OpenRaiser/Intern-Atlas huggingface dataset:
Towards a Robust Classifier: An MDL-Based Method for Generating Adversarial Examples
Venue: arXiv cs.LG 2019
Year: 2019

We address the problem of adversarial examples in machine learning where an adversary tries to misguide a classifier by making functionality-preserving modifications to original samples. We assume a black-box scenario where the adversary has access to only the feature set, and the final hard-decision output of the classifier. We propose a method to generate adversarial examples using the minimum description length (MDL) principle. Our final aim is to improve the robustness of the classifier by considering generated examples in rebuilding the classifier. We evaluate our method for the application of static malware detection in portable executable (PE) files. We consider API calls of PE files as their distinguishing features where the feature vector is a binary vector representing the presence-absence of API calls. In our method, we first create a dataset of benign samples by querying the target classifier. We next construct a code table of frequent patterns for the compression of this dataset using the MDL principle. We finally generate an adversarial example corresponding to a malware sample by selecting and adding a pattern from the benign code table to the malware sample. The selected pattern is the one that minimizes the length of the compressed adversarial example given the code table. This modification preserves the functionalities of the original malware sample as all original API calls are kept, and only some new API calls are added. Considering a neural network, we show that the evasion rate is 78.24 percent for adversarial examples compared to 8.16 percent for original malware samples. This shows the effectiveness of our method in generating examples that need to be considered in rebuilding the classifier.
Calculate the carbon footprint of your IT assets with EcoDiag, an EcoInfo service
Venue: arXiv cs.CY 2019
Year: 2019

You hear a lot about environmental issues, you may have initiated positive personal attitudes, you have a group will in your unit, and you are wondering about the environmental impact of digital technology in your professional environment...EcoDiag is here for you!The CNRS EcoInfo GDS has found that assessing the environmental impacts of digital technology is complex and can discourage the best will, which is why we offer you a simple and effective method to estimate the carbon footprint of your fleet through this new service based on our experience. We chose an indicator that everyone could understand: CO2e emissions (CO2e equivalent). Based on an inventory of the digital services used and the unit's computer equipment, our methodology and expertise will allow you to establish a global estimate of GHG emissions. This evaluation will be accompanied by recommendations and resources (sheets, guide, material) to integrate this work into a Hc{é}res-type report or use them as part of a more global carbon assessment for your structure. The provision of advice will allow you to post a policy to reduce GHG emissions generated by digital technology in the following years!Meet us in front of the poster to discover and leave with our tool for assessing the environmental impacts of your unit's digital activities. You will have the tools and a recommendation guide to control and reduce these impacts in a continuous improvement process. Concrete examples from two units will illustrate our approach and demonstrate the ease of use of the EcoDiag service.
0-Step Capturability, Motion Decomposition and Global Feedback Control of the 3D Variable Height-Inverted Pendulum
Venue: arXiv cs.RO 2019
Year: 2019

One common method for stabilizing robots after a push is the Instantaneous Capture Point, however, this has the fundamental limitation of assuming constant height. Although there are several works for balancing bipedal robots including height variations in 2D, the amount of literature on 3D models is limited. There are optimization methods using variable Center of Pressure (CoP) and reaction force to the ground, although they do not provide the physical region where a robot can step and require a precomputation for the analysis. This work provides the necessary and sufficient conditions to maintain balance of the 3D Variable Height Inverted Pendulum (VHIP) with both, fixed and variable CoP. We also prove that the 3D VHIP with Fixed CoP is the same as its 2D version, and we generalize controllers working on the 2D VHIP to the 3D VHIP. We also show the generalization of the Divergent Component of Motion to the 3D VHIP and we provide an alternative motion decomposition for the analysis of height and CoP strategies independently. This allow us to generalize previous global feedback controllers done in the 2D VHIP to the 3D VHIP with a Variable CoP.
The PlayStation Reinforcement Learning Environment (PSXLE)
Venue: arXiv cs.LG 2019
Year: 2019

We propose a new benchmark environment for evaluating Reinforcement Learning (RL) algorithms: the PlayStation Learning Environment (PSXLE), a PlayStation emulator modified to expose a simple control API that enables rich game-state representations. We argue that the PlayStation serves as a suitable progression for agent evaluation and propose a framework for such an evaluation. We build an action-driven abstraction for a PlayStation game with support for the OpenAI Gym interface and demonstrate its use by running OpenAI Baselines.
Learning Effective Visual Relationship Detector on 1 GPU
Venue: arXiv cs.CV 2019
Year: 2019

