OpenRaiser/Intern-Atlas huggingface dataset:
Reconfigurable Intelligent Surface-Based Wireless Communication: Antenna Design, Prototyping and Experimental Results
Venue: arXiv cs.IT 2019
Year: 2019

One of the key enablers of future wireless communications is constituted by massive multiple-input multiple-output (MIMO) systems, which can improve the spectral efficiency by orders of magnitude. However, in existing massive MIMO systems, conventional phased arrays are used for beamforming, which result in excessive power consumption and hardware cost. Recently, reconfigurable intelligent surface (RIS) has been considered as one of the revolutionary technologies to enable energy-efficient and smart wireless communications, which is a two-dimensional structure with a large number of passive elements. In this paper, we propose and develop a new type of high-gain yet low-cost RIS having 256 elements. The proposed RIS combines the functions of phase shift and radiation together on an electromagnetic surface, where positive intrinsic-negative (PIN) diodes are used to realize 2-bit phase shifting for beamforming. Based on this radical design, the world's first wireless communication prototype using RIS having 256 2-bit elements is designed and developed. Specifically, the prototype conceived consists of modular hardware and flexible software, including the hosts for parameter setting and data exchange, the universal software radio peripherals (USRPs) for baseband and radio frequency (RF) signal processing, as well as the RIS for signal transmission and reception. Our performance evaluation confirms the feasibility and efficiency of RISs in future wireless communications. More particularly, it is shown that a 21.7 dBi antenna gain can be obtained by the proposed RIS at 2.3 GHz, while at the millimeter wave (mmWave) frequency, i.e., 28.5 GHz, a 19.1 dBi antenna gain can be achieved. Furthermore, the over-the-air (OTA) test results show that the RIS-based wireless communication prototype developed is capable of significantly reducing the power consumption.
ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents
Venue: arXiv cs.CV 2019
Year: 2019

This competition investigates the performance of large-scale retrieval of historical document images based on writing style. Based on large image data sets provided by cultural heritage institutions and digital libraries, providing a total of 20 000 document images representing about 10 000 writers, divided in three types: writers of (i) manuscript books, (ii) letters, (iii) charters and legal documents. We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as writer retrieval. The most teams submitted traditional methods not using deep learning techniques. The competition results show that a combination of methods is outperforming single methods. Furthermore, letters are much more difficult to retrieve than manuscripts.
ragamAI: A Network Based Recommender System to Arrange a Indian Classical Music Concert
Venue: arXiv cs.IR 2019
Year: 2019

South Indian classical music (Carnatic music) is best consumed through live concerts. A carnatic recital requires meticulous planning accounting for several parameters like the performers' repertoire, composition variety, musical versatility, thematic structure, the recital's arrangement, etc. to ensure that the audience have a comprehensive listening experience. In this work, we present ragamAI a novel machine learning framework that utilizes the tonic nuances and musical structures in the carnatic music to generate a concert recital that melodically captures the entire range in an octave. Utilizing the underlying idea of playlist and session-based recommender models, the proposed model studies the mathematical structure present in past concerts and recommends relevant items for the playlist/concert. ragamAI ensembles recommendations given by multiple models to learn user idea and past preference of sequences in concerts to extract recommendations. Our experiments on a vast collection of concert show that our model performs 25%-50% better than baseline models. ragamAI's applications are two-fold. 1) it will assist musicians to customize their performance with the necessary variety required to sustain the interest of the audience for the entirety of the concert 2) it will generate carefully curated lists of south Indian classical music so that the listener can discover the wide range of melody that the musical system can offer.
Cost-Sensitive Training for Autoregressive Models
Venue: arXiv cs.LG 2019
Year: 2019

Training autoregressive models to better predict under the test metric, instead of maximizing the likelihood, has been reported to be beneficial in several use cases but brings additional complications, which prevent wider adoption. In this paper, we follow the learning-to-search approach (Daumé III et al., 2009; Leblond et al., 2018) and investigate its several components. First, we propose a way to construct a reference policy based on an alignment between the model output and ground truth. Our reference policy is optimal when applied to the Kendall-tau distance between permutations (appear in the task of word ordering) and helps when working with the METEOR score for machine translation. Second, we observe that the learning-to-search approach benefits from choosing the costs related to the test metrics. Finally, we study the effect of different learning objectives and find that the standard KL loss only learns several high-probability tokens and can be replaced with ranking objectives that target these tokens explicitly.
Hardening Random Forest Cyber Detectors Against Adversarial Attacks
Venue: arXiv cs.CR 2019
Year: 2019

