OpenRaiser/Intern-Atlas huggingface dataset:
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Venue: arXiv cs.HC 2018
Year: 2018

Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the internal complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such performance are challenging and sometimes mystifying to interpret. As deep learning spreads across domains, it is of paramount importance that we equip users of deep learning with tools for understanding when a model works correctly, when it fails, and ultimately how to improve its performance. Standardized toolkits for building neural networks have helped democratize deep learning; visual analytics systems have now been developed to support model explanation, interpretation, debugging, and improvement. We present a survey of the role of visual analytics in deep learning research, which highlights its short yet impactful history and thoroughly summarizes the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How (Why, Who, What, How, When, and Where). We conclude by highlighting research directions and open research problems. This survey helps researchers and practitioners in both visual analytics and deep learning to quickly learn key aspects of this young and rapidly growing body of research, whose impact spans a diverse range of domains.
Opportunism in Dynamic Spectrum Access for 5G: A Concept and Its Application to Duplexing
Venue: arXiv cs.IT 2018
Year: 2018

With the envisioned massive Internet-of-Things (IoT) era, one of the challenges for 5G wireless systems will be handling the unprecedented spectrum crunch. A potential solution has emerged in the form of spectrum sharing, which deviates from a monopolistic spectrum usage system. This article investigates the medium access control (MAC) as a means of increasing the viability of the spectrum sharing technique. We first quantify the opportunity of spectrum access in a probabilistic manner, a method referred to as opportunity probability (OP). Based on the OP framework, we propose a random MAC algorithm in which the access of a node is randomly determined with its own OP value. As a possible application of our OP based random MAC, we propose a hybrid half duplex (HD)/full duplex (FD) communication where each pair decides the duplexing mode according to the OP values of the two pair nodes. This approach fits well with the spectrum sharing system since it enables a flexible operation for the spectrum access according to the spectrum usage level. From the numerical analysis, we validate the feasibility and verify the performance enhancements by implementing an FPGA based real-time prototype. Measurements and numerical results confirm that the proposed architecture can achieve up to 4 times higher system throughput than conventional LTE-TDD (time division duplex) systems.
Convexity of mutual information along the heat flow
Venue: arXiv cs.IT 2018
Year: 2018

We study the convexity of mutual information along the evolution of the heat equation. We prove that if the initial distribution is log-concave, then mutual information is always a convex function of time. We also prove that if the initial distribution is either bounded, or has finite fourth moment and Fisher information, then mutual information is eventually convex, i.e., convex for all large time. Finally, we provide counterexamples to show that mutual information can be nonconvex at small time.
Prioritizing Technical Debt in Database Normalization Using Portfolio Theory and Data Quality Metrics
Venue: arXiv cs.SE 2018
Year: 2018

Database normalization is the one of main principles for designing relational databases. The benefits of normalization can be observed through improving data quality and performance, among the other qualities. We explore a new context of technical debt manifestation, which is linked to ill-normalized databases. This debt can have long-term impact causing systematic degradation of database qualities. Such degradation can be liken to accumulated interest on a debt. We claim that debts are likely to materialize for tables below the fourth normal form. Practically, achieving fourth normal form for all the tables in the database is a costly and idealistic exercise. Therefore, we propose a pragmatic approach to prioritize tables that should be normalized to the fourth normal form based on the metaphoric debt and interest of the ill-normalized tables, observed on data quality and performance. For data quality, tables are prioritized using the risk of data inconsistency metric. Unlike data quality, a suitable metric to estimate the impact of weakly or un-normalized tables on performance is not available. We estimate performance degradation and its costs using Input\Output (IØ) cost of the operations performed on the tables and we propose a model to estimate this cost for each table. We make use of Modern Portfolio Theory to prioritize tables that should be normalized based on the estimated IØcost and the likely risk of cost accumulation in the future. To evaluate our methods, we use a case study from Microsoft, AdventureWorks. The results show that our methods can be effective in reducing normalization debt and improving the quality of the database.
Improved Power Decoding of Interleaved One-Point Hermitian Codes
Venue: arXiv cs.IT 2018
Year: 2018

