Miracle-dz/UniFAR-FTUs huggingface dataset:
Do explanations increase the effectiveness of AI-crowd generated fake news warnings?

Social media platforms are increasingly deploying complex interventions to help users detect false news. Labeling false news using techniques that combine crowd-sourcing with artificial intelligence (AI) offers a promising way to inform users about potentially low-quality information without censoring content, but also can be hard for users to understand. In this study, we examine how users respond in their sharing intentions to information they are provided about a hypothetical human-AI hybrid system. We ask i) if these warnings increase discernment in social media sharing intentions and ii) if explaining how the labeling system works can boost the effectiveness of the warnings. To do so, we conduct a study (N=1473 Americans) in which participants indicated their likelihood of sharing content. Participants were randomly assigned to a control, a treatment where false content was labeled, or a treatment where the warning labels came with an explanation of how they were generated. We find clear evidence that both treatments increase sharing discernment, and directional evidence that explanations increase the warnings' effectiveness. Interestingly, we do not find that the explanations increase self-reported trust in the warning labels, although we do find some evidence that participants found the warnings with the explanations to be more informative. Together, these results have important implications for designing and deploying transparent misinformation warning labels, and AI-mediated systems more broadly.
From Assistants to Friends: Investigating Emotional Intelligence of IPAs in Hindi and English

Intelligent Personal Assistants (IPAs) like Amazon Alexa, Apple Siri, and Google Assistant are increasingly becoming a part of our everyday. As IPAs become ubiquitous and their applications expand, users turn to them for not just routine tasks, but also intelligent conversations. In this study, we measure the emotional intelligence (EI) displayed by IPAs in the English and Hindi languages; to our knowledge, this is a pioneering effort in probing the emotional intelligence of IPAs in Indian languages. We pose utterances that convey the Sadness or Humor emotion and evaluate IPA responses. We build on previous research to propose a quantitative and qualitative evaluation scheme encompassing new criteria from social science perspectives (display of empathy, wit, understanding) and IPA-specific features (voice modulation, search redirects). We find EI displayed by Google Assistant in Hindi is comparable to EI displayed in English, with the assistant employing both voice modulation and emojis in text. However, we do find that IPAs are unable to understand and respond intelligently to all queries, sometimes even offering counter-productive and problematic responses. Our experiment offers evidence and directions to augment the potential for EI in IPAs.
Using Image Transformations to Learn Network Structure

Many learning tasks require observing a sequence of images and making a decision. In a transportation problem of designing and planning for shipping boxes between nodes, we show how to treat the network of nodes and the flows between them as images. These images have useful structural information that can be statistically summarized. Using image compression techniques, we reduce an image down to a set of numbers that contain interpretable geographic information that we call geographic signatures. Using geographic signatures, we learn network structure that can be utilized to recommend future network connectivity. We develop a Bayesian reinforcement algorithm that takes advantage of statistically summarized network information as priors and user-decisions to reinforce an agent's probabilistic decision. Additionally, we show how reinforcement learning can be used with compression directly without interpretation in simple tasks.
Automation Of Transiting Exoplanet Detection, Identification and Habitability Assessment Using Machine Learning Approaches

We are at a unique timeline in the history of human evolution where we may be able to discover earth-like planets around stars outside our solar system where conditions can support life or even find evidence of life on those planets. With the launch of several satellites in recent years by NASA, ESA, and other major space agencies, an ample amount of datasets are at our disposal which can be utilized to train machine learning models that can automate the arduous tasks of exoplanet detection, its identification, and habitability determination. Automating these tasks can save a considerable amount of time and minimize human errors due to manual intervention. To achieve this aim, we conducted our study in three phases Transiting Exoplanet Detection, Identification/Triage, and Habitability Assessment. The first phase analyses the light intensity curves from stars captured by the Kepler telescope to detect the potential curves that exhibit the characteristics of an existence of a possible planetary system. For this detection, along with training conventional models, we propose a stacked GBDT model that can be trained on multiple representations of light signal (Time Domain, Frequency Domain, and Power Spectral Density) simultaneously. The next two successive phases address the automation of identification and habitability determination by leveraging several state-of-the-art machine learning models and ensemble approaches. The identification of exoplanets aims to distinguish false positive instances from the actual instances of exoplanets whereas the habitability assessment groups the exoplanet instances into different clusters based on their habitable characteristics. Additionally, in the final phase, we propose a new metric called Adequate Thermal Adequacy (ATA) score to establish a potential linear relationship between habitable and non-habitable instances. On top of classification tasks, regression analysis is also performed for the computation of Keplar Object of Intrest (KOI) and Earth Similarity Index (ESI) scores in phase 2 and phase 3 respectively. Experimental results suggest that the proposed stacked GBDT model outperformed the conventional models in detecting transiting exoplanets. Furthermore, the incorporation of ATA scores in habitability classification enhanced the performance of models.
Approximating Nash Equilibrium in Random Graphical Games

