Miracle-dz/UniFAR-FTUs huggingface dataset:
Pivoting Image-based Profiles Toward Privacy: Inhibiting Malicious Profiling with Adversarial Additions

Users build up profiles online consisting of items that they have shared or interacted with. In this work, we look at profiles that consist of images. We address the issue of privacy-sensitive information being automatically inferred from these user profiles, against users’ will and best interest. We introduce the concept of a privacy pivot, which is a strategic change that users can make in their sharing that will inhibit malicious profiling. Importantly, the pivot helps put privacy control into the hands of the users. Further, it does not require users to delete any of the existing images in their profiles, nor does it require a radical change in their sharing intentions, i.e., what they would like to communicate with their profile. Previous work has investigated adversarial images for privacy protection, but has focused on individual images. Here, we move further to study image sets comprising image profiles. We define a conceptual formulation of the challenge of the privacy pivot in the form of an “Anti-Profiling Model”. Within this model, we propose a basic pivot solution that uses adversarial additions to effectively inhibit the predictions of profilers using set-based image classification.
Putting ICT in the Hands of the Seafarers

This paper proposes ethnographic action research on expanding participation in an ICT project in the maritime domain. Concepts from actor network theory are derived to provide a useful perspective on the description and analysis of the case. By analyzing two workshops, the paper discusses how power relations are balanced in a new heterogeneous network of actors, whose dynamical features align different actants’ interests toward a common sense. The relevance of the findings is discussed, related to how we might think about the role of researchers in the empowerment of marginalized groups. In this paper, I posit that the creation of a research and development culture to extend participation is helpful and relevant to making a well-organized technological ecosystem to the detriment of seafarers’ community well-being.
A new system for evaluating brand importance: A use case from the fashion industry

Today brand managers and marketing specialists can leverage huge amount of data to reveal patterns and trends in consumer perceptions, monitoring positive or negative associations of brands with respect to desired topics. In this study, we apply the Semantic Brand Score (SBS) indicator to assess brand importance in the fashion industry. To this purpose, we measure and visualize text data using the SBS Business Intelligence App (SBS BI), which relies on methods and tools of text mining and social network analysis. We collected and analyzed about 206,000 tweets that mentioned the fashion brands Fendi, Gucci and Prada, during the period from March 5 to March 12, 2021. From the analysis of the three SBS dimensions - prevalence, diversity and connectivity - we found that Gucci dominated the discourse, with high values of SBS. We use this case study as an example to present a new system for evaluating brand importance and image, through the analysis of (big) textual data.
Real-like MAX-SAT instances and the landscape structure across the phase transition

In contrast with random uniform instances, industrial SAT instances of large size are solvable today by state-of-the-art algorithms. It is believed that this is the consequence of the non-random structure of the distribution of variables into clauses. In order to produce benchmark instances resembling those of real-world formulas with a given structure, generative models have been proposed. In this paper we study the MAX-3SAT problem with model-generated instances having a power-law distribution. Specifically, we target the regions in which computational difficulty undergoes an easy/hard phase transition as a function of clause density and of the power-law exponent. Our approach makes use of a sampling technique to build a graph model (a local optima network) in which nodes are local optima and directed edges are transitions between optima basins. The objective is to relate the structure of the instance fitness landscape with problem difficulty through the transition. We succeed in associating the transition with straightforward network metrics, thus providing a novel and original fitness landscape view of the computational features of the power-law model and its phase transition.
Zeroth-order optimizer benchmarking for 3D performance capture: a real-world use case analysis

In the field of 3D Human Performance Capture, a high-quality 3D scan of the performer is rigged and skinned to an animatable 3D template mesh that is subsequently fitted to the captured performance's RGB-D data. Template fitting is accomplished via solving for the template's pose parameters that better explain the performance data at each recorded frame. In this paper, we challenge open implementations of zeroth-order optimizers to solve the template fitting problem in a human performance capture dataset. The objective function that we employ approximates, the otherwise costly to evaluate, 3D RMS hausdorff distance between the animated template and the 3D mesh reconstructed from the depth data (target mesh) at an individual recorded frame. We distinguish and benchmark the optimizers, in three different real-world scenarios, two of which are based on the geometric proximity of the template to the target in individual frames, while in the third one we fit the template sequentially to all target frames of the recorded sequence. Conclusions of this work can serve as a reference for future optimizer implementations and our findings can server as a baseline for future multi-objective optimization approaches. We make part of our benchmark and experiment setup publicly available (https://github.com/VCL3D/nevergrad, https://github.com/VCL3D/PerformanceCapture/releases/).
Care-full Design Sprints, Online? Addressing Gaps in Cultural Access and Inclusion during Covid-19 with Vulnerable Communities in London and Tokyo

