OpenRaiser/Intern-Atlas huggingface dataset:
Component Outage Estimation based on Support Vector Machine
Venue: arXiv cs.SY 2018
Year: 2018

Predicting power system component outages in response to an imminent hurricane plays a major role in preevent planning and post-event recovery of the power system. An exact prediction of components states, however, is a challenging task and cannot be easily performed. In this paper, a Support Vector Machine (SVM) based method is proposed to help estimate the components states in response to anticipated path and intensity of an imminent hurricane. Components states are categorized into three classes of damaged, operational, and uncertain. The damaged components along with the components in uncertain class are then considered in multiple contingency scenarios of a proposed Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the simultaneous outage of multiple components under an N-m-u reliability criterion. Experimental results on the IEEE 118-bus test system show the merits and the effectiveness of the proposed SVM classifier and the E-SCUC model in improving power system resilience in response to extreme events.
Statistical Analysis of Metrics for Software Quality Improvement
Venue: arXiv cs.SE 2018
Year: 2018

Software product quality can be defined as the features and characteristics of the product that meet the user needs. The quality of any software can be achieved by following a well defined software process. These software process results into various metrics like Project metrics, Product metrics and Process metrics. Software quality depends on the process which is carried out to design and develop software. Even though the process can be carried out with utmost care, still it can introduce some error and defects. Process metrics are very useful from management point of view. Process metrics can be used for improving the software development and maintenance process for defect removal and also for reducing the response time. This paper describes the importance of capturing the Process metrics during the quality audit process and also attempts to categorize them based on the nature of error captured. To reduce such errors and defects found, steps for corrective actions are recommended.
iBoW-LCD: An Appearance-based Loop Closure Detection Approach using Incremental Bags of Binary Words
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we introduce iBoW-LCD, a novel appearance-based loop closure detection method. The presented approach makes use of an incremental Bag-of-Words (BoW) scheme based on binary descriptors to retrieve previously seen similar images, avoiding any vocabulary training stage usually required by classic BoW models. In addition, to detect loop closures, iBoW-LCD builds on the concept of dynamic islands, a simple but effective mechanism to group similar images close in time, which reduces the computational times typically associated to Bayesian frameworks. Our approach is validated using several indoor and outdoor public datasets, taken under different environmental conditions, achieving a high accuracy and outperforming other state-of-the-art solutions.
Measuring Human-perceived Similarity in Heterogeneous Collections
Venue: arXiv cs.AI 2018
Year: 2018

We present a technique for estimating the similarity between objects such as movies or foods whose proper representation depends on human perception. Our technique combines a modest number of human similarity assessments to infer a pairwise similarity function between the objects. This similarity function captures some human notion of similarity which may be difficult or impossible to automatically extract, such as which movie from a collection would be a better substitute when the desired one is unavailable. In contrast to prior techniques, our method does not assume that all similarity questions on the collection can be answered or that all users perceive similarity in the same way. When combined with a user model, we find how each assessor's tastes vary, affecting their perception of similarity.
Improving the Florentine algorithms: recovering algorithms for Motzkin and Schröder paths
Venue: arXiv cs.DS 2018
Year: 2018

We present random sampling procedures for Motzkin and Schröder paths, following previous work on Dyck paths. Our algorithms follow the anticipated rejection method of the Florentine algorithms (Barcucci et al. 1994+), but introduce a recovery idea to greatly reduce the probability of rejection. They use an optimal amount of randomness and achieve a better time complexity than the Florentine algorithms.
Learning multiagent coordination in the absence of communication channels
Venue: arXiv cs.GT 2018
Year: 2018

In this work, we develop a reinforcement learning protocol for a multiagent coordination task in a discrete state and action space: an iterated prisoner's dilemma game extended into a team based, winner-take all tournament, which forces the agents to collude in order to maximize their reward. By disallowing extra communication channels, the agents are forced to embed their coordination strategy into their actions in the prisoner's dilemma game. We develop a representation of the iterated prisoners dilemma that makes it amenable to Q-learning. We find that the reinforcement learning strategy is able to consistently train agents that can win the winner take all iterated prisoners dilemma tournament. By using a game with discrete state and action space, we are able to better analyze and understand both the dynamics and the communication protocols that are established between the agents. We find that the agents adapt a number of interesting behaviors, such as the formation of benevolent dictators, that minimize inequality of scores. We also find that the agents settle on a remarkably consistent symbology in their actions, such that agents from independent trials are able to collude with each other without further training.
The Vertex Sample Complexity of Free Energy is Polynomial
Venue: arXiv cs.LG 2018
Year: 2018

