OpenRaiser/Intern-Atlas huggingface dataset:
Prior-informed Uncertainty Modelling with Bayesian Polynomial Approximations
Venue: arXiv cs.CE 2022
Year: 2022

Orthogonal polynomial approximations form the foundation to a set of well-established methods for uncertainty quantification known as polynomial chaos. These approximations deliver models for emulating physical systems in a variety of computational engineering applications. In this paper, we describe a Bayesian formulation of polynomial approximations capable of incorporating uncertainties in input data. Through different priors in a hierarchical structure, this permits us to incorporate expert knowledge on the inference task via different approaches. These include beliefs of sparsity in the model; approximate knowledge of the polynomial coefficients (e.g. through low-fidelity estimates) or output mean, and correlated models that share similar functional and/or physical behaviours. We show that through a Bayesian framework, such prior knowledge can be leveraged to produce orthogonal polynomial approximations with enhanced predictive accuracy.
A Large Force Haptic Interface with Modular Linear Actuators
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents a haptic interface with modular linear actuators which can address limitations of conventional devices based on rotatory joints. The proposed haptic interface is composed of parallel linear actuators that provide high backdrivability and small inertia. The performance of the haptic interface is compared with the conventional mechanisms in terms of force capability, reflected inertia, and structural stiffness. High stiffness, large range of motion with high force capability are achieved with the proposed mechanism, which are in trade-off relationships in traditional haptic interfaces. The device can apply up to 83 N continuously, which is three times larger than most haptic devices. The theoretical minimum haptic force density and the stiffness of the proposed mechanism were 1.3 to 1.9 times and 37 times of conventional mechanisms in a similar condition, respectively. The system is also scalable because its structural stiffness only depends on the timing belt stiffness, while that of conventional haptic interfaces is inversely proportional to the cube of structural lengths. The modular actuator design enables change of degrees freedom (DOFs) for different applications. The proposed haptic interface was tested by the interaction experiment with a virtual environment with rigid walls.
Fairness for Text Classification Tasks with Identity Information Data Augmentation Methods
Venue: arXiv cs.CL 2022
Year: 2022

Counterfactual fairness methods address the question: How would the prediction change if the sensitive identity attributes referenced in the text instance were different? These methods are entirely based on generating counterfactuals for the given training and test set instances. Counterfactual instances are commonly prepared by replacing sensitive identity terms, i.e., the identity terms present in the instance are replaced with other identity terms that fall under the same sensitive category. Therefore, the efficacy of these methods depends heavily on the quality and comprehensiveness of identity pairs. In this paper, we offer a two-step data augmentation process where (1) the former stage consists of a novel method for preparing a comprehensive list of identity pairs with word embeddings, and (2) the latter consists of leveraging prepared identity pairs list to enhance the training instances by applying three simple operations (namely identity pair replacement, identity term blindness, and identity pair swap). We empirically show that the two-stage augmentation process leads to diverse identity pairs and an enhanced training set, with an improved counterfactual token-based fairness metric score on two well-known text classification tasks.
LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition
Venue: arXiv cs.CL 2022
Year: 2022

Processing complex and ambiguous named entities is a challenging research problem, but it has not received sufficient attention from the natural language processing community. In this short paper, we present our participation in the English track of SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition. Inspired by the recent advances in pretrained Transformer language models, we propose a simple yet effective Transformer-based baseline for the task. Despite its simplicity, our proposed approach shows competitive results in the leaderboard as we ranked 12 over 30 teams. Our system achieved a macro F1 score of 72.50% on the held-out test set. We have also explored a data augmentation approach using entity linking. While the approach does not improve the final performance, we also discuss it in this paper.
On realizing differential-algebraic equations by rational dynamical systems
Venue: arXiv cs.SC 2022
Year: 2022

Real-world phenomena can often be conveniently described by dynamical systems (that is, ODE systems in the state-space form). However, if one observes the state of the system only partially, the observed quantities (outputs) and the inputs of the system can typically be related by more complicated differential-algebraic equations (DAEs). Therefore, a natural question (referred to as the realizability problem) is: given a differential-algebraic equation (say, fitted from data), does it come from a partially observed dynamical system? A special case in which the functions involved in the dynamical system are rational is of particular interest. For a single differential-algebraic equation in a single output variable, Forsman has shown that it is realizable by a rational dynamical system if and only if the corresponding hypersurface is unirational, and he turned this into an algorithm in the first-order case. In this paper, we study a more general case of single-input-single-output equations. We show that if a realization by a rational dynamical system exists, the system can be taken to have the dimension equal to the order of the DAE. We provide a complete algorithm for first-order DAEs. We also show that the same approach can be used for higher-order DAEs using several examples from the literature.
OCR quality affects perceived usefulness of historical newspaper clippings -- a user study
Venue: arXiv cs.IR 2022
Year: 2022