Computing Nash equilibrium in multi-agent games is a longstanding challenge at the interface of game theory and computer science. It is well known that a general normal form game in N players and k strategies requires exponential space simply to write down. This Curse of Multi-Agents prompts the study of succinct games which can be written down efficiently. A canonical example of a succinct game is the graphical game which models players as nodes in a graph interacting with only their neighbors in direct analogy with markov random fields. Graphical games have found applications in wireless, financial, and social networks. However, computing the nash equilbrium of graphical games has proven challenging. Even for polymatrix games, a model where payoffs to an agent can be written as the sum of payoffs of interactions with the agent's neighbors, it has been shown that computing an epsilon approximate nash equilibrium is PPAD hard for epsilon smaller than a constant. The focus of this work is to circumvent this computational hardness by considering average case graph models i.e random graphs. We provide a quasipolynomial time approximation scheme (QPTAS) for computing an epsilon approximate nash equilibrium of polymatrix games on random graphs with edge density greater than poly(k, 1/epsilon, ln(N))$ with high probability. Furthermore, with the same runtime we can compute an epsilon-approximate Nash equilibrium that epsilon-approximates the maximum social welfare of any nash equilibrium of the game. Our primary technical innovation is an"accelerated rounding"of a novel hierarchical convex program for the nash equilibrium problem. Our accelerated rounding also yields faster algorithms for Max-2CSP on the same family of random graphs, which may be of independent interest.
Signatures of the Many Supermassive Black Hole Mergers in a Cosmologically Forming Massive Early-type Galaxy

We model here the merger histories of the supermassive black hole (SMBH) population in the late stages of a cosmological simulation of a ∼ 2 × 1013 M ⊙ galaxy group. The gravitational dynamics around the several tens of SMBHs (M • > 7.5 × 107 M ⊙) hosted by the galaxies in the group is computed at high accuracy using regularized integration with the KETJU code. The 11 SMBHs that form binaries and a hierarchical triplet eventually merge after hardening through dynamical friction, stellar scattering, and gravitational wave (GW) emission. The binaries form at eccentricities of e ∼ 0.3–0.9, with one system evolving to a very high eccentricity of e = 0.998, and merge on timescales of a few tens to several hundred megayears. During the simulation, the merger-induced GW recoil kicks eject one SMBH remnant from the central host galaxy. This temporarily drives the galaxy off the M •–σ ⋆ relation; however, the galaxy returns to the relation due to subsequent galaxy mergers, which bring in new SMBHs. This showcases a possible mechanism contributing to the observed scatter of the M •–σ ⋆ relation. Finally, we show that pulsar timing arrays and LISA would be able to detect parts of the GW signals from the SMBH mergers that occur during the ∼4 Gyr time span simulated with KETJU.
Natural Answer Generation: From Factoid Answer to Full-length Answer using Grammar Correction

Question Answering systems these days typically use template-based language generation. Though adequate for a domain-specific task, these systems are too restrictive and predefined for domain-independent systems. This paper proposes a system that outputs a full-length answer given a question and the extracted factoid answer (short spans such as named entities) as the input. Our system uses constituency and dependency parse trees of questions. A transformer-based Grammar Error Correction model GECToR (2020), is used as a post-processing step for better fluency. We compare our system with (i) Modified Pointer Generator (SOTA) and (ii) Fine-tuned DialoGPT for factoid questions. We also test our approach on existential (yes-no) questions with better results. Our model generates accurate and fluent answers than the state-of-the-art (SOTA) approaches. The evaluation is done on NewsQA and SqUAD datasets with an increment of 0.4 and 0.9 percentage points in ROUGE-1 score respectively. Also the inference time is reduced by 85\% as compared to the SOTA. The improved datasets used for our evaluation will be released as part of the research contribution.
The Partially Observable Asynchronous Multi-Agent Cooperation Challenge