We present our winning solution to the Open Images 2019 Visual Relationship challenge. This is the largest challenge of its kind to date with nearly 9 million training images. Challenge task consists of detecting objects and identifying relationships between them in complex scenes. Our solution has three stages, first object detection model is fine-tuned for the challenge classes using a novel weight transfer approach. Then, spatio-semantic and visual relationship models are trained on candidate object pairs. Finally, features and model predictions are combined to generate the final relationship prediction. Throughout the challenge we focused on minimizing the hardware requirements of our architecture. Specifically, our weight transfer approach enables much faster optimization, allowing the entire architecture to be trained on a single GPU in under two days. In addition to efficient optimization, our approach also achieves superior accuracy winning first place out of over 200 teams, and outperforming the second place team by over $5\%$ on the held-out private leaderboard.
Data Exploration and Validation on dense knowledge graphs for biomedical research
Venue: arXiv cs.AI 2019
Year: 2019

Here we present a holistic approach for data exploration on dense knowledge graphs as a novel approach with a proof-of-concept in biomedical research. Knowledge graphs are increasingly becoming a vital factor in knowledge mining and discovery as they connect data using technologies from the semantic web. In this paper we extend a basic knowledge graph extracted from biomedical literature by context data like named entities and relations obtained by text mining and other linked data sources like ontologies and databases. We will present an overview about this novel network. The aim of this work was to extend this current knowledge with approaches from graph theory. This method will build the foundation for quality control, validation of hypothesis, detection of missing data and time series analysis of biomedical knowledge in general. In this context we tried to apply multiple-valued decision diagrams to these questions. In addition this knowledge representation of linked data can be used as FAIR approach to answer semantic questions. This paper sheds new lights on dense and very large knowledge graphs and the importance of a graph-theoretic understanding of these networks.
Screening of Informed and Uninformed Experts
Venue: arXiv cs.GT 2019
Year: 2019

Testing the validity of claims made by self-proclaimed experts can be impossible when testing them in isolation, even with infinite observations at the disposal of the tester. However, in a multiple expert setting it is possible to design a contract that only informed experts accept and uninformed experts reject. The tester can pit competing forecasts of future events against each other and take advantage of the uncertainty experts have about the other experts' knowledge. This contract will work even when there is only a single data point to evaluate.
Joint Viewpoint and Keypoint Estimation with Real and Synthetic Data
Venue: arXiv cs.CV 2019
Year: 2019

The estimation of viewpoints and keypoints effectively enhance object detection methods by extracting valuable traits of the object instances. While the output of both processes differ, i.e., angles vs. list of characteristic points, they indeed share the same focus on how the object is placed in the scene, inducing that there is a certain level of correlation between them. Therefore, we propose a convolutional neural network that jointly computes the viewpoint and keypoints for different object categories. By training both tasks together, each task improves the accuracy of the other. Since the labelling of object keypoints is very time consuming for human annotators, we also introduce a new synthetic dataset with automatically generated viewpoint and keypoints annotations. Our proposed network can also be trained on datasets that contain viewpoint and keypoints annotations or only one of them. The experiments show that the proposed approach successfully exploits this implicit correlation between the tasks and outperforms previous techniques that are trained independently.
Joint AGC and Receiver Design for Large-Scale MU-MIMO Systems with Coarsely Quantized Signals and C-RANs
Venue: arXiv cs.IT 2019
Year: 2019

In this work, we propose the joint optimization of the automatic gain control (AGC), which works in the remote radio heads (RHHs), and a low-resolution aware (LRA) linear receive filter based on the minimum mean square error (MMSE), which works on the baseband unit (BBU) pool, for large-scale multi-user multiple-input multiple-output (MU-MIMO) systems with coarsely quantized signals in cloud radio access networks. We develop successive interference cancellation receivers based on the proposed joint AGC and LRA-MMSE (AGC-LRA-MMSE) approach. An analysis of the achievable sum rates is also carried out. Simulations show that the proposed design has better error rates and higher achievable rates than existing techniques.
The SBP Algorithm for Maximizing Revenue in Online Dial-a-Ride
Venue: arXiv cs.DS 2019
Year: 2019

