Miracle-dz/UniFAR-FTUs huggingface dataset:
IRelics: Designing a Tangible Interaction Platform for the Popularization of Field Archaeology

We present IRelics, a tangible interaction platform for the popularization of field archeology. IRelics allows users to experience archaeological field work activities as a serious game by using a set of tangible tools. We developed an innovative LWIR (Long Wavelength Infrared Rays) sensing system, which implements the design of tangible tools that provide real manipulation experiences. By interacting with IRelics, a player may experience different archaeological activities such as excavation and cleaning. We conducted two observations to evaluate the usability and effectiveness at archeology popularizing. Findings suggest that the IRelics platform can enhance the engagement of the participants by providing a positive and interactive environment while teaching them unfamiliar knowledge.
A New Lower Bound for Semigroup Orthogonal Range Searching

We report the first improvement in the space-time trade-off of lower bounds for the orthogonal range searching problem in the semigroup model, since Chazelle's result from 1990. This is one of the very fundamental problems in range searching with a long history. Previously, Andrew Yao's influential result had shown that the problem is already non-trivial in one dimension [Yao, 1982]: using m units of space, the query time Q(n) must be Omega(alpha(m,n) + n/(m-n+1)) where alpha(*,*) is the inverse Ackermann's function, a very slowly growing function. In d dimensions, Bernard Chazelle [Chazelle, 1990] proved that the query time must be Q(n) = Omega((log_beta n)^{d-1}) where beta = 2m/n. Chazelle's lower bound is known to be tight for when space consumption is "high" i.e., m = Omega(n log^{d+epsilon}n). We have two main results. The first is a lower bound that shows Chazelle's lower bound was not tight for "low space": we prove that we must have m Q(n) = Omega(n (log n log log n)^{d-1}). Our lower bound does not close the gap to the existing data structures, however, our second result is that our analysis is tight. Thus, we believe the gap is in fact natural since lower bounds are proven for idempotent semigroups while the data structures are built for general semigroups and thus they cannot assume (and use) the properties of an idempotent semigroup. As a result, we believe to close the gap one must study lower bounds for non-idempotent semigroups or building data structures for idempotent semigroups. We develope significantly new ideas for both of our results that could be useful in pursuing either of these directions.
A Method for Reducing the Complexity of Model Predictive Control in Robotics Applications

This letter describes an approach for parametrizing input and state trajectories in model predictive control. The parametrization is designed to be invariant to time shifts, which enables warm-starting the successive optimization problems and reduces the computational complexity of the online optimization. It is shown that in certain cases (e.g., for linear time-invariant dynamics with input and state constraints) the parametrization leads to inherent stability and the recursive feasibility guarantees without additional terminal set constraints. Due to the fact that the number of decision variables are greatly reduced through the parametrization, the warm-starting capabilities are preserved, and the approach is suitable for applications where the available computational resources (memory and CPU-power) are limited.
Offensive Language Analysis using Deep Learning Architecture

SemEval-2019 Task 6 (Zampieri et al., 2019b) requires us to identify and categorise offensive language in social media. In this paper we will describe the process we took to tackle this challenge. Our process is heavily inspired by Sosa (2017) where he proposed CNN-LSTM and LSTM-CNN models to conduct twitter sentiment analysis. We decided to follow his approach as well as further his work by testing out different variations of RNN models with CNN. Specifically, we have divided the challenge into two parts: data processing and sampling and choosing the optimal deep learning architecture. In preprocessing, we experimented with two techniques, SMOTE and Class Weights to counter the imbalance between classes. Once we are happy with the quality of our input data, we proceed to choosing the optimal deep learning architecture for this task. Given the quality and quantity of data we have been given, we found that the addition of CNN layer provides very little to no additional improvement to our model's performance and sometimes even lead to a decrease in our F1-score. In the end, the deep learning architecture that gives us the highest macro F1-score is a simple BiLSTM-CNN.
A New Frontier for Pull-Based Graph Processing