Multi-agent reinforcement learning (MARL) has received increasing attention for its applications in various domains. Researchers have paid much attention on its partially observable and cooperative settings for meeting real-world requirements. For testing performance of different algorithms, standardized environments are designed such as the StarCraft Multi-Agent Challenge, which is one of the most successful MARL benchmarks. To our best knowledge, most of current environments are synchronous, where agents execute actions in the same pace. However, heterogeneous agents usually have their own action spaces and there is no guarantee for actions from different agents to have the same executed cycle, which leads to asynchronous multi-agent cooperation. Inspired from the Wargame, a confrontation game between two armies abstracted from real world environment, we propose the first Partially Observable Asynchronous multi-agent Cooperation challenge (POAC) for the MARL community. Specifically, POAC supports two teams of heterogeneous agents to fight with each other, where an agent selects actions based on its own observations and cooperates asynchronously with its allies. Moreover, POAC is a light weight, flexible and easy to use environment, which can be configured by users to meet different experimental requirements such as self-play model, human-AI model and so on. Along with our benchmark, we offer six game scenarios of varying difficulties with the built-in rule-based AI as opponents. Finally, since most MARL algorithms are designed for synchronous agents, we revise several representatives to meet the asynchronous setting, and the relatively poor experimental results validate the challenge of POAC. Source code is released in \url{http://turingai.ia. ac.cn/data\_center/show}.
Gravitational-wave geodesy: Defining false alarm probabilities with respect to correlated noise

Kamiel Janssens , 2 Thomas A. Callister , Nelson Christensen , Michael W. Coughlin , Ioannis Michaloliakos , Jishnu Suresh , 7 and Nick van Remortel 1 Universiteit Antwerpen, Prinsstraat 13, 2000 Antwerpen, Belgium Artemis, Université Côte d’Azur, Observatoire Côte d’Azur, Nice, France Center for Computational Astrophysics, Flatiron Institute, New York, NY 10010, USA School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, USA University of Florida, Gainesville, Florida 32611, USA Institute for Cosmic Ray Research, The University of Tokyo, Kashiwa City, Chiba 277-8582, Japan Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium (Dated: December 9, 2021)
Early Warnings of Binary Neutron Star Coalescence Using the SPIIR Search

Gravitational waves from binary neutron star mergers can be used as alerts to enable prompt follow-up observations. In particular, capturing prompt electromagnetic and astroparticle emissions from the moment of a binary merger presents unique constraints on the timescale and sky localization for online gravitational-wave detection. Here we present the expected performance of the SPIIR online detection pipeline that is designed for this purpose in the upcoming international LIGO–Virgo’s 4th Science Run (O4). Using simulated Gaussian data for the two LIGO observatories with expected O4 sensitivity, we demonstrate that there is a nonnegligible opportunity to deliver premerger warnings at least 10 s before the final plunge. These alerts are expected to be issued at a nominal rate of one binary neutron star coalescence per year and localized within a median searched area of 300 deg2. We envision such detection to be extremely useful for follow-up observatories with a large field of view such as the Murchison Widefield Array radio facility in Western Australia.
Fractional anisotropic Calder\'on problem on closed Riemannian manifolds

In this paper we solve the fractional anisotropic Calder\'on problem on closed Riemannian manifolds of dimensions two and higher. Specifically, we prove that the knowledge of the local source-to-solution map for the fractional Laplacian, given on an arbitrary small open nonempty a priori known subset of a smooth closed connected Riemannian manifold, determines the Riemannian manifold up to an isometry. This can be viewed as a nonlocal analog of the anisotropic Calder\'on problem in the setting of closed Riemannian manifolds, which is wide open in dimensions three and higher.
Does Proprietary Software Still Offer Protection of Intellectual Property in the Age of Machine Learning? - A Case Study using Dual Energy CT Data

In the domain of medical image processing, medical device manufacturers protect their intellectual property in many cases by shipping only compiled software, i.e. binary code which can be executed but is difficult to be understood by a potential attacker. In this paper, we investigate how well this procedure is able to protect image processing algorithms. In particular, we investigate whether the computation of mono-energetic images and iodine maps from dual energy CT data can be reverse-engineered by machine learning methods. Our results indicate that both can be approximated using only one single slice image as training data at a very high accuracy with structural similarity greater than 0.98 in all investigated cases.
Ground-Truth, Whose Truth? - Examining the Challenges with Annotating Toxic Text Datasets