Effects of Optical Character Recognition (OCR) quality on historical information retrieval have so far been studied in data-oriented scenarios regarding the effectiveness of retrieval results. Such studies have either focused on the effects of artificially degraded OCR quality (see, e.g., [1-2]) or utilized test collections containing texts based on authentic low quality OCR data (see, e.g., [3]). In this paper the effects of OCR quality are studied in a user-oriented information retrieval setting. Thirty-two users evaluated subjectively query results of six topics each (out of 30 topics) based on pre-formulated queries using a simulated work task setting. To the best of our knowledge our simulated work task experiment is the first one showing empirically that users' subjective relevance assessments of retrieved documents are affected by a change in the quality of optically read text. Users of historical newspaper collections have so far commented effects of OCR'ed data quality mainly in impressionistic ways, and controlled user environments for studying effects of OCR quality on users' relevance assessments of the retrieval results have so far been missing. To remedy this The National Library of Finland (NLF) set up an experimental query environment for the contents of one Finnish historical newspaper, Uusi Suometar 1869-1918, to be able to compare users' evaluation of search results of two different OCR qualities for digitized newspaper articles. The query interface was able to present the same underlying document for the user based on two alternatives: either based on the lower OCR quality, or based on the higher OCR quality, and the choice was randomized. The users did not know about quality differences in the article texts they evaluated. The main result of the study is that improved optical character recognition quality affects perceived usefulness of historical newspaper articles significantly. The mean average evaluation score for the improved OCR results was 7.94% higher than the mean average evaluation score of the old OCR results.
Academic mobility as an organizational mechanism of intercultural interaction
Venue: arXiv cs.CY 2022
Year: 2022

The trends of academic mobility of students in the context of internationalization are presented. The results of a study de-voted to the students' attitudes towards international education, cultural and educational exchange and academic mobility programs are presented. It is concluded that the organization of academic mobility, as a part of intercultural environment of the university, is a necessary tool of motivation for the educational process and organizational mechanism for intercultural communication.
Mid-Air Helicopter Delivery at Mars Using a Jetpack
Venue: arXiv cs.RO 2022
Year: 2022

Mid-Air Helicopter Delivery (MAHD) is a new Entry, Descent and Landing (EDL) architecture to enable in situ mobility for Mars science at lower cost than previous missions. It uses a jetpack to slow down a Mars Science Helicopter (MSH) after separation from the backshell, and reach aerodynamic conditions suitable for helicopter take-off in mid air. For given aeroshell dimensions, only MAHD's lander-free approach leaves enough room in the aeroshell to accommodate the largest rotor option for MSH. This drastically improves flight performance, notably allowing +150\% increased science payload mass. Compared to heritage EDL approaches, the simpler MAHD architecture is also likely to reduce cost, and enables access to more hazardous and higher-elevation terrains on Mars. This paper introduces a design for the MAHD system architecture and operations. We present a mechanical configuration that fits both MSH and the jetpack within the 2.65-m Mars heritage aeroshell, and a jetpack control architecture which fully leverages the available helicopter avionics. We discuss preliminary numerical models of the flow dynamics resulting from the interaction between the jets, the rotors and the side winds. We define a force-torque sensing architecture capable of handling the wind and trimming the rotors to prepare for safe take-off. Finally, we analyze the dynamic environment and closed-loop control simulation results to demonstrate the preliminary feasibility of MAHD.
Biological, Family and Cultural Predictors of Personality Structure analysis based on personality prediction models constructed by open data source
Venue: arXiv cs.CY 2022
Year: 2022

Objective: This study takes further step on understanding personality structure in order to cope with the mental health during the COVID-19 global pandemic situation. Methods: Categorized the independent variables into biological, family and cultural predictors according to the datasets of the Big-5 personality survey online. And established multiple regression prediction models and exhaustive CHAID decision tree model of each personality trait. Results: Females are different from males in personality. The personality changes when growing. One-handed dominants are less agreeable and open than those who use both hands. Different sexual orientation does have variety personality. Native language used and education attainment is significantly related to personality accordingly. Marriage did help shaping personality to be more extroverted, less neurotic or agreeable and more conscientious and open. People raised in urban are more agreeable and open. Neurotic and open people often come from small families. person participated in voting are more extroverted, conscientious and open but less neurotic and agreeable. Different religions and races have different characteristics in each dimension of personality and there is no clear pattern have been found. Conclusion: Personality traits are indeed affected by multiple confounding factors. but the exploration on multiple cultures predictors still needed more details
Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace
Venue: arXiv cs.CY 2022
Year: 2022

