Reviewerly/exHarmony huggingface dataset:
Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline

Object detection is a comprehensively studied problem in autonomous driving. However, it has been relatively less explored in the case of fisheye cameras. The standard bounding box fails in fisheye cameras due to the strong radial distortion, particularly in the image's periphery. We explore better representations like oriented bounding box, ellipse, and generic polygon for object detection in fisheye images in this work. We use the IoU metric to compare these representations using accurate instance segmentation ground truth. We design a novel curved bounding box model that has optimal properties for fisheye distortion models. We also design a curvature adaptive perimeter sampling method for obtaining polygon vertices, improving relative mAP score by 4.9% compared to uniform sampling. Overall, the proposed polygon model improves mIoU relative accuracy by 40.3%. It is the first detailed study on object detection on fisheye cameras for autonomous driving scenarios to the best of our knowledge. The dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> comprising of 10,000 images along with all the object representations ground truth will be made public to encourage further research. We summarize our work in a short video with qualitative results at https://youtu.be/iLkOzvJpL-A.
Exploring the Effects of Blur and Deblurring to Visual Object Tracking

The existence of motion blur can inevitably influence the performance of visual object tracking. However, in contrast to the rapid development of visual trackers, the quantitative effects of increasing levels of motion blur on the performance of visual trackers still remain unstudied. Meanwhile, although image-deblurring can produce visually sharp videos for pleasant visual perception, it is also unknown whether visual object tracking can benefit from image deblurring or not. In this paper, we present a Blurred Video Tracking (BVT) benchmark to address these two problems, which contains a large variety of videos with different levels of motion blurs, as well as ground-truth tracking results. To explore the effects of blur and deblurring to visual object tracking, we extensively evaluate 25 trackers on the proposed BVT benchmark and obtain several new interesting findings. Specifically, we find that light motion blur may improve the accuracy of many trackers, but heavy blur usually hurts the tracking performance. We also observe that image deblurring is helpful to improve tracking accuracy on heavily-blurred videos but hurts the performance of lightly-blurred videos. According to these observations, we then propose a new general GAN-based scheme to improve a tracker's robustness to motion blur. In this scheme, a fine-tuned discriminator can effectively serve as an adaptive blur assessor to enable selective frames deblurring during the tracking process. We use this scheme to successfully improve the accuracy of 6 state-of-the-art trackers on motion-blurred videos.
Compositional Learning of Image-Text Query for Image Retrieval

In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (imagetext) query. Specifically, the query text prompts some modification in the query image and the task is to retrieve images with the desired modifications. For instance, a user of an E-Commerce platform is interested in buying a dress, which should look similar to her friend's dress, but the dress should be of white color with a ribbon sash. In this case, we would like the algorithm to retrieve some dresses with desired modifications in the query dress. We propose an autoencoder based model, ComposeAE, to learn the composition of image and text query for retrieving images. We adopt a deep metric learning approach and learn a metric that pushes composition of source image and text query closer to the target images. We also propose a rotational symmetry constraint on the optimization problem. Our approach is able to outperform the state-of-the-art method TIRG [24] on three benchmark datasets, namely: MIT-States, Fashion200k and Fashion IQ. In order to ensure fair comparison, we introduce strong baselines by enhancing TIRG method. To ensure reproducibility of the results, we publish our code here: https://github.com/ecom-research/ComposeAE.
For You, or For"You"?

Online communities provide spaces for people who are vulnerable and underserved to seek support and build community, such as LGBTQ+ people. Today, some online community spaces are mediated by algorithms. Scholarship has found that algorithms have become deeply embedded in the systems that mediate our routine engagements with the world. Yet, little is known about how these systems impact those who are most vulnerable in society. In this paper, we focus on people's everyday experiences with one algorithmic system, the short video sharing application TikTok. TikTok recently received press that it was suppressing and oppressing the identities of its growing LGBTQ+ user population through algorithmic and human moderation of LGBTQ+ creators and content related to LGBTQ+ identity. Through an interview study with 16 LGBTQ+ TikTok users, we explore people's everyday engagements and encounters with the platform. We find that TikTok's For You Page algorithm constructs contradictory identity spaces that at once support LGBTQ+ identity work and reaffirm LGBTQ+ identity, while also transgressing and violating the identities of individual users. We also find that people are developing self-organized practices in response to these transgressions and violations. We discuss the implications of algorithmic systems on people's identity work, and introduce the concept of algorithmic exclusion, and explore how people are building resilience following moments of algorithmic exclusion.
Audio- and Gaze-driven Facial Animation of Codec Avatars

