OpenRaiser/Intern-Atlas huggingface dataset:
Integrating Variable Reduction Strategy with Evolutionary Algorithm for Solving Nonlinear Equations Systems
Venue: arXiv cs.NE 2020
Year: 2020

Nonlinear equations systems (NESs) are widely used in real-world problems while they are also difficult to solve due to their characteristics of nonlinearity and multiple roots. Evolutionary algorithm (EA) is one of the methods for solving NESs, given their global search capability and an ability to locate multiple roots of a NES simultaneously within one run. Currently, the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs. By contrast, the problem domain knowledge of NESs is particularly investigated in this study, using which we propose to incorporate the variable reduction strategy (VRS) into EAs to solve NESs. VRS makes full use of the systems of expressing a NES and uses some variables (i.e., core variable) to represent other variables (i.e., reduced variables) through the variable relationships existing in the equation systems. It enables to reduce partial variables and equations and shrink the decision space, thereby reducing the complexity of the problem and improving the search efficiency of the EAs. To test the effectiveness of VRS in dealing with NESs, this paper integrates VRS into two existing state-of-the-art EA methods (i.e., MONES and DRJADE), respectively. Experimental results show that, with the assistance of VRS, the EA methods can significantly produce better results than the original methods and other compared methods.
PolicyKit: Building Governance in Online Communities
Venue: arXiv cs.CY 2020
Year: 2020

The software behind online community platforms encodes a governance model that represents a strikingly narrow set of governance possibilities focused on moderators and administrators. When online communities desire other forms of government, such as ones that take many members' opinions into account or that distribute power in non-trivial ways, communities must resort to laborious manual effort. In this paper, we present PolicyKit, a software infrastructure that empowers online community members to concisely author a wide range of governance procedures and automatically carry out those procedures on their home platforms. We draw on political science theory to encode community governance into policies, or short imperative functions that specify a procedure for determining whether a user-initiated action can execute. Actions that can be governed by policies encompass everyday activities such as posting or moderating a message, but actions can also encompass changes to the policies themselves, enabling the evolution of governance over time. We demonstrate the expressivity of PolicyKit through implementations of governance models such as a random jury deliberation, a multi-stage caucus, a reputation system, and a promotion procedure inspired by Wikipedia's Request for Adminship (RfA) process.
SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
Venue: arXiv cs.CL 2020
Year: 2020

In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020). We also release and describe our Hinglish (Hindi-English) and Spanglish (Spanish-English) corpora annotated with word-level language identification and sentence-level sentiment labels. These corpora are comprised of 20K and 19K examples, respectively. The sentiment labels are - Positive, Negative, and Neutral. SentiMix attracted 89 submissions in total including 61 teams that participated in the Hinglish contest and 28 submitted systems to the Spanglish competition. The best performance achieved was 75.0% F1 score for Hinglish and 80.6% F1 for Spanglish. We observe that BERT-like models and ensemble methods are the most common and successful approaches among the participants.
Secure Simultaneous Information and Power Transfer for Downlink Multi-user Massive MIMO
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, downlink secure transmission in simultaneous information and power transfer (SWIPT) system enabled with massive multiple-input multiple-output (MIMO) is studied. A base station (BS) with a large number of antennas transmits energy and information signals to its intended users, but these signals are also received by an active eavesdropper. The users and eavesdropper employ a power splitting technique to simultaneously decode information and harvest energy. Massive MIMO helps the BS to focus energy to the users and prevent information leakage to the eavesdropper. The harvested energy by each user is employed for decoding information and transmitting uplink pilot signals for channel estimation. It is assumed that the active eavesdropper also harvests energy in the downlink and then contributes during the uplink training phase. Achievable secrecy rate is considered as the performance criterion and a closed-form lower bound for it is derived. To provide secure transmission, the achievable secrecy rate is then maximized through an optimization problem with constraints on the minimum harvested energy by the user and the maximum harvested energy by the eavesdropper. Numerical results show the effectiveness of using massive MIMO in providing physical layer security in SWIPT systems and also show that our closed-form expressions for the secrecy rate are accurate.
Uncertainty Quantification of Structural Systems with Subset of Data
Venue: arXiv cs.CE 2020
Year: 2020