AI plays a key role in current cyberspace and future immersive ecosystems that pinpoint user experiences. Thus, the trustworthiness of such AI systems is vital as failures in these systems can cause serious user harm. Although there are related works on exploring trustworthy AI (TAI) metrics in the current cyberspace, ecosystems towards user-centered services, such as the metaverse, are much more complicated in terms of system performance and user experience assessment, thus posing challenges for the applicability of existing approaches. Thus, we give an overlook on fairness, privacy and robustness, across the historical path from existing approaches. Eventually, we propose a research agenda towards systematic yet user-centered TAI in immersive ecosystems.
Robustness and Usefulness in AI Explanation Methods
Venue: arXiv cs.LG 2022
Year: 2022

Explainability in machine learning has become incredibly important as machine learning-powered systems become ubiquitous and both regulation and public sentiment begin to demand an understanding of how these systems make decisions. As a result, a number of explanation methods have begun to receive widespread adoption. This work summarizes, compares, and contrasts three popular explanation methods: LIME, SmoothGrad, and SHAP. We evaluate these methods with respect to: robustness, in the sense of sample complexity and stability; understandability, in the sense that provided explanations are consistent with user expectations; and usability, in the sense that the explanations allow for the model to be modified based on the output. This work concludes that current explanation methods are insufficient; that putting faith in and adopting these methods may actually be worse than simply not using them.
Class Fairness in Online Matching
Venue: arXiv cs.GT 2022
Year: 2022

In the classical version of online bipartite matching, there is a given set of offline vertices (aka agents) and another set of vertices (aka items) that arrive online. When each item arrives, its incident edges -- the agents who like the item -- are revealed and the algorithm must irrevocably match the item to such agents. We initiate the study of class fairness in this setting, where agents are partitioned into a set of classes and the matching is required to be fair with respect to the classes. We adopt popular fairness notions from the fair division literature such as envy-freeness (up to one item), proportionality, and maximin share fairness to our setting. Our class versions of these notions demand that all classes, regardless of their sizes, receive a fair treatment. We study deterministic and randomized algorithms for matching indivisible items (leading to integral matchings) and for matching divisible items (leading to fractional matchings). We design and analyze three novel algorithms. For matching indivisible items, we propose an adaptive-priority-based algorithm, MATCH-AND-SHIFT, prove that it achieves 1/2-approximation of both class envy-freeness up to one item and class maximin share fairness, and show that each guarantee is tight. For matching divisible items, we design a water-filling-based algorithm, EQUAL-FILLING, that achieves (1-1/e)-approximation of class envy-freeness and class proportionality; we prove (1-1/e) to be tight for class proportionality and establish a 3/4 upper bound on class envy-freeness. Finally, we build upon EQUAL-FILLING to design a randomized algorithm for matching indivisible items, EQAUL-FILLING-OCS, which achieves 0.593-approximation of class proportionality. The algorithm and its analysis crucially leverage the recently introduced technique of online correlated selection (OCS) [Fahrbach et al., 2020].
Double-Sided Beamforming in OWC Systems Using Omni-Digital Reconfigurable Intelligent Surfaces
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we introduce a variant of reconfigurable intelligent surfaces (RISs) called omni-digital-RISs (DRISs), which allow multiple physical processes, with application to optical wireless communications systems. The proposed omni-DRIS contains both reflectors and refractive elements, as well as elements that perform both simultaneously. We describe and explain the concept of omni-DRIS, suggest and analyze an omni-DRIS coding structure, discuss metamaterials to be used, and provide a design example. Furthermore, we demonstrate that the achievable rate of an omni-DRIS system depends on the number of omni-DRIS elements, bits per phase shift, and the number of unused elements. In addition, we show that the achievable rate upper bound is related to the number of omni-DRIS elements, and conclude by discussing future research directions.
Aggregate Queries on Knowledge Graphs: Fast Approximation with Semantic-aware Sampling
Venue: arXiv cs.DB 2022
Year: 2022

