Reviewerly/exHarmony huggingface dataset:
Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video Classification

Video classification researches have recently attracted attention in the fields of temporal modeling and efficient 3D convolutional architectures. However, the temporal modeling methods are not efficient, and there is little interest in how to deal with temporal modeling in the 3D efficient architectures. To build an efficient 3D architecture for temporal modeling, we propose a new 3D backbone network, called VoV3D, that consists of a temporal one-shot aggregation (T-OSA) module and a depthwise factorized component, D(2+1)D. The T-OSA is devised to build a feature hierarchy by aggregating spatiotemporal features with different temporal receptive fields. Stacking this T-OSA enables the network itself to model short-range as well as long-range temporal relationships across frames without any external modules. We also design a depthwise spatiotemporal factorization module, D(2+1)D, that decomposes a 3D depthwise convolution into two spatial and temporal depthwise convolutions for efficient architecture. Through the proposed temporal modeling method (T-OSA) and the efficient factorization module (D(2+1)D), we construct two types of VoV3D networks: VoV3D-M and VoV3D-L. Thanks to its efficiency and effectiveness of their temporal modeling, VoV3D-L has 4× fewer model parameters and 14× less computation, surpassing the state-of-the-art TEA model [22] on both Something-Something and Kinetics-400 datasets. Furthermore, VoV3D shows better performance for temporal modeling than the efficient X3D [7]. We hope that VoV3D can serve as a baseline for efficient temporal modeling architecture. The code and models are available at https://github.com/youngwanLEE/VoV3D.
Dynamic Depth Fusion and Transformation for Monocular 3D Object Detection

Visual-based 3D detection is drawing a lot of attention recently. Despite the best efforts from the computer vision researchers visual-based 3D detection remains a largely unsolved problem. This is primarily due to the lack of accurate depth perception provided by LiDAR sensors. Previous works struggle to fuse 3D spatial information and the RGB image effectively. In this paper, we propose a novel monocular 3D detection framework to address this problem. Specifically, we propose to primary contributions: (i) We design an Adaptive Depth-guided Instance Normalization layer to leverage depth features to guide RGB features for high quality estimation of 3D properties. (ii) We introduce a Dynamic Depth Transformation module to better recover accurate depth according to semantic context learning and thus facilitate the removal of depth ambiguities that exist in the RGB image. Experiments show that our approach achieves state-of-the-art on KITTI 3D detection benchmark among current monocular 3D detection works.
SoK: Privacy-Preserving Reputation Systems

Abstract Trust and user-generated feedback have become increasingly vital to the normal functioning of the modern internet. However, deployed systems that currently incorporate such feedback do not guarantee users much in the way of privacy, despite a wide swath of research on how to do so spanning over 15 years. Meanwhile, research on systems that maintain user privacy while helping them to track and update each others’ reputations has failed to standardize terminology, or converge on what privacy guarantees should be important. Too often, this leads to misunderstandings of the tradeoffs underpinning design decisions. Further, key insights made in some approaches to designing such systems have not circulated to other approaches, leaving open significant opportunity for new research directions. This SoK investigates 42 systems describing privacy-preserving reputation systems from 2003–2019 in order to organize previous work and suggest directions for future work. Our three key contributions are the systematization of this body of research, the detailing of the tradeoffs implied by overarching design choices, and the identification of underresearched areas that provide promising opportunities for future work.
Dual Attention Model for Citation Recommendation

Based on an exponentially increasing number of academic articles, discovering and citing comprehensive and appropriate resources has become a non-trivial task.Conventional citation recommender methods suffer from severe information loss.For example, they do not consider the section of the paper that the user is writing and for which they need to find a citation, the relatedness between the words in the local context (the text span that describes a citation), or the importance on each word from the local context.These shortcomings make such methods insufficient for recommending adequate citations to academic manuscripts.In this study, we propose a novel embedding-based neural network called "dual attention model for citation recommendation (DACR)" to recommend citations during manuscript preparation.Our method adapts embedding of three semantic information: words in the local context, structural contexts 1 , and the section on which a user is working.A neural network model is designed to maximize the similarity between the embedding of the three input (local context words, section and structural contexts) and the target citation appearing in the context.The core of the neural network model is composed of self-attention and additive attention, where the former aims to capture the relatedness between the contextual words and structural context, and the latter aims to learn the importance of them.The experiments on real-world datasets demonstrate the effectiveness of the proposed approach.
BUT-FIT at SemEval-2020 Task 5: Automatic Detection of Counterfactual Statements with Deep Pre-trained Language Representation Models

