Reviewerly/exHarmony huggingface dataset:
Predicting 3D Human Dynamics From Video

Given a video of a person in action, we can easily guess the 3D future motion of the person. In this work, we present perhaps the first approach for predicting a future 3D mesh model sequence of a person from past video input. We do this for periodic motions such as walking and also actions like bowling and squatting seen in sports or workout videos. While there has been a surge of future prediction problems in computer vision, most approaches predict 3D future from 3D past or 2D future from 2D past inputs. In this work, we focus on the problem of predicting 3D future motion from past image sequences, which has a plethora of practical applications in autonomous systems that must operate safely around people from visual inputs. Inspired by the success of autoregressive models in language modeling tasks, we learn an intermediate latent space on which we predict the future. This effectively facilitates autoregressive predictions when the input differs from the output domain. Our approach can be trained on video sequences obtained in-the-wild without 3D ground truth labels. The project website with videos can be found at https://jasonyzhang.com/phd.
Inducing Relational Knowledge from BERT

One of the most remarkable properties of word embeddings is the fact that they capture certain types of semantic and syntactic relationships. Recently, pre-trained language models such as BERT have achieved groundbreaking results across a wide range of Natural Language Processing tasks. However, it is unclear to what extent such models capture relational knowledge beyond what is already captured by standard word embeddings. To explore this question, we propose a methodology for distilling relational knowledge from a pre-trained language model. Starting from a few seed instances of a given relation, we first use a large text corpus to find sentences that are likely to express this relation. We then use a subset of these extracted sentences as templates. Finally, we fine-tune a language model to predict whether a given word pair is likely to be an instance of some relation, when given an instantiated template for that relation as input.
Bitcoin volatility, stock market and investor sentiment. Are they connected?

Bitcoin is the cryptocurrency with the largest market capitalization, and many studies have examined its role in financial markets. In this manuscript, we contribute to the extant body of knowledge by analyzing the Bitcoin behavior and the effect that investor sentiment, S&P 500 returns, and VIX returns have on Bitcoin volatility using GARCH and EGARCH models. The results suggest that Bitcoin volatility is more unstable in speculative periods. In stable periods, S&P 500 returns, VIX returns, and sentiment influence Bitcoin volatility.
Detecting weak and strong Islamophobic hate speech on social media

Islamophobic hate speech on social media is a growing concern in contemporary Western politics and society. It can inflict considerable harm on any victims who are targeted, create a sense of fear and exclusion amongst their communities, toxify public discourse and motivate other forms of extremist and hateful behavior. Accordingly, there is a pressing need for automated tools to detect and classify Islamophobic hate speech robustly and at scale, thereby enabling quantitative analyses of large textual datasets, such as those collected from social media. Previous research has mostly approached the automated detection of hate speech as a binary task. However, the varied nature of Islamophobia means that this is often inappropriate for both theoretically informed social science and effective monitoring of social media platforms. Drawing on in-depth conceptual work we build an automated software tool which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and balanced accuracy is 83%. Our tool enables future quantitative research into the drivers, spread, prevalence and effects of Islamophobic hate speech on social media.
Feature learning for Human Activity Recognition using Convolutional Neural Networks

Abstract The use of Convolutional Neural Networks (CNNs) as a feature learning method for Human Activity Recognition (HAR) is becoming more and more common. Unlike conventional machine learning methods, which require domain-specific expertise, CNNs can extract features automatically. On the other hand, CNNs require a training phase, making them prone to the cold-start problem. In this work, a case study is presented where the use of a pre-trained CNN feature extractor is evaluated under realistic conditions. The case study consists of two main steps: (1) different topologies and parameters are assessed to identify the best candidate models for HAR, thus obtaining a pre-trained CNN model. The pre-trained model (2) is then employed as feature extractor evaluating its use with a large scale real-world dataset. Two CNN applications were considered: Inertial Measurement Unit (IMU) and audio based HAR. For the IMU data, balanced accuracy was 91.98% on the UCI-HAR dataset, and 67.51% on the real-world Extrasensory dataset. For the audio data, the balanced accuracy was 92.30% on the DCASE 2017 dataset, and 35.24% on the Extrasensory dataset.
Unsupervised Traffic Accident Detection in First-Person Videos

Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems. However, most work on video anomaly detection suffers from two crucial drawbacks. First, they assume cameras are fixed and videos have static backgrounds, which is reasonable for surveillance applications but not for vehicle-mounted cameras. Second, they pose the problem as one-class classification, relying on arduously hand-labeled training datasets that limit recognition to anomaly categories that have been explicitly trained. This paper proposes an unsupervised approach for traffic accident detection in first-person (dashboard-mounted camera) videos. Our major novelty is to detect anomalies by predicting the future locations of traffic participants and then monitoring the prediction accuracy and consistency metrics with three different strategies. We evaluate our approach using a new dataset of diverse traffic accidents, AnAn Accident Detection (A3D), as well as another publicly-available dataset. Experimental results show that our approach outperforms the state-of-the-art. Code and the dataset developed in this work are available at: https:llgithub.comlMoonBtvdltad-IROS2019.
ALTRO: A Fast Solver for Constrained Trajectory Optimization

Trajectory optimization is a widely used tool for robot motion planning and control. Existing solvers for these problems either rely on off-the-shelf nonlinear programming solvers that are numerically robust and capable of handling arbitrary constraints, but tend to be slow because they are general purpose; or they use custom numerical methods that take advantage of the problem structure to be fast, but often lack robustness and have limited or no ability to reason about constraints. This paper presents ALTRO (Augmented Lagrangian TRajectory optimizer), a solver for constrained trajectory optimization problems that handles general nonlinear state and input constraints and offers fast convergence and numerical robustness thanks to careful exploitation of problem structure. We demonstrate its performance on a set of benchmark motion-planning problems and offer comparisons to the standard direct collocation method with large-scale sequential quadratic programming and interior-point solvers.
When Blockchain meets Online Social Networks

Online Social Networks (OSNs) have become one of the most popular application of the daily life of users in the worldwide. Today the number of Social Media users is about 3 billion, and this trend increases year per year with high impact on the privacy issue. During the last years, decentralization of social services has been considered a big opportunity to overcome the main issues in OSNs. Blockchain technology represents today the most well known decentralized technique, which has been taken into account to develop the new generation of decentralized social platforms. Nevertheless, the real benefit of the introduction of blockchain technology in real social platforms is still unknown. In this paper we propose an overview of the main Blockchain-based Online Social Media platforms. We describe in detail these platforms by highlighting the main features and services they offer, but also the common drawbacks of these platforms. Finally, we propose a new model of Blockchain-based Online Social Network, which takes into account the role of the user as the center of the system, instead of the role of the content as in current proposals.
Escaping Plato’s Cave: 3D Shape From Adversarial Rendering

We introduce PLATONICGAN to discover the 3D structure of an object class from an unstructured collection of 2D images, i. e., where no relation between photos is known, except that they are showing instances of the same category. The key idea is to train a deep neural network to generate 3D shapes which, when rendered to images, are indistinguishable from ground truth images (for a discriminator) under various camera poses. Discriminating 2D images instead of 3D shapes allows tapping into unstructured 2D photo collections instead of relying on accurated (e. g., aligned, annotated, etc.) 3D data sets. To establish constraints between 2D image observation and their 3D interpretation, we suggest a family of rendering layers that are effectively differentiable. This family includes visual hull, absorption-only (akin to x-ray), and emission-absorption. We can successfully reconstruct 3D shapes from unstructured 2D images and extensively evaluate PLATONICGAN on a range of synthetic and real data sets achieving consistent improvements over baseline methods. We further show that PLATONICGAN can be combined with 3D supervision to improve on and in some cases even surpass the quality of 3D-supervised methods.
Multi-Frame Track-Before-Detect Algorithm for Maneuvering Target Tracking

Multi-frame track-before-detect (MF-TBD) is a model-based batch processing method. Assuming a particular model for the evolution of target states (e.g. a constant velocity model) within a batch processing time, MF-TBD integrates the target energy by taking advantage of the space-time correlations between a number of consecutive frames. Its performance is known to be heavily dependent on the accuracy of the motion model, and to substantially degrade when target maneuvers occur and motions do not follow the presumed model. We make two contributions towards addressing this problem. Firstly, we analyze and summarize the direct strategies to incorporate the effect of target maneuvers into MF-TBD. Our analysis shows that although these strategies are straightforward to implement, they either suffer from considerable performance loss or are computationally expensive. Motivated by the analysis, we propose a general measurement-directed (MD) strategy to address target maneuvers. It carries out an on-line study of target dynamics from the observations, and is capable of achieving both low computational complexity and high adaptability to different target maneuvers. Secondly, as the proposed MD strategy is a general framework without any particular model assumptions, we further derive its detailed implementation equations for linear motion and measurement models. Simulation results for various tracking scenarios are presented to demonstrate the effectiveness of the proposed MD strategy.
Issues Related to Face Recognition Accuracy Varying Based on Race and Skin Tone

