Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Perceptions and Behaviors of Google Personal Account Holders in Saudi Arabia

While privacy perceptions and behaviors have been investigated in Western societies, little is known about these issues in non-Western societies. To bridge this gap, we interviewed 30 Google personal account holders in Saudi Arabia about their privacy perceptions and behaviors regarding the activity data that Google saves about them. Our study focuses on Google's Activity Controls, which enable users to control whether, and how, Google saves their Web \&App Activity, Location History, and YouTube History. Our results show that although most participants have some level of awareness about Google's data practices and the Activity Controls, many have only vague awareness, and the majority have not used the available controls. When participants viewed their saved activity data, many were surprised by what had been saved. While many participants find Google's use of their data to improve the services provided to them acceptable, the majority find the use of their data for ad purposes unacceptable. We observe that our Saudi participants exhibit similar trends and patterns in privacy awareness, attitudes, preferences, concerns, and behaviors to what has been found in studies in the US. Our results emphasize the need for: 1) improved techniques to inform users about privacy settings during account sign-up, to remind users about their settings, and to raise awareness about privacy settings; 2) improved privacy setting interfaces to reduce the costs that deter many users from changing the settings; and 3) further research to explore privacy concerns in non-Western cultures.
Fixed-Parameter Algorithms for Computing RAC Drawings of Graphs

In a right-angle crossing (RAC) drawing of a graph, each edge is represented as a polyline and edge crossings must occur at an angle of exactly $90^\circ$, where the number of bends on such polylines is typically restricted in some way. While structural and topological properties of RAC drawings have been the focus of extensive research, little was known about the boundaries of tractability for computing such drawings. In this paper, we initiate the study of RAC drawings from the viewpoint of parameterized complexity. In particular, we establish that computing a RAC drawing of an input graph $G$ with at most $b$ bends (or determining that none exists) is fixed-parameter tractable parameterized by either the feedback edge number of $G$, or $b$ plus the vertex cover number of $G$.
An Efficient High-Dimensional Gene Selection Approach based on Binary Horse Herd Optimization Algorithm for Biological Data Classification

The Horse Herd Optimization Algorithm (HOA) is a new meta-heuristic algorithm based on the behaviors of horses at different ages. The HOA was introduced recently to solve complex and high-dimensional problems. This paper proposes a binary version of the Horse Herd Optimization Algorithm (BHOA) in order to solve discrete problems and select prominent feature subsets. Moreover, this study provides a novel hybrid feature selection framework based on the BHOA and a minimum Redundancy Maximum Relevance (MRMR) filter method. This hybrid feature selection, which is more computationally efficient, produces a beneficial subset of relevant and informative features. Since feature selection is a binary problem, we have applied a new Transfer Function (TF), called X-shape TF, which transforms continuous problems into binary search spaces. Furthermore, the Support Vector Machine (SVM) is utilized to examine the efficiency of the proposed method on ten microarray datasets, namely Lymphoma, Prostate, Brain-1, DLBCL, SRBCT, Leukemia, Ovarian, Colon, Lung, and MLL. In comparison to other state-of-the-art, such as the Gray Wolf (GW), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), the proposed hybrid method (MRMR-BHOA) demonstrates superior performance in terms of accuracy and minimum selected features. Also, experimental results prove that the X-Shaped BHOA approach outperforms others methods.
Multiples among B stars in the Scorpius-Centaurus association

We discuss the properties of companions to B stars in the Scorpius-Centaurus association (age ~15 Myr, 181 B-stars). We gathered available data combining high contrast imaging samples with evidence of companions from Gaia, from eclipsing binaries, and from spectroscopy. We evaluated the completeness of the binary search and estimated the mass and semi-major axis for all detected companions. These data provide a complete sample of stellar secondaries for separation>3 au, and they are highly informative as to closer companions. We found evidence for 200 companions around 181 stars. The fraction of single star is 15.2\pm 4.1% for stars with M_A>3.5 Msun while it is 31.5\pm 5.9% for lower-mass stars. The median semi-major axis of the orbits of the companions is smaller for B than in A stars, confirming a turn-over previously found for OB stars. The mass distribution of the very wide (a>1000 au) and closer companions is different. Very few companions of massive stars M_A>5.0 Msun have a mass below solar and even fewer are M stars with a semi-major axis<1000 au. The scarcity of low-mass companions extends throughout the whole sample. Most early B stars are in compact systems with massive secondaries, while lower-mass stars are mainly in wider systems with a larger spread in mass ratios. We interpret our results as the formation of secondaries with a semi-major axis<1000 au (about 80% of the total) by fragmentation of the disk of the primary and selective mass accretion on the secondaries. The observed trends with primary mass may be explained by a more prolonged phase of accretion episodes on the disk and by a more effective inward migration. We detected twelve new stellar companions from the BEAST survey and of a new BD companion at 9.6 arcsec from HIP74752 using Gaia data, and we discuss the cases of possible BD and low-mass stellar companions to HIP59173, HIP62058, and HIP64053.
cantnlp@LT-EDI-2023: Homophobia/Transphobia Detection in Social Media Comments using Spatio-Temporally Retrained Language Models