This paper describes BUT-FIT's submission at SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals.The challenge focused on detecting whether a given statement contains a counterfactual (Subtask 1) and extracting both antecedent and consequent parts of the counterfactual from the text (Subtask 2).We experimented with various state-of-the-art language representation models (LRMs).We found RoBERTa LRM to perform the best in both subtasks.We achieved the first place in both exact match and F1 for Subtask 2 and ranked second for Subtask 1.
Kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification

Code switching is a linguistic phenomenon that may occur within a multilingual setting where speakers share more than one language.With the increasing communication between groups with different languages, this phenomenon is more and more popular.However, there are little research and data in this area, especially in code-mixing sentiment classification.In this work, the domain transfer learning from state-of-the-art uni-language model ERNIE is tested on the code-mixing dataset, and surprisingly, a strong baseline is achieved.Furthermore, the adversarial training with a multi-lingual model is used to achieve 1st place of SemEval-
Analysing Simulated Phishing Campaigns for Staff

In an attempt to stop phishing attacks, an increasing number of organisations run Simulated Phishing Campaigns to train their staff not to click on suspicious links. Organisations can buy toolkits to craft and run their own campaigns, or hire a specialist company to provide such campaigns as a service. To what extent this activity reduces the vulnerability of an organisation to such attacks is debated in both the research and practitioner communities, but an increasing number of organisations do it because it seems common practice, and are convinced by vendors’ claims about the reduction in clickrates that can be achieved. But most are not aware that effective security is not just about reducing clickrates for simulated phishing messages, that there are many different ways of running such campaigns, and that there are security, legal, and trust issues associated with those choices. The goal of this paper is to equip organisational decision makers with tools for making those decisions. A closer examination of costs and benefits of the choice reveals that it may be possible to run a legally compliant campaign, but that it is costly and time-consuming. Additionally, the impact of Simulated Phishing Campaigns on employees’ self-efficacy and trust in the organisation may negatively affect other organisational goals. We conclude that for many organisations, a joined-up approach of (1) improving technical security measures, (2) introducing and establishing adequate security incident reporting, and (3) increasing staff awareness through other means may deliver better protection at lower cost.
HOLMS: Alternative Summary Evaluation with Large Language Models

Efficient document summarization requires evaluation measures that can not only rank a set of systems based on an average score, but also highlight which individual summary is better than another.However, despite the very active research on summarization approaches, few works have proposed new evaluation measures in the recent years.The standard measures relied upon for the development of summarization systems are most often ROUGE and BLEU which, despite being efficient in overall system ranking, remain lexical in nature and have a limited potential when it comes to training neural networks.In this paper, we present a new hybrid evaluation measure for summarization, called HOLMS, that combines both language models pre-trained on large corpora and lexical similarity measures.Through several experiments, we show that HOLMS outperforms ROUGE and BLEU substantially in its correlation with human judgments on several extractive summarization datasets for both linguistic quality and pyramid scores.
Simultaneous shape control and transport with multiple robots

Autonomous transport of objects may require multiple robots when the object is large or heavy. Besides, in the case of deformable objects, a set of robots may also be needed to maintain or adapt the shape of the object to the task requirements. The task we address consists in transporting an object, represented as a two dimensional shape or contour, along a desired path. Simultaneously, the team of robots grasping the object are controlled to the desired contour points configuration. Since the mobile robots of the team obey nonholonomic motion constraints, admissible trajectories are designed to keep the integrity of the object while following the prescribed path. Additionally, the simultaneous control of the object's shape is smoothly performed to respect the admissible deformation of the object. The main contribution lies in the definition of the grasping robots' trajectories dealing with the involved constraints. Different simulations, where the deformable object dynamics are modelled with consensus-based techniques, illustrate the performance of the approach.
Transformation-Aware Embeddings for Image Provenance

A dramatic rise in the flow of manipulated image content on the Internet has led to a prompt response from the media forensics research community. New mitigation efforts leverage cutting-edge data-driven strategies and increasingly incorporate usage of techniques from computer vision and machine learning to detect and profile the space of image manipulations. This paper addresses Image Provenance Analysis, which aims at discovering relationships among different manipulated image versions that share content. One important task in provenance analysis, like most visual understanding problems, is establishing a visual description and dissimilarity computation method that connects images that share full or partial content. But the existing handcrafted or learned descriptors - generally appropriate for tasks such as object recognition - may not sufficiently encode the subtle differences between near-duplicate image variants, which significantly characterize the provenance of any image. This paper introduces a novel data-driven learning-based approach that provides the context for ordering images that have been generated from a single image source through various transformations. Our approach learns transformation-aware embeddings using weak supervision via composited transformations and a rank-based Edit Sequence Loss. To establish the effectiveness of the proposed approach, comparisons are made with state-of-the-art handcrafted and deep-learning-based descriptors, as well as image matching approaches. Further experimentation validates the proposed approach in the context of image provenance analysis and improves upon existing approaches.
Tableau (version 2020.3)