In the Online-Dial-a-Ride Problem (OLDARP) a server travels through a metric space to serve requests for rides. We consider a variant where each request specifies a source, destination, release time, and revenue that is earned for serving the request. The goal is to maximize the total revenue earned within a given time limit. We prove that no non-preemptive deterministic online algorithm for OLDARP can be guaranteed to earn more than twice the revenue earned by an optimal offline solution. We then investigate the \textsc{segmented best path} ($SBP$) algorithm of~\cite{atmos17} for the general case of weighted graphs. The previously-established lower and upper bounds for the competitive ratio of $SBP$ are 4 and 6, respectively, under reasonable assumptions about the input instance. We eliminate the gap by proving that the competitive ratio is 5 (under the same reasonable assumptions). We also prove that when revenues are uniform, $SBP$ has competitive ratio 4. Finally, we provide a competitive analysis of $SBP$ on complete bipartite graphs.
RSSI-based Secure Localization in the Presence of Malicious Nodes in Sensor Networks
Venue: arXiv cs.CR 2019
Year: 2019

The ability of a sensor node to determine its location in a sensor network is important in many applications. The infrastructure for the location-based services is an easy target for malicious attacks. We address scenarios where malicious node(s) attempt to disrupt, in an uncoordinated or coordinated manner, the localization process of a target node. We propose four techniques for secure localization: weighted least square (WLS), secure weighted least square (SWLS), and $\ell_1$-norm based techniques LN-1 and LN-1E, in a network that includes one or more compromised anchor nodes. WLS and SWLS techniques are shown to offer significant advantage over existing techniques by assigning larger weights to the anchor nodes that are closer to the target node, and by detecting the malicious nodes and eliminating their measurements from the localization process. In a coordinated attack, the localization problem can be posed as a plane fitting problem where the measurements from non-malicious and malicious anchor nodes lie on two different planes. LN-1E technique estimates these two planes and prevents disruption of the localization process. The Cramer-Rao lower bound (CRLB) for the position estimate is also derived. The proposed techniques are shown to provide better localization accuracy than the existing algorithms.
Seizure Prediction Using Bidirectional LSTM
Venue: arXiv cs.LG 2019
Year: 2019

Approximately, 50 million people in the world are affected by epilepsy. For patients, the anti-epileptic drugs are not always useful and these drugs may have undesired side effects on a patient's health. If the seizure is predicted the patients will have enough time to take preventive measures. The purpose of this work is to investigate the application of bidirectional LSTM for seizure prediction. In this paper, we trained EEG data from canines on a double Bidirectional LSTM layer followed by a fully connected layer. The data was provided in the form of a Kaggle competition by American Epilepsy Society. The main task was to classify the interictal and preictal EEG clips. Using this model, we obtained an AUC of 0.84 on the test dataset. Which shows that our classifier's performance is above chance level on unseen data. The comparison with the previous work shows that the use of bidirectional LSTM networks can achieve significantly better results than SVM and GRU networks.
Real-time texturing for 6D object instance detection from RGB Images
Venue: arXiv cs.CV 2019
Year: 2019

For objected detection, the availability of color cues strongly influences detection rates and is even a prerequisite for many methods. However, when training on synthetic CAD data, this information is not available. We therefore present a method for generating a texture-map from image sequences in real-time. The method relies on 6 degree-of-freedom poses and a 3D-model being available. In contrast to previous works this allows interleaving detection and texturing for upgrading the detector on-the-fly. Our evaluation shows that the acquired texture-map significantly improves detection rates using the LINEMOD detector on RGB images only. Additionally, we use the texture-map to differentiate instances of the same object by surface color.
RDD-Eclat: Approaches to Parallelize Eclat Algorithm on Spark RDD Framework
Venue: arXiv cs.DC 2019
Year: 2019

Initially, a number of frequent itemset mining (FIM) algorithms have been designed on the Hadoop MapReduce, a distributed big data processing framework. But, due to heavy disk I/O, MapReduce is found to be inefficient for such highly iterative algorithms. Therefore, Spark, a more efficient distributed data processing framework, has been developed with in-memory computation and resilient distributed dataset (RDD) features to support the iterative algorithms. On the Spark RDD framework, Apriori and FP-Growth based FIM algorithms have been designed, but Eclat-based algorithm has not been explored yet. In this paper, RDD-Eclat, a parallel Eclat algorithm on the Spark RDD framework is proposed with its five variants. The proposed algorithms are evaluated on the various benchmark datasets, which shows that RDD-Eclat outperforms the Spark-based Apriori by many times. Also, the experimental results show the scalability of the proposed algorithms on increasing the number of cores and size of the dataset.
Thinging as a Way of Modeling in Poiesis: Applications in Software Engineering
Venue: arXiv cs.SE 2019
Year: 2019