Face recognition technology has recently become controversial over concerns about possible bias due to accuracy varying based on race or skin tone. We explore three important aspects of face recognition technology related to this controversy. Using two different deep convolutional neural network face matchers, we show that for a fixed decision threshold, the African-American image cohort has a higher false match rate (FMR), and the Caucasian cohort has a higher false nonmatch rate. We present an analysis of the impostor distribution designed to test the premise that darker skin tone causes a higher FMR, and find no clear evidence to support this premise. Finally, we explore how using face recognition for one-to-many identification can have a very low false-negative identification rate and still present concerns related to the false-positive identification rate. Both the ArcFace and VGGFace2 matchers and the MORPH dataset used in our experiments are available to the research community so that others should be able to reproduce or reanalyze our results.
Blockchain Technology Toward Green IoT: Opportunities and Challenges

In recent decades, the dramatic increase in global industrial activity has resulted in a significant rise in the use of fossil fuel energy resources, while technological progress has increased the carbon footprint and hence global warming. The significant increase in energy consumption caused by the Internet of Things (IoT) technology has created a new challenge and diverted our focus toward creating a more eco-friendly IoT ecosystem. The green IoT is an emerging field that has attracted a great deal of attention from researchers and industries, as it provides energy efficient services and enables the production and use of renewable energy. Meanwhile, blockchain technology has become a highly adoptable technology in the IoT and is attracting considerable interest from energy supply companies, business start-ups, financial institutions, governments and researchers. This study focuses on the role of the emerging blockchain technology in the green IoT ecosystem, presents the crucial factors that need to be taken into consideration in order to build a green IoT ecosystem, and discusses how blockchain technology contributes to make the IoT ecosystem greener. It also presents the open issues and future research directions to be considered when creating a sustainable green IoT ecosystem using blockchain technology.
A Survey of End-to-End Driving: Architectures and Training Methods

Autonomous driving is of great interest to industry and academia alike. The use of machine learning approaches for autonomous driving has long been studied, but mostly in the context of perception. In this article, we take a deeper look on the so-called end-to-end approaches for autonomous driving, where the entire driving pipeline is replaced with a single neural network. We review the learning methods, input and output modalities, network architectures, and evaluation schemes in end-to-end driving literature. Interpretability and safety are discussed separately, as they remain challenging for this approach. Beyond providing a comprehensive overview of existing methods, we conclude the review with an architecture that combines the most promising elements of the end-to-end autonomous driving systems.
Blockchain-Enabled Information Sharing Within a Supply Chain: A Systematic Literature Review

A supply chain consists of many stakeholders such as suppliers, carriers and customers. It is often complex due to the rapid development of economic globalization and the intense competition pressure in the market which resulted in information sharing within a supply chain to be fragmented. Blockchain technology can solve this problem by having only a “one trusted ledger” that could reshape the element of data trust. The goal of this paper is to identify and understand the impact of blockchain technology for information sharing within a supply chain. The decentralized nature of blockchain technology offers a high level of transparency and has gained the attention from various sectors to deploy this technology. A systematic literature review in the academic literature was conducted using different databases. Blockchain-enabled information sharing can add value to enhance collaborative work in different types of supply chains such as health and medical, construction and smart city. From our findings, one potential impact of deploying blockchain-enabled information sharing within a supply chain is that it ensures all members in the chain can obtain verified information which enhances collaborative partnerships. Through this in-depth research, we highlighted potential barriers that could impede the development of blockchain technology in supply chain such as the lack of understanding of blockchain technology in businesses and conflict of interests. Future work such as information hiding, in parallel with information sharing, could close the gap in deploying this technology within a supply chain. Understanding the nature of different supply chain is also important to better prepare the deployment of blockchain. We acknowledge that our approach in selecting literatures in our systematic review may exclude certain literatures. Nonetheless, we tried to include as many relevant literatures as possible, to develop a roadmap on the current situation of blockchain-enabled information sharing within a supply chain.
Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: A study on blockchain technology trend analysis

