Reviewerly/exHarmony huggingface dataset:
Do You Care Who Flagged This Post? Effects of Moderator Visibility on Bystander Behavior

Abstract This study evaluates whether increasing information visibility around the identity of a moderator influences bystanders’ likelihood to flag subsequent unmoderated harassing comments. In a 2-day preregistered experiment conducted in a realistic social media simulation, participants encountered ambiguous or unambiguous harassment comments, which were ostensibly flagged by either other users, an automated system (AI), or an unidentified moderation source. The results reveal that visibility of a content moderation source inhibited participants’ flagging of a subsequent unmoderated harassment comment, presumably because their efforts were seen as dispensable, compared to when the moderation source was unknown. On the contrary, there was an indirect effect of other users versus AI as moderation source on subsequent flagging through changes in perceived social norms. Overall, this research shows that the effects of moderation transparency are complex, as increasing visibility of a content moderator may inadvertently inhibit bystander intervention. Lay Summary This study examines the effects of flagging unmoderated offensive posts on social media, and how this changes the users’ subsequent behavior. We examined users’ reactions to the flagging of these posts by other users, an automated system, or an unspecified process to determine whether this affects the users’ ensuing behavior. A 2-day experiment on a simulated social media site showed that the visibility of the “flagger” impacts how users perceive social norms and think about the accountability for their own online actions. The results showed that the visibility of the person/system that flagged the material generally deterred subsequent flagging. The analysis also shows that the effect was stronger when the users thought that it was other users, and not an automated system, that had flagged the online harassment.
A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations

The COVID-19 pandemic is changing daily routines for many citizens with a high impact on the economy in some sectors. Small-medium enterprises of some sectors need to be aware of both the pandemic evolution and the corresponding sentiments of customers in order to figure out which are the best commercialization techniques. This article proposes an expert system based on the combination of machine learning and sentiment analysis in order to support business decisions with data fusion through web scraping. The system uses human-centric artificial intelligence for automatically generating explanations. The expert system feeds from online content from different sources using a scraping module. It allows users to interact with the expert system providing feedback, and the system uses this feedback to improve its recommendations with supervised learning.
JANOS: An Integrated Predictive and Prescriptive Modeling Framework

Business research practice is witnessing a surge in the integration of predictive modeling and prescriptive analysis. We describe a modeling framework JANOS that seamlessly integrates the two streams of analytics, allowing researchers and practitioners to embed machine learning models in an end-to-end optimization framework. JANOS allows for specifying a prescriptive model using standard optimization modeling elements such as constraints and variables. The key novelty lies in providing modeling constructs that enable the specification of commonly used predictive models within an optimization model, have the features of the predictive model as variables in the optimization model, and incorporate the output of the predictive models as part of the objective. The framework considers two sets of decision variables: regular and predicted. The relationship between the regular and the predicted variables is specified by the user as pretrained predictive models. JANOS currently supports linear regression, logistic regression, and neural network with rectified linear activation functions. In this paper, we demonstrate the flexibility of the framework through an example on scholarship allocation in a student enrollment problem and provide a numeric performance evaluation. Summary of Contribution. This paper describes a new software tool, JANOS, that integrates predictive modeling and discrete optimization to assist decision making. Specifically, the proposed solver takes as input user-specified pretrained predictive models and formulates optimization models directly over those predictive models by embedding them within an optimization model through linear transformations.
A Skeleton-Driven Neural Occupancy Representation for Articulated Hands

We present Hand ArticuLated Occupancy (HALO), a novel representation of articulated hands that bridges the advantages of 3D keypoints and neural implicit surfaces and can be used in end-to-end trainable architectures. Unlike existing statistical parametric hand models (e.g. MANO), HALO directly leverages the 3D joint skeleton as input and produces a neural occupancy volume representing the posed hand surface. The key benefits of HALO are (1) it is driven by 3D keypoints, which have benefits in terms of accuracy and are easier to learn for neural networks than the latent hand-model parameters; (2) it provides a differentiable volumetric occupancy representation of the posed hand, (3) it can be trained end-to-end, allowing the formulation of losses on the hand surface that benefit the learning of 3D keypoints. We demonstrate the applicability of HALO to the task of conditional generation of hands that grasp 3D objects. The differentiable nature of HALO is shown to improve the quality of the synthesized hands both in terms of physical plausibility and user preference.
Engaging Part-Whole Hierarchies and Contrast Cues for Salient Object Detection

