Reviewerly/exHarmony huggingface dataset:
Contiguous identities

In October 2021, Facebook, Inc. changed its name to “Meta Platforms, Inc.” A key part of this rebranding involved Facebook’s long-term focus on virtual reality (VR) and the future of the supposed Metaverse. Significantly, as of November 2021, users have only been able to access the VR space of Meta if they sign in through their social networking account. While Facebook has suggested it will change this in the future, the specifics of this future have yet to be explained. The purpose of this article, therefore, is to explore the implications of identity within VR if identity within this space is tethered to a corresponding social networking account. Here we suggest the virtual self currently being forged in Meta forms a connective tissue between (1) early accounts of online communication enabling the body to be left behind and (2) more embodied approaches to identity in the context of digitality. We then argue that the virtual self is not a singular entity per se but forms a contiguous connection between the lived experience of VR and data gathered through social media about the identity associated with said experience. Finally, we argue conceptualizations of identity in VR (and Meta’s role in identity) will only become more pressing as Meta attempts to build its supposed “Metaverse” in the coming years.
Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization

In the traditional particle swarm optimization algorithm, the particles always choose to learn from the well-behaved particles in the population during the population iteration. Nevertheless, according to the principles of particle swarm optimization, we know that the motion of each particle has an impact on other individuals, and even poorly behaved particles can provide valuable information. Based on this consideration, we propose Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization, called LFIACL-PSO. In the LFIACL-PSO algorithm, First, when the particle is trapped in the local optimum and cannot jump out, inverse learning is used, and the learning step size is obtained through the Lévy flight. Second, to increase the diversity of the algorithm and prevent it from prematurely converging, a comprehensive learning strategy and Ring-type topology are used as part of the learning paradigm. In addition, use the adaptive update to update the acceleration coefficients for each learning paradigm. Finally, the comprehensive performance of LFIACL-PSO is measured using 16 benchmark functions and a real engineering application problem and compared with seven other classical particle swarm optimization algorithms. Experimental comparison results show that the comprehensive performance of the LFIACL-PSO outperforms comparative PSO variants.
Toward a Configurable Thing Composition Language for the SIoT

With the advent of the Social Internet-of-Things (SIoT), which builds upon the success of IoT, different IoT devices engage in relationships to achieve common goals calling for a composition language that would dictate who will do what, when, and where. Since the number of service interactions may become overwhelming the composition plan may have many alternatives. For that reason, variability is an important concern for IoT and SIoT. A configurable composition model could provide a consolidated view of multiple variants of composition plans. It promotes the reuse of proven practices by providing analysts with a generic modeling approach from which to derive individual composition plan models. Unfortunately, the scope of existing notations for configurable composition modeling is restricted or even non-existent in the IoT domain. In this paper, we propose a variability awareness IoT service Composition Framework. The new Framework handles both configurable and classical IoT service composition. We represent the new primitives, the configurable composition language, and algorithms of customization. Finally we demonstrate the features of the framework through a proof of concept application.
Multiple-weak-value quantum measurement for precision estimation of time delay

In quantum weak measurement, a weak value is a quantity related to a pointer shift of a measuring device which is usually determined by pre- and postselection states. For conventional weak measurement, a single weak-value model has been well addressed in precision metrology of time delay, but it only holds for a narrowband light source. To further improve the measurement accuracy, a broad-spectrum light source is needed. We find that the single weak-value model no longer holds, due to the occurrence of modified preselection states. Here, a multiple weak-value model is established by introducing the modified preselection states in the estimation of time delay. For broad-spectrum light source with 50-nm spectral width, the multiple weak-value scheme enables us to achieve a high resolution of $1.6\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}4}\phantom{\rule{0.16em}{0ex}}\mathrm{as}$. Our proposed multiple weak-value amplification may have important applications in various fields involving precise time-delay detection.
A Fairy Tale Gone Wrong: Social media, Recursive Hate and the Politicisation of Drag Queen Storytime

