Reviewerly/exHarmony huggingface dataset:
ASFD: Automatic and Scalable Face Detector

Along with current multi-scale based detectors, Feature Aggregation and Enhancement (FAE) modules have shown superior performance gains for cutting-edge object detection. However, these hand-crafted FAE modules show inconsistent improvements on face detection, which is mainly due to the significant distribution difference between its training and applying corpus, i.e. COCO vs. WIDER Face. To tackle this problem, we essentially analyse the effect of data distribution, and consequently propose to search an effective FAE architecture, termed AutoFAE by a differentiable architecture search, which outperforms all existing FAE modules in face detection with a considerable margin. Upon the found AutoFAE and existing backbones, a supernet is further built and trained, which automatically obtains a family of detectors under the different complexity constraints. Extensive experiments conducted on popular benchmarks, i.e. WIDER Face and FDDB, demonstrate the state-of-the-art performance-efficiency trade-off for the proposed automatic and scalable face detector (ASFD) family. In particular, our strong ASFD-D6 outperforms the best competitor with AP 96.7/96.2/92.1 on WIDER Face test, and the lightweight ASFD-D0 costs about 3.1 ms, i.e. more than 320 FPS, on the V100 GPU with VGA-resolution images.
Analysis of Compact Block Propagation Delay in Bitcoin Network

Bitcoin is a blockchain-based network where thousands of nodes are directly connected and communicate through a gossip-based flooding protocol. Mined blocks are propagated to all participating nodes in the network through compact block relay (CBR) protocol. Therefore, reducing the block relay time between nodes can reduce the block propagation time to all nodes and ultimately improve the performance of Bitcoin. In order to reduce the block relay time, the delivery time between nodes must be measured and analyzed to find the cause of the delay and provide ways to resolve it. Therefore, in this paper, we measure the CBR time between directly connected Bitcoin nodes and analyze the cause of the relay delay. Our results show that the delivery time delay is affected by whether or not a transaction is requested when assembling the compact block. In addition, the reason for requesting a transaction is due to the transaction propagation method and the characteristics of the transaction itself.
Enhancing Mixture-of-Experts by Leveraging Attention for Fine-Grained Recognition

Differentiating subcategories of a common visual category is challenging because of the similar appearance shared among different classes in fine-grained recognition. Existing mixture-of-expert based methods divide the fine-grained space into some specific regions and solve the integrated problem by conquering subspace ones. However, it is not feasible to learn diverse experts directly through data partition strategy because of limited data available for fine-grained recognition problems. To address the issue, we leverage visual attention to learn an enhanced experts' mixture. Specifically, we introduce a gradually-enhanced learning strategy from model attention. The strategy promotes diversity among experts by feeding each expert with full-size data distinct in granularity. We further promote expert's learning by providing it with a larger data space, which is achieved by swapping attentive regions within positive pairs. Our method learns new experts on the dataset with the prior knowledge from former experts sequentially and enforces the experts to learn more diverse but discriminative representation. These enhanced experts are finally combined to make stronger predictions. We conduct extensive experiments on fine-grained benchmarks. The results show that our method consistently outperforms the state-of-the-art method in both weakly supervised localization and fine-grained image classification. Our code is publicly available at https://github.com/lbzhang/Enhanced-Expert-FGVC-Pytorch.git.
Tracking clusters and anomalies in evolving data streams

Abstract Data‐driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) scores as anomalies. This presents a practical limitation on the applicability of such methods, since most methods are sensitive to the choice of the threshold, and it is challenging to set optimal thresholds. The issue is exacerbated in a streaming scenario, where the optimal thresholds vary with time. We present a probabilistic framework to explicitly model the normal and anomalous behaviors and probabilistically reason about the data. An extreme value theory based formulation is proposed to model the anomalous behavior as the extremes of the normal behavior. As a specific instantiation, a joint nonparametric clustering and anomaly detection algorithm (INCAD) is proposed that models the normal behavior as a Dirichlet process mixture model. Results on a variety of datasets, including streaming data, show that the proposed method provides effective and simultaneous clustering and anomaly detection without requiring strong initialization and threshold parameters.
Action Recognition Using Close-Up of Maximum Activation and ETRI-Activity3D LivingLab Dataset

