OpenRaiser/Intern-Atlas huggingface dataset:
Erasure decoding of convolutional codes using first order representations
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we employ the linear systems representation of a convolutional code to develop a decoding algorithm for convolutional codes over the erasure channel. We study the decoding problem using the state space description and this provides in a natural way additional information. With respect to previously known decoding algorithms, our new algorithm has the advantage that it is able to reduce the decoding delay as well as the computational effort in the erasure recovery process. We describe which properties a convolutional code should have in order to obtain a good decoding performance and illustrate it with an example.
A stabilized finite element method for delamination analysis of composites using cohesive elements
Venue: arXiv cs.CE 2020
Year: 2020

We demonstrate the ability of a stabilized finite element method, inspired by the weighted Nitsche approach, to alleviate spurious traction oscillations at interlaminar interfaces in multi-ply multi-directional composite laminates. In contrast with the standard (penalty-like) method, the stabilized method allows the use of arbitrarily large values of cohesive stiffness and obviates the need for engineering approaches to estimate minimum cohesive stiffness necessary for accurate delamination analysis. This is achieved by defining a weighted interface traction in the stabilized method, which allows a gradual transition from penalty-like method for soft elastic contact to Nitsche-like method for rigid contact. We conducted several simulation studies involving constant strain patch tests and benchmark delamination tests under mode-I, mode-II and mixed-mode loadings. Our results show clear evidence of traction oscillations with the standard method with structured and perturbed finite element meshes, and that the stabilized method alleviates these oscillations, thus illustrating its robustness.
Adventures in Mathematical Reasoning
Venue: arXiv cs.AI 2020
Year: 2020

"Mathematics is not a careful march down a well-cleared highway, but a journey into a strange wilderness, where the explorers often get lost. Rigour should be a signal to the historian that the maps have been made, and the real explorers have gone elsewhere." W.S. Anglin, the Mathematical Intelligencer, 4 (4), 1982.
The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems
Venue: arXiv cs.HC 2020
Year: 2020

Domain-specific intelligent systems are meant to help system users in their decision-making process. Many systems aim to simultaneously support different users with varying levels of domain expertise, but prior domain knowledge can affect user trust and confidence in detecting system errors. While it is also known that user trust can be influenced by first impressions with intelligent systems, our research explores the relationship between ordering bias and domain expertise when encountering errors in intelligent systems. In this paper, we present a controlled user study to explore the role of domain knowledge in establishing trust and susceptibility to the influence of first impressions on user trust. Participants reviewed an explainable image classifier with a constant accuracy and two different orders of observing system errors (observing errors in the beginning of usage vs. in the end). Our findings indicate that encountering errors early-on can cause negative first impressions for domain experts, negatively impacting their trust over the course of interactions. However, encountering correct outputs early helps more knowledgable users to dynamically adjust their trust based on their observations of system performance. In contrast, novice users suffer from over-reliance due to their lack of proper knowledge to detect errors.
Mechanisms for QoE optimisation of Video Traffic: A review paper
Venue: arXiv cs.NI 2020
Year: 2020

Transmission of video traffic over the Internet has grown exponentially in the past few years with no sign of waning. This increasing demand for video services has changed user expectation of quality. Various mechanisms have been proposed to optimise the Quality of Experience (QoE) of end users video. Studying these approaches are necessary for new methods to be proposed or combination of existing ones to be tailored. We discuss challenges facing the optimisation of QoE for video traffic in this paper. It surveys and classifies these mechanisms based on their functions. The limitation of each of them is identified and future directions are highlighted.
PyTorch Metric Learning
Venue: arXiv cs.CV 2020
Year: 2020

Deep metric learning algorithms have a wide variety of applications, but implementing these algorithms can be tedious and time consuming. PyTorch Metric Learning is an open source library that aims to remove this barrier for both researchers and practitioners. The modular and flexible design allows users to easily try out different combinations of algorithms in their existing code. It also comes with complete train/test workflows, for users who want results fast. Code and documentation is available at https://www.github.com/KevinMusgrave/pytorch-metric-learning.
A quality of experience-aware cross-layer architecture for optimizing video streaming services
Venue: arXiv cs.NI 2020
Year: 2020