The trade-off between pull-based and push-based graph processing engines is well-understood. On one hand, pull-based engines can achieve higher throughput because their workloads are read-dominant, rather than write-dominant, and can proceed without synchronization between threads. On the other hand, push-based engines are much better able to take advantage of the frontier optimization, which leverages the fact that often only a small subset of the graph needs to be accessed to complete an iteration of a graph processing application. Hybrid engines attempt to overcome this trade-off by dynamically switching between push and pull, but there are two key disadvantages with this approach. First, applications must be implemented twice (once for push and once for pull), and second, processing throughput is reduced for iterations that run with push. We propose a radically different solution: rebuild the frontier optimization entirely such that it is well-suited for a pull-based engine. In doing so, we remove the only advantage that a push-based engine had over a pull-based engine, making it possible to eliminate the push-based engine entirely. We introduce Wedge, a pull-only graph processing framework that transforms the traditional source-oriented vertex-based frontier into a pull-friendly format called the Wedge Frontier. The transformation itself is expensive even when parallelized, so we introduce two key optimizations to make it practical. First, we perform the transformation only when the resulting Wedge Frontier is sufficiently sparse. Second, we coarsen the granularity of the representation of elements in the Wedge Frontier. These optimizations respectively improve Wedge's performance by up to 5x and 2x, enabling it to outperform Grazelle, Ligra, and GraphMat respectively by up to 2.8x, 4.9x, and 185.5x.
Identity-Free Facial Expression Recognition Using Conditional Generative Adversarial Network

A novel Identity-Free conditional Generative Adversarial Network (IF-GAN) was proposed for Facial Expression Recognition (FER) to explicitly reduce high inter-subject variations caused by identity-related facial attributes, e.g., age, race, and gender. As part of an end-to-end system, a cGAN was designed to transform a given input facial expression to an “average” identity face with the same expression as the input. Then, identity-free FER is possible since the generated images have the same synthetic “average” identity and differ only in their displayed expressions. Experiments on four facial expression datasets, one with spontaneous expressions, show that IF-GAN outperforms the baseline CNN and achieves state-of-the-art performance for FER.
Fisher Discriminative Least Square Regression with Self-Adaptive Weighting for Face Recognition

As a supervised classification method, least square regression (LSR) has shown promising performance in multiclass face recognition tasks. However, the latest LSR based classification methods mainly focus on learning a relaxed regression target to replace traditional zero-one label matrix while ignoring the discriminability of transformed features. Based on the assumption that the transformed features of samples from the same class have similar structure while those of samples from different classes are uncorrelated, in this paper we propose a novel discriminative LSR method based on the Fisher discrimination criterion (FDLSR), where the projected features have small within-class scatter and large inter-class scatter simultaneously. Moreover, different from other methods, we explore relax regression from the view of transformed features rather than the regression targets. Specifically, we impose a dynamic non-negative weight matrix on the transformed features to enlarge the margin between the true and the false classes by self-adaptively assigning appropriate weights to different features. Above two factors can encourage the learned transformation for regression to be more discriminative and thus achieving better classification performance. Extensive experiments on various databases demonstrate that the proposed FDLSR method achieves superior performance to other state-of-the-art LSR based classification methods.
Tangible Signals - Physical Representation of Sound and Haptic Control Feedback

This paper describes the Ph.D. research project "Tangible Signals" which is currently in its initial phase. This project investigates the dynamic physical representation and haptic feedback control of computer music and sound data using motorized and augmented objects. The research focuses on artistic and performative contributions that this approach offers. Special attention is given to the collaboration with visually impaired people, as they are very limited in the use of exclusively GUI-based interaction. On the following pages the background of the project will be described and methods, research questions and work progress will be presented.
Exploring the Potential for Tangible Social Technologies for Childhood Cancer Patients within the Hospital

Through a multi-phased mixed method study with childhood cancer patients (8-12 years old) and their team of caregivers within US and Canadian hospitals we will explore (1) the ways the cancer experience impacts patient's social/emotional well-being, (2) how existing technologies fail to provide feelings of connectedness to friends/peers, and (3) how novel tangible technology could improve connectedness. We aim to (1) empower children with cancer by allowing them to voice their own experiences with isolation, loneliness, and loss of a normal childhood, as well as how technology may better support their needs, (2) contribute design knowledge about how to support meaningful social interaction and play that is age and 'ability' appropriate, and (3) provide insight for future design and evaluation studies by better understanding constraints/opportunities for social tangible technologies intended for use in a real world pediatric hospitals.
Exact Gaussian Processes on a Million Data Points

Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data. However, computational constraints with standard inference procedures have limited exact GPs to problems with fewer than about ten thousand training points, necessitating approximations for larger datasets. In this paper, we develop a scalable approach for exact GPs that leverages multi-GPU parallelization and methods like linear conjugate gradients, accessing the kernel matrix only through matrix multiplication. By partitioning and distributing kernel matrix multiplies, we demonstrate that an exact GP can be trained on over a million points, a task previously thought to be impossible with current computing hardware, in less than 2 hours. Moreover, our approach is generally applicable, without constraints to grid data or specific kernel classes. Enabled by this scalability, we perform the first-ever comparison of exact GPs against scalable GP approximations on datasets with $10^4 \! -\! 10^6$ data points, showing dramatic performance improvements.
WindyWall: Exploring Creative Wind Simulations

Wind simulations are typically one-off implementations for specific applications. We introduce WindyWall, a platform for creative design and exploration of wind simulations. WindyWall is a three-panel 90-fan array that encapsulates users with 270? of wind coverage. We describe the design and implementation of the array panels, discussing how the panels can be re-arranged, where various wind simulations can be realized as simple effects. To understand how people perceive "wind" generated from WindyWall, we conducted a pilot study of wind magnitude perception using different wind activation patterns from WindyWall. Our findings suggest that: horizontal wind activations are perceived more readily than vertical ones, and that people's perceptions of wind are highly variable-most individuals will rate airflow differently in subsequent exposures. Based on our findings, we discuss the importance of developing a method for characterizing wind simulations, and provide design directions for others using fan arrays to simulate wind.
ONEDAY Shoes: A Maker Toolkit to Understand the Role of Co-Manufacturing in Personalization

Personalization of shoes is of increasing importance to designers, design researchers, and manufacturers as mass customization progresses towards ultra personalized product service systems. Many attempts have been made to design co-creation platforms that allow end users to personalize their own shoes. Those co-creation platforms primarily concentrate on color selection. This research takes a different approach and designs a toolkit for maker-oriented users to co-manufacture their own shoes. The toolkit was designed in different levels and deployed to makers via crowdsharing worldwide. Backers were surveyed before deployment and interviewed after two years to understand personalization over a larger amount of time with the research product. We find that users who have greater bespoke tools and materials in their toolkits are more likely to personalize their shoes while co-manufacturing. The research provides guidelines for researchers and designers creating toolkits, designing personalization product service systems/configurators and engaging in tangible bespoke processes.
Hooze: A Kinetic Fashion Accessory for Touch and Play

Recent innovations in fashion and smart textiles have contributed new visions of wearable computing, addressing the body through the cultural and social self. In this work, we draw on speculative design, maker technologies, and zoomorphism to explore how wearables might support sociability, and present Hooze, a fashion accessory that entices touch through its zoomorphic qualities and visual appearance. We describe our design and prototyping process, and reflect on how Hooze inspires transformative designs of wearables.
FormFab: Continuous Interactive Fabrication

Several systems have illustrated the concept of interactive fabrication, i.e. rather than working through a digital editor, users make edits directly on the physical workpiece. However, so far the interaction has been limited to turn-taking, i.e., users first perform a command and then the system responds with physical feedback. In this paper, we present a first step towards interactive fabrication that changes the workpiece continuously while the user is manipulating it. To achieve this, our system FormFab does not add or subtract material but instead reshapes it (formative fabrication). A heat gun attached to a robotic arm warms up a thermoplastic sheet until it becomes compliant; users then control a pneumatic system that applies either pressure or vacuum thereby pushing the material outwards or pulling it inwards. Since FormFab reshapes the workpiece continuously while users are moving their hands, users can interactively explore different sizes of a shape with a single interaction.
compare-mt: A Tool for Holistic Comparison of Language Generation Systems

In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation. The main goal of the tool is to give the user a high-level and coherent view of the salient differences between systems that can then be used to guide further analysis or system improvement. It implements a number of tools to do so, such as analysis of accuracy of generation of particular types of words, bucketed histograms of sentence accuracies or counts based on salient characteristics, and extraction of characteristic n-grams for each system. It also has a number of advanced features such as use of linguistic labels, source side data, or comparison of log likelihoods for probabilistic models, and also aims to be easily extensible by users to new types of analysis. compare-mt is a pure-Python open source package, that has already proven useful to generate analyses that have been used in our published papers. Demo Video: https://youtu.be/NyJEQT7t2CA
Corners for Layout: End-to-End Layout Recovery From 360 Images

