGNET (Modular Applied GeNeral Equilibrium Tool) modeling framework. In developing and applying the CERES scenarios, we have demonstrated that the basic architecture is sufficiently flexible to be used at a wide diversity of scales. We urge the climate science community to adopt a similar scenarios framework, based around SSPs, to facilitate global cross-comparison of fisheries and aquaculture model outputs more broadly and to harmonize communication regarding potential future bioeconomic impacts of climate change.

Identifying Fracture-Filling Material in Oil-Based Mud with Dielectric Borehole Imaging

Natural fractures maintain a significant role in many hydrocarbon plays, in both conventional and unconventional reservoirs. In exploration and development scenarios, specific fracture properties, such as orientation and density, are important. However, more critical is their internal architecture: are the fractures open to fluid flow or filled with minerals? Borehole microresistivity imaging tools are widely used to determine these fracture characteristics. In wells drilled with water-based muds, open fractures are filled with conductive borehole fluid that enables distinguishing open, water-filled fractures from resistive, mineral-filled fractures and the surrounding rock. However, many wells today are drilled with oil-based muds. In this case, mineral-filled fractures and oil-based-mud-filled fractures are equally highly resistive and cannot be directly distinguished using resistivity images only. The latest-generation wireline oil-based-mud microresistivity imagers operate in the megahertz frequency range, radiating the electrical current capacitively through the nonconductive mud column and delivering photorealistic borehole images. Both electrical conductivity and dielectric permittivity components constitute the measured signal, from which button standoff, formation resistivity, and dielectric permittivity are inverted. Our example case shows highly resistive, high-angle fractures from the resistivity images with their orientation and density. The standoff image determines if the mud column penetrates the fracture plane, showing an apparently high standoff compared with the surrounding rock. If the standoff appears high in the fracture plane, the fracture is classified as open to fluid flow. However, are these fractures indeed fully dilated and open, or are they filled with different materials—are they partially mineralized with calcite and partially open, filled with mud? To further determine the fracture fill and susceptibility to fluid flow, a new workflow employs the material dependency of the relative dielectric permittivity. The relative permittivity is estimated as a function of resistivity and frequency pixel by pixel on the resistivity image. The estimate formula is based on several hundred laboratory measurements on core plugs with different fluid saturations and salinities. The resulting borehole image enables distinguishing materials in the volume of investigation, where low values correspond to mud-dominated oil in open fracture planes, medium values correspond to rock-forming minerals, and high values are attributed to shales and other clay-rich rocks. Fracture planes filled with patches of both low- and medium-permittivity values are classified as partially open.

TopoAct: Visually Exploring the Shape of Activations in Deep Learning

AbstractDeep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e.combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present TopoAct, a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using TopoAct that provide valuable insights into learned representations of neural networks. We expect TopoAct to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.

Alternative futures for shared mobility systems: impacts on transport planning tools and techniques

On-demand mobility services are changing the way we move in cities, fostered by digitalisation, vehicle automation and Mobility-as-a-Service (MaaS) platforms. Transport planning tools and techniques are expected to keep up to date to support decision-makers on the successful integration of these new mobility options in the urban transport mix. However, the evolution of the emerging mobility solutions is highly uncertain, as it is a function of complex factors that go beyond the influence of transport stakeholders. This poses a challenge for the definition of the requirements that data analysis techniques and modelling tools will face. This paper presents a series of explorative scenarios that provide different contexts for the evolution of urban mobility in Europe, in order to grasp the plausible pathways that shared mobility services will follow in the upcoming decades. The scenarios are adapted from those developed by the climate change research community and are used in a Delphi poll to gather expert visions on how shared mobility services will evolve. The availability of a range of possible futures for shared mobility facilitates the identification of the capabilities that transport decision-makers will demand from data analysis techniques and modelling tools.

