Reviewerly/exHarmony huggingface dataset:
Prevalence and Psychological Effects of Hateful Speech in Online College Communities

Hateful speech bears negative repercussions and is particularly damaging in college communities. The efforts to regulate hateful speech on college campuses pose vexing socio-political problems, and the interventions to mitigate the effects require evaluating the pervasiveness of the phenomenon on campuses as well the impacts on students' psychological state.Given the growing use of social media among college students, we target the above issues by studying the online aspect of hateful speech in a dataset of 6 million Reddit comments shared in 174 college communities. To quantify the prevelence of hateful speech in an online college community, we devise College Hate Index (CHX). Next, we examine its distribution across the categories of hateful speech, behavior, class, disability, ethnicity, gender, physical appearance, race, religion, and sexual orientation. We then employ a causal-inference framework to study the psychological effects of hateful speech, particularly in the form of individuals' online stress expression. Finally, we characterize their psychological endurance to hateful speech by analyzing their language- their discriminatory keyword use, and their personality traits.We find that hateful speech is prevalent in college subreddits, and 25% of them show greater hateful speech than non-college subreddits. We also find that the exposure to hate leads to greater stress expression. However, everybody exposed is not equally affected; some show lower psychological endurance than others. Low endurance individuals are more vulnerable to emotional outbursts, and are more neurotic than those with higher endurance.Our work bears implications for policy-making and intervention efforts to tackle the damaging effects of online hateful speech in colleges. From technological perspective, our work caters to mental health support provisions on college campuses, and to moderation efforts in online college communities. In addition, given the charged aspect of speech dilemma, we highlight the ethical implications of our work. Our work lays the foundation for studying the psychological impacts of hateful speech in online communities in general, and situated communities in particular (the ones that have both an offline and an online analog).
The Power of Tiling for Small Object Detection

Deep neural network based techniques are state-of-the-art for object detection and classification with the help of the development in computational power and memory efficiency. Although these networks are adapted for mobile platforms with sacrifice in accuracy; the resolution increase in visual sources makes the problem even harder by raising the expectations to leverage all the details in images. Real-time small object detection in low power mobile devices has been one of the fundamental problems of surveillance applications. In this study, we address the detection of pedestrians and vehicles onboard a micro aerial vehicle (MAV) with high-resolution imagery. For this purpose, we exploit PeleeNet, to our best knowledge the most efficient network model on mobile GPUs, as the backbone of an SSD network as well as 38x38 feature map in the earlier layer. After illustrating the low accuracy of state-of-the-art object detectors under the MAV scenario, we introduce a tiling based approach that is applied in both training and inference phases. The proposed technique limits the detail loss in object detection while feeding the network with a fixed size input. The improvements provided by the proposed approach are shown by in-depth experiments performed along Nvidia Jetson TX1 and TX2 using the VisDrone2018 dataset.
Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments

This letter presents a method for local motion planning in unstructured environments with static and moving obstacles, such as humans. Given a reference path and speed, our optimization-based receding-horizon approach computes a local trajectory that minimizes the tracking error while avoiding obstacles. We build on nonlinear model-predictive contouring control (MPCC) and extend it to incorporate a static map by computing, online, a set of convex regions in free space. We model moving obstacles as ellipsoids and provide a correct bound to approximate the collision region, given by the Minkowsky sum of an ellipse and a circle. Our framework is agnostic to the robot model. We present experimental results with a mobile robot navigating in indoor environments populated with humans. Our method is executed fully onboard without the need of external support and can be applied to other robot morphologies such as autonomous cars.
Unsupervised object discovery and co-localization by deep descriptor transformation

Reusable model design becomes desirable with the rapid expansion of computer vision and pattern recognition applications. In this paper, we focus on the reusability of pre-trained deep convolutional models. Specifically, different from treating pre-trained models as feature extractors, we reveal more treasures beneath convolutional layers, i.e., the convolutional activations could act as a detector for the common object in the object co-localization problem. We propose a simple yet effective method, termed Deep Descriptor Transformation (DDT), for evaluating the correlations of descriptors and then obtaining the category-consistent regions, which can accurately locate the common object in a set of unlabeled images, i.e., object co-localization. Empirical studies validate the effectiveness of the proposed DDT method. On benchmark object co-localization datasets, DDT consistently outperforms existing state-of-the-art methods by a large margin. Moreover, DDT also demonstrates good generalization ability for unseen categories and robustness for dealing with noisy data. Beyond those, DDT can be also employed for harvesting web images into valid external data sources for improving performance of both image recognition and object detection.
End-to-End Navigation Strategy With Deep Reinforcement Learning for Mobile Robots