Real-world scenes always exhibit objects with clutter backgrounds, posing great challenges for deep salient object detection models. In this paper, we propose salient object detection by engaging two saliency cues, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the part-whole hierarchies and contrast cues, resulting in a PWHCNet. Specifically, two branches, which consists of a Dynamic Grouping Capsules (DGC) branch and a DenseHRNet branch, are put in place to learn the part-whole hierarchies and contrast cues, respectively. Moreover, to help highlight the whole salient object in complex scenes, a Background Suppression (BS) module is proposed to guide the shallow features of DenseHRNet with the aid of the part-whole relational cues captured by DGC. Subsequently, these two saliency cues are integrated via a Self-Channel and Mutual-Spatial (SCMS) attention mechanism. Experimental results on five benchmarks demonstrate that the proposed PWHCNet achieves state-of-the-art performance while obtaining the whole salient objects with fine details.
Wnet ++: A Nested W-shaped Network with Multiscale Input and Adaptive Deep Supervision for Osteosarcoma Segmentation

In this paper, a novel and more powerful architecture W-net++ was proposed based on two cascaded U-Nets and dense skip connections to realize the automatic and more accurate segmentation of osteosarcoma lesion in computed tomography (CT) images. In this network, multiscale inputs were applied to the architecture to recover the missing spatial details caused by multiple encoding and subsampling of the encoder; adaptive deep supervision was introduced to guide the multi-scale learning of the network to speed up convergence and improve the performance of the network; channel attention module (CAM) was incorporated to selectively emphasize the interdependent channel graphs and further improve the feature representation of the model. We have evaluated the proposed architecture and compared it with the-state-of-the-art methods by 5-fold cross validation on a home-built dataset of osteosarcoma CT images. Our experiments demonstrated that our method achieves an average DSC gain of 6.17 points, 1.91 points, 1.55 points over U-Net, U-Net++, MSRN, respectively.
Modeling the Process by Which People Try to Explain Complex Things to Others

The process of explaining something to another person is more than offering a statement. Explaining means taking the perspective and knowledge of the Learner into account and determining whether the Learner is satisfied. While the nature of explanation—conceived of as a set of statements—has been explored philosophically and empirically, the process of explaining, as an activity, has received less attention. We conducted an archival study, looking at 73 cases of explaining. We were particularly interested in cases in which the explanations focused on the workings of complex systems or technologies. The results generated two models: local explaining to address why a device (such an intelligent system) acted in a surprising way, and global explaining about how a device works. The examination of the processes of explaining as it occurs in natural settings revealed a number of mistaken beliefs about how explaining happens, and what constitutes an explanation that encourages learning.
DARE: Deceiving Audio–Visual speech Recognition model

Audio–Visual speech recognition (AVSR) is an effective way to predict text corresponding to the spoken words using both audio and face videos, even in a noisy environment. These models find extensive applications in various fields like assisting hearing-impaired, biometric verification and speaker verification. Adversarial examples are created by adding imperceptible perturbations to the original input resulting in an incorrect classification by the deep learning models. Attacking an AVSR model is quite challenging, as both audio and visual modalities complement each other. Moreover, the correlation between audio and video features decreases while crafting an adversarial example, which can be used for detecting the adversarial example. We propose an end-to-end targeted attack, Deceiving Audio–visual speech Recognition model (DARE), which successfully performs an imperceptible adversarial attack while remaining undetected by the existing synchronisation-based detection network, SyncNet. To this end, we are the first to perform an adversarial attack that fools the AVSR model and SyncNet simultaneously. Experimental results on the publicly available dataset using state-of-the-art AVSR model reveal that the proposed attack can successfully deceive the AVSR model while remaining undetected. Furthermore, our DARE attack circumvents the well-known defences while maintaining a 100% targeted attack success rate.
Alexa, Who Am I Speaking To?: Understanding Users’ Ability to Identify Third-Party Apps on Amazon Alexa

Many Internet of Things devices have voice user interfaces. One of the most popular voice user interfaces is Amazon’s Alexa, which supports more than 50,000 third-party applications (“skills”). We study how Alexa’s integration of these skills may confuse users. Our survey of 237 participants found that users do not understand that skills are often operated by third parties, that they often confuse third-party skills with native Alexa functions, and that they are unaware of the functions that the native Alexa system supports. Surprisingly, users who interact with Alexa more frequently are more likely to conclude that a third-party skill is a native Alexa function. The potential for misunderstanding creates new security and privacy risks: attackers can develop third-party skills that operate without users’ knowledge or masquerade as native Alexa functions. To mitigate this threat, we make design recommendations to help users better distinguish native functionality and third-party skills, including audio and visual indicators of native and third-party contexts, as well as a consistent design standard to help users learn what functions are and are not possible on Alexa.
DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles

Safe UAV navigation is challenging due to the complex environment structures, dynamic obstacles, and uncertainties from measurement noises and unpredictable moving obstacle behaviors. Although plenty of recent works achieve safe navigation in complex static environments with sophisticated mapping algorithms, such as occupancy map and ESDF map, these methods cannot reliably handle dynamic environments due to the mapping limitation from moving obstacles. To address the limitation, this paper proposes a trajectory planning framework to achieve safe navigation considering complex static environments with dynamic obstacles. To reliably handle dynamic obstacles, we divide the environment representation into static mapping and dynamic object representation, which can be obtained from computer vision methods. Our framework first generates a static trajectory based on the proposed iterative corridor shrinking algorithm. Then, reactive chance-constrained model predictive control with temporal goal tracking is applied to avoid dynamic obstacles with uncertainties. The simulation results in various environments demonstrate the ability of our algorithm to navigate safely in complex static environments with dynamic obstacles.
BINet: Bidirectional interactive network for salient object detection

Existing state-of-the-art methods for salient object detection mainly achieve significant progress by integrating multi-level features. However, most of them simply resize the multi-level features to the same spatial resolution and aggregate them by channel-wise concatenation or element-wise addition. Thus, they are limited to integrating features of adjacent layers due to large discrepancy between these multi-level features. Besides, most existing methods adopt the U-shape architecture where high-level semantic information is gradually transmitted to shallower layers to suppress the background noises. Nonetheless, little attention has been paid to refine high-level features. In this paper, we propose the BINet to solve the above problems. Specifically, we design the feature interaction module (FIM) to exploit complementary information between multiple features, which is then utilized to simultaneously refine these features. Besides, we propose the cascaded bidirectional interaction decoder (CBID) to further refine multi-level features iteratively. Our CBID progressively refines multi-level features with feedback mechanisms and employs a channel attention module (CAM) to assign larger weights to channels showing higher response to salient regions. Equipped with these modules, our BINet is capable of segmenting salient regions accurately and quickly. Experimental results on six widely used benchmark datasets validate that our BINet outperforms 16 other state-of-the-art methods in terms of 7 standard evaluation metrics. Our code will be publicly available at https://github.com/clelouch/BINet.
Macroscopic Circuit Quantum Electrodynamics: A New Look Toward Developing Full-Wave Numerical Models

Devices built using superconducting circuits are a popular approach being pursued to develop quantum information processing hardware. These systems involve the quantized interactions of electromagnetic fields with large coherent collections of charges in superconductors, and so are typically referred to as macroscopic circuit quantum electrodynamics (QED) devices or simply circuit QED devices. Although significant progress has been made recently, substantial engineering efforts are required to improve performance and scale these devices so they can address problems of practical interest. Performing these engineering efforts is difficult due to the absence of rigorous numerical modeling approaches. This work begins to address this by providing a new mathematical description of a commonly used circuit QED system, a transmon qubit coupled to microwave transmission lines. Expressed in terms of three-dimensional vector fields, our model is better suited to developing rigorous numerical solvers than approximate lumped element circuit descriptions used in the literature. We present details on the quantization of our model, and derive equations of motion for the coupled field-transmon system. These results can be used in developing full-wave solvers in the future. To make this work more accessible, we assume a limited amount of training in quantum physics and provide many background details.
Improving Pedestrian Attribute Recognition with Multi-Scale Spatial Calibration

Pedestrian Attribute Recognition (PAR) has attracted increasing attention since it could provide important structural information of pedestrians for Smart Video Analysis. However, the pedestrian images are taken from a far distance significantly increase the difficulty of PAR for fine-grained attributes. To address these problems, and further improve the effects of PAR, we proposed a Multi-Scale Spatial Calibration (MSSC) module. More specifically, the module includes two submodules: first, a Spatial Calibrated Module (SCM) is proposed to extract more discriminative features of inconspicuous attributes from its surrounding regions by gathering the contextual information across different receptive fields. Moreover, in order to build the long-range dependencies of pyramid feature maps in different spatial scales, we also propose Multi-Scale Feature Fusion (MSFF) to integrate the multiple branches of low-level detailed features and high-level semantics features by non-local attention mechanism. Extensive experiments show that our proposed model could achieve state-of-the-art results on three pedestrian attribute datasets, including RAPv1, PA-100K, and RAPv2. Especially, the proposed model significantly improves the recognition effects of fine-grained attributes in low-resolution images in terms of mean Accuracy (mA) and recall. Code is available at https://github.com/iceicei/MSSC.
Public-Key Authenticated Encryption with Keyword Search: A Generic Construction and Its Quantum-Resistant Instantiation