Machine learning algorithms are effective in several applications, but they are not as much successful when applied to intrusion detection in cyber security. Due to the high sensitivity to their training data, cyber detectors based on machine learning are vulnerable to targeted adversarial attacks that involve the perturbation of initial samples. Existing defenses assume unrealistic scenarios; their results are underwhelming in non-adversarial settings; or they can be applied only to machine learning algorithms that perform poorly for cyber security. We present an original methodology for countering adversarial perturbations targeting intrusion detection systems based on random forests. As a practical application, we integrate the proposed defense method in a cyber detector analyzing network traffic. The experimental results on millions of labelled network flows show that the new detector has a twofold value: it outperforms state-of-the-art detectors that are subject to adversarial attacks; it exhibits robust results both in adversarial and non-adversarial scenarios.
Effective Attention Modeling for Neural Relation Extraction
Venue: arXiv cs.CL 2019
Year: 2019

Relation extraction is the task of determining the relation between two entities in a sentence. Distantly-supervised models are popular for this task. However, sentences can be long and two entities can be located far from each other in a sentence. The pieces of evidence supporting the presence of a relation between two entities may not be very direct, since the entities may be connected via some indirect links such as a third entity or via co-reference. Relation extraction in such scenarios becomes more challenging as we need to capture the long-distance interactions among the entities and other words in the sentence. Also, the words in a sentence do not contribute equally in identifying the relation between the two entities. To address this issue, we propose a novel and effective attention model which incorporates syntactic information of the sentence and a multi-factor attention mechanism. Experiments on the New York Times corpus show that our proposed model outperforms prior state-of-the-art models.
Big Data Security Issues and Challenges in Healthcare
Venue: arXiv cs.CR 2019
Year: 2019

This paper embodies the usage of Big Data in Healthcare. It is important to note that big data in terms of Architecture and implementation might be or has already or will continue to assist the continuous growth in the field of healthcare. The main important aspects of this study are the general importance of big data in healthcare, the positives big data will help tackle and enhance in this field and not to also forget to mention the tremendous downside big data has on healthcare that is still needed to improve or putting extensive research on. We believe there is still a long way in which institutions and individuals understand the hidden truth about big data. We have highlighted the various ways one could be confidently relied on big data and on the other hand highlighted the weighted importance of big problem big data and expected solutions.
Variability-aware Datalog
Venue: arXiv cs.PL 2019
Year: 2019

Variability-aware computing is the efficient application of programs to different sets of inputs that exhibit some variability. One example is program analyses applied to Software Product Lines (SPLs). In this paper we present the design and development of a variability-aware version of the Soufflé Datalog engine. The engine can take facts annotated with Presence Conditions (PCs) as input, and compute the PCs of its inferred facts, eliminating facts that do not exist in any valid configuration. We evaluate our variability-aware Soufflé implementation on several fact sets annotated with PCs to measure the associated overhead in terms of processing time and database size.
Robust Network Function Virtualization
Venue: arXiv cs.NI 2019
Year: 2019

Network function virtualization (NFV) enables on-demand network function (NF) deployment providing agile and dynamic network services. Through an evaluation metric that quantifies the minimal reliability among all NFs for all demands, service providers and operators may better facilitate flexible NF service recovery and migration, thus offer higher service reliability. In this paper, we present evaluation metrics on NFV reliability and solution approaches to solve robust NFV under random NF-enabled node failure(s). We demonstrate how to construct an auxiliary NF-enabled network and its mapping onto the physical substrate network. With constructed NF-enabled network, we develop pseudo-polynomial algorithms to solve the robust NF and SFC $s-t$ path problems -- subproblems of robust NFV. We also present approximation algorithms for robust NFV with the SFC-Fork as the NF forwarding graph. Furthermore, we propose exact solution approaches via mixed-integer linear programming (MILP) under the general setting. Computational results show that our proposed solution approaches are capable of managing robust NFV in a large-size network.
MITAS: A Compressed Time-Domain Audio Separation Network with Parameter Sharing
Venue: arXiv cs.SD 2019
Year: 2019

Deep learning methods have brought substantial advancements in speech separation (SS). Nevertheless, it remains challenging to deploy deep-learning-based models on edge devices. Thus, identifying an effective way to compress these large models without hurting SS performance has become an important research topic. Recently, TasNet and Conv-TasNet have been proposed. They achieved state-of-the-art results on several standardized SS tasks. Moreover, their low latency natures make them definitely suitable for real-time on-device applications. In this study, we propose two parameter-sharing schemes to lower the memory consumption on TasNet and Conv-TasNet. Accordingly, we derive a novel so-called MiTAS (Mini TasNet). Our experimental results first confirmed the robustness of our MiTAS on two types of perturbations in mixed audio. We also designed a series of ablation experiments to analyze the relation between SS performance and the amount of parameters in the model. The results show that MiTAS is able to reduce the model size by a factor of four while maintaining comparable SS performance with improved stability as compared to TasNet and Conv-TasNet. This suggests that MiTAS is more suitable for real-time low latency applications.
Nova -- A rainbow cloud over the Alps
Venue: arXiv cs.DC 2019
Year: 2019