The development of action recognition models has shown great performance on various video datasets. Nevertheless, because there is no rich data on target actions in existing datasets, it is insufficient to perform action recognition applications required by industries. To satisfy this requirement, datasets composed of target actions with high availability have been created, but it is difficult to capture various characteristics in actual environments because video data are generated in a specific environment. In this paper, we introduce a new ETRI-Activity3D-LivingLab dataset, which provides action sequences in actual environments and helps to handle a network generalization issue due to the dataset shift. When the action recognition model is trained on the ETRI-Activity3D and KIST SynADL datasets and evaluated on the ETRI-Activity3D-LivingLab dataset, the performance can be severely degraded because the datasets were captured in different environments domains. To reduce this dataset shift between training and testing datasets, we propose a close-up of maximum activation, which magnifies the most activated part of a video input in detail. In addition, we present various experimental results and analysis that show the dataset shift and demonstrate the effectiveness of the proposed method.
SSA-Net: Spatial Scale Attention Network for Image-Based Geo-Localization

Image-based geo-localization is estimating the location of a query image by matching it to a large amount of images in geo-tagged database. This matching task is very challenging due to the vast differences in visual appearance or modality of image pairs on different platforms, for example, one image from the RGB camera, the other from the light detection and ranging (LiDAR) sensor. The spatial layout of the scene can provide important clues and significantly reduce matching ambiguity. Therefore, we propose a novel deep network that embeds spatial configuration of the scenes into feature representation. Specifically, we design a spatial-scale attention (SSA) module to highlight the salience correspondence layout features at different scales. The encoded features not only represent the emergence of certain objects, but also reflect the relative locations of the objects. By this way, we learn more discriminative deep feature representations, leading to a higher recall. The experimental results on two standard cross-view benchmark datasets (CVUSA and CVACT) and a cross-modal dataset (GRAL) demonstrate that our method performs better than the state-of-the-art methods. Remarkably, the recall rate@top-1 improves from 27.6% to 40.5% on the GRAL dataset.
Evolutionary Optimization of Neural Architectures in Remote Sensing Classification Problems

BigEarthNet is one of the standard large remote sensing datasets. It has been shown previously that neural networks are effective tools to classify the image patches in this data. However, finding the optimum network hyperparameters and architecture to accurately classify the image patches in BigEarthNet remains a challenge. Searching for more accurate models manually is extremely time consuming and labour intensive. Hence, a systematic approach is advisable. One possibility is automated evolutionary Neural Architecture Search (NAS). With this NAS many of the commonly used network hyperparameters, such as loss functions, are eliminated and a more accurate network is determined.
Reconciling Hand-Crafted and Self-Supervised Deep Priors for Video Directional Rain Streaks Removal

Removing rain streaks in videos has recently received much attention. Existing hand-crafted priors-based methods suffer from limited representation abilities, and supervised deep learning methods need high-quality training data. This paper proposes a novel video rain streaks removal method by reconciling hand-crafted and self-supervised deep priors. The hand-crafted priors include the learned gradient prior, the sparse prior, and the temporal local smooth prior. Meanwhile, a deep convolutional neural network is employed to self-supervisedly capture the deep prior of the clean video without any training data. Our method organically integrates hand-crated priors and self-supervised deep priors to achieve both high generalization abilities and representation abilities. Thus, our method can faithfully remove directional rain streaks in real world videos. To address the resulting model, we introduce an alternating direction multiplier method algorithm. Extensive experiments validate the superiority of our method over state-of-the-art methods.
The ACM Multimedia 2021 Meet Deadline Requirements Grand Challenge

Delay-sensitive multimedia streaming applications require their data to be delivered before a deadline to be useful. The data transmitted by these applications can usually be partitioned into blocks with different priorities, assigned based on the impact of a block on the Quality of Experience (QoE) if it misses its delivery deadline. Meet their deadline requirements is challenging due to the dynamics of the network and these applications' high demand on network resources. To encourage the research community to address this challenge, we organize the "Meet Deadline Requirements" Grand Challenge at ACM Multimedia 2021. This grand challenge provides a simulation platform onto which the participants can implement their block scheduler and bandwidth estimator and then benchmark against each other using a common set of application traces and network traces.
HMMN: Online metric learning for human re-identification via hard sample mining memory network

Effective metric learning is important in various applications, especially for re-identification. Compared with most existing re-identification methods which are not suitable for a real-time update mode, we exploit a novel memory-based strategy for mining hard triplets in online metric learning. This strategy is realized with an end-to-end deep learning based framework using an external memory pool. Our proposed pipeline is able to explicitly provide hard negative and positive samples to generate effective triplets, which are important for online metric learning due to the representative triplets could provide distinctive information to help understand the concept of metric learning between categories. In addition, a “focal-triplet loss” function is proposed to deal with the lack of positive or negative samples for one anchor, and the imbalance between easy and hard triplets for mini-batch. Experimental results on Market-1501, CUHK03 and DukeMTMC-reID demonstrate the effectiveness of our method, and its performance even outperforms that of some existing offline methods.
Investigation of Cyber Situation Awareness via SIEM tools: a constructive review