Abstract The industrial Internet of Things (IIoT) integrates sensors, instruments, equipment and industrial applications, enabling traditional industries to automate and intelligently process data. To reduce the cost and demand of required service equipment, IIoT relies on cloud computing to further process and store data. Public-key encryption with keyword search (PEKS) plays an important role, due to its search functionality, to ensure the privacy and confidentiality of the outsourced data and the maintenance of flexibility in the use of the data. Recently, Huang and Li proposed the ‘public-key authenticated encryption with keyword search’ (PAEKS) to avoid the insider keyword guessing attacks (IKGAs) in the previous PEKS schemes. However, all current PAEKS schemes are based on the discrete logarithm assumption and are therefore vulnerable to quantum attacks. In this study, we first introduce a generic PAEKS construction, with the assistance of a trusted authority, that enjoys the security against IKGA in the standard model, if all building blocks are secure under standard model. Based on the framework, we further propose a novel instantiation of quantum-resistant PAEKS that is based on NTRU assumption under random oracle. Compared with its state-of-the-art counterparts, the experiment result indicates that our instantiation is more efficient and secure.
Performance-Efficiency Trade-Offs in Unsupervised Pre-Training for Speech Recognition

This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW-D (Squeezed and Efficient Wav2vec with Disentangled Attention), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW-D achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25–50% across different model sizes.
Automatic segmentation of blood cells from microscopic slides: A comparative analysis

With the recent developments in deep learning, automatic cell segmentation from images of microscopic examination slides seems to be a solved problem as recent methods have achieved comparable results on existing benchmark datasets. However, most of the existing cell segmentation benchmark datasets either contain a single cell type, few instances of the cells, not publicly available. Therefore, it is unclear whether the performance improvements can generalize on more diverse datasets. In this paper, we present a large and diverse cell segmentation dataset BBBC041Seg1, which consists both of uninfected cells (i.e., red blood cells/RBCs, leukocytes) and infected cells (i.e., gametocytes, rings, trophozoites, and schizonts). Additionally, all cell types do not have equal instances, which encourages researchers to develop algorithms for learning from imbalanced classes in a few shot learning paradigm. Furthermore, we conduct a comparative study using both classical rule-based and recent deep learning state-of-the-art (SOTA) methods for automatic cell segmentation and provide them as strong baselines. We believe the introduction of BBBC041Seg will promote future research towards clinically applicable cell segmentation methods from microscopic examinations, which can be later used for downstream tasks such as detecting hematological diseases (i.e., malaria).
Magnitude Estimation for Earthquake Early Warning with Multiple Parameter Inputs and a Support Vector Machine

Abstract Accurately estimating the magnitude within the initial seconds after the P-wave arrival is of great significance in earthquake early warning (EEW). Over the past few decades, single-parameter approaches such as the τc and Pd methods have been applied to EEW magnitude estimation studies considering the first 3 s after the P-wave onset. However, these methods present considerable scatter and are affected by the signal-to-noise ratio (SNR) and epicentral distance. In this study, using Japanese K-NET strong-motion data, we propose a machine-learning method comprising multiple parameter inputs, namely, the support vector machine magnitude estimation (SVM-M) model, to determine earthquake magnitudes and resolve the aforementioned problems. Our results using a single seismological station record show that the standard deviation of the magnitude prediction errors of the SVM-M model is 0.297, which is less than those of the τc (1.637) and Pd (0.425) methods. The magnitudes estimated by the SVM-M model within 3 s after the P-wave arrival are not obviously affected by the SNR or epicentral distance, and not overestimated for MJMA≤5. In addition, in an offline EEW application, the magnitude estimation error of the SVM-M model gradually decreases with increasing time after the first station is triggered, and the underestimation of event magnitudes for 6.5≤MJMA gradually improves. These results demonstrate that the proposed SVM-M model can robustly estimate earthquake magnitudes and has potential for EEW.