This paper describes our multiclass classification system developed as part of the LT-EDI@RANLP-2023 shared task. We used a BERT-based language model to detect homophobic and transphobic content in social media comments across five language conditions: English, Spanish, Hindi, Malayalam, and Tamil. We retrained a transformer-based cross-language pretrained language model, XLM-RoBERTa, with spatially and temporally relevant social media language data. We found the inclusion of this spatio-temporal data improved the classification performance for all language and task conditions when compared with the baseline. We also retrained a subset of models with simulated script-mixed social media language data with varied performance. The results from the current study suggests that transformer-based language classification systems are sensitive to register-specific and language-specific retraining.
Conceptual Perspectives for Explaining Spatio-Temporal Behaviour in Urban Areas

This paper's goal is to elaborate tools for understanding trips in urban areas, using an activity based theoretical approach as a starting point. The paper will (1) propose a systematic conceptual framework for analyzing and explaining the travel behavior of urban residents, and (2) underline the multidimensionality of decision making processes and of rules of behavior and their evolutions. The paper argues that the economic and temporal dimensions should not be as dominant as they have been, and that the spatial and temporal dimensions of behavior should be considered simultaneously and given equal weight. The paper recommends four priority areas for further development of research tools. They are: better integration of social norms in the survey methods; more information about the workings of the influence of social status; more knowledge about the reassessment of behavioral rules by an individual based on accumulated experience; and a stronger framework in the interpersonal domain as many decisions about daily mobility are made at the household level.
Sampling From Autoencoders’ Latent Space via Quantization And Probability Mass Function Concepts

In this study, we focus on sampling from the latent space of generative models built upon autoencoders so as the reconstructed samples are lifelike images. To do to, we introduce a novel post-training sampling algorithm rooted in the concept of probability mass functions, coupled with a quantization process. Our proposed algorithm establishes a vicinity around each latent vector from the input data and then proceeds to draw samples from these defined neighborhoods. This strategic approach ensures that the sampled latent vectors predominantly inhabit high-probability regions, which, in turn, can be effectively transformed into authentic real-world images. A noteworthy point of comparison for our sampling algorithm is the sampling technique based on Gaussian mixture models (GMM), owing to its inherent capability to represent clusters. Remarkably, we manage to improve the time complexity from the previous $\mathcal{O}(n \times d \times k \times i)$ associated with GMM sampling to a much more streamlined $\mathcal{O}(n \times d)$, thereby resulting in substantial speedup during runtime. Moreover, our experimental results, gauged through the Fréchet inception distance (FID) for image generation, underscore the superior performance of our sampling algorithm across a diverse range of models and datasets. On the MNIST benchmark dataset, our approach outperforms GMM sampling by yielding a noteworthy improvement of up to 0.89 in FID value. Furthermore, when it comes to generating images of faces and ocular images, our approach showcases substantial enhancements with FID improvements of 1.69 and 0.87 respectively, as compared to GMM sampling, as evidenced on the CelebA and MOBIUS datasets. Lastly, we substantiate our methodology’s efficacy in estimating latent space distributions in contrast to GMM sampling, particularly through the lens of the Wasserstein distance.
YORC: Yoruba Reading Comprehension dataset

In this paper, we create YORC: a new multi-choice Yoruba Reading Comprehension dataset that is based on Yoruba high-school reading comprehension examination. We provide baseline results by performing cross-lingual transfer using existing English RACE dataset based on a pre-trained encoder-only model. Additionally, we provide results by prompting large language models (LLMs) like GPT-4.
Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning

: Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of nearby drivers. In this work, we introduce a distributed multi-agent reinforcement learning (MARL) algorithm for joint trajectory and intent prediction for autonomous vehicles in dense and heterogeneous environments. Our approach for intent-aware planning, i PLAN , allows agents to infer nearby drivers’ intents solely from their local observations. We model an explicit representation of agents’ private incentives : Behavioral Incentive for high-level decision-making strategy that sets planning sub-goals and Instant Incentive for low-level motion planning to execute sub-goals. Our approach enables agents to infer their opponents’ behavior incentives and integrate this inferred information into their decision-making and motion-planning processes. We perform experiments on two simulation environments, Non-Cooperative Navigation and Heterogeneous Highway. In Heterogeneous Highway, results show that, compared with centralized training decentralized execution (CTDE) MARL baselines such as QMIX and MAPPO, our method yields a 4 . 3% and 38 . 4% higher episodic reward in mild and chaotic traffic, with 48 . 1% higher success rate and 80 . 6% longer survival time in chaotic traffic. We also compare with a decentralized training de-centralized execution (DTDE) baseline IPPO and demonstrate a higher episodic reward of 12 . 7% and 6 . 3% in mild traffic and chaotic traffic, 25 . 3% higher success rate, and 13 . 7% longer survival time.
Exploring Dynamic Vibrotactile Feedback for Layer-based Interaction on Elastic Displays