We propose a new partial decoding algorithm for $h$-interleaved one-point Hermitian codes that can decode-under certain assumptions-an error of relative weight up to $1-(\tfrac{k+g}{n})^{\frac{h}{h+1}}$, where $k$ is the dimension, $n$ the length, and $g$ the genus of the code. Simulation results for various parameters indicate that the new decoder achieves this maximal decoding radius with high probability. The algorithm is based on a recent generalization of Rosenkilde's improved power decoder to interleaved Reed-Solomon codes, does not require an expensive root-finding step, and improves upon the previous best decoding radius by Kampf at all rates. In the special case $h=1$, we obtain an adaption of the improved power decoding algorithm to one-point Hermitian codes, which for all simulated parameters achieves a similar observed failure probability as the Guruswami-Sudan decoder above the latter's guaranteed decoding radius.
Large-Scale Analysis of Framework-Specific Exceptions in Android Apps
Venue: arXiv cs.SE 2018
Year: 2018

Mobile apps have become ubiquitous. For app developers, it is a key priority to ensure their apps' correctness and reliability. However, many apps still suffer from occasional to frequent crashes, weakening their competitive edge. Large-scale, deep analyses of the characteristics of real-world app crashes can provide useful insights to guide developers, or help improve testing and analysis tools. However, such studies do not exist -- this paper fills this gap. Over a four-month long effort, we have collected 16,245 unique exception traces from 2,486 open-source Android apps, and observed that framework-specific exceptions account for the majority of these crashes. We then extensively investigated the 8,243 framework-specific exceptions (which took six person-months): (1) identifying their characteristics (e.g., manifestation locations, common fault categories), (2) evaluating their manifestation via state-of-the-art bug detection techniques, and (3) reviewing their fixes. Besides the insights they provide, these findings motivate and enable follow-up research on mobile apps, such as bug detection, fault localization and patch generation. In addition, to demonstrate the utility of our findings, we have optimized Stoat, a dynamic testing tool, and implemented ExLocator, an exception localization tool, for Android apps. Stoat is able to quickly uncover three previously-unknown, confirmed/fixed crashes in Gmail and Google+; ExLocator is capable of precisely locating the root causes of identified exceptions in real-world apps. Our substantial dataset is made publicly available to share with and benefit the community.
Multi-Source Social Feedback of Online News Feeds
Venue: arXiv cs.SI 2018
Year: 2018

The profusion of user generated content caused by the rise of social media platforms has enabled a surge in research relating to fields such as information retrieval, recommender systems, data mining and machine learning. However, the lack of comprehensive baseline data sets to allow a thorough evaluative comparison has become an important issue. In this paper we present a large data set of news items from well-known aggregators such as Google News and Yahoo! News, and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The data collected relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation.
Towards Automated Tuberculosis detection using Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

Tuberculosis(TB) in India is the world's largest TB epidemic. TB leads to 480,000 deaths every year. Between the years 2006 and 2014, Indian economy lost US$340 Billion due to TB. This combined with the emergence of drug resistant bacteria in India makes the problem worse. The government of India has hence come up with a new strategy which requires a high-sensitivity microscopy based TB diagnosis mechanism. We propose a new Deep Neural Network based drug sensitive TB detection methodology with recall and precision of 83.78% and 67.55% respectively for bacillus detection. This method takes a microscopy image with proper zoom level as input and returns location of suspected TB germs as output. The high accuracy of our method gives it the potential to evolve into a high sensitivity system to diagnose TB when trained at scale.
An Overview of Wireless IoT Protocol Security in the Smart Home Domain
Venue: arXiv cs.CR 2018
Year: 2018

While the application of IoT in smart technologies becomes more and more proliferated, the pandemonium of its protocols becomes increasingly confusing. More seriously, severe security deficiencies of these protocols become evident, as time-to- market is a key factor, which satisfaction comes at the price of a less thorough security design and testing. This applies especially to the smart home domain, where the consumer-driven market demands quick and cheap solutions. This paper presents an overview of IoT application domains and discusses the most important wireless IoT protocols for smart home, which are KNX-RF, EnOcean, Zigbee, Z-Wave and Thread. Finally, it describes the security features of said protocols and compares them with each other, giving advice on whose protocols are more suitable for a secure smart home.
User assisted and automatic inverse procedural road modelling at the city scale
Venue: arXiv cs.CE 2018
Year: 2018