Blockchain has become one of the core technologies in Industry 4.0. To help decision-makers establish action plans based on blockchain, it is an urgent task to analyze trends in blockchain technology. However, most of existing studies on blockchain trend analysis are based on effort demanding full-text investigation or traditional bibliometric methods whose study scope is limited to a frequency-based statistical analysis. Therefore, in this paper, we propose a new topic modeling method called Word2vec-based Latent Semantic Analysis (W2V-LSA), which is based on Word2vec and Spherical k-means clustering to better capture and represent the context of a corpus. We then used W2V-LSA to perform an annual trend analysis of blockchain research by country and time for 231 abstracts of blockchain-related papers published over the past five years. The performance of the proposed algorithm was compared to Probabilistic LSA, one of the common topic modeling techniques. The experimental results confirmed the usefulness of W2V-LSA in terms of the accuracy and diversity of topics by quantitative and qualitative evaluation. The proposed method can be a competitive alternative for better topic modeling to provide direction for future research in technology trend analysis and it is applicable to various expert systems related to text mining.
B<scp>reak</scp> It Down: A Question Understanding Benchmark

Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through natural language, that are necessary for answering a question. We develop a crowdsourcing pipeline, showing that quality QDMRs can be annotated at scale, and release the Break dataset, containing over 83K pairs of questions and their QDMRs. We demonstrate the utility of QDMR by showing that (a) it can be used to improve open-domain question answering on the HotpotQA dataset, (b) it can be deterministically converted to a pseudo-SQL formal language, which can alleviate annotation in semantic parsing applications. Last, we use Break to train a sequence-to-sequence model with copying that parses questions into QDMR structures, and show that it substantially outperforms several natural baselines.
PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

With the widespread usage of cloud computing to benefit from its services, cloud service providers have invested in constructing large scale data centers.Consequently, a tremendous increase in energy consumption has arisen in conjunction with its results, including a remarkable rise in costs of operating and cooling servers.Besides, increasing energy consumption has a significant impact on the environment due to emissions of carbon dioxide.Dynamic consolidation of Virtual Machines (VMs) into the minimal number of Physical Machines (PMs) is considered as one of the magic solutions to manage power consumption.The virtual machine placement problem is a critical issue for good VM consolidation.This paper proposes a Power-Aware technique depending on Particle Swarm Optimization (PAPSO) to determine the near-optimal placement for the migrated VMs.A discrete version of Particle Swarm Optimization (PSO) is adopted based on a decimal encoding to map the migrated VMs to the best appropriate PMs.Furthermore, an effective minimization fitness function is employed to reduce power consumption without violating the Service Level Agreement (SLA).Specifically, PAPSO consolidates the migrated VMs into the minimum number of PMs with a major constraint to decrease the number of overloaded hosts as much as possible.Therefore, the number of VM migrations can be reduced drastically by taking into consideration the main sources for VM migrations; overloaded hosts and underloaded ones.PAPSO is implemented in CloudSim and the experimental results on random workloads with different sizes of VMs and PMs show that PAPSO does not violate SLA and outperforms the Power-Aware Best Fit Decreasing algorithm (PABFD).It can reduce about 8.01%, 39.65%, 66.33%, and 11.87% on average in terms of consumed energy, number of VM migrations, number of host shutdowns and the combined metric Energy SLA Violation (ESV), respectively.
Folk theories of algorithmic recommendations on Spotify: Enacting data assemblages in the global South

This paper examines folk theories of algorithmic recommendations on Spotify in order to make visible the cultural specificities of data assemblages in the global South. The study was conducted in Costa Rica and draws on triangulated data from 30 interviews, 4 focus groups with 22 users, and the study of “rich pictures” made by individuals to graphically represent their understanding of algorithmic recommendations. We found two main folk theories: one that personifies Spotify (and conceives of it as a social being that provides recommendations thanks to surveillance) and another one that envisions it as a system full of resources (and a computational machine that offers an individualized musical experience through the appropriate kind of “training”). Whereas the first theory emphasizes local conceptions of social relations to make sense of algorithms, the second one stresses the role of algorithms in providing a global experience of music and technology. We analyze why people espouse either one of these theories (or both) and how these theories provide users with resources to enact different modalities of power and resistance in relation to recommendation algorithms. We argue that folk theories thus offer a productive way to broaden understanding of what agency means in relation to algorithms.
Interpretable and Accurate Fine-grained Recognition via Region Grouping

We present an interpretable deep model for fine-grained visual recognition. At the core of our method lies the integration of region-based part discovery and attribution within a deep neural network. Our model is trained using image-level object labels, and provides an interpretation of its results via the segmentation of object parts and the identification of their contributions towards classification. To facilitate the learning of object parts without direct supervision, we explore a simple prior of the occurrence of object parts. We demonstrate that this prior, when combined with our region-based part discovery and attribution, leads to an interpretable model that remains highly accurate. Our model is evaluated on major fine-grained recognition datasets, including CUB-200, CelebA and iNaturalist. Our results compares favourably to state-of-the-art methods on classification tasks, and outperforms previous approaches on the localization of object parts.