Quantification of the impact of uncertainty in material properties as well as the input ground motion on structural responses is an important step in implementing a performance-based earthquake engineering (PBEE) framework. Among various sources of uncertainty, the variability in the input ground motions, a.k.a. record-to-record, greatly affects the assessment results. The objective of this paper is to quantify the uncertainty in structural response with hybrid uncertainty sources. In this paper, multiple matrix completion methods are proposed and applied on a case study structure. The matrix completion method is a means to estimate the analyses results for the entire set of input parameters by conducting analysis for only a small subset of analyses. The main algorithmic contributions of our proposed method are twofold. First, we develop a sampling technique for choosing a subset of representative simulations, which allows improving the accuracy of the estimated response. An unsupervised machine learning technique is used for this purpose. Next, the proposed matrix completion method for uncertainty quantification is further refined by incorporating a regression model that is trained on the available partial simulations. The regression model improves the initial sampling as it provides a rough estimation of the structural responses. Finally, the proposed algorithm is applied to a multi-degree-of-freedom system, and the structural responses (i.e., displacements and base shear) are estimated. Results show that the proposed algorithm can effectively estimate the response from a full set of nonlinear simulations by conducting analyses only on a small portion of the set.
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop
Venue: arXiv cs.LG 2020
Year: 2020

DeepDrummer is a drum loop generation tool that uses active learning to learn the preferences (or current artistic intentions) of a human user from a small number of interactions. The principal goal of this tool is to enable an efficient exploration of new musical ideas. We train a deep neural network classifier on audio data and show how it can be used as the core component of a system that generates drum loops based on few prior beliefs as to how these loops should be structured. We aim to build a system that can converge to meaningful results even with a limited number of interactions with the user. This property enables our method to be used from a cold start situation (no pre-existing dataset), or starting from a collection of audio samples provided by the user. In a proof of concept study with 25 participants, we empirically demonstrate that DeepDrummer is able to converge towards the preference of our subjects after a small number of interactions.
Locating Cephalometric X-Ray Landmarks with Foveated Pyramid Attention
Venue: arXiv cs.CV 2020
Year: 2020

CNNs, initially inspired by human vision, differ in a key way: they sample uniformly, rather than with highest density in a focal point. For very large images, this makes training untenable, as the memory and computation required for activation maps scales quadratically with the side length of an image. We propose an image pyramid based approach that extracts narrow glimpses of the of the input image and iteratively refines them to accomplish regression tasks. To assist with high-accuracy regression, we introduce a novel intermediate representation we call 'spatialized features'. Our approach scales logarithmically with the side length, so it works with very large images. We apply our method to Cephalometric X-ray Landmark Detection and get state-of-the-art results.
A Synthetic Network Generator for Covert Network Analytics
Venue: arXiv cs.SI 2020
Year: 2020

We study social networks and focus on covert (also known as hidden) networks, such as terrorist or criminal networks. Their structures, memberships and activities are illegal. Thus, data about covert networks is often incomplete and partially incorrect, making interpreting structures and activities of such networks challenging. For legal reasons, real data about active covert networks is inaccessible to researchers. To address these challenges, we introduce here a network generator for synthetic networks that are statistically similar to a real network but void of personal information about its members. The generator uses statistical data about a real or imagined covert organization network. It generates randomized instances of the Stochastic Block model of the network groups but preserves this network organizational structure. The direct use of such anonymized networks is for training on them the research and analytical tools for finding structure and dynamics of covert networks. Since these synthetic networks differ in their sets of edges and communities, they can be used as a new source for network analytics. First, they provide alternative interpretations of the data about the original network. The distribution of probabilities for these alternative interpretations enables new network analytics. The analysts can find community structures which are frequent, therefore stable under perturbations. They may also analyze how the stability changes with the strength of perturbation. For covert networks, the analysts can quantify statistically expected outcomes of interdiction. This kind of analytics applies to all complex network in which the data are incomplete or partially incorrect.
SafetyOps
Venue: arXiv cs.SE 2020
Year: 2020

Safety assurance is a paramount factor in the large-scale deployment of various autonomous systems (e.g., self-driving vehicles). However, the execution of safety engineering practices and processes have been challenged by an increasing complexity of modern safety-critical systems. This attribute has become more critical for autonomous systems that involve artificial intelligence (AI) and data-driven techniques along with the complex interactions of the physical world and digital computing platforms. In this position paper, we highlight some challenges of applying current safety processes to modern autonomous systems. Then, we introduce the concept of SafetyOps - a set of practices, which combines DevOps, TestOps, DataOps, and MLOps to provide an efficient, continuous and traceable system safety lifecycle. We believe that SafetyOps can play a significant role in scalable integration and adaptation of safety engineering into various industries relying on AI and data.
Comprehensiveness of Archives: A Modern AI-enabled Approach to Build Comprehensive Shared Cultural Heritage
Venue: arXiv cs.CY 2020
Year: 2020