Cities are structured by roads. Having up to date and detailed maps of these is thus an important challenge for urban planing, civil engineering and transportation. Those maps are traditionally created manually, which represents a massive amount of work, and may discard recent or temporary changes. For these reasons, automated map building has been a long time goal, either for road network reconstruction or for local road surface reconstruction from low level observations. In this work, we navigate between these two goals. Starting from an approximate road axis (+ width) network as a simple road modelling, we propose to use observations of street features and optimisation to improve the coarse model. Observations are generic, and as such, can be derived from various data, such as aerial images, street images and street Lidar, other GIS data, and complementary user input. Starting from an initial road modelling which is at a median distance of 1.5 metre from the sidewalk ground-truth, our method has the potential to robustly optimise the road modelling so the median distance reaches 0.45 metre fully automatically, with better results possible using user inputs and/or more precise observations. The robust non linear least square optimisation used is extremely fast, with computing time from few minutes (whole Paris) to less than a second for a few blocks. The proposed approach is simple, very fast and produces a credible road model. These promising results open the way to various applications, such as integration in an interactive framework, or integration in a more powerful optimisation method, which would be able to further segment road network and use more complex road model.
Reflexion in mathematical models of decision-making
Venue: arXiv cs.GT 2018
Year: 2018

The paper is devoted to a generalization of static and dynamic mathematical models of behavior with explicitly stated reflexive models of agents' decision-making. Reflexion is considered as agent's beliefs about nature, opponents' beliefs and opponents' decision-making principles in the framework of game theory, collective behavior theory and learning models.
Get Your Workload in Order: Game Theoretic Prioritization of Database Auditing
Venue: arXiv cs.AI 2018
Year: 2018

For enhancing the privacy protections of databases, where the increasing amount of detailed personal data is stored and processed, multiple mechanisms have been developed, such as audit logging and alert triggers, which notify administrators about suspicious activities; however, the two main limitations in common are: 1) the volume of such alerts is often substantially greater than the capabilities of resource-constrained organizations, and 2) strategic attackers may disguise their actions or carefully choosing which records they touch, making incompetent the statistical detection models. For solving them, we introduce a novel approach to database auditing that explicitly accounts for adversarial behavior by 1) prioritizing the order in which types of alerts are investigated and 2) providing an upper bound on how much resource to allocate for each type. We model the interaction between a database auditor and potential attackers as a Stackelberg game in which the auditor chooses an auditing policy and attackers choose which records to target. A corresponding approach combining linear programming, column generation, and heuristic search is proposed to derive an auditing policy. For testing the policy-searching performance, a publicly available credit card application dataset are adopted, on which it shows that our methods produce high-quality mixed strategies as database audit policies, and our general approach significantly outperforms non-game-theoretic baselines.
Low-level Active Visual Navigation: Increasing robustness of vision-based localization using potential fields
Venue: arXiv cs.RO 2018
Year: 2018

This paper proposes a low-level visual navigation algorithm to improve visual localization of a mobile robot. The algorithm, based on artificial potential fields, associates each feature in the current image frame with an attractive or neutral potential energy, with the objective of generating a control action that drives the vehicle towards the goal, while still favoring feature rich areas within a local scope, thus improving the localization performance. One key property of the proposed method is that it does not rely on mapping, and therefore it is a lightweight solution that can be deployed on miniaturized aerial robots, in which memory and computational power are major constraints. Simulations and real experimental results using a mini quadrotor equipped with a downward looking camera demonstrate that the proposed method can effectively drive the vehicle to a designated goal through a path that prevents localization failure.
Convergence of Value Aggregation for Imitation Learning
Venue: arXiv cs.LG 2018
Year: 2018

Value aggregation is a general framework for solving imitation learning problems. Based on the idea of data aggregation, it generates a policy sequence by iteratively interleaving policy optimization and evaluation in an online learning setting. While the existence of a good policy in the policy sequence can be guaranteed non-asymptotically, little is known about the convergence of the sequence or the performance of the last policy. In this paper, we debunk the common belief that value aggregation always produces a convergent policy sequence with improving performance. Moreover, we identify a critical stability condition for convergence and provide a tight non-asymptotic bound on the performance of the last policy. These new theoretical insights let us stabilize problems with regularization, which removes the inconvenient process of identifying the best policy in the policy sequence in stochastic problems.
Optimistic Execution in Key-Value Store
Venue: arXiv cs.DC 2018
Year: 2018

