Reviewerly/exHarmony huggingface dataset:
Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models

Ability to generate intelligent and generalizable facial expressions is essential for building human-like social robots. At present, progress in this field is hindered by the fact that each facial expression needs to be programmed by humans. In order to adapt robot behavior in real time to different situations that arise when interacting with human subjects, robots need to be able to train themselves without requiring human labels, as well as make fast action decisions and generalize the acquired knowledge to diverse and new contexts. We addressed this challenge by designing a physical animatronic robotic face with soft skin and by developing a vision-based self-supervised learning framework for facial mimicry. Our algorithm does not require any knowledge of the robot's kinematic model, camera calibration or predefined expression set. By decomposing the learning process into a generative model and an inverse model, our framework can be trained using a single motor babbling dataset. Comprehensive evaluations show that our method enables accurate and diverse face mimicry across diverse human subjects.
Online Informative Path Planning for Active Information Gathering of a 3D Surface

This paper presents an online informative path planning approach for active information gathering on three-dimensional surfaces using aerial robots. Most existing works on surface inspection focus on planning a path offline that can provide full coverage of the surface, which inherently assumes the surface information is uniformly distributed hence ignoring potential spatial correlations of the information field. In this paper, we utilize manifold Gaussian processes (mGPs) with geodesic kernel functions for mapping surface information fields and plan informative paths online in a receding horizon manner. Our approach actively plans information-gathering paths based on recent observations that respect dynamic constraints of the vehicle and a total flight time budget. We provide planning results for simulated temperature modeling for simple and complex 3D surface geometries (a cylinder and an aircraft model). We demonstrate that our informative planning method outperforms traditional approaches such as 3D coverage planning and random exploration, both in reconstruction error and information-theoretic metrics. We also show that by taking spatial correlations of the information field into planning using mGPs, the information gathering efficiency is significantly improved.
Reflections on Assets-Based Design: A Journey Towards A Collective of Assets-Based Thinkers

The field of Computer-Supported Cooperative Work (CSCW) has long recognized a socio-technical gap complicating the design of technologies that can sustainably meet social needs. In response, a growing body of research advocates for assets-based design, an approach that seeks to build upon what the individuals and community already have. The emphasis on positioning assets rather than needs at the center of the process can complicate designers' decisions on what activities to foster, how to conduct them, and what outcomes to expect. In this paper, we reflect on two different assets-based design endeavors with vulnerable populations. Our reflections present assets-based design as an ongoing process that prioritizes the formation and evolution of a collective of assets-based thinkers who continually learn about their assets and ways to use them to attain desirable change. From that reflection, we contribute three methodological commitments for assets-based design to the growing CSCW scholarship on supporting vulnerable communities to attain emancipatory transformations: (1) embedding trust-building elements throughout the journey; (2) facilitating the formation of an interdependent collective; and (3) making moves towards incremental transformations. Further, we contribute a discussion on the change of perspective that entails for researchers and designers interested in undertaking assets-based design. In particular, we underscore the need to recognize the value of work before the work, to see technology as an intermediary rather than an inevitable end, and embrace impact in the shape of slow incremental transformation.
<i>Weight of Evidence</i>: Participatory Methods and Bayesian Updating to Contextualize Evidence Synthesis in Stakeholders’ Knowledge

Mixed methods research is well-suited to grapple with questions of what counts as valid knowledge across different contexts and perspectives. This article introduces Weight of Evidence as a transformative procedure for stakeholders to interpret, expand on and prioritize evidence from evidence syntheses, with a focus on engaging populations historically excluded from planning and decision making. This article presents the procedure's five steps using pilot data on perinatal care of immigrant women in Canada, engaging family physicians and birth companions. Fuzzy cognitive mapping offers an accessible and systematic way to generate priors to update published literature with stakeholder priorities. Weight of Evidence is a transparent procedure to broaden what counts as expertise, contributing to a more comprehensive, context-specific, and actionable understanding.
Extracting Useful Knowledge from Noisy Web Images via Data Purification for Fine-Grained Recognition

Fine-grained visual recognition tasks typically require training data with reliable acquisition and annotation processes. Acquiring such datasets with precise fine-grained annotations is very expensive and time-consuming. Conversely, a vast amount of web data is relatively easy to obtain with nearly no human effort. Nevertheless, the presence of label noise in web images becomes a huge obstacle for training robust fine-grained recognition models. In this work, we investigate the noisy label problem and propose a method that can specifically distinguish in- and out-of-distribution noisy samples. It can purify the web training data by discarding out-of-distribution noisy images and relabeling in-distribution ones. After purification, we can train the model on a less noisy web training set to achieve better robustness and performance. Extensive experiments on three real-world web datasets for fine-grained visual recognition demonstrate the superiority of our approach.
Sarcasm Detection with Commonsense Knowledge