What does it mean to invite vulnerable communities to the table in times of crisis not just as subjects, but as co-designers, in ways that facilitate nourishing and care-full relations? In this paper, we present the case of an online design sprint involving two groups of diverse participants in London and Tokyo as the Covid-19 pandemic unfolded. This modified design sprint model, which we describe as a ’care-full design stroll’, integrated co-design approaches with ethics of care to offer remote cultural experiences aimed at addressing inequalities of access and inclusion faced by the arts and cultural sectors in Japan and the UK. We analyse data from ethnographic observations, interviews and surveys in both nations to illustrate the challenges and opportunities of facilitating design sprints online. Our findings show how care-full co-design, underpinned by concepts of thinking-with and working-alongside, can be facilitated in online-only and/or limited terrains, in ways that nourish cultural organisations and their publics in times of great uncertainty. We conclude with a set of six design principles which provide practical recommendations for the effective facilitation of future care-full co-design sprints for groups working in a variety of settings.
Personalisation in Cyber-Physical-Social Systems: A Multi-stakeholder aware Recommendation and Guidance

The evolution of smart devices has led to the transformation of many physical spaces to the so-called smart environments collectively termed as Cyber-Physical-Social System (CPSS). In CPSS users co-exist with different stakeholders influencing each other while being influenced by different environmental factors. Additionally, these environments often have their own desired goals and corresponding set of rules in place expecting people to behave in certain ways. Hence, in such settings classical approaches to personalisation which solely optimise for user satisfaction are often encumbered by competing objectives and environmental constraints which are yet to be addressed jointly. In this work we set out to (i) formalise the general problem of personalisation in CPSS from a multi-stakeholder perspective taking into account the full environmental complexity, (ii) extend the general formalisation to the case of exhibition areas and propose a personalised Multi-stakeholder aware Recommendation and Guidance method on a case study of National Gallery, London.
A genetic algorithm approach to virtual topology design for multi-layer communication networks

The core networks of current telecommunication infrastructures are typically engineered as multi-layer networks. The uppermost layer is defined by the virtual topology, which determines the logical connections between core network routers. This topology is realized by optical paths in the lower layer, which is defined by optical fiber connections between network nodes. Minimizing the hardware cost incurred by these optical paths for a given set of traffic demands is a common combinatorial optimization problem in network planning, often approached by Mixed-Integer Linear Programming. However, increasing network densities and the introduction of additional constraints will impact tractability of future network problems. In order to provide a more scalable method, we suggest a Genetic Algorithm-based approach that optimizes the virtual topology and subsequently derives the remaining parameters from it. Our genetic encoding utilizes a combination of spanning trees and augmentation links to quickly form meaningful topologies. We compare the results of our approach to known linear programming solutions in simple scenarios and to a competing heuristic based on Simulated Annealing in large-scale problems.
Pedestrian Trajectory Prediction with MLP-social-GRU

When crossing a crowded area, a person can predict dangers or collisions in advance around him/her, and then makes a suitable decision which direction he/she should take. The pedestrian trajectory prediction aims at simulating such ability of humans in a crowded environment. Most of the existing trajectory predictions are all based on the traditional hand-crafted methods that often ignore critical factors and can only be adapted to specific environments. Based on deep learning technology, this paper proposes a data-driven pedestrian trajectory predictor called MLP-social-GRU. First, the proposed predictor processes a pedestrian trajectory with a Multilayer Perceptron (MLP). Then, it adopts Gated Recurrent Units (GRU) to get hidden features of a pedestrian motion patterns, from which relationships between pedestrians can be simulated. Next, the social-pooling is used to receive and merge the hidden status information to get the mutual influence of adjacent pedestrians. Finally, a unified pedestrian trajectory prediction framework is designed based on abovementioned modules. We evaluate our predictor on two publicly available datasets, ETH and UCY, and the results show that it is superior to popular models such as LSTM, social-LSTM, and goal-social-array.
Landscape features and automated algorithm selection for multi-objective interpolated continuous optimisation problems