Inversion of 1D frequency- and time-domain electromagnetic data with convolutional neural networks

Inversion of electromagnetic data finds applications in many areas of geophysics. The inverse problem is commonly solved with either deterministic optimization methods (such as the nonlinear conjugate gradient or Gauss-Newton) which are prone to getting trapped in a local minimum, or probabilistic methods which are very computationally demanding. A recently emerging alternative is to employ deep neural networks for predicting subsurface model properties from measured data. This approach is entirely data-driven, does not employ traditional misfit optimization methods and provides a guess to the model instantaneously. In this study, we examine the feasibility of using deep convolutional neural networks for the inversion of marine frequency-domain controlled-source electromagnetic (CSEM) data as well as onshore time-domain electromagnetic (TEM) data. Our approach yields accurate results both on synthetic and real data and provides them instantaneously. Using several networks and combining their outputs from various training epochs can also provide insights into the uncertainty distribution, which are found to be higher in the regions where resistivity anomalies are present. The proposed method opens up possibilities to estimate the subsurface resistivity distribution in exploration scenarios in real time.

Organizational Happiness Dimensions as a Contribution to Sustainable Development Goals: A Prospective Study in Higher Education Institutions in Chile, Colombia and Spain

The aim of this study is to reveal the perception of current and future organizational happiness in the context of higher education institutions (HEIs) in a cross-national study, to reveal exploratory scenarios supporting the UN’s sustainable development goals (SDGs). Six dimensions of organizational happiness were considered for this study: Meaning, reliable relationships, positive emotions, engagement, achievement and recognition, and personal and professional development. To do so, Delphi methodology was used. An online survey was addressed to academics and support staff from three HEIs in different countries (Chile, Colombia and Spain), whose answers were analyzed using the Approximation of Qualitative Profiles mathematical technique. The results of this analysis show that there is a favorable prospective for happy workplaces, in all the dimensions for all countries. The implications of this research will serve HEIs’ human management practitioners to formulate effective policies for sustainable workplaces based on organizational happiness.

Azimuthal amplitude variation with offset parameterization and inversion for fracture weaknesses in tilted transversely isotropic media

The characterization of fracture-induced tilted transverse isotropy (TTI) seems to be more suitable to actual scenarios of geophysical exploration for fractured reservoirs. Fracture weaknesses enable us to describe fracture-induced anisotropy. With the incident and reflected PP-wave in TTI media, we have adopted a robust method of azimuthal amplitude variation with offset (AVO) parameterization and inversion for fracture weaknesses in a fracture-induced reservoir with TTI symmetry. Combining the linear-slip model with the Bond transformation, we have derived the stiffness matrix of a dipping-fracture-induced TTI medium characterized by normal and tangential fracture weaknesses and a tilt angle. Integrating the first-order perturbations in the stiffness matrix of a TTI medium and scattering theory, we adopt a method of azimuthal AVO parameterization for PP-wave reflection coefficient for the case of a weak-contrast interface separating two homogeneous weakly anisotropic TTI layers. We then adopt an iterative inversion method by using the partially incidence-angle-stacked seismic data with different azimuths to estimate the fracture weaknesses of a TTI medium when the tilt angle is estimated based on the image well logs prior to the seismic inversion. Synthetic examples confirm that the fracture weaknesses of a TTI medium are stably estimated from the azimuthal seismic reflected amplitudes for the case of moderate noise. A field data example demonstrates that geologically reasonable results of fracture weaknesses can be determined when the tilt angle is treated as the prior information. We determine that the azimuthal AVO inversion approach provides an available tool for fracture prediction in a dipping-fracture-induced TTI reservoir.

