OpenRaiser/Intern-Atlas huggingface dataset:
Panoptic Edge Detection
Venue: arXiv cs.CV 2019
Year: 2019

Pursuing more complete and coherent scene understanding towards realistic vision applications drives edge detection from category-agnostic to category-aware semantic level. However, finer delineation of instance-level boundaries still remains unexcavated. In this work, we address a new finer-grained task, termed panoptic edge detection (PED), which aims at predicting semantic-level boundaries for stuff categories and instance-level boundaries for instance categories, in order to provide more comprehensive and unified scene understanding from the perspective of edges.We then propose a versatile framework, Panoptic Edge Network (PEN), which aggregates different tasks of object detection, semantic and instance edge detection into a single holistic network with multiple branches. Based on the same feature representation, the semantic edge branch produces semantic-level boundaries for all categories and the object detection branch generates instance proposals. Conditioned on the prior information from these two branches, the instance edge branch aims at instantiating edge predictions for instance categories. Besides, we also devise a Panoptic Dual F-measure (F2) metric for the new PED task to uniformly measure edge prediction quality for both stuff and instances. By joint end-to-end training, the proposed PEN framework outperforms all competitive baselines on Cityscapes and ADE20K datasets.
Transmit Power Policy and Ergodic Multicast Rate Analysis of Cognitive Radio Networks in Generalized Fading
Venue: arXiv cs.IT 2019
Year: 2019

This paper determines the optimum secondary user power allocation and ergodic multicast rate of point-to-multipoint communication in a cognitive radio network in the presence of outage constraints for the primary users. Using tools from extreme value theory (EVT), it is first proved that the limiting distribution of the minimum of independent and identically distributed (i.i.d.) signal-to-interference ratio (SIR) random variables (RVs) is a Weibull distribution, when the user signal and the interferer signals undergo independent and non-identically distributed (i.n.i.d.) $κ-μ$ shadowed fading. Also, the rate of convergence of the actual minimum distribution to the Weibull distribution is derived. This limiting distribution is then used for determining the optimum transmit power of a secondary network in an underlay cognitive radio network subject to outage constraints at the primary network in a generalized fading scenario. Furthermore, the asymptotic ergodic multicast rate of secondary users is analyzed for varying channel fading parameters.
Decentralized Spectrum Learning for IoT Wireless Networks Collision Mitigation
Venue: arXiv cs.NI 2019
Year: 2019

This paper describes the principles and implementation results of reinforcement learning algorithms on IoT devices for radio collision mitigation in ISM unlicensed bands. Learning is here used to improve both the IoT network capability to support a larger number of objects as well as the autonomy of IoT devices. We first illustrate the efficiency of the proposed approach in a proof-of-concept based on USRP software radio platforms operating on real radio signals. It shows how collisions with other RF signals present in the ISM band are diminished for a given IoT device. Then we describe the first implementation of learning algorithms on LoRa devices operating in a real LoRaWAN network, that we named IoTligent. The proposed solution adds neither processing overhead so that it can be ran in the IoT devices, nor network overhead so that no change is required to LoRaWAN. Real life experiments have been done in a realistic LoRa network and they show that IoTligent device battery life can be extended by a factor 2 in the scenarios we faced during our experiment.
A Direct $\tilde{O}(1/ε)$ Iteration Parallel Algorithm for Optimal Transport
Venue: arXiv cs.DS 2019
Year: 2019

Optimal transportation, or computing the Wasserstein or ``earth mover's'' distance between two distributions, is a fundamental primitive which arises in many learning and statistical settings. We give an algorithm which solves this problem to additive $ε$ with $\tilde{O}(1/ε)$ parallel depth, and $\tilde{O}\left(n^2/ε\right)$ work. Barring a breakthrough on a long-standing algorithmic open problem, this is optimal for first-order methods. Blanchet et. al. '18, Quanrud '19 obtained similar runtimes through reductions to positive linear programming and matrix scaling. However, these reduction-based algorithms use complicated subroutines which may be deemed impractical due to requiring solvers for second-order iterations (matrix scaling) or non-parallelizability (positive LP). The fastest practical algorithms run in time $\tilde{O}(\min(n^2 / ε^2, n^{2.5} / ε))$ (Dvurechensky et. al. '18, Lin et. al. '19). We bridge this gap by providing a parallel, first-order, $\tilde{O}(1/ε)$ iteration algorithm without worse dependence on dimension, and provide preliminary experimental evidence that our algorithm may enjoy improved practical performance. We obtain this runtime via a primal-dual extragradient method, motivated by recent theoretical improvements to maximum flow (Sherman '17).
Peut-on former les enseignant$\cdot$e$\cdot$s en un rien de temps ?
Venue: arXiv cs.CY 2019
Year: 2019

