Reviewerly/exHarmony huggingface dataset:
Upgrading Optical Flow to 3D Scene Flow Through Optical Expansion

We describe an approach for upgrading 2D optical flow to 3D scene flow. Our key insight is that dense optical expansion - which can be reliably inferred from monocular frame pairs - reveals changes in depth of scene elements, e.g., things moving closer will get bigger. When integrated with camera intrinsics, optical expansion can be converted into a normalized 3D scene flow vectors that provide meaningful directions of 3D movement, but not their magnitude (due to an underlying scale ambiguity). Normalized scene flow can be further “upgraded” to the true 3D scene flow knowing depth in one frame. We show that dense optical expansion between two views can be learned from annotated optical flow maps or unlabeled video sequences, and applied to a variety of dynamic 3D perception tasks including optical scene flow, LiDAR scene flow, time-to-collision estimation and depth estimation, often demonstrating significant improvement over the prior art.
Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm

A popular method for anomaly detection is to use the generator of an adversarial network to formulate anomaly score over reconstruction loss of input. Due to the rare occurrence of anomalies, optimizing such networks can be a cumbersome task. Another possible approach is to use both generator and discriminator for anomaly detection. However, attributed to the involvement of adversarial training, this model is often unstable in a way that the performance fluctuates drastically with each training step. In this study, we propose a framework that effectively generates stable results across a wide range of training steps and allows us to use both the generator and the discriminator of an adversarial model for efficient and robust anomaly detection. Our approach transforms the fundamental role of a discriminator from identifying real and fake data to distinguishing between good and bad quality reconstructions. To this end, we prepare training examples for the good quality reconstruction by employing the current generator, whereas poor quality examples are obtained by utilizing an old state of the same generator. This way, the discriminator learns to detect subtle distortions that often appear in reconstructions of the anomaly inputs. Extensive experiments performed on Caltech-256 and MNIST image datasets for novelty detection show superior results. Furthermore, on UCSD Ped2 video dataset for anomaly detection, our model achieves a frame-level AUC of 98.1%, surpassing recent state-of-the-art methods.
Articulation-Aware Canonical Surface Mapping

We tackle the tasks of: 1) predicting a Canonical Surface Mapping (CSM) that indicates the mapping from 2D pixels to corresponding points on a canonical template shape , and 2) inferring the articulation and pose of the template corresponding to the input image. While previous approaches rely on keypoint supervision for learning, we present an approach that can learn without such annotations. Our key insight is that these tasks are geometrically related, and we can obtain supervisory signal via enforcing consistency among the predictions. We present results across a diverse set of animal object categories, showing that our method can learn articulation and CSM prediction from image collections using only foreground mask labels for training. We empirically show that allowing articulation helps learn more accurate CSM prediction, and that enforcing the consistency with predicted CSM is similarly critical for learning meaningful articulation.
Oops! Predicting Unintentional Action in Video

From just a short glance at a video, we can often tell whether a person's action is intentional or not. Can we train a model to recognize this? We introduce a dataset of in-the-wild videos of unintentional action, as well as a suite of tasks for recognizing, localizing, and anticipating its onset. We train a supervised neural network as a baseline and analyze its performance compared to human consistency on the tasks. We also investigate self-supervised representations that leverage natural signals in our dataset, and show the effectiveness of an approach that uses the intrinsic speed of video to perform competitively with highly-supervised pretraining. However, a significant gap between machine and human performance remains.
Data-Driven Based Tiny-YOLOv3 Method for Front Vehicle Detection Inducing SPP-Net

In order to solve the problem of low recognition rate and low real-time performance of vehicle detection in complex road environment, a data-driven forward vehicle detection algorithm based on improved tiny-YOLOv3 is proposed. Based on tiny-YOLOv3, the context feature information is combined to increase the two scale detections of tiny-YOLOv3 to three. The spatial pyramid pooling (SPP) module is added to increase the number of feature channels to improve the network feature extraction ability. According to the dense arrangement of vehicles on the horizontal axis in the road image ahead, we change the grid size of tiny-YOLOv3 and increase the number of candidate boxes on the horizontal axis. In addition, combined with the characteristics of the vehicle size in the road image ahead, K-means clustering method is used to select the appropriate number and size of target candidate boxes. We obtain the optimal detection model by multi-scale training of the improved network. The experimental results show that the average accuracy of the improved algorithm on the KITTI datasets is 91.03%, which is 7.12% higher than that of tiny-YOLOv3. And the detection speed of improved network is 144 frames/s, which meets the real-time requirements.
End-to-End Adversarial-Attention Network for Multi-Modal Clustering

