OpenRaiser/Intern-Atlas huggingface dataset:
Deep Neural Networks for Marine Debris Detection in Sonar Images
Venue: arXiv cs.CV 2019
Year: 2019

Garbage and waste disposal is one of the biggest challenges currently faced by mankind. Proper waste disposal and recycling is a must in any sustainable community, and in many coastal areas there is significant water pollution in the form of floating or submerged garbage. This is called marine debris. Submerged marine debris threatens marine life, and for shallow coastal areas, it can also threaten fishing vessels [Iñiguez et al. 2016, Renewable and Sustainable Energy Reviews]. Submerged marine debris typically stays in the environment for a long time (20+ years), and consists of materials that can be recycled, such as metals, plastics, glass, etc. Many of these items should not be disposed in water bodies as this has a negative effect in the environment and human health. This thesis performs a comprehensive evaluation on the use of DNNs for the problem of marine debris detection in FLS images, as well as related problems such as image classification, matching, and detection proposals. We do this in a dataset of 2069 FLS images that we captured with an ARIS Explorer 3000 sensor on marine debris objects lying in the floor of a small water tank. The objects we used to produce this dataset contain typical household marine debris and distractor marine objects (tires, hooks, valves, etc), divided in 10 classes plus a background class. Our results show that for the evaluated tasks, DNNs are a superior technique than the corresponding state of the art. There are large gains particularly for the matching and detection proposal tasks. We also study the effect of sample complexity and object size in many tasks, which is valuable information for practitioners. We expect that our results will advance the objective of using Autonomous Underwater Vehicles to automatically survey, detect and collect marine debris from underwater environments.
Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds
Venue: arXiv cs.CV 2019
Year: 2019

Autonomous vehicles may make wrong decisions due to inaccurate detection and recognition. Therefore, an intelligent vehicle can combine its own data with that of other vehicles to enhance perceptive ability, and thus improve detection accuracy and driving safety. However, multi-vehicle cooperative perception requires the integration of real world scenes and the traffic of raw sensor data exchange far exceeds the bandwidth of existing vehicular networks. To the best our knowledge, we are the first to conduct a study on raw-data level cooperative perception for enhancing the detection ability of self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse the sensor data collected from different positions and angles of connected vehicles. A point cloud based 3D object detection method is proposed to work on a diversity of aligned point clouds. Experimental results on KITTI and our collected dataset show that the proposed system outperforms perception by extending sensing area, improving detection accuracy and promoting augmented results. Most importantly, we demonstrate it is possible to transmit point clouds data for cooperative perception via existing vehicular network technologies.
When Do People Trust Their Social Groups?
Venue: arXiv cs.SI 2019
Year: 2019

Trust facilitates cooperation and supports positive outcomes in social groups, including member satisfaction, information sharing, and task performance. Extensive prior research has examined individuals' general propensity to trust, as well as the factors that contribute to their trust in specific groups. Here, we build on past work to present a comprehensive framework for predicting trust in groups. By surveying 6,383 Facebook Groups users about their trust attitudes and examining aggregated behavioral and demographic data for these individuals, we show that (1) an individual's propensity to trust is associated with how they trust their groups, (2) smaller, closed, older, more exclusive, or more homogeneous groups are trusted more, and (3) a group's overall friendship-network structure and an individual's position within that structure can also predict trust. Last, we demonstrate how group trust predicts outcomes at both individual and group level such as the formation of new friendship ties.
Affine Variational Autoencoders: An Efficient Approach for Improving Generalization and Robustness to Distribution Shift
Venue: arXiv cs.NE 2019
Year: 2019

In this study, we propose the Affine Variational Autoencoder (AVAE), a variant of Variational Autoencoder (VAE) designed to improve robustness by overcoming the inability of VAEs to generalize to distributional shifts in the form of affine perturbations. By optimizing an affine transform to maximize ELBO, the proposed AVAE transforms an input to the training distribution without the need to increase model complexity to model the full distribution of affine transforms. In addition, we introduce a training procedure to create an efficient model by learning a subset of the training distribution, and using the AVAE to improve generalization and robustness to distributional shift at test time. Experiments on affine perturbations demonstrate that the proposed AVAE significantly improves generalization and robustness to distributional shift in the form of affine perturbations without an increase in model complexity.
Current Mode Neuron for the Memristor based synapse
Venue: arXiv cs.ET 2019
Year: 2019