From a software design perspective, a clear definition of design can enhance project success and development productivity. Even though the focus is on software engineering, in this paper, we view the notion of design from the wider point of view of poiesis, the field of the study of the phenomena of creation and production of the artifacts. In poiesis, design operates through the medium of modeling. According to several sources, there is as yet no systematic consolidated body of knowledge that a practitioner can refer to when designing a computer-based modeling language. Modeling languages such as UML are practice-based and seldom underpinned with a solid theory-be it mathematical, ontological or concomitant with language use. In this paper, we propose adopting a recent addition to the diagrammatic languages, the thinging machine (abbreviated TM), as a design language in the general area of Poiesis and we exemplify TM by applying it to software engineering design. We show intermediate steps of design that led to producing a TM model for a case study. The case study is taken from a source where a full UML-based design was given. Contrasting the models produced by the two methodologies points to the viability of TM as an integrating and unifying modeling language in the design field.
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk Minimization
Venue: arXiv cs.LG 2019
Year: 2019

We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving empirical risk minimization (ERM) problems with a nonsmooth regularization term. Our algorithm is applicable to both the primal and the dual ERM problem. Current second-order and quasi-Newton methods for this problem either do not work well in the distributed setting or work only for specific regularizers. Our algorithm uses successive quadratic approximations of the smooth part, and we describe how to maintain an approximation of the (generalized) Hessian and solve subproblems efficiently in a distributed manner. When applied to the distributed dual ERM problem, unlike state of the art that takes only the block-diagonal part of the Hessian, our approach is able to utilize global curvature information and is thus magnitudes faster. The proposed method enjoys global linear convergence for a broad range of non-strongly convex problems that includes the most commonly used ERMs, thus requiring lower communication complexity. It also converges on non-convex problems, so has the potential to be used on applications such as deep learning. Computational results demonstrate that our method significantly improves on communication cost and running time over the current state-of-the-art methods.
Reducing selfish routing inefficiencies using traffic lights
Venue: arXiv cs.GT 2019
Year: 2019

Traffic congestion games abstract away from the costs of junctions in transport networks, yet, in urban environments, these often impact journey times significantly. In this paper we equip congestion games with traffic lights, modelled as junction-based waiting cycles, therefore enabling more realistic route planning strategies. Using the SUMO simulator, we show that our modelling choices coincide with realistic routing behaviours, in particular, that drivers' decisions about route choices are based on the proportion of red light time for their direction of travel. Drawing upon the experimental results, we show that the effects of the notorious Braess' paradox can be avoided in theory and significantly reduced in practice, by allocating the appropriate traffic light cycles along a transport network.
On asymptotically optimal tests for random number generators
Venue: arXiv cs.IT 2019
Year: 2019

The problem of constructing effective statistical tests for random number generators (RNG) is considered. Currently, statistical tests for RNGs are a mandatory part of cryptographic information protection systems, but their effectiveness is mainly estimated based on experiments with various RNGs. We find an asymptotic estimate for the p-value of an optimal test in the case where the alternative hypothesis is a known stationary ergodic source, and then describe a family of tests each of which has the same asymptotic estimate of the p-value for any (unknown) stationary ergodic source.
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
Venue: arXiv cs.AI 2019
Year: 2019

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last three decades. Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then some methodologies combining reasoning and learning are reviewed (such as inductive logic programming, neuro-symbolic reasoning, formal concept analysis, rule-based representations and ML, uncertainty in ML, or case-based reasoning and analogical reasoning), before discussing examples of synergies between KRR and ML (including topics such as belief functions on regression, EM algorithm versus revision, the semantic description of vector representations, the combination of deep learning with high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and recommendation). This paper is the first step of a work in progress aiming at a better mutual understanding of research in KRR and ML, and how they could cooperate.
Estimating Early Fundraising Performance of Innovations via Graph-based Market Environment Model
Venue: arXiv cs.LG 2019
Year: 2019

Well begun is half done. In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms. However, estimating the early fundraising performance before the project published is very challenging and still under-explored. To that end, in this paper, we present a focused study on this important problem in a market modeling view. Specifically, we propose a Graph-based Market Environment model (GME) for estimating the early fundraising performance of the target project by exploiting the market environment. In addition, we discriminatively model the market competition and market evolution by designing two graph-based neural network architectures and incorporating them into the joint optimization stage. Finally, we conduct extensive experiments on the real-world crowdfunding data collected from Indiegogo.com. The experimental results clearly demonstrate the effectiveness of our proposed model for modeling and estimating the early fundraising performance of the target project.