Reviewerly/exHarmony huggingface dataset:
Neuro-inspired edge feature fusion using Choquet integrals

It is known that the human visual system performs a hierarchical information process in which early vision cues (or primitives) are fused in the visual cortex to compose complex shapes and descriptors. While different aspects of the process have been extensively studied, such as lens adaptation or feature detection, some other aspects, such as feature fusion, have been mostly left aside. In this work, we elaborate on the fusion of early vision primitives using generalizations of the Choquet integral, and novel aggregation operators that have been extensively studied in recent years. We propose to use generalizations of the Choquet integral to sensibly fuse elementary edge cues, in an attempt to model the behaviour of neurons in the early visual cortex. Our proposal leads to a fully-framed edge detection algorithm whose performance is put to the test in state-of-the-art edge detection datasets.
Revisiting agglomerative clustering

Hierarchical agglomerative methods stand out as particularly effective and popular approaches for clustering data. Yet, these methods have not been systematically compared regarding the important issue of false positives while searching for clusters. A model of clusters involving a higher density nucleus surrounded by a transition, followed by outliers is adopted as a means to quantify the relevance of the obtained clusters and address the problem of false positives. Six traditional methodologies, namely the single, average, median, complete, centroid and Ward’s linkage criteria are compared with respect to the adopted model. Unimodal and bimodal datasets obeying uniform, gaussian, exponential and power-law distributions are considered for this comparison. The obtained results include the verification that many methods detect two clusters in unimodal data. The single-linkage method was found to be more resilient to false positives. Also, several methods detected clusters not corresponding directly to the nucleus.
STEM: An approach to Multi-source Domain Adaptation with Guarantees

Multi-source Domain Adaptation (MSDA) is more practical but challenging than the conventional unsupervised domain adaptation due to the involvement of diverse multiple data sources. Two fundamental challenges of MSDA are: (i) how to deal with the diversity in the multiple source domains and (ii) how to cope with the data shift between the target domain and the source domains. In this paper, to address the first challenge, we propose a theoretical-guaranteed approach to combine domain experts locally trained on its own source domain to achieve a combined multi-source teacher that globally predicts well on the mixture of source domains. To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor. Together with bridging the gap in the latent space, we train a student to mimic the predictions of the teacher expert on both source and target examples. In addition, our approach is guaranteed with rigorous theory offered insightful justifications of how each component influences the transferring performance. Extensive experiments conducted on three benchmark datasets show that our proposed method achieves state-of-the-art performances to the best of our knowledge.
Contagion of offensive speech online: An interactional analysis of political swearing

Despite their positive effects in promoting participatory politics, digital publics have also manifested an offensive vernacular culture. This study takes a social network analytic approach to explain the contagion of offensive speech in online discussion contexts. The study examines four social interactional mechanisms underlying a user's adoption of political swearing: generalized reciprocity, direct reciprocity, leader-mimicry, and peer-mimicry. The empirical context of this study is a highly popular online discussion forum in Hong Kong. The study examines the effects of social interactional mechanisms on the occurrences of political swearing by analyzing five years of user comments. Findings show that peer-mimicry contributes to the contagion process the most, followed by generalized reciprocity and direct mimicry. The study demonstrates how individual-level speech behaviors spiral into a collective norm that potentially hinders a healthy discussion culture in mediated social spaces.
On Exploring Image Resizing for Optimizing Criticality-based Machine Perception

On-board computing capacity remains a key bottleneck in modern machine inference pipelines that run on embedded hardware, such as aboard autonomous drones or cars. To mitigate this bottleneck, recent work proposed an architecture for segmenting input frames of complex modalities, such as video, and prioritizing downstream machine perception tasks based on criticality of the respective segments of the perceived scene. Criticality-based prioritization allows limited machine resources (of lower-end embedded GPUs) to be spent more judiciously on tracking more important objects first. This paper explores a novel dimension in criticality-based prioritization of machine perception; namely, the role of criticality-dependent image resizing as a way to improve the trade-off between perception quality and timeliness. Given an assessment of criticality (e.g., an object’s distance from the autonomous car), the scheduler is allowed to choose from several image resizing options (and related inference models) before passing the resized images to the perception module. Experiments on an AI-powered embedded platform with a real-world driving dataset demonstrate significant improvements in the trade-off between perception accuracy and response time when the proposed resizing algorithm is used. The improvement is attributed to two advantages of the proposed scheme: (i) improved preferential treatment of more critical objects by reducing time spent on less critical ones, and (ii) improved image batching within the GPU, thanks to re-sizing, leading to better resource utilization.
Long Short View Feature Decomposition via Contrastive Video Representation Learning