We study the following question: given a massive Markov random field on $n$ nodes, can a small sample from it provide a rough approximation to the free energy $\mathcal{F}_n = \log{Z_n}$? Results in graph limit literature by Borgs, Chayes, Lovász, Sós, and Vesztergombi show that for Ising models on $n$ nodes and interactions of strength $Θ(1/n)$, an $ε$ approximation to $\log Z_n / n$ can be achieved by sampling a randomly induced model on $2^{O(1/ε^2)}$ nodes. We show that the sampling complexity of this problem is {\em polynomial in} $1/ε$. We further show a polynomial dependence on $ε$ cannot be avoided. Our results are very general as they apply to higher order Markov random fields. For Markov random fields of order $r$, we obtain an algorithm that achieves $ε$ approximation using a number of samples polynomial in $r$ and $1/ε$ and running time that is $2^{O(1/ε^2)}$ up to polynomial factors in $r$ and $ε$. For ferromagnetic Ising models, the running time is polynomial in $1/ε$. Our results are intimately connected to recent research on the regularity lemma and property testing, where the interest is in finding which properties can tested within $ε$ error in time polynomial in $1/ε$. In particular, our proofs build on results from a recent work by Alon, de la Vega, Kannan and Karpinski, who also introduced the notion of polynomial vertex sample complexity. Another critical ingredient of the proof is an effective bound by the authors of the paper relating the variational free energy and the free energy.
A Unified Framework for Planning in Adversarial and Cooperative Environments
Venue: arXiv cs.AI 2018
Year: 2018

Users of AI systems may rely upon them to produce plans for achieving desired objectives. Such AI systems should be able to compute obfuscated plans whose execution in adversarial situations protects privacy, as well as legible plans which are easy for team members to understand in cooperative situations. We develop a unified framework that addresses these dual problems by computing plans with a desired level of comprehensibility from the point of view of a partially informed observer. For adversarial settings, our approach produces obfuscated plans with observations that are consistent with at least k goals from a set of decoy goals. By slightly varying our framework, we present an approach for goal legibility in cooperative settings which produces plans that achieve a goal while being consistent with at most j goals from a set of confounding goals. In addition, we show how the observability of the observer can be controlled to either obfuscate or clarify the next actions in a plan when the goal is known to the observer. We present theoretical results on the complexity analysis of our problems. We demonstrate the execution of obfuscated and legible plans in a cooking domain using a physical robot Fetch. We also provide an empirical evaluation to show the feasibility and usefulness of our approaches using IPC domains.
Drift-Free Indoor Navigation Using Simultaneous Localization and Mapping of the Ambient Heterogeneous Magnetic Field
Venue: arXiv cs.RO 2018
Year: 2018

In the absence of external reference position information (e.g. GNSS) SLAM has proven to be an effective method for indoor navigation. The positioning drift can be reduced with regular loop-closures and global relaxation as the backend, thus achieving a good balance between exploration and exploitation. Although vision-based systems like laser scanners are typically deployed for SLAM, these sensors are heavy, energy inefficient, and expensive, making them unattractive for wearables or smartphone applications. However, the concept of SLAM can be extended to non-optical systems such as magnetometers. Instead of matching features such as walls and furniture using some variation of the ICP algorithm, the local magnetic field can be matched to provide loop-closure and global trajectory updates in a Gaussian Process (GP) SLAM framework. With a MEMS-based inertial measurement unit providing a continuous trajectory, and the matching of locally distinct magnetic field maps, experimental results in this paper show that a drift-free navigation solution in an indoor environment with millimetre-level accuracy can be achieved. The GP-SLAM approach presented can be formulated as a maximum a posteriori estimation problem and it can naturally perform loop-detection, feature-to-feature distance minimization, global trajectory optimization, and magnetic field map estimation simultaneously. Spatially continuous features (i.e. smooth magnetic field signatures) are used instead of discrete feature correspondences (e.g. point-to-point) as in conventional vision-based SLAM. These position updates from the ambient magnetic field also provide enough information for calibrating the accelerometer and gyroscope bias in-use. The only restriction for this method is the need for magnetic disturbances (which is typically not an issue indoors); however, no assumptions are required for the general motion of the sensor.
Numerical solution of boundary value problems for the eikonal equation in an anisotropic medium
Venue: arXiv cs.NA 2018
Year: 2018

A Dirichlet problem is considered for the eikonal equation in an anisotropic medium. The nonlinear boundary value problem (BVP) formulated in the present work is the limit of the diffusion-reaction problem with a reaction parameter tending to infinity. To solve numerically the singularly perturbed diffusion-reaction problem, monotone approximations are employed. Numerical examples are presented for a two-dimensional BVP for the eikonal equation in an anisotropic medium. The standard piecewise-linear finite-element approximation in space is used in computations.
Network Centrality Analysis of Tehran Urban and Suburban Railway System
Venue: arXiv cs.SI 2018
Year: 2018