Codec Avatars are a recent class of learned, photorealistic face models that accurately represent the geometry and texture of a person in 3D (i.e., for virtual reality), and are almost indistinguishable from video [28]. In this paper we describe the first approach to animate these parametric models in real-time which could be deployed on commodity virtual reality hardware using audio and/or eye tracking. Our goal is to display expressive conversations between individuals that exhibit important social signals such as laughter and excitement solely from la-tent cues in our lossy input signals. To this end we collected over 5 hours of high frame rate 3D face scans across three participants including traditional neutral speech as well as expressive and conversational speech. We investigate a multimodal fusion approach that dynamically identifies which sensor encoding should animate which parts of the face at any time. See the supplemental video which demonstrates our ability to generate full face motion far beyond the typically neutral lip articulations seen in competing work: https://research.fb.com/videos/audio-and-gaze-driven-facial-animation-of-codec-avatars/.
A Privacy-Preserving Homomorphic Scheme With Multiple Dimensions and Fault Tolerance for Metering Data Aggregation in Smart Grid

Advanced Metering Infrastructure (AMI) facilitates the communication between smart meters and network operators in smart grid. For better demand-response management, smart meters are supposed to send live or sometimes periodic consumption reports. If such reports are intercepted or eavesdropped by a malicious entity, customers’ privacy is compromised, since vital information can be inferred from power consumption data. In this article, we propose a novel homomorphic privacy-preserving protocol (called NHP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) for data aggregation that has a low computational cost compared to its rivals. It is fault-tolerant, supports multi-category aggregation, and can do batch verification at the intermediate aggregator as well as the central system. The proposed protocol is secure even when the gateway or aggregator turns malicious. It does not allow any compromised meter to find other users’ consumption information either. Moreover, in this scheme, the central system cannot infer any usage data even if it is curious and gains access to the data packets sent from meters to the intermediate aggregator. A comprehensive and comparative analysis is carried out at the end of this article which shows the advantages of the proposed scheme in terms of security features and cost.
New organizational changes with blockchain: a focus on the supply chain

Purpose The paper aims to present a systematic literature review (SLR) showing the benefits, challenges and future research of blockchain technology (BT) for the supply chain (SC), also suggesting how the features of BT can change the organizational aspects of the SC. Design/methodology/approach An SLR has been conducted to detect papers that contained the word “Blockchain” in their titles, keywords or abstracts. Consequently, a second filter to analyze BT papers for the SC was applied. Findings This paper shows through 31 variables classified into positive, negative and future directions of technology for the SC. For instance, BT will reduce time consuming of operations management and payments using smart contracts. In addition, integrating BT with other technologies will allow product tracking and sustainable production management. Research limitations/implications The selection of papers is limited to Scopus database and specifically to the Management Journal . Practical implications BT creates collaborative peer-to-peer and business-to-business markets. The technology automates several tasks such as order management, payment for goods, waste reduction and process control. Therefore, its use within the SCs will improve the productivity and profits of the participants. Originality/value This paper is focused on BT for the SC area with 60 articles analyzed. In addition, 13 variables on benefits, eight variables on challenges and 12 points on future research directions were analyzed. This work will help researchers and entrepreneurs to deepen about the changes that BT offers in SC.
Blockchain-empowered cloud architecture based on secret sharing for smart city

Recent advances in information technology go beyond the simple convenience offered to individuals, leading to smart city technology that provides urban facilities and better quality of life. Smart City is a heterogeneous system consisting of a large IoT-based network, offering various applications for citizens by collecting and analyzing real-time information. As intelligent, efficient digital systems, smart cities manage data by digitalizing all information. Despite the potential benefits of smart cities for citizen convenience, this digital information service can give rise to security and privacy issues. In particular, the challenge of relying on the Cloud Service Provider (CSP) is that it is difficult for individuals to manage large amounts of data in smart city environments. In this paper, we study blockchain and Secret Sharing to address personal information protection issues in external cloud services and improve data integrity and security by designing a distributed system. CSPs are connected in a blockchain to validate user data integrity and provide easy data access through Transaction. CSP uses the blockchain to store distributed users' information safely through the Secret Sharing algorithm as a distributed system with improved security of existing centralized systems. The proposed approach demonstrates improved security and privacy through security analysis and has faster transaction speed and data storage efficiency than existing research.
User Concerns &amp; Tradeoffs in Technology-facilitated COVID-19 Response