Sarcasm is commonly used in today's social media platforms such as Twitter and Reddit. Sarcasm detection is necessary for analysing people's real sentiments as people usually use sarcasm to express a flipped emotion against the literal meaning. However, the current works neglect the fact that commonsense knowledge is crucial for sarcasm recognition. In this paper, we propose a novel architecture in deep learning for sarcasm detection by integrating commonsense knowledge. To be specific, we apply the pre-trained COMET model to generate relevant commonsense knowledge. Besides, we compare two kinds of knowledge selection strategies to investigate how commonsense knowledge influences performance. Finally, a knowledge-text integration module is designed to model both text and knowledge. The experimental results demonstrate our model's effectiveness on three datasets, including two Twitter datasets and a Reddit dataset.
Two-pronged Strategy

Hashing learns compact binary codes to store and retrieve massive data efficiently. Particularly, unsupervised deep hashing is supported by powerful deep neural networks and has the desirable advantage of label independence. It is a promising technique for scalable image retrieval. However, deep models introduce a large number of parameters, which is hard to optimize due to the lack of explicit semantic labels and brings considerable training cost. As a result, the retrieval accuracy and training efficiency of existing unsupervised deep hashing are still limited. To tackle the problems, in this paper, we propose a simple and efficient Lightweight Augmented Graph Network Hashing (LAGNH) method with a two-pronged strategy. For one thing, we extract the inner structure of the image as the auxiliary semantics to enhance the semantic supervision of the unsupervised hash learning process. For another, we design a lightweight network structure with the assistance of the auxiliary semantics, which greatly reduces the number of network parameters that needs to be optimized and thus greatly accelerates the training process. Specifically, we design a cross-modal attention module based on the auxiliary semantic information to adaptively mitigate the adverse effects in the deep image features. Besides, the hash codes are learned by multi-layer message passing within an adversarial regularized graph convolutional network. Simultaneously, the semantic representation capability of hash codes is further enhanced by reconstructing the similarity graph. Experimental results show that our method achieves significant performance improvement compared with the state-of-the-art unsupervised deep hashing methods in terms of both retrieval accuracy and efficiency. Notably, on MS-COCO dataset, our method achieves more than 10% improvement on retrieval precision and 2.7x speedup on training time compared with the second best result.
Adversarial Training is Not Ready for Robot Learning

Adversarial training is an effective method to train deep learning models that are resilient to norm-bounded perturbations, with the cost of nominal performance drop. While adversarial training appears to enhance the robustness and safety of a deep model deployed in open-world decision-critical applications, counterintuitively, it induces undesired behaviors in robot learning settings. In this paper, we show theoretically and experimentally that neural controllers obtained via adversarial training are subjected to three types of defects, namely transient, systematic, and conditional errors. We first generalize adversarial training to a safety-domain optimization scheme allowing for more generic specifications. We then prove that such a learning process tends to cause certain error profiles. We support our theoretical results by a thorough experimental safety analysis in a robot-learning task. Our results suggest that adversarial training is not yet ready for robot learning.
The Epistemology of the Internet and the Regulation of Speech in America

(This is a contribution to a symposium issue on "The Ethics of Free Speech.")The Internet is the epistemological crisis of the 21st-century: it has fundamentally altered the social epistemology of societies with relative freedom to access it. Most of what we think we know about the world is due to reliance on epistemic authorities, individuals or institutions that tell us what we ought to believe about Newtonian mechanics, evolution by natural selection, climate change, resurrection from the dead, or the Holocaust. The most practically fruitful epistemic norm of modernity, empiricism, demands that knowledge be grounded in sensory experience, but almost no one who believes in evolution by natural selection or the reality of the Holocaust has any sensory evidence in support of those beliefs. Instead, we rely on epistemic authorities—biologists and historians, for example. Epistemic authority cannot be sustained by empiricist criteria, for obvious reasons: salient anecdotal evidence, the favorite tool of propagandists, appeals to ordinary faith in the senses, but is easily exploited given that most people understand neither the perils of induction nor the finer points of sampling and Bayesian inference. Sustaining epistemic authority depends, crucially, on social institutions that inculcate reliable second-order norms about whom to believe about what. The traditional media were crucial, in the age of mass democracy, with promulgating and sustaining such norms. The Internet has obliterated the intermediaries who made that possible (and, in the process, undermined the epistemic standing of experts), while even the traditional media in the U.S., thanks to the demise of the “Fairness Doctrine,” has contributed to the same phenomenon. I argue that this crisis cries out for changes in the regulation of speech in cyberspace—including liability for certain kinds of false speech, incitement, and hate speech--but also a restoration of a version of the Fairness Doctrine for the traditional media.
Seeing Like an Infrastructure: Low-resolution Citizens and the Aadhaar Identification Project