Multi-modal clustering aims to cluster data into different groups by exploring complementary information from multiple modalities or views. Little work learns the deep fused representations and simutaneously discovers the cluster structure with a discriminative loss. In this paper, we present an End-to-end Adversarial-attention network for Multi-modal Clustering (EAMC), where adversarial learning and attention mechanism are leveraged to align the latent feature distributions and quantify the importance of modalities respectively. To benefit from the joint training, we introducea divergence-based clustering objective that not only encourages the separation and compactness of the clusters but also enjoy a clear cluster structure by embedding the simplex geometry of the output space into the loss. The proposed network consists of modality-specific feature learning, modality fusion and cluster assignment three modules. It can be trained from scratch with batch-mode based optimization and avoid an autoencoder pretraining stage. Comprehensive experiments conducted on five real-world datasets show the superiority and effectiveness of the proposed clustering method.
Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media

The availability of large annotated corpora from social media and the development of powerful classification approaches have contributed in an unprecedented way to tackle the challenge of monitoring users’ opinions and sentiments in online social platforms across time. Such linguistic data are strongly affected by events and topic discourse, and this aspect is crucial when detecting phenomena such as hate speech, especially from a diachronic perspective. We address this challenge by focusing on a real case study: the “Contro l’odio” platform for monitoring hate speech against immigrants in the Italian Twittersphere. We explored the temporal robustness of a BERT model for Italian (AlBERTo), the current benchmark on non-diachronic detection settings. We tested different training strategies to evaluate how the classification performance is affected by adding more data temporally distant from the test set and hence potentially different in terms of topic and language use. Our analysis points out the limits that a supervised classification model encounters on data that are heavily influenced by events. Our results show how AlBERTo is highly sensitive to the temporal distance of the fine-tuning set. However, with an adequate time window, the performance increases, while requiring less annotated data than a traditional classifier.
Urban rail transit operation safety evaluation based on an improved CRITIC method and cloud model

Urban rail transit operation safety based on a cloud model and improved Criteria Importance Through Intercriteria Correlation (CRITIC) method was evaluated, considering both existing expertise and uncertainties such as fuzziness and randomness of such evaluation. A two-level index system was constructed to evaluate urban rail transit operation safety. The evaluation scale was then chosen. The cloud model index system to obtain the evaluation set and cloud was determined. An improved CRITIC method was adopted to specify the weights of all indices. The normal cloud model for the comprehensive index cloud and evaluation set was measured next. The rationale and validity of the model were verified, and the results noted. The index system could indeed be scientifically, systematically and objectively evaluated based on a cloud model and improved CRITIC method. The evaluation results agreed well with practical results. This study provides a reference and theoretical basis for the operational safety planning and management for urban rail transit.
A Systematic Study on the Recommender Systems in the E-Commerce

Electronic commerce or e-commerce includes the service and good exchange through electronic support like the Internet. It plays a crucial role in today's business and users' experience. Also, e-commerce platforms produce a vast amount of information. So, Recommender Systems (RSs) are a solution to overcome the information overload problem. They provide personalized recommendations to improve user satisfaction. The present article illustrates a comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of e-commerce recommender systems. We reviewed the selected papers to identify the gaps and significant issues of the RSs' traditional methods, which guide the researchers to do future work. So, we provided the traditional techniques, challenges, and open issues concerning traditional methods of the field of review based on the selected papers. This review includes five categories of the RSs' algorithms, including Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic-Based Filtering (DBF), hybrid filtering, and Knowledge-Based Filtering (KBF). Also, the salient points of each selected paper are briefly reported. The publication time of the selected papers ranged from 2008 to 2019. Also, we provided a comparison table of important issues of the selected papers as well as the tables of advantages and disadvantages. Moreover, we provided a comparative table of metrics and review issues for the selected papers. And finally, the conclusions can, to a great extent, provide valuable guidelines for future studies.
Fast sparse coding networks for anomaly detection in videos