In this article, we develop a navigation strategy based on deep reinforcement learning (DRL) for mobile robots. Because of the large difference between simulation and reality, most of the trained DRL models cannot be directly migrated into real robots. Moreover, how to explore in a sparsely rewarded environment is also a long-standing problem of DRL. This article proposes an end-to-end navigation planner that translates sparse laser ranging results into movement actions. Using this highly abstract data as input, agents trained by simulation can be extended to the real scene for practical application. For map-less navigation across obstacles and traps, it is difficult to reach the target via random exploration. Curiosity is used to encourage agents to explore the state of an environment that has not been visited and as an additional reward for exploring behavior. The agent relies on the self-supervised model to predict the next state, based on the current state and the executed action. The prediction error is used as a measure of curiosity. The experimental results demonstrate that without any manual design features and previous demonstrations, the proposed method accomplishes map-less navigation in complex environments. Through a reward signal that is enhanced by intrinsic motivation, the agent explores more efficiently, and the learned strategy is more reliable.
The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English

For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available.Besides the technical challenges of learning with limited supervision, it is difficult to evaluate methods trained on lowresource language pairs because of the lack of freely and publicly available benchmarks.In this work, we introduce the FLORES evaluation datasets for Nepali-English and Sinhala-English, based on sentences translated from Wikipedia.Compared to English, these are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available.We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised.Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on lowresource MT.Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores.
Evaluating the use of interactive virtual reality technology with older adults living in residential aged care

As technologies gain traction within the aged care community, better understanding their impact becomes vital. This paper reports on a study that explored the deployment of virtual reality (VR) as a tool to engage older adults in Residential Aged Care Facilities (RACF). The paper has two aims: 1) to identify the benefits and challenges associated with using VR with residents in aged care settings, and 2) to gather the views of older adult residents in RACF about the potential uses of VR in aged care. Five RACF residents and five RACF staff members took part in an intensive two-week evaluation of a VR system. Qualitative data was collected from multiple interviews and via researcher notes and video recordings made during the VR sessions. Results highlight the usability issues that impacted on the aged care residents' ability to use interactive VR technology and the potential negative impact head mounted displays can have on those living with dementia; the role that VR can play in engaging residents who might otherwise self-isolate, and how this can extend to increased engagement with family and friends. We discuss the design challenges that will need to be met in order to ensure that interactive VR technology can be used by residents living in aged care, and the potential for VR to be used as a tool to improve the quality of life of some older residents, particularly those for whom traditional social activities do not appeal.
Blockchain based uniform price double auctions for energy markets

The energy sector is undergoing a massive transformation that greatly affects and is being affected by its market mechanisms. This paper presents the design, the prototype implementation and the analysis of a decentralized, real-time, uniform-price double auction energy market. The proposed market is a distributed and decentralized application and its rules may be specified through a smart contract. Market participants interact with the smart contract sending their asks and offers and the distributed application clears the market based on a uniform-price economic dispatch model. We propose and analyze three different implementation approaches on an Ethereum blockchain network. A model representing a physical demonstration project conducted in Seattle, USA, is utilized to simulate the power grid and to provide real-life scenarios for our analysis. We systematically evaluate our three implementation approaches and elucidate several blockchain issues regarding the decentralization of next-generation energy markets. In particular, we identify that certain computation modules may be installed on smart consuming devices in order to increase efficiency, security and decentralization and reduce the overall blockchain overhead cost. Furthermore, we show that in order to fulfill the market’s high demand for step timing, we ought to have the block generation time several magnitudes smaller compared to the auction period.
Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering

This paper proposes a splicing forgery detection method with two parts: a coarse-to-refined convolutional neural network (C2RNet) and diluted adaptive clustering. The proposed C2RNet cascades a coarse convolutional neural network (C-CNN) and a refined CNN (R-CNN) and extracts the differences in the image properties between un-tampered and tampered regions from image patches with different scales. Further, to decrease the computational complexity, an image-level CNN is introduced to replace patch-level CNN in C2RNet. The proposed detection method learns the differences of various image properties to guarantee a stable detection performance, and the image-level CNN tremendously decreases its computational time. After the suspicious forgery regions are located by the proposed C2RNet, the final detected forgery regions are generated by applying the proposed adaptive clustering approach. The experiment results demonstrate that the proposed detection method achieves relatively promising results compared with state-of-the-art splicing forgery detection methods, even under various attack conditions.
Symbiotic Graph Neural Networks for 3D Skeleton-Based Human Action Recognition and Motion Prediction

3D skeleton-based action recognition and motion prediction are two essential problems of human activity understanding. In many previous works: 1) they studied two tasks separately, neglecting internal correlations; and 2) they did not capture sufficient relations inside the body. To address these issues, we propose a symbiotic model to handle two tasks jointly; and we propose two scales of graphs to explicitly capture relations among body-joints and body-parts. Together, we propose symbiotic graph neural networks, which contain a backbone, an action-recognition head, and a motion-prediction head. Two heads are trained jointly and enhance each other. For the backbone, we propose multi-branch multiscale graph convolution networks to extract spatial and temporal features. The multiscale graph convolution networks are based on joint-scale and part-scale graphs. The joint-scale graphs contain actional graphs, capturing action-based relations, and structural graphs, capturing physical constraints. The part-scale graphs integrate body-joints to form specific parts, representing high-level relations. Moreover, dual bone-based graphs and networks are proposed to learn complementary features. We conduct extensive experiments for skeleton-based action recognition and motion prediction with four datasets, NTU-RGB+D, Kinetics, Human3.6M, and CMU Mocap. Experiments show that our symbiotic graph neural networks achieve better performances on both tasks compared to the state-of-the-art methods.
A flexible high-performance simulator for verifying and benchmarking quantum circuits implemented on real hardware

Here we present qFlex, a flexible tensor network based quantum circuit simulator. qFlex can compute both exact amplitudes, essential for the verification of the quantum hardware, as well as low fidelity amplitudes, in order to mimic sampling from Noisy Intermediate-Scale Quantum (NISQ) devices. In this work, we focus on random quantum circuits (RQCs) in the range of sizes expected for supremacy experiments. Fidelity $f$ simulations are performed at a cost that is $1/f$ lower than perfect fidelity ones. We also present a technique to eliminate the overhead introduced by rejection sampling in most tensor network approaches. We benchmark the simulation of square lattices and Google's Bristlecone QPU. Our analysis is supported by extensive simulations on NASA HPC clusters Pleiades and Electra. For our most computationally demanding simulation, the two clusters combined reached a peak of 20 PFLOPS (single precision), i.e., $64\%$ of their maximum achievable performance, which represents the largest numerical computation in terms of sustained FLOPs and number of nodes utilized ever run on NASA HPC clusters. Finally, we introduce a novel multithreaded, cache-efficient tensor index permutation algorithm of general application.
Deep Meta Learning for Real-Time Target-Aware Visual Tracking

In this paper, we propose a novel on-line visual tracking framework based on the Siamese matching network and meta-learner network, which run at real-time speeds. Conventional deep convolutional feature-based discriminative visual tracking algorithms require continuous re-training of classifiers or correlation filters, which involve solving complex optimization tasks to adapt to the new appearance of a target object. To alleviate this complex process, our proposed algorithm incorporates and utilizes a meta-learner network to provide the matching network with new appearance information of the target objects by adding target-aware feature space. The parameters for the target-specific feature space are provided instantly from a single forward-pass of the meta-learner network. By eliminating the necessity of continuously solving complex optimization tasks in the course of tracking, experimental results demonstrate that our algorithm performs at a real-time speed while maintaining competitive performance among other state-of-the-art tracking algorithms.
On Network Design Spaces for Visual Recognition

Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity. Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape. As a case study, we examine design spaces used in neural architecture search (NAS). We find significant statistical differences between recent NAS design space variants that have been largely overlooked. Furthermore, our analysis reveals that the design spaces for standard model families like ResNeXt can be comparable to the more complex ones used in recent NAS work. We hope these insights into distribution analysis will enable more robust progress toward discovering better networks for visual recognition.
DeepFovea