Controversy over Drag Queen Storytime (DQS) childhood literacy events in many Western liberal democracies between 2018 and 2020 occurred within a broader rise in reported ‘hate crime’, notable in the United States and in England and Wales. Outrage from conservative US Christian anti-LGBTQ hate groups and activists, and far-right groups, included online bias-motivated harassment, in-person bias-motivated harassment, and alleged assault and property damage. This article analyses news media and public discourse on hateful conduct against DQS events in the United States and related conduct in the UK and Australia. This hateful conduct draws on legacies of the criminalisation and pathologisation of same-sex attraction, and the framing of non-conforming gender identities as ‘deceptive’. The findings emphasise the recursive nature of bias-motivated hateful conduct based on medico-legal and pseudo-scientific stigmatisation of same-sex attraction and gender fluidity, and the amplificatory capacity of social media networks to engender hateful conduct. The article analyses the broader implications of hateful conduct given the limits of the criminal law in the United States (and other Western liberal democracies such as the UK and Australia) in addressing the translation of online hate speech into bias-motivated in-person hateful conduct. In the United States, these limits are often defined through the unqualified invocation of freedom of speech protections. The article applies ‘digiqueer’ criminology as a frame to consider the harms of bigotry against LGBTQ people amplified through digital media technologies and their intersection, particularly in the US, with Christian righteousness and right-wing ideology through a ‘hate feedback loop’.
Adaptive Multi-Pedestrian Tracking by Multi-Sensor: Track-to-Track Fusion Using Monocular 3D Detection and MMW Radar

Accurate and reliable tracking of multi-pedestrian is of great importance for autonomous driving, human-robot interaction and video surveillance. Since different scenarios have different best-performing sensors, sensor fusion perception plans are believed to have complementary modalities and be capable of handling situations which are challenging for single sensor. In this paper, we propose a novel track-to-track fusion strategy for multi-pedestrian tracking by using a millimeter-wave (MMW) radar and a monocular camera. Pedestrians are firstly tracked by each sensor according to the sensor characteristic. Specifically, the 3D monocular pedestrian detections are obtained by a convolutional neural network (CNN). The trajectory is formed by the tracking-by-detection approach, combined with Bayesian estimation. The measurement noise of the 3D monocular detection is modeled by a detection uncertainty value obtained from the same CNN, as an approach to estimate the pedestrian state more accurately. The MMW radar utilizes the track-before-detection method due to the sparseness of the radar features. Afterwards, the pedestrian trajectories are obtained by the proposed track-to-track fusion strategy, which can work adaptively under challenging weather conditions, low-illumination conditions and clutter scenarios. A group of tests are carried out to validate our pedestrian tracking strategy. Tracking trajectories and optimal sub-pattern assignment (OSPA) metric demonstrate the accuracy and robustness of the proposed multi-sensor multi-pedestrian tracking system.
Tone colour in music and bifurcation control

An intrinsic characteristic of musical sounds is the qualitative evolution of their timbre (tone colour). In this paper, we interpret each qualitative change by a bifurcation. We employ spectral and temporal envelopes as two of the most salient features of tone colour. Fourier analysis of a note generates its n-leading harmonic partials and an amplitude spectral n-vector A for n≥2. We introduce harmonic manifolds associated with amplitude spectral vectors. This is to accommodate the n-leading harmonic partials of the note. Then, harmonic partials are modelled by solutions of a radial coupling of n-tuple parametric Hopf oscillators. We use one bifurcation parameter and interpolate the qualitative evolution of temporal envelope over a set of consecutive time-intervals via bifurcation analysis and control. Bifurcation scenarios involve harmonic asymptotic sets as time-asymptotic limits of harmonic partials. Each harmonic asymptotic set is homeomorphic to a k-tori, where k≤n is the number of contributing harmonic partials. Bifurcation classification of tone colour is then drawn up by encoding the spectral geometry of acoustic signals into harmonic manifolds and harmonic asymptotic sets. For instance, we treat audio C♯4 files obtained from piano and violin. Different bifurcation scenarios distinguish temporal amplitude bifurcations of these sounds. A complete hysteresis cycle is observed within the temporal amplitude bifurcations of C♯4 note played by violin.
Zero-shot Query Contextualization for Conversational Search

Current conversational passage retrieval systems cast conversational search into ad-hoc search by using an intermediate query resolution step that places the user's question in context of the conversation. While the proposed methods have proven effective, they still assume the availability of large-scale question resolution and conversational search datasets. To waive the dependency on the availability of such data, we adapt a pre-trained token-level dense retriever on ad-hoc search data to perform conversational search with no additional fine-tuning. The proposed method allows to contextualize the user question within the conversation history, but restrict the matching only between question and potential answer. Our experiments demonstrate the effectiveness of the proposed approach. We also perform an analysis that provides insights of how contextualization works in the latent space, in essence introducing a bias towards salient terms from the conversation.
GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment

Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i.e., number of labeled nodes, is limited, which is expected as GNNs are trained solely based on the supervision obtained from the labeled nodes. On the other hand,recent self-supervised learning paradigm aims to train GNNs by solving pretext tasks that do not require any labeled nodes, and it has shown to even outperform GNNs trained with few labeled nodes. However, a major drawback of self-supervised methods is that they fall short of learning class discriminative node representations since no labeled information is utilized during training. To this end, we propose a novel semi-supervised method for graphs, GraFN, that leverages few labeled nodes to ensure nodes that belong to the same class to be grouped together, thereby achieving the best of both worlds of semi-supervised and self-supervised methods. Specifically, GraFN randomly samples support nodes from labeled nodes and anchor nodes from the entire graph. Then, it minimizes the difference between two predicted class distributions that are non-parametrically assigned by anchor-supports similarity from two differently augmented graphs. We experimentally show that GraFN surpasses both the semi-supervised and self-supervised methods in terms of node classification on real-world graphs. The source code for GraFN is available at https://github.com/Junseok0207/GraFN.
Improved Meta Learning for Low Resource Speech Recognition

We propose a new meta learning based framework for low resource speech recognition that improves the previous model agnostic meta learning (MAML) approach. The MAML is a simple yet powerful meta learning approach. However, the MAML presents some core deficiencies such as training instabilities and slower convergence speed. To address these issues, we adopt multi-step loss (MSL). The MSL aims to calculate losses at every step of the inner loop of MAML and then combines them with a weighted importance vector. The importance vector ensures that the loss at the last step has more importance than the previous steps. Our empirical evaluation shows that MSL significantly improves the stability of the training procedure and it thus also improves the accuracy of the overall system. Our proposed system outperforms MAML based low resource ASR system on various languages in terms of character error rates and stable training behavior.
Bias-Eliminated Semantic Refinement for Any-Shot Learning

When training samples are scarce, the semantic embedding technique, i. e., describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic descriptions are usually obtained in an external paradigm, such as manual annotation, resulting in weak consistency between descriptions and visual features. In this paper, we refine the coarse-grained semantic description for any-shot learning tasks, i. e., zero-shot learning (ZSL), generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new model, namely, the semantic refinement Wasserstein generative adversarial network (SRWGAN) model, is designed with the proposed multihead representation and hierarchical alignment techniques. Unlike conventional methods, semantic refinement is performed with the aim of identifying a bias-eliminated condition for disjoint-class feature generation and is applicable in both inductive and transductive settings. We extensively evaluate model performance on six benchmark datasets and observe state-of-the-art results for any-shot learning; e. g., we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the standard GZSL setting. Various visualizations are also provided to show the bias-eliminated generation of SRWGAN. Our code is available. 1.
Triple-Level Model Inferred Collaborative Network Architecture for Video Deraining

Video deraining is an important issue for outdoor vision systems and has been investigated extensively. However, designing optimal architectures by the aggregating model formation and data distribution is a challenging task for video deraining. In this paper, we develop a model-guided triple-level optimization framework to deduce network architecture with cooperating optimization and auto-searching mechanism, named Triple-level Model Inferred Cooperating Searching (TMICS), for dealing with various video rain circumstances. In particular, to mitigate the problem that existing methods cannot cover various rain streaks distribution, we first design a hyper-parameter optimization model about task variable and hyper-parameter. Based on the proposed optimization model, we design a collaborative structure for video deraining. This structure includes Dominant Network Architecture (DNA) and Companionate Network Architecture (CNA) that is cooperated by introducing an Attention-based Averaging Scheme (AAS). To better explore inter-frame information from videos, we introduce a macroscopic structure searching scheme that searches from Optical Flow Module (OFM) and Temporal Grouping Module (TGM) to help restore latent frame. In addition, we apply the differentiable neural architecture searching from a compact candidate set of task-specific operations to discover desirable rain streaks removal architectures automatically. Extensive experiments on various datasets demonstrate that our model shows significant improvements in fidelity and temporal consistency over the state-of-the-art works. Source code is available at https://github.com/vis-opt-group/TMICS.
Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks

Purpose: To objectively assess new medical imaging technologies via computer-simulations, it is important to account for the variability in the ensemble of objects to be imaged. This source of variability can be described by stochastic object models (SOMs). It is generally desirable to establish SOMs from experimental imaging measurements acquired by use of a well-characterized imaging system, but this task has remained challenging. Approach: A generative adversarial network (GAN)-based method that employs AmbientGANs with modern progressive or multiresolution training approaches is proposed. AmbientGANs established using the proposed training procedure are systematically validated in a controlled way using computer-simulated magnetic resonance imaging (MRI) data corresponding to a stylized imaging system. Emulated single-coil experimental MRI data are also employed to demonstrate the methods under less stylized conditions. Results: The proposed AmbientGAN method can generate clean images when the imaging measurements are contaminated by measurement noise. When the imaging measurement data are incomplete, the proposed AmbientGAN can reliably learn the distribution of the measurement components of the objects. Conclusions: Both visual examinations and quantitative analyses, including task-specific validations using the Hotelling observer, demonstrated that the proposed AmbientGAN method holds promise to establish realistic SOMs from imaging measurements.
MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series

In this paper, we introduce Masked Anomaly Detection (MAD), a general self-supervised learning task for multivariate time series anomaly detection. With the increasing availability of sensor data from industrial systems, being able to detecting anomalies from streams of multivariate time series data is of significant importance. Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned normal representations can empower anomaly detection as the model has learned to capture certain key underlying data regularities. A typical formulation is to learn a predictive model, i.e., use a window of time series data to predict future data values. In this paper, we propose an alternative self-supervised learning task. By randomly masking a portion of the inputs and training a model to estimate them using the remaining ones, MAD is an improvement over the traditional left-to-right next step prediction (NSP) task. Our experimental results demonstrate that MAD can achieve better anomaly detection rates over traditional NSP approaches when using exactly the same neural network (NN) base models, and can be modified to run as fast as NSP models during test time on the same hardware, thus making it an ideal upgrade for many existing NSP-based NN anomaly detection models.
Refined Commonsense Knowledge from Large-Scale Web Contents

Commonsense knowledge (CSK) about concepts and their properties is helpful for AI applications. Prior works, such as ConceptNet, have compiled large CSK collections. However, they are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and strings for P and O. This paper presents a method called ASCENT++ to automatically build a large-scale knowledge base (KB) of CSK assertions, with refined expressiveness and both better precision and recall than prior works. ASCENT++ goes beyond SPO triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter is essential to express the temporal and spatial validity of assertions and further qualifiers. Furthermore, ASCENT++ combines open information extraction (OpenIE) with judicious cleaning and ranking by typicality and saliency scores. For high coverage, our method taps into the large-scale crawl C4 with broad web contents. The evaluation with human judgments shows the superior quality of the ASCENT++ KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of ASCENT++. A web interface, data, and code can be accessed at https://ascentpp.mpi-inf.mpg.de/.
BBM92 quantum key distribution over a free space dusty channel of 200 meters

Abstract Free-space quantum communication assumes importance as it is a precursor for satellite-based quantum communication needed for secure key distribution over longer distances. Prepare and measure quantum key distribution (QKD) protocols like BB84 consider the satellite as a trusted device, which is fraught with security threat looking at the current trend for satellite-based optical communication. Therefore, entanglement-based protocols must be preferred, so that one can consider the satellite as an untrusted device. The current work reports the effect of atmospheric aerosols on the key rate obtained with BBM92 QKD protocol, an entanglement-based QKD protocol over 200 m distance, using an indigenous facility developed at Physical Research Laboratory (PRL), Ahmedabad, India. Our results show that concentration and extinction coefficient of atmospheric aerosols play a major role in influencing the observed sift key rate, and eventually, the secure key rate. Such experiments are important to validate the models to account for the atmospheric effects on the key rates achieved through satellite-based QKD.
Gender and Racial Bias in Visual Question Answering Datasets

Vision-and-language tasks have increasingly drawn more attention as a means to evaluate human-like reasoning in machine learning models. A popular task in the field is visual question answering (VQA), which aims to answer questions about images. However, VQA models have been shown to exploit language bias by learning the statistical correlations between questions and answers without looking into the image content: e.g., questions about the color of a banana are answered with yellow, even if the banana in the image is green. If societal bias (e.g., sexism, racism, ableism, etc.) is present in the training data, this problem may be causing VQA models to learn harmful stereotypes. For this reason, we investigate gender and racial bias in five VQA datasets. In our analysis, we find that the distribution of answers is highly different between questions about women and men, as well as the existence of detrimental gender-stereotypical samples. Likewise, we identify that specific race-related attributes are underrepresented, whereas potentially discriminatory samples appear in the analyzed datasets. Our findings suggest that there are dangers associated to using VQA datasets without considering and dealing with the potentially harmful stereotypes. We conclude the paper by proposing solutions to alleviate the problem before, during, and after the dataset collection process.