Due to many limitations of Von Neumann architecture such as speed, memory bandwidth, efficiency of global interconnects and increase in the application of artificial neural network, researchers have been pushed to look into alternative architectures such as Neuromorphic computing system. Memristors (memristive crossbar memory RCM) are used as synapses due to its high packing density and energy efficiency and CMOS blocks as neurons. The increase in the terminal resistance of the RCM can degrade its energy efficiency and bandwidth. A more energy efficient current mode neuron has been proposed in this paper which can operate at lower voltages as compared to conventional voltage mode neuron circuit.
Wireless Edge Computing with Latency and Reliability Guarantees
Venue: arXiv cs.NI 2019
Year: 2019

Edge computing is an emerging concept based on distributing computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth generation (5G) wireless systems and beyond. While current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This article will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultra-reliable and low-latency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so forth. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.
Computing and Testing Small Vertex Connectivity in Near-Linear Time and Queries
Venue: arXiv cs.DS 2019
Year: 2019

We present a new, simple, algorithm for the local vertex connectivity problem (LocalVC) introduced by Nanongkai~et~al. [STOC'19]. Roughly, given an undirected unweighted graph $G$, a seed vertex $x$, a target volume $ν$, and a target separator size $k$, the goal of LocalVC is to remove $k$ vertices `near' $x$ (in terms of $ν$) to disconnect the graph in `local time', which depends only on parameters $ν$ and $k$. In this paper, we present a simple randomized algorithm with running time $O(νk^2)$ and correctness probability $2/3$. Plugging our new localVC algorithm in the generic framework of Nanongkai~et~al. immediately lead to a randomized $\tilde O(m+nk^3)$-time algorithm for the classic $k$-vertex connectivity problem on undirected graphs. ($\tilde O(T)$ hides $\text{polylog}(T)$.) This is the first near-linear time algorithm for any $4\leq k \leq \text{polylog} n$. Previous fastest algorithm for small $k$ takes $\tilde O(m+n^{4/3}k^{7/3})$ time [Nanongkai~et~al., STOC'19]. This work is inspired by the algorithm of Chechik~et~al. [SODA'17] for computing the maximal $k$-edge connected subgraphs. Forster and Yang [arXiv'19] has independently developed local algorithms similar to ours, and showed that they lead to an $\tilde O(k^3/ε)$ bound for testing $k$-edge and -vertex connectivity, resolving two long-standing open problems in property testing since the work of Goldreich and Ron [STOC'97] and Orenstein and Ron [Theor. Comput. Sci.'11]. Inspired by this, we use local approximation algorithms to obtain bounds that are near-linear in $k$, namely $\tilde O(k/ε)$ and $\tilde O(k/ε^2)$ for the bounded and unbounded degree cases, respectively. For testing $k$-edge connectivity for simple graphs, the bound can be improved to $\tilde O(\min(k/ε, 1/ε^2))$.
Graph Attribute Aggregation Network with Progressive Margin Folding
Venue: arXiv cs.LG 2019
Year: 2019

Graph convolutional neural networks (GCNNs) have been attracting increasing research attention due to its great potential in inference over graph structures. However, insufficient effort has been devoted to the aggregation methods between different convolution graph layers. In this paper, we introduce a graph attribute aggregation network (GAAN) architecture. Different from the conventional pooling operations, a graph-transformation-based aggregation strategy, progressive margin folding, PMF, is proposed for integrating graph features. By distinguishing internal and margin elements, we provide an approach for implementing the folding iteratively. And a mechanism is also devised for preserving the local structures during progressively folding. In addition, a hypergraph-based representation is introduced for transferring the aggregated information between different layers. Our experiments applied to the public molecule datasets demonstrate that the proposed GAAN outperforms the existing GCNN models with significant effectiveness.
Spectral Approximate Inference
Venue: arXiv cs.LG 2019
Year: 2019

Given a graphical model (GM), computing its partition function is the most essential inference task, but it is computationally intractable in general. To address the issue, iterative approximation algorithms exploring certain local structure/consistency of GM have been investigated as popular choices in practice. However, due to their local/iterative nature, they often output poor approximations or even do not converge, e.g., in low-temperature regimes (hard instances of large parameters). To overcome the limitation, we propose a novel approach utilizing the global spectral feature of GM. Our contribution is two-fold: (a) we first propose a fully polynomial-time approximation scheme (FPTAS) for approximating the partition function of GM associating with a low-rank coupling matrix; (b) for general high-rank GMs, we design a spectral mean-field scheme utilizing (a) as a subroutine, where it approximates a high-rank GM into a product of rank-1 GMs for an efficient approximation of the partition function. The proposed algorithm is more robust in its running time and accuracy than prior methods, i.e., neither suffers from the convergence issue nor depends on hard local structures, as demonstrated in our experiments.
Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis
Venue: arXiv cs.CV 2019
Year: 2019