In this paper, we demonstrate the application of features from landscape analysis, initially proposed for multi-objective combinatorial optimisation, to a benchmark set of 1 200 randomly-generated multiobjective interpolated continuous optimisation problems (MO-ICOPs). We also explore the benefits of evaluating the considered landscape features on the basis of a fixed-size sampling of the search space. This allows fine control over cost when aiming for an efficient application of feature-based automated performance prediction and algorithm selection. While previous work shows that the parameters used to generate MO-ICOPs are able to discriminate the convergence behaviour of four state-of-the-art multi-objective evolutionary algorithms, our experiments reveal that the proposed (black-box) landscape features used as predictors deliver a similar accuracy when combined with a classification model. In addition, we analyse the relative importance of each feature for performance prediction and algorithm selection.
Making Space for Support: An Exploratory Analysis of Pandemic-Response Mutual Aid Platforms

The COVID-19 pandemic ushered in an era of unprecedented hardship across the United States. In response, local community members leveraged mutual aid as a form of citizen-based, peer-to-peer care. In this paper, we are interested in teasing out significant design features that support the facilitation of mutual aid on online platforms. To this end, we conducted a scenario-based claims analysis of the two most widely used platforms for mutual aid, based on three primary user groups. Our analysis suggests that design for mutual aid platforms considers features which support request standardization and balanced visibility alongside validation and conversational interaction.
An efficient computational approach for automatic itinerary planning on web servers

The automatic itinerary planning service requires to generate multiple-day schedules automatically under user-specified POIs and constraints. Knowing as an NP-Hard optimization problem, the task is commonly solved by (meta-)heuristic algorithms such as the genetic algorithms (GAs). However, considering the concurrent requests received by a web server in practice, the time efficiency of the existing itinerary planners can be rather unsatisfactory. To address the issue, this paper proposes a computational approach that hybridizing a GA with the reinforcement learning (RL) technology. The benefit is that we no longer need to re-execute the GA for each new request arrived. Instead, the approach keeps the historical solutions in track and maintains a RL agent to sequentially decide how to handle each new request. Experimental results show that the proposed approach is able to stably provide high-quality solutions, while greatly reducing the average time overhead of the web server.
How Do User Opinions Influence Their Interaction With Web Search Results?

Understanding the influence of users’ opinions on their search behavior together with their inherent biases in web search has garnered widespread interest in recent times. This is largely due to the implications of promoting critical thinking, explaining phenomena such as political polarization, or the manifestation of echo chambers. It is important to understand how personal opinions can bias users’ interaction with search results. Moreover, there is a lack of understanding of the impact of user search intents, namely non-purposeful browsing versus searching with a pre-defined goal, on users’ interactions with search results. We take a step towards bridging this knowledge gap through an empirical study in this paper. To do so, we select two controversial topics in abortion and gun control, and invite users to learn about them through ‘Purposeless’ and ‘Purposeful’ web searching. Our findings suggest that users with strong personal opinions exhibit biased interactions with the search results. However, the effect of users’ opinions on their interactions with search results can differ depending on whether users search purposelessly or with a purpose. Our findings advance the current understanding of the effect of users’ opinions in web search sessions, and show that users’ search intents shape their interaction with search results. This work has broad design implications on dealing with bias in interactive information retrieval systems.
Multi-Objective Optimal Design of Excitation Systems of Synchronous Condensers for HVDC Systems Based on MOEA/D

In order to optimize the reactive power characteristics of synchronous condensers and improve the capability of condensers to support the voltage of AC systems, in this paper, the outer loop control of the reactive power of condensers and the outer loop control of the voltage of AC systems are introduced into the design of the main excitation systems of condensers in high voltage direct current (HVDC) systems. Meanwhile, taking the integral values, peak values and steady-state values of voltage deviations of AC systems as objective functions, the multi-objective optimization design of the proportional adjustment coefficients in the outer loop control of the reactive power of condensers and the voltage of AC systems is carried out via utilizing a multi-objective evolutionary algorithm based on decomposition (MOEA/D) combining with fuzzy control method. Its purpose is to alleviate the overvoltage problems of power grids caused by the feedback of the reactive power of condensers and the voltage of AC systems. Lastly, the simulation model of ±100 kV HVDC system with a synchronous condenser is established. The simulation results show that the optimal design method of excitation systems of synchronous condensers proposed in this paper can optimize the reactive power characteristics of the condenser, ensure the rapid regulation of the voltage of the AC system by the condenser, and solve the overvoltage problem in the AC system caused by the reactive power regulation of the condenser which can not change suddenly and the feedback links of the reactive power of the condenser and the voltage of the AC system in the excitation system.
Personality and Engagement with Digital Mental Health Interventions