The popularity of the video services on the Internet has evolved various mechanisms that target the Quality of Experience (QoE) optimization of video traffic. The video quality has been enhanced through adapting the sending bitrates. However, rate adaptation alone is not sufficient for maintaining a good video QoE when congestion occurs. This paper presents a cross-layer architecture for video streaming that is QoE-aware. It combines adaptation capabilities of video applications and QoE-aware admission control to optimize the trade-off relationship between QoE and the number of admitted sessions. Simulation results showed the efficiency of the proposed architecture in terms of QoE and number of sessions compared to two other architectures (adaptive architecture and non-adaptive architecture).
Learning to Abstract and Predict Human Actions
Venue: arXiv cs.CV 2020
Year: 2020

Human activities are naturally structured as hierarchies unrolled over time. For action prediction, temporal relations in event sequences are widely exploited by current methods while their semantic coherence across different levels of abstraction has not been well explored. In this work we model the hierarchical structure of human activities in videos and demonstrate the power of such structure in action prediction. We propose Hierarchical Encoder-Refresher-Anticipator, a multi-level neural machine that can learn the structure of human activities by observing a partial hierarchy of events and roll-out such structure into a future prediction in multiple levels of abstraction. We also introduce a new coarse-to-fine action annotation on the Breakfast Actions videos to create a comprehensive, consistent, and cleanly structured video hierarchical activity dataset. Through our experiments, we examine and rethink the settings and metrics of activity prediction tasks toward unbiased evaluation of prediction systems, and demonstrate the role of hierarchical modeling toward reliable and detailed long-term action forecasting.
COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce
Venue: arXiv cs.IR 2020
Year: 2020

In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE. The dataset is constructed from an Amazon review corpus by integrating both user-agent dialogue and custom knowledge graphs for recommendation. Specifically, we first construct a unified knowledge graph and extract key entities between user--product pairs, which serve as the skeleton of a conversation. Then we simulate conversations mirroring the human coarse-to-fine process of choosing preferred items. The proposed baselines and experiments demonstrate that our dataset is able to provide innovative opportunities for conversational recommendation.
Logic-Induced Bisimulations
Venue: arXiv cs.LO 2020
Year: 2020

We define a new logic-induced notion of bisimulation (called $ρ$-bisimulation) for coalgebraic modal logics given by a logical connection, and investigate its properties. We show that it is structural in the sense that it is defined only in terms of the coalgebra structure and the one-step modal semantics and, moreover, can be characterised by a form of relation lifting. Furthermore we compare $ρ$-bisimulations to several well-known equivalence notions, and we prove that the collection of bisimulations between two models often forms a complete lattice. The main technical result is a Hennessy-Milner type theorem which states that, under certain conditions, logical equivalence implies $ρ$-bisimilarity. In particular, the latter does \emph{not} rely on a duality between functors $\mathsf{T}$ (the type of the coalgebras) and $\mathsf{L}$ (which gives the logic), nor on properties of the logical connection $ρ$.
Algorithmic Transparency with Strategic Users
Venue: arXiv cs.GT 2020
Year: 2020

Should firms that apply machine learning algorithms in their decision-making make their algorithms transparent to the users they affect? Despite growing calls for algorithmic transparency, most firms have kept their algorithms opaque, citing potential gaming by users that may negatively affect the algorithm's predictive power. We develop an analytical model to compare firm and user surplus with and without algorithmic transparency in the presence of strategic users and present novel insights. We identify a broad set of conditions under which making the algorithm transparent benefits the firm. We show that, in some cases, even the predictive power of machine learning algorithms may increase if the firm makes them transparent. By contrast, users may not always be better off under algorithmic transparency. The results hold even when the predictive power of the opaque algorithm comes largely from correlational features and the cost for users to improve on them is close to zero. Overall, our results show that firms should not view manipulation by users as bad. Rather, they should use algorithmic transparency as a lever to motivate users to invest in more desirable features.
ATG-PVD: Ticketing Parking Violations on A Drone
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framework consists of: 1) SwiftFlow, an efficient and accurate convolutional neural network (CNN) for unsupervised optical flow estimation; 2) Flow-RCNN, a flow-guided CNN for car detection and classification; and 3) an illegally parked car (IPC) candidate investigation module developed based on visual SLAM. The proposed framework was successfully embedded in a drone from ATG Robotics. The experimental results demonstrate that, firstly, our proposed SwiftFlow outperforms all other state-of-the-art unsupervised optical flow estimation approaches in terms of both speed and accuracy; secondly, IPC candidates can be effectively and efficiently detected by our proposed Flow-RCNN, with a better performance than our baseline network, Faster-RCNN; finally, the actual IPCs can be successfully verified by our investigation module after drone re-localization.
Explainable Recommender Systems via Resolving Learning Representations
Venue: arXiv cs.LG 2020
Year: 2020

Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing more effective models in various scenarios, the exploration on the explainability of recommender systems is running behind. Explanations could help improve user experience and discover system defects. In this paper, after formally introducing the elements that are related to model explainability, we propose a novel explainable recommendation model through improving the transparency of the representation learning process. Specifically, to overcome the representation entangling problem in traditional models, we revise traditional graph convolution to discriminate information from different layers. Also, each representation vector is factorized into several segments, where each segment relates to one semantic aspect in data. Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge. In this way, the proposed model can learn interpretable and meaningful representations for users and items. Unlike traditional methods that need to make a trade-off between explainability and effectiveness, the performance of our proposed explainable model is not negatively affected after considering explainability. Finally, comprehensive experiments are conducted to validate the performance of our model as well as explanation faithfulness.
Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples
Venue: arXiv cs.CV 2020
Year: 2020

Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain. However, these algorithms will be infeasible when only a few labeled data exist in the source domain, and thus the performance decreases significantly. To address this challenge, we propose a Domain-invariant Graph Learning (DGL) approach for domain adaptation with only a few labeled source samples. Firstly, DGL introduces the Nystrom method to construct a plastic graph that shares similar geometric property as the target domain. And then, DGL flexibly employs the Nystrom approximation error to measure the divergence between plastic graph and source graph to formalize the distribution mismatch from the geometric perspective. Through minimizing the approximation error, DGL learns a domain-invariant geometric graph to bridge source and target domains. Finally, we integrate the learned domain-invariant graph with the semi-supervised learning and further propose an adaptive semi-supervised model to handle the cross-domain problems. The results of extensive experiments on popular datasets verify the superiority of DGL, especially when only a few labeled source samples are available.
Schematic Representation of Large Biconnected Graphs
Venue: arXiv cs.DS 2020
Year: 2020

Suppose that a biconnected graph is given, consisting of a large component plus several other smaller components, each separated from the main component by a separation pair. We investigate the existence and the computation time of schematic representations of the structure of such a graph where the main component is drawn as a disk, the vertices that take part in separation pairs are points on the boundary of the disk, and the small components are placed outside the disk and are represented as non-intersecting lunes connecting their separation pairs. We consider several drawing conventions for such schematic representations, according to different ways to account for the size of the small components. We map the problem of testing for the existence of such representations to the one of testing for the existence of suitably constrained $1$-page book-embeddings and propose several polynomial-time algorithms.
Combining Control Barrier Functions and Behavior Trees for Multi-Agent Underwater Coverage Missions
Venue: arXiv cs.RO 2020
Year: 2020

Robot missions typically involve a number of desired objectives, such as avoiding collisions, staying connected to other robots, gathering information using sensors and returning to the charging station before the battery runs out. Some of these objectives need to be taken into account at the same time, such as avoiding collisions and staying connected, while others are focused upon during different parts of the executions, such as returning to the charging station and connectivity maintenance. In this paper, we show how Control Barrier Functions(CBFs) and Behavior Trees(BTs) can be combined in a principled manner to achieve both types of task compositions, with performance guarantees in terms of mission completion. We illustrate our method with a simulated underwater coverage mission.
Graph learning under spectral sparsity constraints
Venue: arXiv cs.IT 2020
Year: 2020

Graph inference plays an essential role in machine learning, pattern recognition, and classification. Signal processing based approaches in literature generally assume some variational property of the observed data on the graph. We make a case for inferring graphs on which the observed data has high variation. We propose a signal processing based inference model that allows for wideband frequency variation in the data and propose an algorithm for graph inference. The proposed inference algorithm consists of two steps: 1) learning orthogonal eigenvectors of a graph from the data; 2) recovering the adjacency matrix of the graph topology from the given graph eigenvectors. The first step is solved by an iterative algorithm with a closed-form solution. In the second step, the adjacency matrix is inferred from the eigenvectors by solving a convex optimization problem. Numerical results on synthetic data show the proposed inference algorithm can effectively capture the meaningful graph topology from observed data under the wideband assumption.
Investigating Differences in Crowdsourced News Credibility Assessment: Raters, Tasks, and Expert Criteria
Venue: arXiv cs.HC 2020
Year: 2020

