OpenRaiser/Intern-Atlas huggingface dataset:
A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry
Venue: arXiv cs.SI 2020
Year: 2020

Relational learning in networked data has been shown to be effective in a number of studies. Relational learners, composed of relational classifiers and collective inference methods, enable the inference of nodes in a network given the existence and strength of links to other nodes. These methods have been adapted to predict customer churn in telecommunication companies showing that incorporating them may give more accurate predictions. In this research, the performance of a variety of relational learners is compared by applying them to a number of CDR datasets originating from the telecommunication industry, with the goal to rank them as a whole and investigate the effects of relational classifiers and collective inference methods separately. Our results show that collective inference methods do not improve the performance of relational classifiers and the best performing relational classifier is the network-only link-based classifier, which builds a logistic model using link-based measures for the nodes in the network.
Discriminator Soft Actor Critic without Extrinsic Rewards
Venue: arXiv cs.LG 2020
Year: 2020

It is difficult to be able to imitate well in unknown states from a small amount of expert data and sampling data. Supervised learning methods such as Behavioral Cloning do not require sampling data, but usually suffer from distribution shift. The methods based on reinforcement learning, such as inverse reinforcement learning and generative adversarial imitation learning (GAIL), can learn from only a few expert data. However, they often need to interact with the environment. Soft Q imitation learning addressed the problems, and it was shown that it could learn efficiently by combining Behavioral Cloning and soft Q-learning with constant rewards. In order to make this algorithm more robust to distribution shift, we propose Discriminator Soft Actor Critic (DSAC). It uses a reward function based on adversarial inverse reinforcement learning instead of constant rewards. We evaluated it on PyBullet environments with only four expert trajectories.
Distributionally Robust Bayesian Quadrature Optimization
Venue: arXiv cs.LG 2020
Year: 2020

Bayesian quadrature optimization (BQO) maximizes the expectation of an expensive black-box integrand taken over a known probability distribution. In this work, we study BQO under distributional uncertainty in which the underlying probability distribution is unknown except for a limited set of its i.i.d. samples. A standard BQO approach maximizes the Monte Carlo estimate of the true expected objective given the fixed sample set. Though Monte Carlo estimate is unbiased, it has high variance given a small set of samples; thus can result in a spurious objective function. We adopt the distributionally robust optimization perspective to this problem by maximizing the expected objective under the most adversarial distribution. In particular, we propose a novel posterior sampling based algorithm, namely distributionally robust BQO (DRBQO) for this purpose. We demonstrate the empirical effectiveness of our proposed framework in synthetic and real-world problems, and characterize its theoretical convergence via Bayesian regret.
SlideImages: A Dataset for Educational Image Classification
Venue: arXiv cs.CV 2020
Year: 2020

In the past few years, convolutional neural networks (CNNs) have achieved impressive results in computer vision tasks, which however mainly focus on photos with natural scene content. Besides, non-sensor derived images such as illustrations, data visualizations, figures, etc. are typically used to convey complex information or to explore large datasets. However, this kind of images has received little attention in computer vision. CNNs and similar techniques use large volumes of training data. Currently, many document analysis systems are trained in part on scene images due to the lack of large datasets of educational image data. In this paper, we address this issue and present SlideImages, a dataset for the task of classifying educational illustrations. SlideImages contains training data collected from various sources, e.g., Wikimedia Commons and the AI2D dataset, and test data collected from educational slides. We have reserved all the actual educational images as a test dataset in order to ensure that the approaches using this dataset generalize well to new educational images, and potentially other domains. Furthermore, we present a baseline system using a standard deep neural architecture and discuss dealing with the challenge of limited training data.
Contact with coupled adhesion and friction: Computational framework, applications, and new insights
Venue: arXiv cs.CE 2020
Year: 2020

Contact involving soft materials often combines dry adhesion, sliding friction, and large deformations. At the local level, these three aspects are rarely captured simultaneously, but included in the theoretical models by Mergel et al. (2019). We here develop a corresponding finite element framework that captures 3D finite-strain contact of two deformable bodies. This framework is suitable to investigate sliding friction even under tensile normal loads. First, we demonstrate the capabilities of our finite element model using both 2D and 3D test cases, which range from compliant tapes to structures with high stiffness, and include deformable-rigid and deformable-deformable contact. We then provide new results on the onset of sliding of smooth elastomer-glass interfaces, a setup that couples nonlinear material behavior, adhesion, and large frictional stresses. Our simulations not only agree well with both experimental and theoretical findings, they also provide new insights into the current debate on the shear-induced reduction of the contact area in elastomeric contact.
Optimal Resource Allocation in Ground Wireless Networks Supporting Unmanned Aerial Vehicle Transmissions
Venue: arXiv cs.IT 2020
Year: 2020