Personalisation is key to creating successful digital health applications. Recent evidence links personality and preference for digital experience — suggesting that psychometric traits can be a promising basis for personalisation of digital mental health services. However, there is still little quantitative evidence from actual app usage. In this study, we explore how different personality types engage with different intervention content in a commercial mental health application. Specifically, we collected the Big Five personality traits alongside the app usage data of 126 participants using a mobile mental health app for seven days. We found that personality traits significantly correlate with the engagement and user ratings of different intervention content. These findings open a promising research avenue that can inform the personalised delivery of digital mental health content and the creation of recommender systems, ultimately improving the effectiveness of mental health interventions.
Low-cost Influence-Limiting Defense against Adversarial Machine Learning Attacks in Cooperative Spectrum Sensing

Cooperative spectrum sensing aims to improve the reliability of spectrum sensing by individual sensors for better utilization of the scarce spectrum bands, which gives the feasibility for secondary spectrum users to transmit their signals when primary users remain idle. However, there are various vulnerabilities experienced in cooperative spectrum sensing, especially when machine learning techniques are applied. The influence-limiting defense is proposed as a method to defend the data fusion center when a small number of spectrum sensing devices is controlled by an intelligent attacker to send erroneous sensing results. Nonetheless, this defense suffers from a computational complexity problem. In this paper, we propose a low-cost version of the influence-limiting defense and demonstrate that it can decrease the computation cost significantly (the time cost is reduced to less than 20% of the original defense) while still maintaining the same level of defense performance.
Evolving gaits for damage control in a hexapod robot

Autonomous robots are increasingly used in remote and hazardous environments, where damage to sensory-actuator systems cannot be easily repaired. Such robots must therefore have controllers that continue to function effectively given unexpected malfunctions and damage to robot morphology. This study applies the Intelligent Trial and Error (IT&E) algorithm to adapt hexapod robot control to various leg failures and demonstrates the IT&E map-size parameter as a critical parameter in influencing IT&E adaptive task performance. We evaluate robot adaptation for multiple leg failures on two different map-sizes in simulation and validate evolved controllers on a physical hexapod robot. Results demonstrate a trade-off between adapted gait speed and adaptation duration, dependent on adaptation task complexity (leg damage incurred), where map-size is crucial for generating behavioural diversity required for adaptation.
Information Hiding in Cyber Physical Systems: Challenges for Embedding, Retrieval and Detection using Sensor Data of the SWAT Dataset

In this paper, we present an Information Hiding approach that would be suitable for exfiltrating sensible information of Industrial Control Systems (ICS) by leveraging the long-term storage of process data in historian databases. We show how hidden messages can be embedded in sensor measurements as well as retrieved asynchronously by accessing the historian. We evaluate this approach at the example of water-flow and water-level sensors of the Secure Water Treatment (SWAT) dataset from iTrust. To generalize from specific cover channels (sensors and their transmitted data), we reflect upon general challenges that arise in such Information Hiding scenarios creating network covert channels and discuss aspects of cover channel selection and and sender receiver synchronisation as well as temporal aspects such as the potential persistence of hidden messages in Cyber Physical Systems (CPS). For an empirical evaluation we design and implement a covert channel that makes use of different embedding strategies to perform an adaptive approach in regards to the noise in sensor measurements, resulting in dynamic capacity and bandwidth selection to reduce detection probability. The results of this evaluation show that, using such methods, the exfiltration of sensible information in long-term scaled attacks would indeed be possible. Additionally, we present two detection approaches for the introduced hidden channel and carry out an extensive evaluation of our detectors with multiple test data sets and different parameters. We determine a detection accuracy of up to 87.8% on test data at a false positive rate (FPR) of 0%.
Characterising Student Engagement Modes through Low-Level Activity Patterns

Existing approaches to characterise engagement in online learning focus on features of the interaction of students with the learning platform including the number of posts in forums, downloads of learning materials and time spent watching videos. However, little is known about what students actually do within the learning resources and whether these activities are indicators of learning outcomes. To bridge this gap, we associate low-level activity patterns with particular student engagement modes on a connectivist MOOC (cMOOC) that ran for four weeks and involved 224 students. Our findings indicate that our approach isolates meaningful interactive behavioural markers that are indicators of engagement, and are amenable to computation.