The semi-supervised video anomaly detection assumes that only normal video clips are available for training. Therefore, the intuitive idea is either to learn a dictionary by sparse coding or to train encoding-decoding neural networks by minimizing the reconstruction errors. For the former, the optimization of sparse coefficients is extremely time-consuming. For the latter, this manner cannot guarantee that an abnormal data corresponds to a larger reconstruction error due to the strong generalization of neural networks. To remedy their weaknesses and leverage their strengths, we propose a Fast Sparse Coding Network (FSCN) based on High-level Features. First, we propose a two-stream neural network to extract Spatial-Temporal Fusion Features (STFF) in hidden layers. With the STFF at hand, we use a Fast Sparse Coding Network to build a normal dictionary. By leveraging the predictor to produce approximate sparse coefficients, our FSCN generates sparse coefficients within a forward pass, which is simple and computationally efficient. Compared with traditional sparse coding based methods, FSCN is hundreds of or even thousands of times faster at the test stage. Extensive experiments on benchmark datasets demonstrate that our method reaches the state-of-the-art level.1
Improved prototypical networks for few-Shot learning

Few-Shot Learning (FSL) aims at recognizing the target classes that only a few samples are available for training. The current approaches mostly address FSL by learning a generalized class-level metric while neglect the intra-class distribution information. In this work, we propose Improved Prototypical Networks (IPN) to address this issue. Inspired by the observation that the intra-class samples differ greatly in revealing the class distribution, we first propose an attention-analogous strategy to explore the class distribution information by distributing different weights to samples based on their representativeness. Besides, to further explore the discriminative information across classes, we propose a distance scaling strategy to reduce the intra-class difference while enlarge the inter-class difference. The experimental results on two benchmark datasets show the superiority of the proposed model against the state-of-the-art approaches.
Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils , eosinophils , lymphocytes , monocytes , and basophils , where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks. Generally, laboratory experiments are used for determining the type of a white blood cell. The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors. Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well. As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types. Furthermore, we explore initializing the DNNs’ weights randomly or using weights pretrained on the CIFAR-100 dataset. In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images. The results of extensive experiments show that the proposed method can successfully classify white blood cells. The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%. Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.
Leveraging Deep Learning Techniques for Malaria Parasite Detection Using Mobile Application

Malaria is a contagious disease that affects millions of lives every year. Traditional diagnosis of malaria in laboratory requires an experienced person and careful inspection to discriminate healthy and infected red blood cells (RBCs). It is also very time-consuming and may produce inaccurate reports due to human errors. Cognitive computing and deep learning algorithms simulate human intelligence to make better human decisions in applications like sentiment analysis, speech recognition, face detection, disease detection, and prediction. Due to the advancement of cognitive computing and machine learning techniques, they are now widely used to detect and predict early disease symptoms in healthcare field. With the early prediction results, healthcare professionals can provide better decisions for patient diagnosis and treatment. Machine learning algorithms also aid the humans to process huge and complex medical datasets and then analyze them into clinical insights. This paper looks for leveraging deep learning algorithms for detecting a deadly disease, malaria, for mobile healthcare solution of patients building an effective mobile system. The objective of this paper is to show how deep learning architecture such as convolutional neural network (CNN) which can be useful in real-time malaria detection effectively and accurately from input images and to reduce manual labor with a mobile application. To this end, we evaluate the performance of a custom CNN model using a cyclical stochastic gradient descent (SGD) optimizer with an automatic learning rate finder and obtain an accuracy of 97.30% in classifying healthy and infected cell images with a high degree of precision and sensitivity. This outcome of the paper will facilitate microscopy diagnosis of malaria to a mobile application so that reliability of the treatment and lack of medical expertise can be solved.
Security in smart cities: A brief review of digital forensic schemes for biometric data

A smart city is engineered to be a self-sustained ecosystem driven by Internet-of-Things (IoT) devices. Smooth functioning of smart cities is conditioned on seamless communication between users and devices. Smart devices equipped with biometric authentication can offer security as well as personalized experience to the end users. Currently, a number of smart devices employ face, fingerprint, and voice modalities for user verification. However, the biometric data acquired by these devices can be digitally manipulated or tampered with, that can compromise the security of the smart environment. Further, the preponderance of biometric data such as face and voice in social media applications, necessitates the validation of their integrity. In this work, we review state-of-the-art digital forensic schemes for audio-visual biometric data that can be leveraged by applications designed for smart cities.
Revocable Attribute-Based Signature for Blockchain-Based Healthcare System