Limitations of CAP theorem imply that if availability is desired in the presence of network partitions, one must sacrifice sequential consistency, a consistency model that is more natural for system design. We focus on the problem of what a designer should do if she has an algorithm that works correctly with sequential consistency but is faced with an underlying key-value store that provides a weaker (e.g., eventual or causal) consistency. We propose a detect-rollback based approach: The designer identifies a correctness predicate, say P , and continue to run the protocol, as our system monitors P . If P is violated (because the underlying key-value store provides a weaker consistency), the system rolls back and resumes the computation at a state where P holds. We evaluate this approach in the Voldemort key-value store. Our experiments with deployment of Voldemort on Amazon AWS shows that using eventual consistency with monitoring can provide 20 - 40% increase in throughput when compared with sequential consistency. We also show that the overhead of the monitor itself is small (typically less than 8%) and the latency of detecting violations is very low. For example, more than 99.9% violations are detected in less than 1 second.
The Importance of Communities for Learning to Influence
Venue: arXiv cs.SI 2018
Year: 2018

We consider the canonical problem of influence maximization in social networks. Since the seminal work of Kempe, Kleinberg, and Tardos, there have been two largely disjoint efforts on this problem. The first studies the problem associated with learning the parameters of the generative influence model. The second focuses on the algorithmic challenge of identifying a set of influencers, assuming the parameters of the generative model are known. Recent results on learning and optimization imply that in general, if the generative model is not known but rather learned from training data, no algorithm can yield a constant factor approximation guarantee using polynomially-many samples, drawn from any distribution. In this paper, we design a simple heuristic that overcomes this negative result in practice by leveraging the strong community structure of social networks. Although in general the approximation guarantee of our algorithm is necessarily unbounded, we show that this algorithm performs well experimentally. To justify its performance, we prove our algorithm obtains a constant factor approximation guarantee on graphs generated through the stochastic block model, traditionally used to model networks with community structure.
Mean-Field Game Theoretic Edge Caching in Ultra-Dense Networks
Venue: arXiv cs.NI 2018
Year: 2018

This paper investigates a cellular edge caching problem under a very large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), conventional caching algorithms are inapplicable as their complexity increases with the number of small base stations (SBSs). Furthermore, the performance of UDCN is highly sensitive to the dynamics of user demand and inter-SBS interference. To overcome such difficulties, we propose a distributed caching algorithm under a stochastic geometric network model, as well as a spatio-temporal user demand model that characterizes the content popularity dynamics. By exploiting mean-field game (MFG) theory, the complexity of the proposed UDCN caching algorithm becomes independent of the number of SBSs. Numerical evaluations validate that the proposed caching algorithm reduces not only the long run average cost of the network but also the redundant cached data respectively by 24% and 42%, compared to a baseline caching algorithm. The simulation results also show that the proposed caching algorithm is robust to imperfect popularity information, while ensuring a low computational complexity.
Dense 3-D Mapping with Spatial Correlation via Gaussian Filtering
Venue: arXiv cs.RO 2018
Year: 2018

Constructing an occupancy representation of the environment is a fundamental problem for robot autonomy. Many accurate and efficient methods exist that address this problem but most assume that the occupancy states of different elements in the map representation are statistically independent. The focus of this paper is to provide a model that captures correlation of the occupancy of map elements. Correlation is important not only for improved accuracy but also for quantifying uncertainty in the map and for planning autonomous mapping trajectories based on the correlation among known and unknown areas. Recent work proposes Gaussian Process (GP) regression to capture covariance information and enable resolution-free occupancy estimation. The drawback of techniques based on GP regression (or classification) is that the computation complexity scales cubically with the length of the measurement history. Our main contribution is a new approach for occupancy mapping that models the binary nature of occupancy measurements precisely, via a Bernoulli distribution, and provides an efficient approximation of GP classification with complexity that does not scale with time. We prove that the error between the estimates provided by our method and those provided by GP classification is negligible. The proposed method is evaluated using both simulated data and real data collected using a Velodyne Puck 3-D range sensor.
Onion Curve: A Space Filling Curve with Near-Optimal Clustering
Venue: arXiv cs.CG 2018
Year: 2018

Space filling curves (SFCs) are widely used in the design of indexes for spatial and temporal data. Clustering is a key metric for an SFC, that measures how well the curve preserves locality in moving from higher dimensions to a single dimension. We present the {\em onion curve}, an SFC whose clustering performance is provably close to optimal for the cube and near-cube shaped query sets, irrespective of the side length of the query. We show that in contrast, the clustering performance of the widely used Hilbert curve can be far from optimal, even for cube-shaped queries. Since the clustering performance of an SFC is critical to the efficiency of multi-dimensional indexes based on the SFC, the onion curve can deliver improved performance for data structures involving multi-dimensional data.