Archives play a crucial role in the construction and advancement of society. Humans place a great deal of trust in archives and depend on them to craft public policies and to preserve languages, cultures, self-identity, views and values. Yet, there are certain voices and viewpoints that remain elusive in the current processes deployed in the classification and discoverability of records and archives. In this paper, we explore the ramifications and effects of centralized, due process archival systems on marginalized communities. There is strong evidence to prove the need for progressive design and technological innovation while in the pursuit of comprehensiveness, equity and justice. Intentionality and comprehensiveness is our greatest opportunity when it comes to improving archival practices and for the advancement and thrive-ability of societies at large today. Intentionality and comprehensiveness is achievable with the support of technology and the Information Age we live in today. Reopening, questioning and/or purposefully including others voices in archival processes is the intention we present in our paper. We provide examples of marginalized communities who continue to lead "community archive" movements in efforts to reclaim and protect their cultural identity, knowledge, views and futures. In conclusion, we offer design and AI-dominant technological considerations worth further investigation in efforts to bridge systemic gaps and build robust archival processes.
Context Reinforced Neural Topic Modeling over Short Texts
Venue: arXiv cs.IR 2020
Year: 2020

As one of the prevalent topic mining tools, neural topic modeling has attracted a lot of interests for the advantages of high efficiency in training and strong generalisation abilities. However, due to the lack of context in each short text, the existing neural topic models may suffer from feature sparsity on such documents. To alleviate this issue, we propose a Context Reinforced Neural Topic Model (CRNTM), whose characteristics can be summarized as follows. Firstly, by assuming that each short text covers only a few salient topics, CRNTM infers the topic for each word in a narrow range. Secondly, our model exploits pre-trained word embeddings by treating topics as multivariate Gaussian distributions or Gaussian mixture distributions in the embedding space. Extensive experiments on two benchmark datasets validate the effectiveness of the proposed model on both topic discovery and text classification.
PiNet: Attention Pooling for Graph Classification
Venue: arXiv cs.LG 2020
Year: 2020

We propose PiNet, a generalised differentiable attention-based pooling mechanism for utilising graph convolution operations for graph level classification. We demonstrate high sample efficiency and superior performance over other graph neural networks in distinguishing isomorphic graph classes, as well as competitive results with state of the art methods on standard chemo-informatics datasets.
Holdout SGD: Byzantine Tolerant Federated Learning
Venue: arXiv cs.DC 2020
Year: 2020

This work presents a new distributed Byzantine tolerant federated learning algorithm, HoldOut SGD, for Stochastic Gradient Descent (SGD) optimization. HoldOut SGD uses the well known machine learning technique of holdout estimation, in a distributed fashion, in order to select parameter updates that are likely to lead to models with low loss values. This makes it more effective at discarding Byzantine workers inputs than existing methods that eliminate outliers in the parameter-space of the learned model. HoldOut SGD first randomly selects a set of workers that use their private data in order to propose gradient updates. Next, a voting committee of workers is randomly selected, and each voter uses its private data as holdout data, in order to select the best proposals via a voting scheme. We propose two possible mechanisms for the coordination of workers in the distributed computation of HoldOut SGD. The first uses a truthful central server and corresponds to the typical setting of current federated learning. The second is fully distributed and requires no central server, paving the way to fully decentralized federated learning. The fully distributed version implements HoldOut SGD via ideas from the blockchain domain, and specifically the Algorand committee selection and consensus processes. We provide formal guarantees for the HoldOut SGD process in terms of its convergence to the optimal model, and its level of resilience to the fraction of Byzantine workers. Empirical evaluation shows that HoldOut SGD is Byzantine-resilient and efficiently converges to an effectual model for deep-learning tasks, as long as the total number of participating workers is large and the fraction of Byzantine workers is less than half (<1/3 for the fully distributed variant).
Deep UAV Localization with Reference View Rendering
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents a framework for the localization of Unmanned Aerial Vehicles (UAVs) in unstructured environments with the help of deep learning. A real-time rendering engine is introduced that generates optical and depth images given a six Degrees-of-Freedom (DoF) camera pose, camera model, geo-referenced orthoimage, and elevation map. The rendering engine is embedded into a learning-based six-DoF Inverse Compositional Lucas-Kanade (ICLK) algorithm that is able to robustly align the rendered and real-world image taken by the UAV. To learn the alignment under environmental changes, the architecture is trained using maps spanning multiple years at high resolution. The evaluation shows that the deep 6DoF-ICLK algorithm outperforms its non-trainable counterparts by a large margin. To further support the research in this field, the real-time rendering engine and accompanying datasets are released along with this publication.
Measure the Impact of Institution and Paper via Institution-citation Network
Venue: arXiv cs.DL 2020
Year: 2020