Self-supervised video representation methods typically focus on the representation of temporal attributes in videos. However, the role of stationary versus non-stationary attributes is less explored: Stationary features, which remain similar throughout the video, enable the prediction of video-level action classes. Non-stationary features, which represent temporally varying attributes, are more beneficial for downstream tasks involving more fine-grained temporal understanding, such as action segmentation. We argue that a single representation to capture both types of features is sub-optimal, and propose to decompose the representation space into stationary and non-stationary features via contrastive learning from long and short views, i.e. long video sequences and their shorter sub-sequences. Stationary features are shared between the short and long views, while non-stationary features aggregate the short views to match the corresponding long view. To empirically verify our approach, we demonstrate that our stationary features work particularly well on an action recognition downstream task, while our non-stationary features perform better on action segmentation. Furthermore, we analyse the learned representations and find that stationary features capture more temporally stable, static attributes, while non-stationary features encompass more temporally varying ones.
Matching images and texts with multi-head attention network for cross-media hashing retrieval

The cross-media hashing retrieval generally encodes multimedia data into a common binary hash space, which can effectively measure the correlation between samples from different modalities. However, in the cross-media retrieval, supervised methods require a lot of manual labels, which leads to the problem of high labor in practical application. Simultaneously, most unsupervised methods do not achieve good results by preserving the correlation between or within modalities. To attack these problems and further improve retrieval performance, this paper proposes an unsupervised cross-media hashing retrieval method based on multi-head attention network, which contains rich semantic information to match images and texts better. Specifically, we make use of a multi-head attention network for generating binary hash code better. At the same time, an auxiliary similarity matrix is constructed to integrate the original neighborhood information from different modalities. Therefore, this method can capture the potential relationships between inter-modal and intra-modal correlations. Furthermore, the method is unsupervised and requires no additional semantic labels, so it has the potential to achieve large-scale cross-media retrieval. In addition, two strategies of batch normalization and replacing hash code generation functions are adopted to optimize the model, and two loss functions are designed to make the performance of our method exceed that of many supervised cross-media hashing retrieval methods. Experiments on three baseline datasets show that our method performs much better than many state-of-the-art methods. The results demonstrate the effectiveness and superiority of our method.
Impact of data visualization on decision-making and its implications for public health practice: a systematic literature review

Data visualization tools have the potential to support decision-making for public health professionals. This review summarizes the science and evidence regarding data visualization and its impact on decision-making behavior as informed by cognitive processes such as understanding, attitude, or perception.An electronic literature search was conducted using six databases, including reference list reviews. Search terms were pre-defined based on research questions.Sixteen studies were included in the final analysis. Data visualization interventions in this review were found to impact attitude, perception, and decision-making compared to controls. These relationships between the interventions and outcomes appear to be explained by mediating factors such as perceived trustworthiness and quality, domain-specific knowledge, basic beliefs shared by social groups, and political beliefs.Visualization appears to bring advantages by increasing the amount of information delivered and decreasing the cognitive and intellectual burden to interpret information for decision-making. However, understanding data visualization interventions specific to public health leaders' decision-making is lacking, and there is little guidance for understanding a participant's characteristics and tasks. The evidence from this review suggests positive effects of data visualization can be identified, depending on the control of confounding factors on attitude, perception, and decision-making.
Towards the simulation of large scale protein–ligand interactions on NISQ-era quantum computers