In France, we recently realized that it is urgent to stop waiting to start teaching our girls and boys bases of computer science, to master digital technology, and the Digital Science and Technology (SNT) topic is now offered in Year 11 (classe de 2nd), starting at the 2019 school year. All highschool students are concerned. But how to train teachers? It is a challenge and it should be done in almost no time. By using quantitative data about the Class'Code training program and putting the methodological elements developed and experimented in perspective, we report here what did work and, more interesting, where we stumbled in order to set the limits of the approach and show the importance of a greater place regarding these formations.
Federated Hierarchical Hybrid Networks for Clickbait Detection
Venue: arXiv cs.IR 2019
Year: 2019

Online media outlets adopt clickbait techniques to lure readers to click on articles in a bid to expand their reach and subsequently increase revenue through ad monetization. As the adverse effects of clickbait attract more and more attention, researchers have started to explore machine learning techniques to automatically detect clickbaits. Previous work on clickbait detection assumes that all the training data is available locally during training. In many real-world applications, however, training data is generally distributedly stored by different parties (e.g., different parties maintain data with different feature spaces), and the parties cannot share their data with each other due to data privacy issues. It is challenging to build models of high-quality federally for detecting clickbaits effectively without data sharing. In this paper, we propose a federated training framework, which is called federated hierarchical hybrid networks, to build clickbait detection models, where the titles and contents are stored by different parties, whose relationships must be exploited for clickbait detection. We empirically demonstrate that our approach is effective by comparing our approach to the state-of-the-art approaches using datasets from social media.
Every nonnegative real number is an abelian critical exponent
Venue: arXiv cs.FL 2019
Year: 2019

The abelian critical exponent of an infinite word $w$ is defined as the maximum ratio between the exponent and the period of an abelian power occurring in $w$. It was shown by Fici et al. that the set of finite abelian critical exponents of Sturmian words coincides with the Lagrange spectrum. This spectrum contains every large enough positive real number. We construct words whose abelian critical exponents fill the remaining gaps, that is, we prove that for each nonnegative real number $θ$ there exists an infinite word having abelian critical exponent $θ$. We also extend this result to the $k$-abelian setting.
NeuralVis: Visualizing and Interpreting Deep Learning Models
Venue: arXiv cs.SE 2019
Year: 2019

Deep Neural Network(DNN) techniques have been prevalent in software engineering. They are employed to faciliatate various software engineering tasks and embedded into many software applications. However, analyzing and understanding their behaviors is a difficult task for software engineers. In this paper, to support software engineers in visualizing and interpreting deep learning models, we present NeuralVis, an instance-based visualization tool for DNN. NeuralVis is designed for: 1). visualizing the structure of DNN models, i.e., components, layers, as well as connections; 2). visualizing the data transformation process; 3). integrating existing adversarial attack algorithms for test input generation; 4). comparing intermediate outputs of different instances to guide the test input generation; To demonstrate the effectiveness of NeuralVis, we conduct an user study involving ten participants on two classic DNN models, i.e., LeNet and VGG-12. The result shows NeuralVis can assist developers in identifying the critical features that determines the prediction results. Video: https://youtu.be/hRxCovrOZFI
Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense networks (UDNs). Our contribution is three-fold. First, this work proposes a two-layer architecture to solve the large-scale load balancing problem in a self-organized manner. The proposed architecture can alleviate the global traffic variations by dynamically grouping small cells into self-organized clusters according to their historical loads, and further adapt to local traffic variations through intra-cluster load balancing afterwards. Second, for the intra-cluster load balancing, this paper proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB policy under an asynchronous parallel learning framework, without any prior knowledge assumed over the underlying UDN environments. Moreover, the algorithm enables joint exploration with multiple behavior policies, such that the traditional MLB methods can be used to guide the learning process thereby improving the learning efficiency and stability. Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way. Empirical results verify that the proposed framework outperforms the existing MLB methods in general UDN environments featured with irregular network topologies, coupled interferences, and random user movements, in terms of the load balancing performance.
Static Code Analysis of Multilanguage Software Systems
Venue: arXiv cs.SE 2019
Year: 2019