Large-scale data systems play a central role in how state bureaucracies come to know and manage citizens. Such systems are endemically uneven in implementation, producing distributed and exclusionary consequences that are among their most important effects. These systems also operate as infrastructures in the rich and complex sense of the term that has been a core contribution of CSCW to the wider computing and social science fields. Building on James Scott's work on 'seeing like a state', we conceptualize 'seeing like an infrastructure' as a more supple analytic perspective that maps the distributed work and uneven consequences through which designers, bureaucrats, and users (here, citizens) assign or claim representation in the consequential data systems that increasingly shape and define citizenship. Drawing on eighteen months of ethnographic fieldwork into Aadhaar, India's biometrics-based identification project, and studies of infrastructure, marginalization, and citizenship in CSCW and allied fields, we argue that this perspective provides crucial insight into the strategies and mechanisms by which effective access to the basic rights and entitlements of citizenship are granted, claimed, and at times undermined. More specifically, we show how challenges in implementing Aadhaar's three key processes-enrollment, seeding, and authentication-give rise to a spectrum of resolution in which the rights and entitlements of 'high-resolution citizens' are expanded, while those of 'low-resolution citizens' are curtailed.?
SiamSTA: Spatio-Temporal Attention based Siamese Tracker for Tracking UAVs

With the growing threat of unmanned aerial vehicle (UAV) intrusion, anti-UAV techniques are becoming increasingly demanding. Object tracking, especially in thermal infrared (TIR) videos, though provides a promising solution, struggles with challenges like small scale and fast movement that commonly occur in anti-UAV scenarios. To mitigate this, we propose a simple yet effective spatio-temporal attention based Siamese network, dubbed SiamSTA, to track UAV robustly by performing reliable local tracking and wide-range re-detection alternatively. Concretely, tracking is carried out by posing spatial and temporal constraints on generating candidate proposals within local neighborhoods, hence eliminating background distractors to better perceive small targets. Complementarily, in case of target lost from local regions due to fast movement, a three-stage re-detection mechanism is introduced to re-detect targets from a global view by exploiting valuable motion cues through a correlation filter based on change detection. Finally, a state-aware switching policy is adopted to adaptively integrate local tracking and global re-detection and take their complementary strengths for robust tracking. Extensive experiments on the 1st and 2nd anti-UAV datasets well demonstrate the superiority of SiamSTA over other competing counterparts. Notably, SiamSTA is the foundation of the 1st-place winning entry in the 2nd Anti-UAV Challenge.
Chip-Based Quantum Key Distribution against Trojan-Horse Attack

Silicon photonics offers a stable, miniaturized platform for quantum key distribution (QKD). As such, the chip-based system is vital for a wider adoption of QKD in the near future. However, the practical security issues presented in this alternative platform have not been fully studied. Here, we theoretically and experimentally evaluate the security of a chip-based QKD system under a powerful source attack, namely, Trojan-horse attack. We analyze the finite-key security of two prominent QKD protocols---BB84 and measurement-device-independent QKD---with the information leakage from both the polarization modulator and the intensity modulator integrated on a silicon photonic chip. Furthermore, we prove that our chip-based measurement-device-independent QKD system is feasibly resistant to the Trojan-horse attack by adding 232-dB isolation to the chip. In this sense, our work provides a guideline for the secure implementation of a leaky transmitter chip in the QKD system.
Uncertainty-Aware Human Mesh Recovery from Video by Learning Part-Based 3D Dynamics

Despite the recent success of 3D human reconstruction methods, recovering the accurate and smooth 3D human motion from video is still challenging. Designing a temporal model in the encoding stage is not sufficient enough to settle the trade-off problem between the per-frame accuracy and the motion smoothness. To address this problem, we approach some of the fundamental problems of 3D reconstruction tasks, simultaneously predicting 3D pose and 3D motion dynamics. First, we utilize the power of uncertainty to address the problem of multiple 3D configurations resulting in the same 2D projections. Second, we confirmed that dividing the body into local regions shows outstanding results for estimating 3D motion dynamics. In this paper, we propose (i) an encoder that makes two different estimations: a static feature that presents 2D pose feature as distribution and a dynamic feature that includes optical flow information and (ii) a decoder that divides the body into five different local regions to estimate the 3D motion dynamics of each region. We demonstrate how our method recovers the accurate and smooth motion and achieves the state-of-the-art results for both constrained and in-the-wild videos.
Skeleton Graph Scattering Networks for 3D Skeleton-based Human Motion Prediction