In order to provide an immersive visual experience, modern displays require head mounting, high image resolution, low latency, as well as high refresh rate. This poses a challenging computational problem. On the other hand, the human visual system can consume only a tiny fraction of this video stream due to the drastic acuity loss in the peripheral vision. Foveated rendering and compression can save computations by reducing the image quality in the peripheral vision. However, this can cause noticeable artifacts in the periphery, or, if done conservatively, would provide only modest savings. In this work, we explore a novel foveated reconstruction method that employs the recent advances in generative adversarial neural networks. We reconstruct a plausible peripheral video from a small fraction of pixels provided every frame. The reconstruction is done by finding the closest matching video to this sparse input stream of pixels on the learned manifold of natural videos. Our method is more efficient than the state-of-the-art foveated rendering, while providing the visual experience with no noticeable quality degradation. We conducted a user study to validate our reconstruction method and compare it against existing foveated rendering and video compression techniques. Our method is fast enough to drive gaze-contingent head-mounted displays in real time on modern hardware. We plan to publish the trained network to establish a new quality bar for foveated rendering and compression as well as encourage follow-up research.
Deep Contextual Attention for Human-Object Interaction Detection

Human-object interaction detection is an important and relatively new class of visual relationship detection tasks, essential for deeper scene understanding. Most existing approaches decompose the problem into object localization and interaction recognition. Despite showing progress, these approaches only rely on the appearances of humans and objects and overlook the available context information, crucial for capturing subtle interactions between them. We propose a contextual attention framework for human-object interaction detection. Our approach leverages context by learning contextually-aware appearance features for human and object instances. The proposed attention module then adaptively selects relevant instance-centric context information to highlight image regions likely to contain human-object interactions. Experiments are performed on three benchmarks: V-COCO, HICO-DET and HCVRD. Our approach outperforms the state-of-the-art on all datasets. On the V-COCO dataset, our method achieves a relative gain of 4.4% in terms of role mean average precision (mAP role ), compared to the existing best approach.
Preserving Balance Between Privacy and Data Integrity in Edge-Assisted Internet of Things

Internet of Things (IoT) devices and the edge jointly broaden the IoT's sensing capability and the monitoring scope for various applications. Though accessing sensing data and making decisions through IoT smart devices turns out to be commonplace, it is challenging to guarantee user privacy and preserve the accuracy (integrity) of the collected data. The IoT smart devices frequently lose either IoT user's privacy or data integrity. This also makes it crucial to put a threshold on the cost of computation and load of the IoT devices, as gradually more IoT services demand access to the resources that devices offer. In this article, we propose BalancePIC, a scheme that attempts to preserve a balance in the three aspects (user privacy, data integrity in edge-assisted IoT devices, and the computational cost). It achieves the balance through a balanced truth discovery approach and a proposed enhanced technique for data privacy, which are used in IoT devices and edge server interactions. It authenticates the IoT user participation with privacy in the truth discovery process through a biometric-ECC-based authentication algorithm. The nature of the BalancePIC scheme is to straightforwardly provide the likelihood for a simple amendment on the cryptography technique and weight assignment. This lessens the overall computational cost for the IoT user devices but also restricts the communications between the user devices and the edge server, which is important for data integrity. We present an enhanced technique to preserve privacy by guarding the user from potential threats and suspicious data collection parties. To achieve this, BalancePIC takes steps to blur the original sensory data of the device by processing results in groups called zones. Simulation result analysis provides evidence for the balance preservation in the three aspects.
Re-ID Driven Localization Refinement for Person Search

Person search aims at localizing and identifying a query person from a gallery of uncropped scene images. Different from person re-identification (re-ID), its performance also depends on the localization accuracy of a pedestrian detector. The state-of-the-art methods train the detector individually, and the detected bounding boxes may be sub-optimal for the following re-ID task. To alleviate this issue, we propose a re-ID driven localization refinement framework for providing the refined detection boxes for person search. Specifically, we develop a differentiable ROI transform layer to effectively transform the bounding boxes from the original images. Thus, the box coordinates can be supervised by the re-ID training other than the original detection task. With this supervision, the detector can generate more reliable bounding boxes, and the downstream re-ID model can produce more discriminative embeddings based on the refined person localizations. Extensive experimental results on the widely used benchmarks demonstrate that our proposed method performs favorably against the state-of-the-art person search methods.