Identifying dependency call graphs of multilanguage software systems using static code analysis is challenging. The different languages used in developing today's systems often have different lexical, syntactical, and semantic rules that make thorough analysis difficult. Also, they offer different modularization and dependency mechanisms, both within and between components. Finally, they promote and--or require varieties of frameworks offering different sets of services, which introduce hidden dependencies, invisible with current static code analysis approaches. In this paper, we identify five important challenges that static code analysis must overcome with multilanguage systems and we propose requirements to handle them. Then, we present solutions of these requirements to handle JEE applications, which combine server-side Java source code with a number of client-side Web dialects (e.g., JSP, JSF) while relying on frameworks (e.g., Web and EJB containers) that create hidden dependencies. Finally, we evaluate our implementations of the solutions by developing a set of tools to analyze JEE applications to build a dependency call graph and by applying these tools on two sample JEE applications. Our evaluation shows that our tools can solve the identified challenges and improve the recall in the identification of multilanguage dependencies compared to standard JEE static code analysis and, thus, indirectly that the proposed requirements are useful to build multilanguage static code analysis.
Can Women Break the Glass Ceiling?: An Analysis of #MeToo Hashtagged Posts on Twitter
Venue: arXiv cs.SI 2019
Year: 2019

In October 2017, there happened the uprising of an unprecedented online movement on social media by women across the world who started publicly sharing their untold stories of being sexually harassed along with the hashtag #MeToo (or some variants of it). Those stories did not only strike the silence that had long hid the perpetrators, but also allowed women to discharge some of their bottled-up grievances, and revealed many important information surrounding sexual harassment. In this paper, we present our analysis of about one million such tweets collected between October 15 and October 31, 2017 that reveals some interesting patterns and attributes of the people, place, emotions, actions, and reactions related to the tweeted stories. Based on our analysis, we also advance the discussion on the potential role of online social media in breaking the silence of women by factoring in the strengths and limitations of these platforms.
Temporal Density Extrapolation using a Dynamic Basis Approach
Venue: arXiv cs.LG 2019
Year: 2019

Density estimation is a versatile technique underlying many data mining tasks and techniques,ranging from exploration and presentation of static data, to probabilistic classification, or identifying changes or irregularities in streaming data. With the pervasiveness of embedded systems and digitisation, this latter type of streaming and evolving data becomes more important. Nevertheless, research in density estimation has so far focused on stationary data, leaving the task of of extrapolating and predicting density at time points outside a training window an open problem. For this task, Temporal Density Extrapolation (TDX) is proposed. This novel method models and predicts gradual monotonous changes in a distribution. It is based on the expansion of basis functions, whose weights are modelled as functions of compositional data over time by using an isometric log-ratio transformation. Extrapolated density estimates are then obtained by extrapolating the weights to the requested time point, and querying the density from the basis functions with back-transformed weights. Our approach aims for broad applicability by neither being restricted to a specific parametric distribution, nor relying on cluster structure in the data.It requires only two additional extrapolation-specific parameters, for which reasonable defaults exist. Experimental evaluation on various data streams, synthetic as well as from the real-world domains of credit scoring and environmental health, shows that the model manages to capture monotonous drift patterns accurately and better than existing methods. Thereby, it requires not more than 1.5-times the run time of a corresponding static density estimation approach.
Stochastic Computing for Hardware Implementation of Binarized Neural Networks
Venue: arXiv cs.ET 2019
Year: 2019

Binarized Neural Networks, a recently discovered class of neural networks with minimal memory requirements and no reliance on multiplication, are a fantastic opportunity for the realization of compact and energy efficient inference hardware. However, such neural networks are generally not entirely binarized: their first layer remains with fixed point input. In this work, we propose a stochastic computing version of Binarized Neural Networks, where the input is also binarized. Simulations on the example of the Fashion-MNIST and CIFAR-10 datasets show that such networks can approach the performance of conventional Binarized Neural Networks. We evidence that the training procedure should be adapted for use with stochastic computing. Finally, the ASIC implementation of our scheme is investigated, in a system that closely associates logic and memory, implemented by Spin Torque Magnetoresistive Random Access Memory. This analysis shows that the stochastic computing approach can allow considerable savings with regards to conventional Binarized Neural networks in terms of area (62% area reduction on the Fashion-MNIST task). It can also allow important savings in terms of energy consumption, if we accept reasonable reduction of accuracy: for example a factor 2.1 can be saved, with the cost of 1.4% in Fashion-MNIST test accuracy. These results highlight the high potential of Binarized Neural Networks for hardware implementation, and that adapting them to hardware constrains can provide important benefits.
Mining YouTube - A dataset for learning fine-grained action concepts from webly supervised video data
Venue: arXiv cs.CV 2019
Year: 2019