This review analyzes the new features in Tableau and how it can be used both by librarians and the patrons they serve.
Robust Real-time Face Tracking for People Wearing Face Masks

Due to the outbreak of the novel coronavirus (or known as COVID-19), people are advised to wear masks when they stay outdoors in many countries. This could result in difficulty for some public safety surveillance systems involving face detection or tracking. Therefore, the development of face detection and tracking algorithms for people wearing face masks is particularly important. In this paper, a real-time tracking algorithm for people with or without face masks is proposed. This algorithm is trained on public face datasets with faces without masks. Although the training does not involve face images of people wearing face masks, we show that the proposed algorithm is robust as it is able to perform well in face tracking for people wearing face masks. We also discuss the possible scenarios where the algorithm could lose track of the target when experimenting in tracking masked faces. This can motivate future research in this area.
RGB-D Human Action Recognition of Deep Feature Enhancement and Fusion Using Two-Stream ConvNet

Action recognition is an important research direction of computer vision, whose performance based on video images is easily affected by factors such as background and light, while deep video images can better reduce interference and improve recognition accuracy. Therefore, this paper makes full use of video and deep skeleton data and proposes an RGB-D action recognition based two-stream network (SV-GCN), which can be described as a two-stream architecture that works with two different data. Proposed Nonlocal-stgcn (S-Stream) based on skeleton data, by adding nonlocal to obtain dependency relationship between a wider range of joints, to provide more rich skeleton point features for the model, proposed a video based Dilated-slowfastnet (V-Stream), which replaces traditional random sampling layer with dilated convolutional layers, which can make better use of depth the feature; finally, two stream information is fused to realize action recognition. The experimental results on NTU-RGB+D dataset show that proposed method significantly improves recognition accuracy and is superior to st-gcn and Slowfastnet in both CS and CV.
Investigating efficient CNN architecture for multiple sclerosis lesion segmentation

Purpose: The automatic segmentation of multiple sclerosis lesions in magnetic resonance imaging has the potential to reduce radiologists’ efforts on a daily time-consuming task and to bring more reproducibility. Almost all new segmentation techniques make use of convolutional neural networks with their own different architecture. Architectural choices are rarely explained. We aimed at presenting the relevance of a U-net-like architecture for our specific task and at building an efficient and simple model. Approach: An experimental study was performed by observing the impact of applying different mutations and deletions to a simple U-net-like architecture. Results: The power of the U-net architecture is explained by the joint benefits of using an encoder–decoder architecture and by linking them with long skip connections. Augmenting the number of convolutional layers and decreasing the number of feature maps allowed us to build an exceptionally light and competitive architecture, the minimally parameterized U-net (MPU-net), with only ∼30,000 parameters. Conclusion: The empirical study of the U-net has led to a better understanding of its architecture. It has guided the building of the MPU-net, a model far less parameterized than others (at least by a factor of seven). This neural network achieves a human-level segmentation of multiple sclerosis lesions on fluid-attenuated inversion recovery images only. It shows that this segmentation task does not necessitate overly complicated models to be achieved. This gives the opportunity to build more explainable models that can help such methods to be adopted in a clinical environment.
Maximizing Drift Is Not Optimal for Solving OneMax

It seems very intuitive that for the maximization of the OneMax problem Om(x):=∑i=1nxi the best that an elitist unary unbiased search algorithm can do is to store a best so far solution, and to modify it with the operator that yields the best possible expected progress in function value. This assumption has been implicitly used in several empirical works. In Doerr et al. (2020), it was formally proven that this approach is indeed almost optimal. In this work, we prove that drift maximization is not optimal. More precisely, we show that for most fitness levels between n/2 and 2n/3 the optimal mutation strengths are larger than the drift-maximizing ones. This implies that the optimal RLS is more risk-affine than the variant maximizing the stepwise expected progress. We show similar results for the mutation rates of the classic (1+1) Evolutionary Algorithm (EA) and its resampling variant, the (1+1) EA>0. As a result of independent interest we show that the optimal mutation strengths, unlike the drift-maximizing ones, can be even.
Sequential Learning of Active Subspaces