Pedestrians and vehicles often share the road in complex inner city traffic. This leads to interactions between the vehicle and pedestrians, with each affecting the other's motion. In order to create robust methods to reason about pedestrian behavior and to design interfaces of communication between self-driving cars and pedestrians we need to better understand such interactions. In this paper, we present a data-driven approach to implicitly model pedestrians' interactions with vehicles, to better predict pedestrian behavior. We propose a LSTM model that takes as input the past trajectories of the pedestrian and ego-vehicle, and pedestrian head orientation, and predicts the future positions of the pedestrian. Our experiments based on a real-world, inner city dataset captured with vehicle mounted cameras, show that the usage of such cues improve pedestrian prediction when compared to a baseline that purely uses the past trajectory of the pedestrian.
Tropical probability theory and an application to the entropic cone
Venue: arXiv cs.IT 2019
Year: 2019

In a series of articles, we have been developing a theory of tropical diagrams of probability spaces, expecting it to be useful for information optimization problems in information theory and artificial intelligence. In this article, we give a summary of our work so far and apply the theory to derive a dimension-reduction statement about the shape of the entropic cone.
Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)
Venue: arXiv cs.CV 2019
Year: 2019

Traditional metrics for evaluating the efficacy of image processing techniques do not lend themselves to understanding the capabilities and limitations of modern image processing methods - particularly those enabled by deep learning. When applying image processing in engineering solutions, a scientist or engineer has a need to justify their design decisions with clear metrics. By applying blind/referenceless image spatial quality (BRISQUE), Structural SIMilarity (SSIM) index scores, and Peak signal-to-noise ratio (PSNR) to images before and after image processing, we can quantify quality improvements in a meaningful way and determine the lowest recoverable image quality for a given method.
WatchOut: A Road Safety Extension for Pedestrians on a Public Windshield Display
Venue: arXiv cs.HC 2019
Year: 2019

We conducted a field study to investigate whether public windshield displays are applicable as an additional interactive digital road safety warning sign. We focused on investigating the acceptance and usability of our novel public windshield display and its potential use for future applications. The study has shown that users are open-minded to the idea of an extraverted windshield display regardless the use case, whether it is used for safety purposes or different content. Contrary to our hypothesis most people assumed they would mistrust the system if it were as well established as traffic lights and primarily rely on their own perception.
Measuring and simulating latency in interactive remote rendering systems
Venue: arXiv cs.NI 2019
Year: 2019

Background: The computationally intensive task of real-time rendering can be offloaded to remote cloud systems. However, due to network latency, interactive remote rendering (IRR) introduces the challenge of interaction latency (IL), which is the time between an action and response to that action. Objectives: to model sources of latency, measure it in a real-world network and to use this understanding to simulate latency so that we have a controlled platform for experimental work in latency management. Method: we present a seven-parameter model of latency for a typical IRR system; we describe new, minimally intrusive software methods for measuring latency in a 3D graphics environment and create a novel latency simulator tool in software. Results: We demonstrate our latency simulator is comparable to real-world behavior and confirm that real-world latency exceeds the interactive limit of 70ms over long distance connections. We also find that current approaches to measuring IL are not general enough for most situations and therefore propose a novel general-purpose solution. Conclusion: to ameliorate latency in IRR systems we need controllable simulation tools for experimentation. In addition to a new measurement technique, we propose a new approach that will be of interest to IRR researchers and developers when designing IL compensation techniques.
NGO-GM: Natural Gradient Optimization for Graphical Models
Venue: arXiv cs.LG 2019
Year: 2019

This paper deals with estimating model parameters in graphical models. We reformulate it as an information geometric optimization problem and introduce a natural gradient descent strategy that incorporates additional meta parameters. We show that our approach is a strong alternative to the celebrated EM approach for learning in graphical models. Actually, our natural gradient based strategy leads to learning optimal parameters for the final objective function without artificially trying to fit a distribution that may not correspond to the real one. We support our theoretical findings with the question of trend detection in financial markets and show that the learned model performs better than traditional practitioner methods and is less prone to overfitting.
D2D Assisted Beamforming for Coded Caching
Venue: arXiv cs.IT 2019
Year: 2019