The use of machine learning (ML)-based language models (LMs) to monitor content online is on the rise. For toxic text identification, task-specific fine-tuning of these models are performed using datasets labeled by annotators who provide ground-truth labels in an effort to distinguish between offensive and normal content. These projects have led to the development, improvement, and expansion of large datasets over time, and have contributed immensely to research on natural language. Despite the achievements, existing evidence suggests that ML models built on these datasets do not always result in desirable outcomes. Therefore, using a design science research (DSR) approach, this study examines selected toxic text datasets with the goal of shedding light on some of the inherent issues and contributing to discussions on navigating these challenges for existing and future projects. To achieve the goal of the study, we re-annotate samples from three toxic text datasets and find that a multi-label approach to annotating toxic text samples can help to improve dataset quality. While this approach may not improve the traditional metric of inter-annotator agreement, it may better capture dependence on context and diversity in annotators. We discuss the implications of these results for both theory and practice.
Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds

When analyzing parametric statistical models, a useful approach consists in modeling geometrically the parameter space. However, even for very simple and commonly used hierarchical models like statistical mixtures or stochastic deep neural networks, the smoothness assumption of manifolds is violated at singular points which exhibit non-smooth neighborhoods in the parameter space. These singular models have been analyzed in the context of learning dynamics, where singularities can act as attractors on the learning trajectory and, therefore, negatively influence the convergence speed of models. We propose a general approach to circumvent the problem arising from singularities by using stratifolds, a concept from algebraic topology, to formally model singular parameter spaces. We use the property that specific stratifolds are equipped with a resolution method to construct a smooth manifold approximation of the singular space. We empirically show that using (natural) gradient descent on the smooth manifold approximation instead of the singular space allows us to avoid the attractor behavior and therefore improve the convergence speed in learning.
Improving customer satisfaction through personalization

From the owner of the grocery shop around the corner to the search results of Google: personalization is found in all facets of society. In this thesis, personalization is applied on the online services of the Dutch Railways (NS), in order to improve the customer satisfaction regarding those services. More specifically, personalization is used to reduce customer effort, which is an indicator of customer satisfaction. Using a literature study and real-world case studies, background information about personalization is retrieved and summarized. A design science approach is used to redesign two online services, in order to reduce customer effort. The redesign is demonstrated and validated to estimate its effects on customer effort, and analyzed to determine its impact on the IT landscape of NS. Finally, findings from both literature and the redesign analysis are synthesized to define generic impact factors of personalization on the IT of a company.
Qualitative Analysis for Human Centered AI

Human-centered artificial intelligence (AI) posits that machine learning and AI should be developed and applied in a socially aware way. In this article, we argue that qualitative analysis (QA) can be a valuable tool in this process, supplementing, informing, and extending the possibilities of AI models. We show this by describing how QA can be integrated in the current prediction paradigm of AI, assisting scientists in the process of selecting data, variables, and model architectures. Furthermore, we argue that QA can be a part of novel paradigms towards Human Centered AI. QA can support scientists and practitioners in practical problem solving and situated model development. It can also promote participatory design approaches, reveal understudied and emerging issues in AI systems, and assist policy making.
An advanced thorax-shoulder design for the THOR dummy

Thoracic injuries are one of the main causes of fatally and severely injured casualties in car crashes. Advances in restraint system technology and airbags may be needed to address this problem; however, the crash test dummies available today for studying these injuries have limitations that prevent them from being able to demonstrate the benefits of such innovations. THORAX-FP7 was a collaborative medium scale project under the European Seventh Framework. It focused on the mitigation and prevention of thoracic injuries through an improved understanding of the thoracic injury mechanisms and the implementation of this understanding in an updated design for the thorax-shoulder complex of the THOR dummy. The updated dummy should enable the design and evaluation of advanced restraint systems for a wide variety (gender, age and size) of car occupants. The hardware development involved five steps: 1) Identification of the dominant thoracic injury types from field data, 2) Specification of biomechanical requirements, 3) Identification of injury parameters and necessary instrumentation, 4) Dummy hardware development and 5) Evaluation of the demonstrator dummy. The activities resulted in the definition of new biofidelity and instrumentation requirements for an updated thorax-shoulder complex. Prototype versions were realised and implemented in three THOR dummies for biomechanical evaluation testing. This paper documents the hardware developments and biomechanical evaluation testing carried out.
Virtual Replay Cache

Return caching is a recent strategy that enables efficient minibatch training with multistep estimators (e.g. the {\lambda}-return) for deep reinforcement learning. By precomputing return estimates in sequential batches and then storing the results in an auxiliary data structure for later sampling, the average computation spent per estimate can be greatly reduced. Still, the efficiency of return caching could be improved, particularly with regard to its large memory usage and repetitive data copies. We propose a new data structure, the Virtual Replay Cache (VRC), to address these shortcomings. When learning to play Atari 2600 games, the VRC nearly eliminates DQN({\lambda})'s cache memory footprint and slightly reduces the total training time on our hardware.