Blockchain-Based Healthcare System (HS-BC) is an electronic medical record (EMR) data-sharing system, which significantly facilitates the storage, access, and sharing of EMR data, and provides patients with timely and effective medical diagnosis and treatment.However, due to the high openness of the blockchain, security and privacy still are major concerns in HS-BC.In this paper, we propose an attribute-based signature scheme with attribute revocation to protect the privacy of the user's identity in HS-BC.Under the premise of using attributes to identify users and protect their identity, the user combines the attribute master-key and the attribute update-key to calculate the attribute signing key, where the attribute master-key is related to the user identity and attribute set, and the attribute update-key is related to the attribute revocation.Through making use of the KUNodes algorithm, attribute revocation can effectively achieve.The designed attribute-based signature scheme requires relatively few pairing operations and does not rely on a central authority.Besides, we formally conducted a security analysis and proved that the proposed scheme is unforgeable, collusion resistant and privacy-preserving.Finally, we evaluate the proposed attribute-based signature scheme in terms of storage and efficiency.Comparison and experimental results show that it is feasible.
The influence of stablecoin issuances on cryptocurrency markets

Stablecoins are digital currencies that are pegged to non-volatile assets. As alternatives to fiat currencies, they constitute an important aspect of cryptocurrency markets. We analyze returns of cryptocurrencies around 565 stablecoin issuances events for seven different stablecoins between April 2019 and March 2020. Our event study reveals market downturns in the week before issuance and positive abnormal returns in the twenty-four hours around the issuance. Effects differ and remain insignificant for some stablecoin subsamples and issuance size does not significantly affect the abnormal returns. We conclude that stablecoin issuances contribute to price discovery and market efficiency of cryptocurrencies.
GARG: Anonymous Recommendation of Point-of-Interest in Mobile Networks by Graph Convolution Network

Abstract The advances of mobile equipment and localization techniques put forward the accuracy of the location-based service (LBS) in mobile networks. One core issue for the industry to exploit the economic interest of the LBSs is to make appropriate point-of-interest (POI) recommendation based on users’ interests. Today, the LBS applications expect the recommender systems to recommend the accurate next POI in an anonymous manner, without inquiring users’ attributes or knowing the detailed features of the vast number of POIs. To cope with the challenge, we propose a novel attentive model to recommend appropriate new POIs for users, namely Geographical Attentive Recommendation via Graph (GARG), which takes full advantage of the collaborative, sequential and content-aware information. Unlike previous strategies that equally treat POIs in the sequence or manually define the relationships between POIs, GARG adaptively differentiates the relevance of POIs in the sequence to the prediction, and automatically identifies the POI-wise correlation. Extensive experiments on three real-world datasets demonstrate the effectiveness of GARG and reveal a significant improvement by GARG on the precision, recall and mAP metrics, compared to several state-of-the-art baseline methods.
An Incentive Mechanism Design for Efficient Edge Learning by Deep Reinforcement Learning Approach

Emerging technologies and applications have generated large amounts of data at the network edge. Due to bandwidth, storage, and privacy concerns, it is often impractical to move the collected data to the cloud. With the rapid development of edge computing and distributed machine learning (ML), edge-based ML called federated learning has emerged to overcome the shortcomings of cloud-based ML. Existing works mainly focus on designing efficient learning algorithms, few works focus on designing the incentive mechanisms with heterogeneous edge nodes (EN) and uncertainty of network bandwidth. The incentive mechanisms affect various tradeoffs: (i) between computation and communication latency, and thus (ii) between the edge learning time and payment consumption. We fill this gap by designing an incentive mechanism that captures the tradeoff between latency and payment. Due to the network dynamics and privacy protection, we propose a deep reinforcement learning-based (DRL-based) solution that can automatically learn the best pricing strategy. To the best of our knowledge, this is the first work that applies the advances of DRL to design the incentive mechanism for edge learning. We evaluate the performance of the incentive mechanism using trace-driven experiments. The results demonstrate the superiority of our proposed approach as compared with the baselines.