The problem of 3D layout recovery in indoor scenes has been a core research topic for over a decade. However, there are still several major challenges that remain unsolved. Among the most relevant ones, a major part of the state-of-the-art methods make implicit or explicit assumptions on the scenes –e.g. box-shaped or Manhattan layouts. Also, current methods are computationally expensive and not suitable for real-time applications like robot navigation and AR/VR. In this work we present CFL (Corners for Layout), the first end-to-end model that predicts layout corners for 3D layout recovery on $\mathbf {{360}^\circ }$ images. Our experimental results show that we outperform the state of the art, making less assumptions on the scene than other works, and with lower cost. We also show that our model generalizes better to camera position variations than conventional approaches by using EquiConvs, a convolution applied directly on the spherical projection and hence invariant to the equirectangular distortions.
Taxonomy and Evaluation for Microblog Popularity Prediction

As social networks become a major source of information, predicting the outcome of information diffusion has appeared intriguing to both researchers and practitioners. By organizing and categorizing the joint efforts of numerous studies on popularity prediction, this article presents a hierarchical taxonomy and helps to establish a systematic overview of popularity prediction methods for microblog. Specifically, we uncover three lines of thoughts: the feature-based approach, time-series modelling, and the collaborative filtering approach and analyse them, respectively. Furthermore, we also categorize prediction methods based on their underlying rationale: whether they attempt to model the motivation of users or monitor the early responses. Finally, we put these prediction methods to test by performing experiments on real-life data collected from popular social networks Twitter and Weibo. We compare the methods in terms of accuracy, efficiency, timeliness, robustness, and bias. As far as we are concerned, there is no precedented survey aimed at microblog popularity prediction at the time of submission. By establishing a taxonomy and evaluation for the first time, we hope to provide an in-depth review of state-of-the-art prediction methods and point out directions for further research. Our evaluations show that time-series modelling has the advantage of high accuracy and the ability to improve over time. The feature-based methods using only temporal features performs nearly as well as using all possible features, producing average results. This suggests that temporal features do have strong predictive power and that power is better exploited with time-series models. On the other hand, this implies that we know little about the future popularity of an item before it is posted, which may be the focus of further research.
Pain-Induced Pessimism and Anhedonia: Evidence From a Novel Probability-Based Judgment Bias Test

Judgment bias tests (JBTs) use responses to ambiguous stimuli to infer emotional states in animals. However, with repeated testing, animals can learn to recognize the previously ambiguous stimuli rendering the test less effective. We describe a novel approach to this problem. Calves (n = 9) were trained in a spatial discrimination task to associate five locations with a specific probability of reward/punishment (Positive: 100%/0%; Near-Positive: 75%/25%; Middle: 50%/50%; Near-Negative: 25%/75%; Negative: 0%/100%). As predicted, calves showed increased latencies to touch locations that had higher probabilities of punishment and lower probabilities of reward. To validate our methodology for detecting mood changes, we followed calves in the hours after routine hot-iron disbudding, a time when animals were likely experiencing post-operative inflammatory pain. At 6 h after disbudding, when inflammatory pain was likely to peak, calves expressed increased approach latencies to the Positive, Near-Positive and Middle locations. These results suggest that calves perceived the value of the reward as being lower (i.e., anhedonia) or had lower expectations of positive outcomes (i.e., pessimism). When re-tested at 22 and 70 h after disbudding, we found no evidence of pessimism or anhedonia (i.e., latencies had returned to baseline). We conclude that our probability-based judgment bias task can detect pain-induced mood changes.
Common noctules exploit low levels of the aerosphere

Aerial habitats present a challenge to find food across a large potential search volume, particularly for insectivorous bats that rely on echolocation calls with limited detection range and may forage at heights over 1000 m. To understand how bats use vertical space, we tracked one to five foraging flights of eight common noctules (Nyctalus noctula). Bats were tracked for their full foraging session (87.27 ± 24 min) using high-resolution atmospheric pressure radio transmitters that allowed us to calculate height and wingbeat frequency. Bats used diverse flight strategies, but generally flew lower than 40 m, with scouting flights to 100 m and a maximum of 300 m. We found no influence of weather on height, and high-altitude ascents were not preceded by an increase in foraging effort. Wingbeat frequency was independent from climbing or descending flight, and bats skipped wingbeats or glided in 10% of all observations. Wingbeat frequency was positively related to capture mass, and wingbeat frequency was positively related to time of night, indicating an effect of load increase over a foraging bout. Overall, individuals used a wide range of airspace including altitudes that put them at increased risk from human-made structures. Further work is needed to test the context of these flight decisions, particularly as individuals migrate throughout Europe.