The COVID-19 pandemic spread across the world in late 2019 and early 2020. As the pandemic spread, technologists joined forces with public health officials to develop apps to support COVID-19 response. Yet, for these technological solutions to benefit public health, users must be willing to adopt these apps. This article details a framework of inputs to a user’s decision to adopt a COVID-19 contact-tracing app or other COVID-19 technology along four major axes: technology benefits, solution accuracy, privacy considerations, and mobile-related costs. This framework is empirically validated via both the literature and a demographically-representative survey of 1,000 Americans.
An improved elephant herding optimization using sine–cosine mechanism and opposition based learning for global optimization problems

An improved elephant herding optimization (EHOI) is proposed for continuous function optimization, financial stress prediction problem and two engineering optimization problems in this work. Elephant Herding Optimization (EHO) is a swarm-based algorithm and was inspired by the social behaviour of elephant clans. In the literature, EHO has received great attention from researchers due to its global optimization capability and ease of implementation. However, it has few limitations like random replacing of worst individual and lack of exploitation, which leads to slow convergence. In this work, EHO was enhanced with the help of the position updating mechanism of sine–cosine algorithm (SCA) and opposition-based learning (OBL). The separating operator in original EHO was replaced by the sine–cosine mechanism and followed by opposition-based learning was introduced to increase the performance of EHO. The proposed EHOI was compared with eight well-known meta-heuristic optimization algorithms (MAs) by using 23 classical benchmark functions, 10 modern CEC2019 benchmark test functions and two engineering optimization problems. From the results, it was observed that the proposed EHOI outperformed most of the selected MAs in terms of solution quality. A kernel extreme learning machine (KELM) model was optimized by improved EHO and applied to handle financial stress prediction. The efficiency of the proposed EHOI_KELM model was tested on two popular financial datasets and compared with popular classifiers, EHO_KELM and SCA_KELM models. The results demonstrate that the proposed EHOI_KELM model shows excellent performance than the popular classifiers, EHO_KELM & SCA_KELM models and it can also serve as an effective tool for financial prediction.
Fast and Secure Authentication in Virtual Reality Using Coordinated 3D Manipulation and Pointing

There is a growing need for usable and secure authentication in immersive virtual reality (VR). Established concepts (e.g., 2D authentication schemes) are vulnerable to observation attacks, and most alternatives are relatively slow. We present RubikAuth, an authentication scheme for VR where users authenticate quickly and secure by selecting digits from a virtual 3D cube that leverages coordinated 3D manipulation and pointing. We report on results from three studies comparing how pointing using eye gaze, head pose, and controller tapping impact RubikAuth’s usability, memorability, and observation resistance under three realistic threat models. We found that entering a four-symbol RubikAuth password is fast: 1.69–3.5 s using controller tapping, 2.35–4.68 s using head pose and 2.39 –4.92 s using eye gaze, and highly resilient to observations: 96–99.55% of observation attacks were unsuccessful. RubikAuth also has a large theoretical password space: 45 n for an n -symbols password. Our work underlines the importance of considering novel but realistic threat models beyond standard one-time attacks to fully assess the observation-resistance of authentication schemes. We conclude with an in-depth discussion of authentication systems for VR and outline five learned lessons for designing and evaluating authentication schemes.
Attention and Feature Fusion SSD for Remote Sensing Object Detection

Object detection is a basic topic in the field of remote sensing. However, remote sensing images usually suffer in complicated backgrounds, object scale variations, and small objects, which make remote sensing object detection still difficult. Although the existing two-stage methods have higher accuracy, their detection speed is slow. While the one-stage object detection algorithms such as single-shot detector (SSD) and YOLO, although they can achieve real-time detection, they have poor detection performance, especially for small objects. In this article, in order to further improve the remote sensing object detection performance of one-stage methods, we propose an end-to-end network named attention and feature fusion SSD. First, a multilayer feature fusion structure is designed to enhance the semantic information of the shallow features. Next, a dual-path attention module is introduced to screen the feature information. This module uses spatial attention and channel attention to suppress the background noise and highlight the key feature. Then, the feature representation ability of the network is further enhanced by a multiscale receptive field module. Finally, the loss function is optimized to alleviate the imbalance between the positive and negative samples. The experimental results on the DOTA and NWPU VHR-10 data sets verify the effectiveness of our method.
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems

Face modification systems using deep learning have become increasingly powerful and accessible. Given images of a person’s face, such systems can generate new images of that same person under different expressions and poses. Some systems can also modify targeted attributes such as hair color or age. This type of manipulated images and video have been coined Deepfakes. In order to prevent a malicious user from generating modified images of a person without their consent we tackle the new problem of generating adversarial attacks against such image translation systems, which disrupt the resulting output image. We call this problem disrupting deepfakes. Most image translation architectures are generative models conditioned on an attribute (e.g. put a smile on this person’s face). We are first to propose and successfully apply (1) class transferable adversarial attacks that generalize to different classes, which means that the attacker does not need to have knowledge about the conditioning class, and (2) adversarial training for generative adversarial networks (GANs) as a first step towards robust image translation networks. Finally, in our scenario, the deepfaker can adaptively blur the image and potentially mount a successful defense against disruption. We present a spread-spectrum adversarial attack, which evades blur defenses. We open-source our code.
An Empirical Study of Adversarial Examples on Remote Sensing Image Scene Classification

Deep neural networks (DNNs), which learn a hierarchical representation of features, have shown remarkable performance in big data analytics of remote sensing. However, previous research indicates that DNNs are easily spoofed by adversarial examples, which are crafted images with artificial perturbations that fool DNN models toward wrong predictions. To comprehensively evaluate the impact of adversarial examples on the remote sensing image (RSI) scene classification, this study tests eight state-of-the-art classification DNNs on six RSI benchmarks. These data sets include both optical and synthetic-aperture radar (SAR) images of different spectral and spatial resolutions. In the experiment, we create 48 classification scenarios and use four cutting-edge attack algorithms to investigate the influence of the adversarial example on the classification of RSIs. The experimental result shows that the fooling rates of the attacks are all over 98% across the 48 scenarios. We also find that, for the optical data, the seriousness of the adversarial problem has a negative relationship with the richness of the feature information. Besides, adversarial examples generated from SAR images are used easily for fooling the models with an average fooling rate of 76.01%. By analyzing the class distribution of these adversarial examples, we find that the distribution of the misclassifications is not affected by the types of models and attack algorithms-adversarial examples of RSIs of the same class cluster on fixed several classes. The analysis of classes of adversarial examples not only helps us explore the relationships between data set classes but also provides insights for further designing defensive algorithms.
A Framework for Extended Reality System Development in Manufacturing

This paper presents a framework for developing extended reality (XR) systems within manufacturing context. The aim of this study is to develop a systematic framework to improve the usability and user acceptance of future XR systems. So that manufacturing industry can move from the “wow effect” of XR demonstrators into the stage whereas XR systems can be successfully integrated and improve the conventional work routines. It is essential to ensure the usability and user acceptance of XR systems for the wider adoption in manufacturing. The proposed framework was developed through six case studies that covered different XR system developments for different application areas of manufacturing. The framework consists of five iterative phases: (1) requirements analysis, (2) solution selection, (3) data preparation, (4) system implementation and (5) system evaluation. It is validated through one empirical case and seven identified previous studies, which partly aligned with the proposed framework. The proposed framework provides a clear guideline on the steps needed to integrate XR in manufacturing and it extends the XR usage with increased usability and user acceptance. Furthermore, it strengthens the importance of user-centered approach for XR system development in manufacturing.
Minimizing the Overlapping Degree to Improve Class-Imbalanced Learning Under Sparse Feature Selection: Application to Fraud Detection

In recent years, the classification of class-imbalanced data has obtained increasing attention across different scientific areas such as fraud detection, metabolomics, Cancer diagnosis, etc. This interest comes after proving the negative effect of overlapping on the performance of class-imbalanced learning. Based on augmented R-value, our proposed strategy aims to select features that make data achieve the minimal overlap degree, so improving the performance of classification as well. In this context, we present three feature selection algorithms RONS (Reduce Overlapping with No-sampling), ROS (Reduce Overlapping with SMOTE), and ROA (Reduce Overlapping with ADASYN), which are built through sparse feature selection to minimize the overlapping and perform binary classification. Also, a re-sampling process has been included in both ROS and ROA. Simulation results show that our proposed algorithms as feature selection methods manage the variation of false discovery rate during the selection of main features for the process modeling. For the experiment, four credit card datasets have been selected to test the performance of our algorithms. Using F-measure and Gmean evaluation metrics, the results reveal that our proposed algorithms are considerably recommended compared with classical feature selection methods. Besides, this effective feature selection strategy can be extended as an alternative to deal with class-imbalanced learning problems that involve overlapping.