We consider a fully-loaded ground wireless network supporting unmanned aerial vehicle (UAV) transmission services. To enable the overload transmissions to a ground user (GU) and a UAV, two transmission schemes are employed, namely non-orthogonal multiple access (NOMA) and relaying, depending on whether or not the GU and UAV are served simultaneously. Under the assumption of the system operating with infinite blocklength (IBL) codes, the IBL throughputs of both the GU and the UAV are derived under the two schemes. More importantly, we also consider the scenario in which data packets are transmitted via finite blocklength (FBL) codes, i.e., data transmission to both the UAV and the GU is performed under low-latency and high reliability constraints. In this setting, the FBL throughputs are characterized again considering the two schemes of NOMA and relaying. Following the IBL and FBL throughput characterizations, optimal resource allocation designs are subsequently proposed to maximize the UAV throughput while guaranteeing the throughput of the cellular user.Moreover, we prove that the relaying scheme is able to provide transmission service to the UAV while improving the GU's performance, and that the relaying scheme potentially offers a higher throughput to the UAV in the FBL regime than in the IBL regime. On the other hand, the NOMA scheme provides a higher UAV throughput (than relaying) by slightly sacrificing the GU's performance.
DoS-resilient Cooperative Beacon Verification for Vehicular Communication Systems
Venue: arXiv cs.CR 2020
Year: 2020

Authenticated safety beacons in Vehicular Communication (VC) systems ensure awareness among neighboring vehicles. However, the verification of beacon signatures introduces significant processing overhead for resource-constrained vehicular On-Board Units (OBUs). Even worse in dense neighborhood or when a clogging Denial of Service (DoS) attack is mounted. The OBU would fail to verify for all received (authentic or fictitious) beacons. This could significantly delay the verifications of authentic beacons or even affect the awareness of neighboring vehicle status. In this paper, we propose an efficient cooperative beacon verification scheme leveraging efficient symmetric key based authentication on top of pseudonymous authentication (based on traditional public key cryptography), providing efficient discovery of authentic beacons among a pool of received authentic and fictitious beacons, and can significantly decrease waiting times of beacons in queue before their validations. We show with simulation results that our scheme can guarantee low waiting times for received beacons even in high neighbor density situations and under DoS attacks, under which a traditional scheme would not be workable.
Reinforcement Learning with Probabilistically Complete Exploration
Venue: arXiv cs.LG 2020
Year: 2020

Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL). State-of-the-art RL algorithms suffer from high sample complexity, particularly in the sparse reward case, where they can do no better than to explore in all directions until the first positive rewards are found. To mitigate this, we propose Rapidly Randomly-exploring Reinforcement Learning (R3L). We formulate exploration as a search problem and leverage widely-used planning algorithms such as Rapidly-exploring Random Tree (RRT) to find initial solutions. These solutions are used as demonstrations to initialize a policy, then refined by a generic RL algorithm, leading to faster and more stable convergence. We provide theoretical guarantees of R3L exploration finding successful solutions, as well as bounds for its sampling complexity. We experimentally demonstrate the method outperforms classic and intrinsic exploration techniques, requiring only a fraction of exploration samples and achieving better asymptotic performance.
Quantitative Aspects of Programming Languages and Systems over the past $2^4$ years and beyond
Venue: arXiv cs.PL 2020
Year: 2020

Quantitative aspects of computation are related to the use of both physical and mathematical quantities, including time, performance metrics, probability, and measures for reliability and security. They are essential in characterizing the behaviour of many critical systems and in estimating their properties. Hence, they need to be integrated both at the level of system modeling and within the verification methodologies and tools. Along the last two decades a variety of theoretical achievements and automated techniques have contributed to make quantitative modeling and verification mainstream in the research community. In the same period, they represented the central theme of the series of workshops entitled Quantitative Aspects of Programming Languages and Systems (QAPL) and born in 2001. The aim of this survey is to revisit such achievements and results from the standpoint of QAPL and its community.
Non-Orthogonal Multiple Access with Wireless Caching for 5G-Enabled Vehicular Networks
Venue: arXiv cs.IT 2020
Year: 2020