A knowledge graph (KG) manages large-scale and real-world facts as a big graph in a schema-flexible manner. Aggregate query is a fundamental query over KGs, e.g., "what is the average price of cars produced in Germany?". Despite its importance, answering aggregate queries on KGs has received little attention in the literature. Aggregate queries can be supported based on factoid queries, e.g., "find all cars produced in Germany", by applying an additional aggregate operation on factoid queries' answers. However, this straightforward method is challenging because both the accuracy and efficiency of factoid query processing will seriously impact the performance of aggregate queries. In this paper, we propose a "sampling-estimation" model to answer aggregate queries over KGs, which is the first work to provide an approximate aggregate result with an effective accuracy guarantee, and without relying on factoid queries. Specifically, we first present a semantic-aware sampling to collect a high-quality random sample through a random walk based on knowledge graph embedding. Then, we propose unbiased estimators for COUNT, SUM, and a consistent estimator for AVG to compute the approximate aggregate results based on the random sample, with an accuracy guarantee in the form of confidence interval. We extend our approach to support iterative improvement of accuracy, and more complex queries with filter, GROUP-BY, and different graph shapes, e.g., chain, cycle, star, flower. Extensive experiments over real-world KGs demonstrate the effectiveness and efficiency of our approach.
Trust in AI and Implications for the AEC Research: A Literature Analysis
Venue: arXiv cs.HC 2022
Year: 2022

Engendering trust in technically acceptable and psychologically embraceable systems requires domain-specific research to capture unique characteristics of the field of application. The architecture, engineering, and construction (AEC) research community has been recently harnessing advanced solutions offered by artificial intelligence (AI) to improve project workflows. Despite the unique characteristics of work, workers, and workplaces in the AEC industry, the concept of trust in AI has received very little attention in the literature. This paper presents a comprehensive analysis of the academic literature in two main areas of trust in AI and AI in the AEC, to explore the interplay between AEC projects unique aspects and the sociotechnical concepts that lead to trust in AI. A total of 490 peer-reviewed scholarly articles are analyzed in this study. The main constituents of human trust in AI are identified from the literature and are characterized within the AEC project types, processes, and technologies.
Boosting Mask R-CNN Performance for Long, Thin Forensic Traces with Pre-Segmentation and IoU Region Merging
Venue: arXiv cs.CV 2022
Year: 2022

Mask R-CNN has recently achieved great success in the field of instance segmentation. However, weaknesses of the algorithm have been repeatedly pointed out as well, especially in the segmentation of long, sparse objects whose orientation is not exclusively horizontal or vertical. We present here an approach that significantly improves the performance of the algorithm by first pre-segmenting the images with a PSPNet algorithm. To further improve its prediction, we have developed our own cost functions and heuristics in the form of training strategies, which can prevent so-called (early) overfitting and achieve a more targeted convergence. Furthermore, due to the high variance of the images, especially for PSPNet, we aimed to develop strategies for a high robustness and generalization, which are also presented here.
Globally-Optimal Event Camera Motion Estimation
Venue: arXiv cs.CV 2022
Year: 2022

Event cameras are bio-inspired sensors that perform well in HDR conditions and have high temporal resolution. However, different from traditional frame-based cameras, event cameras measure asynchronous pixel-level brightness changes and return them in a highly discretised format, hence new algorithms are needed. The present paper looks at fronto-parallel motion estimation of an event camera. The flow of the events is modeled by a general homographic warping in a space-time volume, and the objective is formulated as a maximisation of contrast within the image of unwarped events. However, in stark contrast to prior art, we derive a globally optimal solution to this generally non-convex problem, and thus remove the dependency on a good initial guess. Our algorithm relies on branch-and-bound optimisation for which we derive novel, recursive upper and lower bounds for six different contrast estimation functions. The practical validity of our approach is supported by a highly successful application to AGV motion estimation with a downward facing event camera, a challenging scenario in which the sensor experiences fronto-parallel motion in front of noisy, fast moving textures.
A Hierarchical Approach to Active Pose Estimation
Venue: arXiv cs.RO 2022
Year: 2022

Creating mobile robots which are able to find and manipulate objects in large environments is an active topic of research. These robots not only need to be capable of searching for specific objects but also to estimate their poses often relying on environment observations, which is even more difficult in the presence of occlusions. Therefore, to tackle this problem we propose a simple hierarchical approach to estimate the pose of a desired object. An Active Visual Search module operating with RGB images first obtains a rough estimation of the object 2D pose, followed by a more computationally expensive Active Pose Estimation module using point cloud data. We empirically show that processing image features to obtain a richer observation speeds up the search and pose estimation computations, in comparison to a binary decision that indicates whether the object is or not in the current image.