In recent years, active subspace methods (ASMs) have become a popular means of performing subspace sensitivity analysis on black-box functions. Naively applied, however, ASMs require gradient evaluations of the target function. In the event of noisy, expensive, or stochastic simulators, evaluating gradients via finite differencing may be infeasible. In such cases, often a surrogate model is employed, on which finite differencing is performed. When the surrogate model is a Gaussian process (GP), we show that the ASM estimator is available in closed form, rendering the finite-difference approximation unnecessary. We use our closed-form solution to develop acquisition functions focused on sequential learning tailored to sensitivity analysis on top of ASMs. We also show that the traditional ASM estimator may be viewed as a method of moments estimator for a certain class of GPs. We demonstrate how uncertainty on GP hyperparameters may be propagated to uncertainty on the sensitivity analysis, allowing model-based confidence intervals on the active subspace. Our methodological developments are illustrated on several examples. Supplementary files for this article are available online.
An Accurate Linear Method for 3D Line Reconstruction for Binocular or Multiple View Stereo Vision

For the problem of 3D line reconstruction in binocular or multiple view stereo vision, when there are no corresponding points on the line, the method called Direction-then-Point (DtP) can be used, and if there are two pairs of corresponding points on the line, the method called Two Points 3D coordinates (TPS) can be used. However, when there is only one pair of corresponding points on the line, can we get the better accuracy than DtP for 3D line reconstruction? In this paper, a linear and more accurate method called Point-then-Direction (PtD) is proposed. First, we used the intersection method to obtain the 3D point’s coordinate from its corresponding image points. Then, we used this point as a position on the line to calculate the direction of the line by minimizing the image angle residual. PtD is also suitable for multiple camera systems. The simulation results demonstrate that PtD increases the accuracy of both the direction and the position of the 3D line compared to DtP. At the same time, PtD achieves a better result in direction of the 3D line than TPS, but has a lower accuracy in the position of 3D lines than TPS.
The Use of M-Learning: A Perspective of Learners’ Perceptions on M-Blackboard Learn

&lt;p class="0abstract"&gt;The life of the common to the students have changed due to spread of COVID-19 9 all around the globe. All the educational institutions were also set to be functioned remotely following the global instructions to meet the challenges to combat the COVID-19. The of current inquiry is the explore the male and female learners on the use of mobile phones for Blackboard learn. The Study survey method to collect the data. 68 participants of the medical majors of the CFY formed the sample for the present study. The findings of the study indicate that learners preferred to use M-blackboard. The results also indicated that backboard which is a form of e-learning can be beneficial if it is used with the traditional classroom teaching to get the potential benefits of the software.&lt;/p&gt;
Early Detection of Vulnerabilities from News Websites using Machine Learning Models

The drawbacks of traditional methods of cybernetic vulnerability detection relate to the required time to identify new threats, to register them in the Common Vulnerabilities and Exposures (CVE) records, and to score them with the Common Vulnerabilities Scoring System (CVSS). These problems can be mitigated by early vulnerability detection systems relying on social media and open-source data. This paper presents a model that aims to identify emerging cybernetic vulnerabilities in cybersecurity news articles, as part of a system for automatic detection of early cybernetic threats using Open Source Intelligence (OSINT). Three machine learning models were trained on a novel dataset of 1000 labeled news articles to create a strong baseline for classifying cybersecurity articles as relevant (i.e., introducing new security threats), or irrelevant: Support Vector Machines, a Multinomial Naïve Bayes classifier, and a finetuned BERT model. The BERT model obtained the best performance with a mean accuracy of 88.45% on the test dataset. Our experiments support the conclusion that Natural Language Processing (NLP) models are an appropriate choice for early vulnerability detection systems in order to extract relevant information from cybersecurity news articles.
Directed Acyclic Graph Neural Networks

Graph-structured data ubiquitously appears in science and engineering. Graph neural networks (GNNs) are designed to exploit the relational inductive bias exhibited in graphs; they have been shown to outperform other forms of neural networks in scenarios where structure information supplements node features. The most common GNN architecture aggregates information from neighborhoods based on message passing. Its generality has made it broadly applicable. In this paper, we focus on a special, yet widely used, type of graphs---DAGs---and inject a stronger inductive bias---partial ordering---into the neural network design. We propose the directed acyclic graph neural network, DAGNN, an architecture that processes information according to the flow defined by the partial order. DAGNN can be considered a framework that entails earlier works as special cases (e.g., models for trees and models updating node representations recurrently), but we identify several crucial components that prior architectures lack. We perform comprehensive experiments, including ablation studies, on representative DAG datasets (i.e., source code, neural architectures, and probabilistic graphical models) and demonstrate the superiority of DAGNN over simpler DAG architectures as well as general graph architectures.
Bridging the Gap Between Markowitz Planning and Deep Reinforcement Learning

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