PND57 Cost-Effectiveness Analysis of a Hypothetical Disease Modifying Therapy for Parkinson's Disease

To conduct a case study of the lifetime cost-effectiveness of a hypothetical disease modifying therapy (DMT) plus standard of care (SOC) vs SOC for treatment-naïve Parkinson’s disease (PD) in the UK. A patient-level simulation was developed to account for individual variability in progression of PD, and inter-related changes in Movement Disorder Society Revision of Unified PD Rating Scales (MDS-UPDRS I-IV). Predictive equations were based on analyses of Parkinson's Progression Markers Initiative (PPMI) study and National Institute of Neurological Disorders and Stroke Exploratory Trials in PD Long-Term Study 1. The benefits of starting symptomatic treatments (dopaminergic and advanced therapies) and their long-term limitations were simulated. Inputs for mortality, patient and caregiver utilities, and UK costs (2020) were obtained from publicly available sources. Hypothetical DMT was assumed to slow progression in MDS-UPDRS scores by 50% vs SOC. Costs and health outcomes were discounted at 3.5% per annum. Sensitivity analyses explored the main drivers of results. Patient profiles were generated by jointly sampling correlated characteristics observed in PPMI (mean age=61.2 years, male=65%, PD duration=6 months [range 0 – 48], treatment-naïve). Over a lifetime, patients were projected to incur costs of £243,748—with 22% from treatment costs, and 58% non-medical, including nursing home admission. Comparing DMT vs SOC, quality-adjusted life years (QALYs) were 9.0 vs 7.3 (+1.6), and as costs of DMT were not considered, predicted total costs were lowered by 26%. For a subgroup starting treatment earlier (mean PD duration=3 months [range 0 – 6]), benefits and savings were increased: QALY gain of 1.8 and reduction in costs of £57,251 (-32%). Key drivers of cost-effectiveness: efficacy, utilities, non-medical costs, and baseline characteristics. This case study for a hypothetical DMT illustrates how earlier treatment could be beneficial. The flexible model design facilitates exploratory scenarios and assessment of key sources of uncertainty on the results.

Federated Learning Assisted Multi-UAV Networks

Unmanned aerial vehicles (UAVs) have been recognized as a promising technology to be used in a wide range of civilian, public and military applications. However, given their limited payload and flight time, multiple UAVs may have to be harnessed for accomplishing complex high-level tasks, where a control center can be employed for coordinating their actions. In this article, we consider image classification tasks in UAV-aided exploration scenarios, where the coordination of multiple UAVs is implemented by a ground fusion center (GFC) positioned in a strategic, but inaccessible location, such as a mountain top, where recharging the battery is uneconomical or may even be infeasible. On-board cameras are carried by each UAV and then, federated learning (FL) is invoked for reducing the communication cost between the UAVs and the GFC, and the computational complexity imposed on the GFC. In our proposed FL-aided classification approach, initially local training is performed by each UAV based on the locally collected images to create a local model. Then, each UAV sends its locally acquired model to the GFC via a fading wireless channel, where a global model is generated, which is then fed back to each UAV for the next round of their local training. In order to further minimize the computational complexity imposed on the GFC by the UAVs, weighted zero-forcing (WZF) transmit precoding (TPC) is used at each UAV based on realistic imperfect channel state information (CSI). The system performance attained is evaluated by simulations, showing that the proposed system is capable of attaining a high classification accuracy at relatively low communication cost.

Cartographic Design of Cultural Maps.

Throughout history, maps have been used as a tool to explore cities. They visualize a city's urban fabric through its streets, buildings, and points of interest. Besides purely navigation purposes, street names also reflect a city's culture through its commemorative practices. Therefore, cultural maps that unveil socio-cultural characteristics encoded in street names could potentially raise citizens' historical awareness. But designing effective cultural maps is challenging, not only due to data scarcity but also due to the lack of effective approaches to engage citizens with data exploration. To address these challenges, we collected a dataset of 5000 streets across the cities of Paris, Vienna, London, and New York, and built their cultural maps grounded on cartographic storytelling techniques. Through data exploration scenarios, we demonstrated how cultural maps engage users and allow them to discover distinct patterns in the ways these cities are gender-biased, celebrate various professions, and embrace foreign cultures.