This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research.
Author Impact: Evaluations, Predictions, and Challenges
Venue: arXiv cs.DL 2020
Year: 2020

Author impact evaluation and prediction play a key role in determining rewards, funding, and promotion. In this paper, we first introduce the background of author impact evaluation and prediction. Then, we review recent developments of author impact evaluation, including data collection, data pre-processing, data analysis, feature selection, algorithm design, and algorithm evaluation. Thirdly, we provide an in-depth literature review on author impact predictive models and common evaluation metrics. Finally, we look into the representative research issues, including author impact inflation, unified evaluation standards, academic success gene, identification of the origins of hot streaks, and higher-order academic networks analysis. This paper should help the researchers obtain a broader understanding in author impact evaluation and prediction, and provides future research directions.
Fast and Accurate Optical Flow based Depth Map Estimation from Light Fields
Venue: arXiv cs.CV 2020
Year: 2020

Depth map estimation is a crucial task in computer vision, and new approaches have recently emerged taking advantage of light fields, as this new imaging modality captures much more information about the angular direction of light rays compared to common approaches based on stereoscopic images or multi-view. In this paper, we propose a novel depth estimation method from light fields based on existing optical flow estimation methods. The optical flow estimator is applied on a sequence of images taken along an angular dimension of the light field, which produces several disparity map estimates. Considering both accuracy and efficiency, we choose the feature flow method as our optical flow estimator. Thanks to its spatio-temporal edge-aware filtering properties, the different disparity map estimates that we obtain are very consistent, which allows a fast and simple aggregation step to create a single disparity map, which can then converted into a depth map. Since the disparity map estimates are consistent, we can also create a depth map from each disparity estimate, and then aggregate the different depth maps in the 3D space to create a single dense depth map.
ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows
Venue: arXiv cs.CV 2020
Year: 2020

Downscaling is a landmark task in climate science and meteorology in which the goal is to use coarse scale, spatio-temporal data to infer values at finer scales. Statistical downscaling aims to approximate this task using statistical patterns gleaned from an existing dataset of downscaled values, often obtained from observations or physical models. In this work, we investigate the application of deep latent variable learning to the task of statistical downscaling. We present ClimAlign, a novel method for unsupervised, generative downscaling using adaptations of recent work in normalizing flows for variational inference. We evaluate the viability of our method using several different metrics on two datasets consisting of daily temperature and precipitation values gridded at low (1 degree latitude/longitude) and high (1/4 and 1/8 degree) resolutions. We show that our method achieves comparable predictive performance to existing supervised statistical downscaling methods while simultaneously allowing for both conditional and unconditional sampling from the joint distribution over high and low resolution spatial fields. We provide publicly accessible implementations of our method, as well as the baselines used for comparison, on GitHub.
Security should be there by default: Investigating how journalists perceive and respond to risks from the Internet of Things
Venue: arXiv cs.CR 2020
Year: 2020

Journalists have long been the targets of both physical and cyber-attacks from well-resourced adversaries. Internet of Things (IoT) devices are arguably a new avenue of threat towards journalists through both targeted and generalised cyber-physical exploitation. This study comprises three parts: First, we interviewed 11 journalists and surveyed 5 further journalists, to determine the extent to which journalists perceive threats through the IoT, particularly via consumer IoT devices. Second, we surveyed 34 cyber security experts to establish if and how lay-people can combat IoT threats. Third, we compared these findings to assess journalists' knowledge of threats, and whether their protective mechanisms would be effective against experts' depictions and predictions of IoT threats. Our results indicate that journalists generally are unaware of IoT-related risks and are not adequately protecting themselves; this considers cases where they possess IoT devices, or where they enter IoT-enabled environments (e.g., at work or home). Expert recommendations spanned both immediate and long-term mitigation methods, including practical actions that are technical and socio-political in nature. However, all proposed individual mitigation methods are likely to be short-term solutions, with 26 of 34 (76.5%) of cyber security experts responding that within the next five years it will not be possible for the public to opt-out of interaction with the IoT.