Device-to-device (D2D) aided beamforming for coded caching is considered in finite signal-to-noise ratio regime. A novel beamforming scheme is proposed where the local cache content exchange among nearby users is exploited. The transmission is split into two phases: local D2D content exchange and downlink transmission. In the D2D phase, users can autonomously share content with the adjacent users. The downlink phase utilizes multicast beamforming to simultaneously serve all users to fulfill the remaining content requests. We first explain the main procedure via two simple examples and then present the general formulation. Furthermore, D2D transmission scenarios and conditions useful for minimizing the overall delivery time are identified. We also investigate the benefits of using D2D transmission for decreasing the transceiver complexity of multicast beamforming. By exploiting the direct D2D exchange of file fragments, the common multicasting rate for delivering the remaining file fragments in the downlink phase is increased providing greatly enhanced overall content delivery performance.
Robustification of deep net classifiers by key based diversified aggregation with pre-filtering
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we address a problem of machine learning system vulnerability to adversarial attacks. We propose and investigate a Key based Diversified Aggregation (KDA) mechanism as a defense strategy. The KDA assumes that the attacker (i) knows the architecture of classifier and the used defense strategy, (ii) has an access to the training data set but (iii) does not know the secret key. The robustness of the system is achieved by a specially designed key based randomization. The proposed randomization prevents the gradients' back propagation or the creating of a "bypass" system. The randomization is performed simultaneously in several channels and a multi-channel aggregation stabilizes the results of randomization by aggregating soft outputs from each classifier in multi-channel system. The performed experimental evaluation demonstrates a high robustness and universality of the KDA against the most efficient gradient based attacks like those proposed by N. Carlini and D. Wagner and the non-gradient based sparse adversarial perturbations like OnePixel attacks.
On the share of mathematics published by Elsevier and Springer
Venue: arXiv cs.DL 2019
Year: 2019

For-profit editors such as Elsevier and Springer have been subject to sustained criticism from academics and university libraries, including calls to boycott, and discontinued subscriptions. Mathematicians have played a particularly active role in this critique, and have endeavored to imagine new publication practices and create new journals. This motivates the monitoring of the share of articles published by different editors. I used data from MathSciNet over the period 2000-2017, and focused on the 100 journals with highest citations per article. Within this category, the share of articles published by Elsevier and Springer has steadily increased over this period, from about a third to almost half of the total.
Quantitative Logic Reasoning
Venue: arXiv cs.LO 2019
Year: 2019

In this paper we show several similarities among logic systems that deal simultaneously with deductive and quantitative inference. We claim it is appropriate to call the tasks those systems perform as Quantitative Logic Reasoning. Analogous properties hold throughout that class, for whose members there exists a set of linear algebraic techniques applicable in the study of satisfiability decision problems. In this presentation, we consider as Quantitative Logic Reasoning the tasks performed by propositional Probabilistic Logic; first-order logic with counting quantifiers over a fragment containing unary and limited binary predicates; and propositional Lukasiewicz Infinitely-valued Probabilistic Logic
Learning meters of Arabic and English poems with Recurrent Neural Networks: a step forward for language understanding and synthesis
Venue: arXiv cs.CL 2019
Year: 2019

Recognizing a piece of writing as a poem or prose is usually easy for the majority of people; however, only specialists can determine which meter a poem belongs to. In this paper, we build Recurrent Neural Network (RNN) models that can classify poems according to their meters from plain text. The input text is encoded at the character level and directly fed to the models without feature handcrafting. This is a step forward for machine understanding and synthesis of languages in general, and Arabic language in particular. Among the 16 poem meters of Arabic and the 4 meters of English the networks were able to correctly classify poem with an overall accuracy of 96.38\% and 82.31\% respectively. The poem datasets used to conduct this research were massive, over 1.5 million of verses, and were crawled from different nontechnical sources, almost Arabic and English literature sites, and in different heterogeneous and unstructured formats. These datasets are now made publicly available in clean, structured, and documented format for other future research. To the best of the authors' knowledge, this research is the first to address classifying poem meters in a machine learning approach, in general, and in RNN featureless based approach, in particular. In addition, the dataset is the first publicly available dataset ready for the purpose of future computational research.