Action recognition is so far mainly focusing on the problem of classification of hand selected preclipped actions and reaching impressive results in this field. But with the performance even ceiling on current datasets, it also appears that the next steps in the field will have to go beyond this fully supervised classification. One way to overcome those problems is to move towards less restricted scenarios. In this context we present a large-scale real-world dataset designed to evaluate learning techniques for human action recognition beyond hand-crafted datasets. To this end we put the process of collecting data on its feet again and start with the annotation of a test set of 250 cooking videos. The training data is then gathered by searching for the respective annotated classes within the subtitles of freely available videos. The uniqueness of the dataset is attributed to the fact that the whole process of collecting the data and training does not involve any human intervention. To address the problem of semantic inconsistencies that arise with this kind of training data, we further propose a semantical hierarchical structure for the mined classes.
A Surprising Density of Illusionable Natural Speech
Venue: arXiv cs.SD 2019
Year: 2019

Recent work on adversarial examples has demonstrated that most natural inputs can be perturbed to fool even state-of-the-art machine learning systems. But does this happen for humans as well? In this work, we investigate: what fraction of natural instances of speech can be turned into "illusions" which either alter humans' perception or result in different people having significantly different perceptions? We first consider the McGurk effect, the phenomenon by which adding a carefully chosen video clip to the audio channel affects the viewer's perception of what is said (McGurk and MacDonald, 1976). We obtain empirical estimates that a significant fraction of both words and sentences occurring in natural speech have some susceptibility to this effect. We also learn models for predicting McGurk illusionability. Finally we demonstrate that the Yanny or Laurel auditory illusion (Pressnitzer et al., 2018) is not an isolated occurrence by generating several very different new instances. We believe that the surprising density of illusionable natural speech warrants further investigation, from the perspectives of both security and cognitive science. Supplementary videos are available at: https://www.youtube.com/playlist?list=PLaX7t1K-e_fF2iaenoKznCatm0RC37B_k.
A separator-based method for generating weakly chordal graphs
Venue: arXiv cs.DS 2019
Year: 2019

We propose a scheme for generating a weakly chordal graph on n vertices with m edges. In this method, we first construct a tree and then generate an orthogonal layout (which is a weakly chordal graph on the n vertices) based on this tree. In the next and final step, we insert additional edges to give us a weakly chordal graph on m edges. Our algorithm ensures that the graph remains weakly chordal after each edge is inserted. The time complexity of an insertion query is O(n^3) time and an insertion takes constant time. On the other hand, a generation algorithm based on finding a 2-pair takes O(nm) time using the algorithm of Arikati and Rangan [1].
Localization Requirements for Autonomous Vehicles
Venue: arXiv cs.RO 2019
Year: 2019

Autonomous vehicles require precise knowledge of their position and orientation in all weather and traffic conditions for path planning, perception, control, and general safe operation. Here we derive these requirements for autonomous vehicles based on first principles. We begin with the safety integrity level, defining the allowable probability of failure per hour of operation based on desired improvements on road safety today. This draws comparisons with the localization integrity levels required in aviation and rail where similar numbers are derived at 10^-8 probability of failure per hour of operation. We then define the geometry of the problem, where the aim is to maintain knowledge that the vehicle is within its lane and to determine what road level it is on. Longitudinal, lateral, and vertical localization error bounds (alert limits) and 95% accuracy requirements are derived based on US road geometry standards (lane width, curvature, and vertical clearance) and allowable vehicle dimensions. For passenger vehicles operating on freeway roads, the result is a required lateral error bound of 0.57 m (0.20 m, 95%), a longitudinal bound of 1.40 m (0.48 m, 95%), a vertical bound of 1.30 m (0.43 m, 95%), and an attitude bound in each direction of 1.50 deg (0.51 deg, 95%). On local streets, the road geometry makes requirements more stringent where lateral and longitudinal error bounds of 0.29 m (0.10 m, 95%) are needed with an orientation requirement of 0.50 deg (0.17 deg, 95%).
Integrated Access and Backhaul in 5G mmWave Networks: Potentials and Challenges
Venue: arXiv cs.NI 2019
Year: 2019

Integrated Access and Backhaul (IAB) is being investigated as a means to overcome deployment costs of ultra-dense 5G millimeter wave (mmWave) networks by realizing wireless backhaul links to relay the access traffic. For the development of these systems, however, it is fundamental to validate the performance of IAB in realistic scenarios through end-to-end system level simulations. In this paper, we shed light on the most recent standardization activities on IAB, and compare architectures with and without IAB in mmWave deployments. While it is well understood that IAB networks reduce deployment costs by obviating the need to provide wired backhaul to each cellular base-station, in this paper we demonstrate the cell-edge throughput advantage offered by IAB using end-to-end system level simulations. We further highlight some research challenges associated with this architecture that will require further investigations.