We explore the use of symmetry-adapted perturbation theory (SAPT) as a simple and efficient means to compute interaction energies between large molecular systems with a hybrid method combining NISQ-era quantum and classical computers. From the one- and two-particle reduced density matrices of the monomer wavefunctions obtained by the variational quantum eigensolver (VQE), we compute SAPT contributions to the interaction energy [SAPT(VQE)]. At first order, this energy yields the electrostatic and exchange contributions for non-covalently bound systems. We empirically find from ideal statevector simulations that the SAPT(VQE) interaction energy components display orders of magnitude lower absolute errors than the corresponding VQE total energies. Therefore, even with coarsely optimized low-depth VQE wavefunctions, we still obtain sub kcal mol-1 accuracy in the SAPT interaction energies. In SAPT(VQE), the quantum requirements, such as qubit count and circuit depth, are lowered by performing computations on the separate molecular systems. Furthermore, active spaces allow for large systems containing thousands of orbitals to be reduced to a small enough orbital set to perform the quantum portions of the computations. We benchmark SAPT(VQE) (with the VQE component simulated by ideal statevector simulators) against a handful of small multi-reference dimer systems and the iron center containing human cancer-relevant protein lysine-specific demethylase 5 (KDM5A).
A Survey on Radio Frequency Identification as a Scalable Technology to Face Pandemics

The COVID-19 pandemic drastically changed our way of living. To minimize life losses, multi-level strategies requiring collective efforts were adopted while waiting for the vaccines' rollout. The management of such complex processes has taken benefit from the rising framework of the Internet of Things (IoT), and particularly the Radiofrequency Identification (RFID) since it is probably the most suitable approach to both the micro (user) and the macro (processes) scale. Hence, a single infrastructure can support both the logistic and monitoring issues related to the war against a pandemic. Based on the COVID-19 experience, this paper is a survey on how state-of-the-art RFID systems can be employed in facing future pandemic outbreaks. The three pillars of the contrast of the pandemic are addressed: 1) use of Personal Protective Equipment (PPE), 2) access control and social distancing, and 3) early detection of symptoms. For each class, the envisaged RFID devices and procedures are discussed based on the available technology and the current worldwide research. This survey that RFID could generate an extraordinary amount of data so that complementary paradigms of Edge Computing and Artificial intelligence can be tightly integrated to extract profiles and identify anomalous events in compliance with privacy and security.
Contextually Plausible and Diverse 3D Human Motion Prediction

We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses. In this context, a popular approach consists of using a Conditional Variational Autoencoder (CVAE). However, existing approaches that do so either fail to capture the diversity in human motion, or generate diverse but semantically implausible continuations of the observed motion. In this paper, we address both of these problems by developing a new variational framework that accounts for both diversity and context of the generated future motion. To this end, and in contrast to existing approaches, we condition the sampling of the latent variable that acts as source of diversity on the representation of the past observation, thus encouraging it to carry relevant information. Our experiments demonstrate that our approach yields motions not only of higher quality while retaining diversity, but also that preserve the contextual information contained in the observed motion.
Introducing Image Classification Efficacies

Accuracy assessment is essential in all image classification-related fields, ranging from molecular imaging to earth observation. However, existing accuracy metrics are too sensitive to class imbalance or lack explicit interpretations for assessing classification performance. Consequently, their scores may be misleading when they are applied to compare classification algorithms that address different image data sources. These limitations jeopardize the widespread application of deep learning classification methods for classifying different image types. We introduce the metrics of image classification efficacy from medicine and pharmacology to overcome the limitations of accuracy metrics. We use mathematical proofs to derive the metrics of image classification efficacy and apply real-world and hypothetical examples to further examine their usefulness. Image classification efficacies can be applied at the map and class levels and for binary and multiclass classifications. The interpretability and comparability of image classification efficacies facilitate reliable classification method evaluation across data sources. We detail the procedures of classification efficacy assessment for image classification researchers and classifier users.
Optimal pricing decisions for a global fresh product supply chain in the blockchain technology era

This paper considers a global fresh product supply chain consisting of an overseas supplier and a local cross-border e-commerce platform, in which the supplier sells fresh products to the platform and then the platform sells them to consumers at the local market. We develop an analytical model to explore the effects of the adoption of blockchain technology (BCT) on the pricing decisions and profits of the supply chain members. The results show that the wholesale price and the retail price may decrease or increase when the supplier adopts BCT, and the adoption of BCT is not always beneficial to both the supplier and the retailer. The supplier should adopt BCT if the real product freshness is at a relatively high level. Furthermore, compared with the supplier, the retailer is more likely to benefit from the adoption of BCT.
Explainable AI for Chiller Fault-Detection Systems: Gaining Human Trust

We investigate the role of explainable Artificial Intelligence (XAI) for building trust in data-driven fault detection and diagnosis (FDD). We examine use cases for XAI-FDD on a building in Singapore that has six chillers.
Applying augmented reality (AR) technologies in theatrical performances in theme parks: A transcendent experience perspective