Misinformation about critical issues such as climate change and vaccine safety is oftentimes amplified on online social and search platforms. The crowdsourcing of content credibility assessment by laypeople has been proposed as one strategy to combat misinformation by attempting to replicate the assessments of experts at scale. In this work, we investigate news credibility assessments by crowds versus experts to understand when and how ratings between them differ. We gather a dataset of over 4,000 credibility assessments taken from 2 crowd groups---journalism students and Upwork workers---as well as 2 expert groups---journalists and scientists---on a varied set of 50 news articles related to climate science, a topic with widespread disconnect between public opinion and expert consensus. Examining the ratings, we find differences in performance due to the makeup of the crowd, such as rater demographics and political leaning, as well as the scope of the tasks that the crowd is assigned to rate, such as the genre of the article and partisanship of the publication. Finally, we find differences between expert assessments due to differing expert criteria that journalism versus science experts use---differences that may contribute to crowd discrepancies, but that also suggest a way to reduce the gap by designing crowd tasks tailored to specific expert criteria. From these findings, we outline future research directions to better design crowd processes that are tailored to specific crowds and types of content.
ConiVAT: Cluster Tendency Assessment and Clustering with Partial Background Knowledge
Venue: arXiv cs.LG 2020
Year: 2020

The VAT method is a visual technique for determining the potential cluster structure and the possible number of clusters in numerical data. Its improved version, iVAT, uses a path-based distance transform to improve the effectiveness of VAT for "tough" cases. Both VAT and iVAT have also been used in conjunction with a single-linkage(SL) hierarchical clustering algorithm. However, they are sensitive to noise and bridge points between clusters in the dataset, and consequently, the corresponding VAT/iVAT images are often in-conclusive for such cases. In this paper, we propose a constraint-based version of iVAT, which we call ConiVAT, that makes use of background knowledge in the form of constraints, to improve VAT/iVAT for challenging and complex datasets. ConiVAT uses the input constraints to learn the underlying similarity metric and builds a minimum transitive dissimilarity matrix, before applying VAT to it. We demonstrate ConiVAT approach to visual assessment and single linkage clustering on nine datasets to show that, it improves the quality of iVAT images for complex datasets, and it also overcomes the limitation of SL clustering with VAT/iVAT due to "noisy" bridges between clusters. Extensive experiment results on nine datasets suggest that ConiVAT outperforms the other three semi-supervised clustering algorithms in terms of improved clustering accuracy.
Reinforcement Learning-based Admission Control in Delay-sensitive Service Systems
Venue: arXiv cs.PF 2020
Year: 2020

Ensuring quality of service (QoS) guarantees in service systems is a challenging task, particularly when the system is composed of more fine-grained services, such as service function chains. An important QoS metric in service systems is the end-to-end delay, which becomes even more important in delay-sensitive applications, where the jobs must be completed within a time deadline. Admission control is one way of providing end-to-end delay guarantee, where the controller accepts a job only if it has a high probability of meeting the deadline. In this paper, we propose a reinforcement learning-based admission controller that guarantees a probabilistic upper-bound on the end-to-end delay of the service system, while minimizes the probability of unnecessary rejections. Our controller only uses the queue length information of the network and requires no knowledge about the network topology or system parameters. Since long-term performance metrics are of great importance in service systems, we take an average-reward reinforcement learning approach, which is well suited to infinite horizon problems. Our evaluations verify that the proposed RL-based admission controller is capable of providing probabilistic bounds on the end-to-end delay of the network, without using system model information.
Translating the Grid: How a Translational Approach Shaped the Development of Grid Computing
Venue: arXiv cs.DC 2020
Year: 2020

A growing gap between progress in biological knowledge and improved health outcomes inspired the new discipline of translational medicine, in which the application of new knowledge is an explicit part of a research plan. Abramson and Parashar argue that a similar gap between complex computational technologies and ever-more-challenging applications demands an analogous discipline of translational computer science, in which the deliberate movement of research results into large-scale practice becomes a central research focus rather than an afterthought. We revisit from this perspective the development and application of grid computing from the mid-1990s onwards, and find that a translational framing is useful for understanding the technology's development and impact. We discuss how the development of grid computing infrastructure, and the Globus Toolkit, in particular, benefited from a translational approach. We identify lessons learned that can be applied to other translational computer science initiatives.