The proliferation of connected vehicles along with the high demand for rich multimedia services constitute key challenges for the emerging 5G-enabled vehicular networks. These challenges include, but are not limited to, high spectral efficiency and low latency requirements. Recently, the integration of cache-enabled networks with non-orthogonal multiple access (NOMA) has been shown to reduce the content delivery time and traffic congestion in wireless networks. Ac-cordingly, in this article, we envisage cache-aided NOMA as a technology facilitator for 5G-enabled vehicular networks. In particular, we present a cache-aided NOMA architecture, which can address some of the aforementioned challenges in these networks. We demonstrate that the spectral efficiency gain of the proposed architecture, which depends largely on the cached contents, significantly outperforms that of conventional vehicular networks. Finally, we provide deep insights into the challenges, opportunities, and future research trends that will enable the practical realization of cache-aided NOMA in 5G-enabled vehicular networks.
MOEA/D with Random Partial Update Strategy
Venue: arXiv cs.AI 2020
Year: 2020

Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm. These studies share the common characteristic of updating only a fraction of the population at any given iteration of the algorithm. In this work we investigate a new, simpler partial update strategy, in which a random subset of solutions is selected at every iteration. The performance of the MOEA/D using this new resource allocation approach is compared experimentally against that of the standard MOEA/D-DE and the MOEA/D with relative improvement-based resource allocation. The results indicate that using the MOEA/D with this new partial update strategy results in improved HV and IGD values, and a much higher proportion of non-dominated solutions, particularly as the number of updated solutions at every iteration is reduced.
Adaptive Dithering Using Curved Markov-Gaussian Noise in the Quantized Domain for Mapping SDR to HDR Image
Venue: arXiv cs.CV 2020
Year: 2020

High Dynamic Range (HDR) imaging is gaining increased attention due to its realistic content, for not only regular displays but also smartphones. Before sufficient HDR content is distributed, HDR visualization still relies mostly on converting Standard Dynamic Range (SDR) content. SDR images are often quantized, or bit depth reduced, before SDR-to-HDR conversion, e.g. for video transmission. Quantization can easily lead to banding artefacts. In some computing and/or memory I/O limited environment, the traditional solution using spatial neighborhood information is not feasible. Our method includes noise generation (offline) and noise injection (online), and operates on pixels of the quantized image. We vary the magnitude and structure of the noise pattern adaptively based on the luma of the quantized pixel and the slope of the inverse-tone mapping function. Subjective user evaluations confirm the superior performance of our technique.
The Parallelism Motifs of Genomic Data Analysis
Venue: arXiv cs.DC 2020
Year: 2020

Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share this data with the research community, but some of these genomic data analysis problems require large scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high end parallel systems today and place different requirements on programming support, software libraries, and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high performance genomics analysis, including alignment, profiling, clustering, and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or motifs that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing.
Plane Pair Matching for Efficient 3D View Registration
Venue: arXiv cs.CV 2020
Year: 2020

We present a novel method to estimate the motion matrix between overlapping pairs of 3D views in the context of indoor scenes. We use the Manhattan world assumption to introduce lightweight geometric constraints under the form of planes into the problem, which reduces complexity by taking into account the structure of the scene. In particular, we define a stochastic framework to categorize planes as vertical or horizontal and parallel or non-parallel. We leverage this classification to match pairs of planes in overlapping views with point-of-view agnostic structural metrics. We propose to split the motion computation using the classification and estimate separately the rotation and translation of the sensor, using a quadric minimizer. We validate our approach on a toy example and present quantitative experiments on a public RGB-D dataset, comparing against recent state-of-the-art methods. Our evaluation shows that planar constraints only add low computational overhead while improving results in precision when applied after a prior coarse estimate. We conclude by giving hints towards extensions and improvements of current results.
Sampling Graphlets of Multi-layer Networks: A Restricted Random Walk Approach
Venue: arXiv cs.SI 2020
Year: 2020

Graphlets are induced subgraph patterns that are crucial to the understanding of the structure and function of a large network. A lot of efforts have been devoted to calculating graphlet statistics where random walk based approaches are commonly used to access restricted graphs through the available application programming interfaces (APIs). However, most of them merely consider individual networks while overlooking the strong coupling between different networks. In this paper, we estimate the graphlet concentration in multi-layer networks with real-world applications. An inter-layer edge connects two nodes in different layers if they belong to the same person. The access to a multi-layer network is restrictive in the sense that the upper layer allows random walk sampling, whereas the nodes of lower layers can be accessed only though the inter-layer edges and only support random node or edge sampling. To cope with this new challenge, we define a suit of two-layer graphlets and propose a novel random walk sampling algorithm to estimate the proportion of all the 3-node graphlets. An analytical bound on the sampling steps is proved to guarantee the convergence of our unbiased estimator. We further generalize our algorithm to explore the tradeoff between the estimated accuracies of different graphlets when the sample size is split on different layers. Experimental evaluation on real-world and synthetic multi-layer networks demonstrate the accuracy and high efficiency of our unbiased estimators.
BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction
Venue: arXiv cs.IR 2020
Year: 2020

Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities and their inherent mechanisms. Most biomedical relation extraction systems do not resort to external sources of knowledge, such as domain-specific ontologies. However, using deep learning methods, along with biomedical ontologies, has been recently shown to effectively advance the biomedical relation extraction field. To perform relation extraction, our deep learning system, BiOnt, employs four types of biomedical ontologies, namely, the Gene Ontology, the Human Phenotype Ontology, the Human Disease Ontology, and the Chemical Entities of Biological Interest, regarding gene-products, phenotypes, diseases, and chemical compounds, respectively. We tested our system with three data sets that represent three different types of relations of biomedical entities. BiOnt achieved, in F-score, an improvement of 4.93 percentage points for drug-drug interactions (DDI corpus), 4.99 percentage points for phenotype-gene relations (PGR corpus), and 2.21 percentage points for chemical-induced disease relations (BC5CDR corpus), relatively to the state-of-the-art. The code supporting this system is available at https://github.com/lasigeBioTM/BiOnt.
On the Feasibility of Acoustic Attacks Using Commodity Smart Devices
Venue: arXiv cs.CR 2020
Year: 2020

Sound at frequencies above (ultrasonic) or below (infrasonic) the range of human hearing can, in some settings, cause adverse physiological and psychological effects to individuals. In this paper, we investigate the feasibility of cyber-attacks that could make smart consumer devices produce possibly imperceptible sound at both high (17-21kHz) and low (60-100Hz) frequencies, at the maximum available volume setting, potentially turning them into acoustic cyber-weapons. To do so, we deploy attacks targeting different smart devices and take sound measurements in an anechoic chamber. For comparison, we also test possible attacks on traditional devices. Overall, we find that many of the devices tested are capable of reproducing frequencies within both high and low ranges, at levels exceeding those recommended in published guidelines. Generally speaking, such attacks are often trivial to develop and in many cases could be added to existing malware payloads, as they may be attractive to adversaries with specific motivations or targets. Finally, we suggest a number of countermeasures, both platform-specific and generic ones.
Authenticated Secret Key Generation in Delay Constrained Wireless Systems
Venue: arXiv cs.CR 2020
Year: 2020

With the emergence of 5G low latency applications, such as haptics and V2X, low complexity and low latency security mechanisms are sought. Promising lightweight mechanisms include physical unclonable functions (PUF) and secret key generation (SKG) at the physical layer, as considered in this paper. In this framework we propose i) a novel authenticated encryption using SKG; ii) a combined PUF / SKG authentication to reduce computational overhead; iii) a 0-RTT resumption authentication protocol; iv) pipelining of the SKG and the encrypted data transfer. With respect to the latter, we investigate a parallel SKG approach for multi-carrier systems, where a subset of the subcarriers are used for SKG and the rest for data transmission. The optimal resource allocation is identified under security, power and delay constraints, by formulating the subcarrier allocation as a subset-sum $0-1$ knapsack optimization problem. A heuristic approach of linear complexity is proposed and shown to incur negligible loss with respect to the optimal dynamic programming solution. All of the proposed mechanisms, have the potential to pave the way for a new breed of latency aware security protocols.
Smart Parking System Based on Bluetooth Low Energy Beacons with Particle Filtering
Venue: arXiv cs.NI 2020
Year: 2020

Urban centers and dense populations are expanding, hence, there is a growing demand for novel applications to aid in planning and optimization. In this work, a smart parking system that operates both indoor and outdoor is introduced. The system is based on Bluetooth Low Energy (BLE) beacons and uses particle filtering to improve its accuracy. Through simple BLE connectivity with smartphones, an intuitive parking system is designed and deployed. The proposed system pairs each spot with a unique BLE beacon, providing users with guidance to free parking spaces and a secure and automated payment scheme based on real-time usage of the parking space. Three sets of experiments were conducted to examine different aspects of the system. A particle filter is implemented in order to increase the system performance and improve the credence of the results. Through extensive experimentation in both indoor and outdoor parking spaces, the system was able to correctly predict which spot the user has parked in, as well as estimate the distance of the user from the beacon.