A robotically deployable lunar surface science station and its validation in a Moon-analogue environment

This article presents a system design and a surface operations concept for a robotically deployable, small scientific observatory on the lunar surface. The design reference mission scenario considers its implementation as part of larger international exploration mission such as the European Exploration Envelope Programme. The underlying science case particularly addresses scientific objectives for long term observations as part of scientific networks. Considered strawman payload for this surface station focuses on instruments which are – or could be – candidates for geophysical and astronomical observation networks. A seismometer, a radio experiment and an IR telescope have been taken as sizing case to assess the station’s system budgets. First part of the article looks at the station design from engineering perspective whether a small modular station can address common needs of such instruments such as a sustained operation in the lunar environment. Focus is given to design features to enable the station’s deployment by robotic assets. Secondly, the core unit of the conceptualized station has been built as engineering model including its basic system functions, interfaces to neighboring mission elements such as the lander vehicle and the rover and a set of geophones as representative for a science instrument. This hardware realization was used in a functional end-to-end demonstration from robotic deployment to delivery of geo-scientific data. The mission demonstration has been carried out in a Moon analogue field test on Mt. Etna, Sicily/Italy and confirmed the general feasibility of the proposed concept for lunar scientific exploration. Particularly, the evaluation of the acquired seismic data confirmed its suitability for sub-surface exploration. Results from the Moon analogue test are presented together with the design details of the surface station and the necessary conditions for its implementation and use in a robotic exploration scenario.

In-situ visual exploration over big raw data

Data exploration and visual analytics systems are of great importance in Open Science scenarios, where less tech-savvy researchers wish to access and visually explore big raw data files (e.g., json, csv) generated by scientific experiments using commodity hardware and without being overwhelmed in the tedious processes of data loading, indexing and query optimization. In this paper, we present our work for enabling efficient query processing on large raw data files for interactive visual exploration scenarios and analytics. We introduce a framework, named RawVis, built on top of a lightweight in-memory tile-based index, VALINOR, that is constructed on-the-fly given the first user query over a raw file and progressively adapted based on the user interaction. We evaluate the performance of a prototype implementation compared to three other alternatives and show that our method outperforms in terms of response time, disk accesses and memory consumption. Particularly during an exploration scenario, the proposed method in most cases is about 5-10× faster compared to existing solutions, and requires significantly less memory resources.

Integrating environmental sustainability and social justice in housing development: two contrasting scenarios

The article discusses futures in housing development by applying the approaches from ‘future studies’ to design two explorative scenarios reflecting alternative strategies for achieving sustainable and just housing development. The main aim is to develop scenarios that can achieve a specific normative goal: a future housing development that is both environmentally sustainable and socially just. Two scenarios are built – ecological modernisation and degrowth – that reflect different degrees of societal change, ranging from conventional to radical. The scenarios are applied to the two selected cases of the Milan and Oslo regions, drawing on the statistics of the contextual housing system and the document analysis on planning and housing. We further discuss how the specific scenarios can take place and which challenges will be encountered.

Ocular Biometrics Recognition by Analyzing Human Exploration during Video Observations

Soft biometrics provide information about the individual but without the distinctiveness and permanence able to discriminate between any two individuals. Since the gaze represents one of the most investigated human traits, works evaluating the feasibility of considering it as a possible additional soft biometric trait have been recently appeared in the literature. Unfortunately, there is a lack of systematic studies on clinically approved stimuli to provide evidence of the correlation between exploratory paths and individual identities in “natural” scenarios (without calibration, imposed constraints, wearable tools). To overcome these drawbacks, this paper analyzes gaze patterns by using a computer vision based pipeline in order to prove the correlation between visual exploration and user identity. This correlation is robustly computed in a free exploration scenario, not biased by wearable devices nor constrained to a prior personalized calibration. Provided stimuli have been designed by clinical experts and then they allow better analysis of human exploration behaviors. In addition, the paper introduces a novel public dataset that provides, for the first time, images framing the faces of the involved subjects instead of only their gaze tracks.