A pooled and shared on-demand Infrastructure as a Service (IaaS), based on the Openstack software suite, was rolled out on the Grenoble university campus in 2018 and updated in 2019.We present the methods used to deploy and manage the infrastructure: racadm and preseed for basic system installation, then Kolla for Openstack deployment. This latter solution, based on containers for each service, enables a centralised and logged configuration (GitLab) of controllers and calculation nodes. The solution is the benchmark solution for a reproducible deployment of Openstack. We have been able to expand our cloud easily with new nodes. The change in version of the basic OS was also successfully tested despite a few small hitches... As security is a key element in the proper operation of this type of shared service, each project has been made watertight and its data perfectly isolated from other projects, thanks to the encryption of all network flows in VXLANs.This OpenStack infotainment platform is operational. What is it all for? For example, our first users use the Jupyter Notebook through the provision of Jupyterhub servers (web portal); the Distributed Health Assessment IT System (SIDES project); the continuous integration in connection with the GitLab platform; the test for the Kubernetes container scheduler or the calculation and visualisation software, etc. Highly varied uses that other platforms had difficulty offering.Nova, a new platform, was born.
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers
Venue: arXiv cs.LG 2019
Year: 2019

Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation. However, conditioning CNFs on signals of interest for conditional image generation and downstream predictive tasks is inefficient due to the high-dimensional latent code generated by the model, which needs to be of the same size as the input data. In this paper, we propose InfoCNF, an efficient conditional CNF that partitions the latent space into a class-specific supervised code and an unsupervised code that shared among all classes for efficient use of labeled information. Since the partitioning strategy (slightly) increases the number of function evaluations (NFEs), InfoCNF also employs gating networks to learn the error tolerances of its ordinary differential equation (ODE) solvers for better speed and performance. We show empirically that InfoCNF improves the test accuracy over the baseline while yielding comparable likelihood scores and reducing the NFEs on CIFAR10. Furthermore, applying the same partitioning strategy in InfoCNF on time-series data helps improve extrapolation performance.
Temporal Factorization of 3D Convolutional Kernels
Venue: arXiv cs.LG 2019
Year: 2019

3D convolutional neural networks are difficult to train because they are parameter-expensive and data-hungry. To solve these problems we propose a simple technique for learning 3D convolutional kernels efficiently requiring less training data. We achieve this by factorizing the 3D kernel along the temporal dimension, reducing the number of parameters and making training from data more efficient. Additionally we introduce a novel dataset called Video-MNIST to demonstrate the performance of our method. Our method significantly outperforms the conventional 3D convolution in the low data regime (1 to 5 videos per class). Finally, our model achieves competitive results in the high data regime (>10 videos per class) using up to 45% fewer parameters.
Political Elections Under (Social) Fire? Analysis and Detection of Propaganda on Twitter
Venue: arXiv cs.CR 2019
Year: 2019

For many, social networks have become the primary source of news, although the correctness of the provided information and its trustworthiness are often unclear. The investigations of the 2016 US presidential elections have brought the existence of external campaigns to light aiming at affecting the general political public opinion. In this paper, we investigate whether a similar influence on political elections can be observed in Europe as well. To this end, we use the past German federal election as an indicator and inspect the propaganda on Twitter, based on data from a period of 268 days. We find that 79 trolls from the US campaign have also acted upon the German federal election spreading right-wing views. Moreover, we develop a detector for finding automated behavior that enables us to identify 2,414 previously unknown bots.
Location Trace Privacy Under Conditional Priors
Venue: arXiv cs.LG 2019
Year: 2019

Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time. This is primarily due to the tremendous degree of dependence that can be anticipated between points. We propose a Rényi differentially private framework for bounding expected privacy loss for conditionally dependent data. Additionally, we demonstrate an algorithm for achieving this privacy under Gaussian process conditional priors. This framework both exemplifies why conditionally dependent data is so challenging to protect and offers a strategy for preserving privacy to within a fixed radius for every user location in a trace.
Extended- Force vs Nudge : Comparing Users' Pattern Choices on SysPal and TinPal
Venue: arXiv cs.CR 2019
Year: 2019