Awareness, in the sense of security, builds the backbone of operations understanding the current and future cyber activities. Situation awareness has become the focal point of securing systems due to dynamic nature of cyber domain. Technological advancements cause the volatility to transform into upcoming challenges. Understanding those is the key to keep cyber Situation Awareness (SA) progression. Earlier studies define required steps to administer cyber SA. These steps (perceive, comprehend, project, and resolve) are also adapted to cyber domain. Rapid technological changes redefine the content of those and thus, it creates demands improving automated tools, which play as systematic factor in nurturing SA. As a system factor, SIEM tools can be basis for comprehending cyber domain. In this work, we investigate recent studies contributed mainly to SIEM (Security Information and Event Management) tool’s enhancement to evaluate current state and help predict upcoming challenges for maintaining awareness. We use various criteria in our investigation such as; architecture improvement, affected SIEM process, utilized CTI (Cyber Threat Intelligence) artefact, implementation area, and type of produced result. In doing so, we aim to impart upward trends on CSA (Cyber Situation Awareness) to academia and industry professionals.
Flat morphological operators from non-increasing set operators, I: general theory

Abstract Flat morphology is a general method for obtaining increasing operators on grey-level or multivalued images from increasing operators on binary images (or sets). It relies on threshold stacking and superposition; equivalently, Boolean max and min operations are replaced by lattice-theoretical sup and inf operations. In this paper we consider the construction a flat operator on grey-level or colour images from an operator on binary images that is not increasing. Here grey-level and colour images are functions from a space to an interval in ℝ m or ℤ m ( m ≥ 1). Two approaches are proposed. First, we can replace threshold superposition by threshold summation. Next, we can decompose the non-increasing operator on binary images into a linear combination of increasing operators, then apply this linear combination to their flat extensions. Both methods require the operator to have bounded variation, and then both give the same result, which conforms to intuition. Our approach is very general, it can be applied to linear combinations of flat operators, or to linear convolution filters. Our work is based on a mathematical theory of summation of real-valued functions of one variable ranging in a poset. In a second paper, we will study some particular properties of non-increasing flat operators.
Session types in Elixir

This paper proposes an adaptation of session types to provide behavioural information about public functions in Elixir modules. We formalise typechecking rules for the main constructs of the language. This allows us to statically determine whether a function implementation observes its session endpoint specification. Based on this type system, we then construct a tool that automates typechecking for Elixir modules.
Efficient Self-Supervised Data Collection for Offline Robot Learning

A practical approach to robot reinforcement learning is to first collect a large batch of real or simulated robot interaction data, using some data collection policy, and then learn from this data to perform various tasks, using offline learning algorithms. Previous work focused on manually designing the data collection policy, and on tasks where suitable policies can easily be designed, such as random picking policies for collecting data about object grasping. For more complex tasks, however, it may be difficult to find a data collection policy that explores the environment effectively, and produces data that is diverse enough for the downstream task. In this work, we propose that data collection policies should actively explore the environment to collect diverse data. In particular, we develop a simple-yet-effective goal-conditioned reinforcement-learning method that actively focuses data collection on novel observations, thereby collecting a diverse data-set. We evaluate our method on simulated robot manipulation tasks with visual inputs and show that the improved diversity of active data collection leads to significant improvements in the downstream learning tasks.
Cross-View Representation Learning for Multi-View Logo Classification with Information Bottleneck

Multi-view logo classification is a challenging task due to the cross-view misalignment of logo image varies under different viewpoints, large intra-classes and small inter-classes variation of logo appearance. Cross-view data can represent objects from different views and thus provide complementary information for data analysis. However, most existing multi-view algorithms usually maximize the correlation between different views for consistency. Those methods ignore the interaction among different views and may cause semantic bias during the process of common feature learning. In this paper, we investigate the information bottleneck (IB) to the multi-view learning for extracting the different view common features of one category, named Dual-View Information Bottleneck representation (Dual-view IB). To the best of our knowledge, this is the first cross-view learning method for logo classification. Specifically, we maximize the mutual information between the representations of the two views to achieve the preservation of key features in the classification task, while eliminating the redundant information that is not shared between the two views. In addition, due to the unbalance of samples and limited computing resources, we further introduce a novel Pair Batch Data Augmentation (PB) algorithm for Dual-view IB model, which applies augmentations from a learned policy based on replicates instances of two samples within the same batch. Comprehensive experiments on three existing benchmark datasets, which demonstrate the effectiveness of the proposed method that outperforms the methods in the state of the art. The proposed method is expected to further the development of cross-view representation learning.
Borda Count Versus Majority Voting for Credit Card Fraud Detection