To achieve 3D skeleton-based human motion prediction, many graph-convolution-based methods are proposed for promising results; however, due to only preserving low-pass information over graphs, those graph convolution methods suffer from over-smoothing, causing the predicted poses staying the same in the long term. To resolve the over-smoothing issue, we propose a novel skeleton graph scattering network (SGSN), which leverages graph scattering to extract comprehensive motion information from multiple graph spectrum bands. The core of the proposed SGSN is the adaptive graph scattering block (AGSB), including two key modules: i) graph scattering decomposition, which decomposes information into various graph spectrum bands and updates the trainable features in each band, as well as ii) graph spectrum attention, which aggregates those features in various graph spectrum bands via trainable attention weights. Extensive experiments reveal that SGSN outperforms state-of-the-art methods by 8.5%, 9.0% and 3.9% of 3D mean per joint position error (MPJPE) in average on Human3.6M, CMU Mocap and 3DPW datasets, respectively. We also test the mean angle error (MAE) on Human3.6M, which is lower by 3.3% than previous methods. Moreover, SGSN outperforms even more in the long-term prediction because of the alleviation of the over-smoothing.
SParseQA: Sequential word reordering and parsing for answering complex natural language questions over knowledge graphs

One of the effective approaches for answering natural language questions (NLQs) over knowledge graphs consists of two main stages. It first creates a query graph based on the NLQ and then matches this graph over the knowledge graph to construct a structured query. An obstacle in the first stage is the need to build question interpretations with candidate resources, even if some implicit phrases exist in the sentence. In the second stage, a serious problem is to map diverse NLQ relations to their corresponding predicates. To overcome these problems, in this paper, we propose a novel sequential word parsing-based method to construct and refine an uncertain question graph that is disambiguated directly over the knowledge graph. Instead of relying on the syntactic dependency relations and some predefined rules that recognize the relations and their arguments, we consider the identified entities and variables in the NLQ as well as their corresponding place in the structure of a query graph pattern to build question triples. First, by leveraging the ordered dependency tree of an NLQ, sentence words are reordered. Then the question graph structure is constructed by parsing the new sequence backward, starting from the identified items. Subsequently, the question graph is refined by eliminating the useless elements. Additionally, to improve the relation similarity measure in the graph similarity process, we exploit the knowledge hidden in a relation pattern taxonomy. Experimental studies over several benchmarks demonstrate that our proposed approach is effective as it achieves promising results in answering the complex NLQs.
Organizations’ approaches to blockchain: A critical realist perspective

Organizations face manifold implementation barriers in blockchain adoption. Of particular interest is the pre-adoption phase, where knowledge and attitudes guide organizations’ approaches toward a new technology. This paper examines organizations’ approaches to blockchain through a sensemaking lens to identify how blockchain prototype development is guided by perceived business value of and sentiments toward the technology. Taking a critical realist perspective, we examine divergences between organizations’ approaches toward blockchain adoption, i.e., what they do, and why and how they approach blockchain. We differentiate between four types of approaches and provide recommendations how the pre-adoption phase can be considered in academic analyses.
Pursuing the Fundamental Limits for Quantum Communication

The quantum capacity of a noisy quantum channel determines the maximal rate at which we can code reliably over asymptotically many uses of the channel, and it characterizes the channel's ultimate ability to transmit quantum information coherently. In this paper, we derive single-letter upper bounds on the quantum and private capacities of quantum channels. The quantum capacity of a quantum channel is always no larger than the quantum capacity of its extended channels, since the extensions of the channel can be considered as assistance from the environment. By optimizing the parametrized extended channels with specific structures such as the flag structure, we obtain new upper bounds on the quantum capacity of the original quantum channel. Furthermore, we extend our approach to estimating the fundamental limits of private communication and one-way entanglement distillation. As notable applications, we establish improved upper bounds to the quantum and private capacities for fundamental quantum channels of interest in quantum information, some of which are also the sources of noise in superconducting quantum computing. In particular, our upper bounds on the quantum capacities of the depolarizing channel and the generalized amplitude damping channel are strictly better than previously best-known bounds for certain regimes.