Elastic Displays are shape-changing interfaces, typically made from elastic fabric. Surface deformation creates an additional dimension of interaction and enhances the haptic experience for the user compared to rigid touch interfaces. Other forms of shape-changing interfaces such as actuated displays introduce significant mechanical complexity with the benefit of providing active haptic feedback and self-deformation. We investigate ways to make Elastic Displays more ‘active’ while maintaining their simple setup. Our approach adds vibrotactile feedback to the elastic surface, which add explicit haptic feedback when pushing and pulling the surface. In this contribution, we describe the technical setup and introduce concepts for exploring this active haptic feedback. We realized a case study, in which a layered information space is explored. Finally, we report lessons learned from user feedback, showing that solely vibrotactile feedback may have distracting effects on the user, but in combination with visual feedback can improve the user experience.
Using Social Media for E-government Services: A Service-level Perspective of Citizen Adoption

Citizens’ adoption is essential for fully realizing the potential of social media for e-government services. This paper presents a citizen adoption model of government social media services from the perspective of service level and empirically validates it on 1032 samples using a structural equation modeling approach. The results show that the factors affecting the adoption intention of information services include performance expectancy, effort expectancy, forced use perception, and structural assurance; the factors affecting the adoption intention of transactional services are performance expectancy, effort expectancy, forced use perception, and perceived trust; the factors affecting the adoption intention of political services are social influence, forced use perception, and trust in government. Additionally, the three antecedents of perceived trust are trust in government, perceived privacy and security, and structural assurance.
Knowing the Limits – Human-Centered Explanations of Functionality and Limits of AI

Building an appropriate mental model about the functional principles and limitations of technical systems or AI-based applications is crucial, particularly when these systems are applied in domains involving high risk to user safety, like driving. The presented paper describes an upcoming study on applying methods from Explainable AI to facilitate the building of mental models and investigate their effects on user trust. For the interaction with an AI-based system, we use an algorithm designed to support drivers at intersections by predicting turning maneuvers, thus being able to warn a driver of potential cyclists when turning right. Participants will be able to experience the system in a simulated driving environment. We will investigate the effect of receiving comprehensive training about the system's functionality and limitations on mental models, trust, and acceptance.
NOSEwrist: Natural Olfactory Substitution and Extension wrist

In this paper, we present a wearable augmentation device that ultimately aims to substitute and extend olfactory sensation. The device utilizes gas sensors mounted on the wrist, combined with a trained machine learning model, to discriminate liquids such as water, alcohol, fluid accelerant, and vinegar. The device aims to be useful in cases where the sense of taste and smell are compromised, such sometimes occuring during a COVID-19 infection. The paper also discusses potential advancements for this technology to be utilized in a variety of ways, beyond just substituting a broken sense, and how it aligns with the vision of early HCI pioneer Douglas Engelbart, and the concept of Assistive Augmentation. The paper concludes that this technology, in combination with artificial intelligence, has the potential to enrich our physical experience and bring us closer to the idea of a "Cyber-Human" in the future.
Assistance in Virtual Reality Exergames: Preference for Species of Agents in Relation to Personality of Users

Virtual agents (VAs) can be used in a variety of scenarios, e.g. as assistants or in exergame tutorials. One of the advantages of VAs is that they can be customized to the user’s needs. Still, little research is done on preferences for the appearance of agents, especially when user groups are of mixed age. This work explores the effect of the species (abstract, animal, and human) of the agent on several dependent variables: user experience, anthropomorphism, animacy, likeability, and perceived intelligence. Therefore, an experimental study with a within-subject design was conducted. Furthermore, the effect of the user’s personality on the perception was investigated. Main findings were that people that had a high score in the openness to new experiences trait rated embodied agents significantly higher in likeability and animacy than people with a low score. This work highlights the importance of tailoring the appearance of agents to the users.
Advancing EDGE Zones: spatial priorities for the conservation of tetrapod evolutionary history