As financial fraud is increasing day by day, cardholders have been affected seriously by a lot of economic losses. To detect fraud, mostly machine learning algorithms are used. The research proposed in this paper utilizes the European bank transaction dataset, and it is highly imbalanced. Henceforth, three class imbalance techniques—SMOTE, SMOTE + TOMEK, SMOTE + ENN—were used for removing imbalance in the dataset and five machine learning algorithms such as logistic regression, support vector machine, random forest, decision tree, and K-nearest neighbors are applied. Random forest provides better results when compared with other classifiers. Later, by applying two voting methods, viz. Borda Count and majority voting, on each class imbalance technique, the performance is evaluated and compared based on different parameters such as precision, accuracy, F1 score, as well as Matthews correlation. This paper explains the significance of majority voting over Borda Count in detecting fraudulent transactions.
SmartEye

Video analytics with Deep Neural Networks (DNNs) empowers many vision-based applications. However, deploying DNN models for video analytics services must address the challenges of computational capacity, service delay, and cost. Leveraging the edge-cloud collaboration to address these problems has become a growing trend. This paper provides the multimedia research community with an open source framework named SmartEye for real-time video analytics by leveraging the edge-cloud collaboration. The system consists of 1) an edge layer which enables video preprocessing, model selection, on-edge inference, and task offloading; 2) a request forwarding layer which serves as a gateway of the cloud and forwards the offloaded tasks to backend workers; and 3) a backend worker layer that processes the offloaded tasks with specified DNN models. One can easily customize the policies for preprocessing, offloading, model selection, and request forwarding. The framework can facilitate research and development in this field. The project is released as an open source project on GitHub at https://github.com/MSNLAB/SmartEye.
Economic Modelling of the Bitcoin Mining Industry

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Stereo-augmented Depth Completion from a Single RGB-LiDAR image

Depth completion is an important task in computer vision and robotics applications, which aims at predicting accurate dense depth from a single RGB-LiDAR image. Convolutional neural networks (CNNs) have been widely used for depth completion to learn a mapping function from sparse to dense depth. However, recent methods do not exploit any 3D geometric cues during the inference stage and mainly rely on sophisticated CNN architectures. In this paper, we present a cascade and geometrically inspired learning framework for depth completion, consisting of three stages: view extrapolation, stereo matching, and depth refinement. The first stage extrapolates a virtual (right) view using a single RGB (left) and its LiDAR data. We then mimic the binocular stereo-matching, and as a result, explicitly encode geometric constraints during depth completion. This stage augments the final refinement process by providing additional geometric reasoning. We also introduce a distillation framework based on teacher-student strategy to effectively train our network. Knowledge from a teacher model privileged with real stereo pairs is transferred to the student through feature distillation. Experimental results on KITTI depth completion benchmark demonstrate that the proposed method is superior to state-of-the-art methods.
Self-Supervised Auto-Encoding Multi-Transformations for Airplane Classification

In this paper, we present a self-supervised learning method of Auto-Encoding Multi-Transformations (AEMT) for airplane classification. In this method, the image features are learned in an unsupervised way by simultaneously estimating multiple image transformations from the features of original and transformed images instead of reconstructing the input images. Besides, we propose two structure variants of the AEMT method: composite and parallel modes of which the former transforms the images in a composite fashion while the latter does it in parallel. The experimental results demonstrate that the proposed method outperforms the state-of-the-art self-supervised learning methods for the airplane classification task.
Current Status and Future Perspectives of the PRISMA Mission at the Turn of One Year in Operational Usage

PRISMA, in orbit since March the 22nd 2019, opened the user access in spring 2020. After one year, many hundreds of users have gained the capability to program new acquisitions or download image products from the online archive containing more than 67k datasets, under a quasi-open & free data policy and licensing scheme. During this time frame and despite the COVID-19 related difficulties, PRISMA performed normal operations delivering not only nominal verified quality data to users but establishing collaborations with other space agencies, in order to enable and support synergies with other hyperspectral missions. This paper describes the mission and the results achieved in this first period of full operational usage.