This study aims to investigate the attributes of AR theatrical performance in theme parks that impact a visitor's emotional experience and how it would do so. Data were obtained from tourists who had experienced AR theatrical performances at the Fantawild theme park in China. Drawing on a transcendent experience perspective, this study proposed and tested a theoretical model depicting the relationship between the perceived experiential quality of AR theatrical performance with five major dimensions—visual appeal, heightened enjoyment, education and curiosity, escapism, and focused immersion—as well as visitors' emotional responses (i.e., nostalgia, and emotional arousal) and feelings of belonging to the theme park, which were statistically analyzed via partial least squares structural equation modeling. The results showed that AR experiential quality exerted positive effects on visitors' nostalgia and emotional arousal, all of which triggered a feeling of belonging to the theme park.
Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 14 April 2020Accepted: 01 March 2021Published online: 05 October 2021Keywordstemporal difference learning, Polyak--Ruppert averaging, variance reductionAMS Subject Headings68Q25, 68R10, 68U05Publication DataISSN (online): 2577-0187Publisher: Society for Industrial and Applied MathematicsCODEN: sjmdaq
Low-power-consumption physical reservoir computing model based on overdamped bistable stochastic resonance system

The physical implementation of reservoir computing (RC), a brain-inspired computing framework, is attracting increasing attention in various research fields owing to its capability of quick learning and the relatively simple training process. Although several physical RC models have been envisaged and realized, most of them require additional peripherals; it leads to an increase in the power consumption of the system. In this study, we propose a novel RC model that is based on an overdamped bistable system and exhibits a counter-intuitive phenomenon called stochastic resonance, through which it can transfer noise energy to the information-carrying signal to realize learning with a comparatively low power consumption. The proposed model also possesses the functional capability of filtering and amplification and thus does not require additional peripheral equipment. In order to prove the feasibility and determine the desired operation mode of the system, two basic benchmark tests, namely short-term memory and parity check tasks, were employed to assess the proposed model. The results verify that this work will potentially act as a stepping stone towards realizing even better low-power-consumption physical RC.
Compass: Towards Better Causal Analysis of Urban Time Series

The spatial time series generated by city sensors allow us to observe urban phenomena like environmental pollution and traffic congestion at an unprecedented scale. However, recovering causal relations from these observations to explain the sources of urban phenomena remains a challenging task because these causal relations tend to be time-varying and demand proper time series partitioning for effective analyses. The prior approaches extract one causal graph given long-time observations, which cannot be directly applied to capturing, interpreting, and validating dynamic urban causality. This paper presents Compass, a novel visual analytics approach for in-depth analyses of the dynamic causality in urban time series. To develop Compass, we identify and address three challenges: detecting urban causality, interpreting dynamic causal relations, and unveiling suspicious causal relations. First, multiple causal graphs over time among urban time series are obtained with a causal detection framework extended from the Granger causality test. Then, a dynamic causal graph visualization is designed to reveal the time-varying causal relations across these causal graphs and facilitate the exploration of the graphs along the time. Finally, a tailored multi-dimensional visualization is developed to support the identification of spurious causal relations, thereby improving the reliability of causal analyses. The effectiveness of Compass is evaluated with two case studies conducted on the real-world urban datasets, including the air pollution and traffic speed datasets, and positive feedback was received from domain experts.
Insulator Breakage Detection Utilizing a Convolutional Neural Network Ensemble Implemented With Small Sample Data Augmentation and Transfer Learning

Online fault detection of insulators is a necessary requirement for the development of a smart grid, which directly affects the safety and reliability of power system operations. Breakage is an important factor causing insulator abnormalities, and a large number of image samples are often required for training to complete defect identification with the help of AI deep learning. In this paper, we propose a convolutional neural network ensemble implemented using small samples, data augmentation and transfer learning for insulator breakage detection. We used data augmentation to expand the small sample of 200 insulator images by 15 times to 3000 images, built a VGG16 transfer network, pre-trained the VGG16 network convolutional basis weights, and improved the training performance and efficiency of the model with freeze training and fine-tuning training. The training on 2700 images and testing results on 300 images show that the proposed method can achieve an accuracy of 0.9871 for insulator breakage recognition.