The biodiversity crisis is set to prune the Tree of Life in a way that threatens billions of years of evolutionary history. To secure this evolutionary heritage along with the benefits it provides to humanity, there is a need to understand where in space the greatest losses are predicted to occur. We therefore present threatened evolutionary history mapped for all tetrapod groups, globally and within Biodiversity Hotspots, and identify priority regions of Evolutionarily Distinct and Globally Endangered (EDGE) species at both a grid cell and national level. We find that threatened evolutionary history peaks in Cameroon, whilst EDGE species richness peaks in Madagascar. We refined and advanced the 2013 EDGE Zone concept for spatially prioritising phylogenetic diversity using a novel complementarity procedure with uncertainty incorporated for 33,628 tetrapod species. This involved using extinction risk, phylogenetic, and spatial data to iteratively select areas with the highest accumulated threatened evolutionary history driven by unique species compositions. We identify 25 priority EDGE Zones, which are insufficiently protected and disproportionately exposed to high levels of human pressure. Together, the 25 EDGE Zones occupy 0.723% of the world’s surface but harbour one-third of the world’s threatened evolutionary history, half of which is endemic to these grid cells. They also contain part of the distribution of 918 EDGE tetrapod species, representing near one-third of all EDGE species, with 480 being endemic. Our tetrapod EDGE Zones highlight areas of immediate concern for researchers, practitioners, policymakers, and communicators looking to safeguard the Tree of Life.
Familiarity modulated synapses model visual cortical circuit novelty responses

Since environments are constantly in flux, the brain’s ability to identify novel stimuli that fall outside its own internal representation of the world is crucial for an organism’s survival. Within the mammalian neocortex, inhibitory microcircuits are proposed to regulate activity in an experience-dependent manner and different inhibitory neuron subtypes exhibit distinct novelty responses. Discerning the function of diverse neural circuits and their modulation by experience can be daunting unless one has a biologically plausible mechanism to detect and learn from novel experiences that is both understandable and flexible. Here we introduce a learning mechanism, familiarity modulated synapses (FMSs), through which a network response that encodes novelty emerges from unsupervised synaptic modifications depending only on the presynaptic or both the pre- and postsynaptic activity. FMSs stand apart from other familiarity mechanisms in their simplicity: they operate under continual learning, do not require specialized architecture, and can distinguish novelty rapidly without requiring feedback. Implementing FMSs within a model of a visual cortical circuit that includes multiple inhibitory populations, we simultaneously reproduce three distinct novelty effects recently observed in experimental data from visual cortical circuits in mice: absolute, contextual, and omission novelty. Additionally, our model results in a set of diverse physiological responses across cell subpopulations, allowing us to analyze how their connectivity and synaptic dynamics influences their distinct behavior, leading to predictions that can be tested in experiment. Altogether, our findings demonstrate how experimentally-constrained cortical circuit structure can give rise to qualitatively distinct novelty responses using simple plasticity mechanisms. The flexibility of FMSs opens the door to computationally and theoretically investigating how distinct synapse modulations can lead to a variety of experience-dependent responses in a simple, understandable, and biologically plausible setup.
Surface mine reclamation for wildlife: a model reclamation plan for southern Appalachia. Final report

A reclamation plan for use on surface coal mines in southern Appalachia is presented. Included are suggestions relative to the establishment of groundcover and trees on the mine site. Also included are suggestions relative to the retention of surface water on mine sites. All techniques mentioned in the plan benefit wildlife and will assist the operator in achieving bond release. This plan has been implemented cooperatively by TVA and the FWS on a mine site in Campbell County, Tennessee. The costs of reclaiming a coal surface mine in Campbell County, Tennessee to benefit wildlife are described. The reclamation plan implemented on the mine site was designed for forestry and wildlife.
A platform for dissecting force sensitivity and multivalency in actin networks

The physical structure and dynamics of cells are supported by micron-scale actin networks with diverse geometries, protein compositions, and mechanical properties. These networks are composed of actin filaments and numerous actin binding proteins (ABPs), many of which engage multiple filaments simultaneously to crosslink them into specific functional architectures. Mechanical force has been shown to modulate the interactions between several ABPs and individual actin filaments, but it is unclear how this phenomenon contributes to the emergent force-responsive functional dynamics of actin networks. Here, we engineer filament linker complexes and combine them with photo-micropatterning of myosin motor proteins to produce an in vitro reconstitution platform for examining how force impacts the behavior of ABPs within multi-filament assemblies. Our system enables the monitoring of dozens of actin networks with varying architectures simultaneously using total internal reflection fluorescence microscopy, facilitating detailed dissection of the interplay between force-modulated ABP binding and network geometry. We apply our system to study a dimeric form of the critical cell-cell adhesion protein α-catenin, a model force-sensitive ABP. We find that myosin forces increase α-catenin’s engagement of small filament bundles embedded within networks. This activity is absent in a force-sensing deficient mutant, whose binding scales linearly with bundle size in both the presence and absence of force. These data are consistent with filaments in smaller bundles bearing greater per-filament loads that enhance α-catenin binding, a mechanism that could equalize α-catenin’s distribution across actin-myosin networks of varying sizes in cells to regularize their stability and composition.