Android's 3X3 graphical pattern lock scheme is one of the widely used authentication method on smartphone devices. However, users choose 3X3 patterns from a small subspace of all possible 389,112 patterns. The two recently proposed interfaces, SysPal by Cho et al. and TinPal by the authors, demonstrate that it is possible to influence users 3X3 pattern choices by making small modifications in the existing interface. While SysPal forces users to include one, two or three system-assigned random dots in their pattern, TinPal employs a highlighting mechanism to inform users about the set of reachable dots from the current selected dot. Both interfaces improved the security of 3X3 patterns without affecting usability, but no comparison between SysPal and TinPal was presented. To address this gap, we conduct a new user study with 147 participants and collect patterns on three SysPal interfaces, 1-dot, 2-dot and 3-dot. We also consider original and TinPal patterns collected in our previous user study involving 99 participants. We compare patterns created on five different interfaces, original, TinPal, 1-dot, 2-dot and 3-dot using a range of security and usability metrics including pattern length, stroke length, guessability, recall time and login attempts. Our study results show that participants in the TinPal group created significantly longer and complex patterns than participants in the other four groups. Consequently, the guessing resistance of TinPal patterns was the highest among all groups. Further, we did not find any significant difference in memorability of patterns created in the TinPal group and the other groups.
Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem
Venue: arXiv cs.LG 2019
Year: 2019

Understanding the representational power of Deep Neural Networks (DNNs) and how their structural properties (e.g., depth, width, type of activation unit) affect the functions they can compute, has been an important yet challenging question in deep learning and approximation theory. In a seminal paper, Telgarsky highlighted the benefits of depth by presenting a family of functions (based on simple triangular waves) for which DNNs achieve zero classification error, whereas shallow networks with fewer than exponentially many nodes incur constant error. Even though Telgarsky's work reveals the limitations of shallow neural networks, it does not inform us on why these functions are difficult to represent and in fact he states it as a tantalizing open question to characterize those functions that cannot be well-approximated by smaller depths. In this work, we point to a new connection between DNNs expressivity and Sharkovsky's Theorem from dynamical systems, that enables us to characterize the depth-width trade-offs of ReLU networks for representing functions based on the presence of generalized notion of fixed points, called periodic points (a fixed point is a point of period 1). Motivated by our observation that the triangle waves used in Telgarsky's work contain points of period 3 - a period that is special in that it implies chaotic behavior based on the celebrated result by Li-Yorke - we proceed to give general lower bounds for the width needed to represent periodic functions as a function of the depth. Technically, the crux of our approach is based on an eigenvalue analysis of the dynamical system associated with such functions.
A Study on Accelerating Average Consensus Algorithms Using Delayed Feedback
Venue: arXiv cs.MA 2019
Year: 2019

In this paper, we study accelerating a Laplacian-based dynamic average consensus algorithm by splitting the conventional delay-free disagreement feedback into weighted summation of a current and an outdated term. We determine for what weighted sum there exists a range of time delay that results in the higher rate of convergence for the algorithm. For such weights, using the Lambert W function, we obtain the rate increasing range of the time delay, the maximum reachable rate and comment on the value of the corresponding maximizer delay. We also study the effect of use of outdated feedback on the control effort of the agents and show that only for some specific affine combination of the immediate and outdated feedback the control effort of the agents does not go beyond that of the delay-free algorithm. Additionally, we demonstrate that using outdated feedback does not increase the steady state tracking error of the average consensus algorithm. Lastly, we determine the optimum combination of the current and the outdated feedback weights to achieve the maximum increase in the rate of convergence without increasing the control effort of the agents. We demonstrate our results through a numerical example.
Practice of Efficient Data Collection via Crowdsourcing at Large-Scale
Venue: arXiv cs.HC 2019
Year: 2019

Modern machine learning algorithms need large datasets to be trained. Crowdsourcing has become a popular approach to label large datasets in a shorter time as well as at a lower cost comparing to that needed for a limited number of experts. However, as crowdsourcing performers are non-professional and vary in levels of expertise, such labels are much noisier than those obtained from experts. For this reason, in order to collect good quality data within a limited budget special techniques such as incremental relabelling, aggregation and pricing need to be used. We make an introduction to data labeling via public crowdsourcing marketplaces and present key components of efficient label collection. We show how to choose one of real label collection tasks, experiment with selecting settings for the labelling process, and launch label collection project at Yandex.Toloka, one of the largest crowdsourcing marketplace. The projects will be run on real crowds. We also present main algorithms for aggregation, incremental relabelling, and pricing in crowdsourcing. In particular, we, first, discuss how to connect these three components to build an efficient label collection process; and, second, share rich industrial experiences of applying these algorithms and constructing large-scale label collection pipelines (emphasizing best practices and common pitfalls).