Nowadays, Tehran Urban and Suburban Railway System (TUSRS) is going to be completed by eight lines and 149 stations. This complex transportation system contains 168 links between each station pairs and 20 cross-section and Y-branch stations among all eight lines. In this study, we considered TUSRS as a complex network and undertook several analyzes based on graph theory. Examining e.g. centrality measures, we identified central stations within TUSRS. This analysis could be useful for redistributing strategy of the overcrowded stations and improving the organization of maintaining system. These findings are also promising for better designing the systems of tomorrow in other metropolitan areas in Iran.
Automatic Classification of Roof Shapes for Multicopter Emergency Landing Site Selection
Venue: arXiv cs.RO 2018
Year: 2018

Geographic information systems (GIS) now provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems, can exploit additional information such as building roof structure to improve navigation accuracy and safety particularly in urban regions. This paper proposes a method to automatically label building roof shape types. Satellite imagery and LIDAR data from Witten, Germany are fed to convolutional neural networks (CNN) to extract salient feature vectors. Supervised training sets are automatically generated from pre-labeled buildings contained in the OpenStreetMap database. Multiple CNN architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite and LIDAR data fusion is shown to provide greater classification accuracy than through use of either data type individually.
Machine learning for Internet of Things data analysis: A survey
Venue: arXiv cs.LG 2018
Year: 2018

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.
Consensus in Software Engineering: A Cognitive Mapping Study
Venue: arXiv cs.SE 2018
Year: 2018

Background: Philosophers of science including Collins, Feyerabend, Kuhn and Latour have all emphasized the importance of consensus within scientific communities of practice. Consensus is important for maintaining legitimacy with outsiders, orchestrating future research, developing educational curricula and agreeing industry standards. Low consensus contrastingly undermines a field's reputation and hinders peer review. Aim: This paper aims to investigate the degree of consensus within the software engineering academic community concerning members' implicit theories of software engineering. Method: A convenience sample of 60 software engineering researchers produced diagrams describing their personal understanding of causal relationships between core software engineering constructs. The diagrams were then analyzed for patterns and clusters. Results: At least three schools of thought may be forming; however, their interpretation is unclear since they do not correspond to known divisions within the community (e.g. Agile vs. Plan-Driven methods). Furthermore, over one third of participants do not belong to any cluster. Conclusion: Although low consensus is common in social sciences, the rapid pace of innovation observed in software engineering suggests that high consensus is achievable given renewed commitment to empiricism and evidence-based practice.
On Finding Dense Common Subgraphs
Venue: arXiv cs.DS 2018
Year: 2018

We study the recently introduced problem of finding dense common subgraphs: Given a sequence of graphs that share the same vertex set, the goal is to find a subset of vertices $S$ that maximizes some aggregate measure of the density of the subgraphs induced by $S$ in each of the given graphs. Different choices for the aggregation function give rise to variants of the problem that were studied recently. We settle many of the questions left open by previous works, showing NP-hardness, hardness of approximation, non-trivial approximation algorithms, and an integrality gap for a natural relaxation.
DARTS: Deceiving Autonomous Cars with Toxic Signs
Venue: arXiv cs.CR 2018
Year: 2018

Sign recognition is an integral part of autonomous cars. Any misclassification of traffic signs can potentially lead to a multitude of disastrous consequences, ranging from a life-threatening accident to even a large-scale interruption of transportation services relying on autonomous cars. In this paper, we propose and examine security attacks against sign recognition systems for Deceiving Autonomous caRs with Toxic Signs (we call the proposed attacks DARTS). In particular, we introduce two novel methods to create these toxic signs. First, we propose Out-of-Distribution attacks, which expand the scope of adversarial examples by enabling the adversary to generate these starting from an arbitrary point in the image space compared to prior attacks which are restricted to existing training/test data (In-Distribution). Second, we present the Lenticular Printing attack, which relies on an optical phenomenon to deceive the traffic sign recognition system. We extensively evaluate the effectiveness of the proposed attacks in both virtual and real-world settings and consider both white-box and black-box threat models. Our results demonstrate that the proposed attacks are successful under both settings and threat models. We further show that Out-of-Distribution attacks can outperform In-Distribution attacks on classifiers defended using the adversarial training defense, exposing a new attack vector for these defenses.
What We Read, What We Search: Media Attention and Public Attention Among 193 Countries
Venue: arXiv cs.SI 2018
Year: 2018

We investigate the alignment of international attention of news media organizations within 193 countries with the expressed international interests of the public within those same countries from March 7, 2016 to April 14, 2017. We collect fourteen months of longitudinal data of online news from Unfiltered News and web search volume data from Google Trends and build a multiplex network of media attention and public attention in order to study its structural and dynamic properties. Structurally, the media attention and the public attention are both similar and different depending on the resolution of the analysis. For example, we find that 63.2% of the country-specific media and the public pay attention to different countries, but local attention flow patterns, which are measured by network motifs, are very similar. We also show that there are strong regional similarities with both media and public attention that is only disrupted by significantly major worldwide incidents (e.g., Brexit). Using Granger causality, we show that there are a substantial number of countries where media attention and public attention are dissimilar by topical interest. Our findings show that the media and public attention toward specific countries are often at odds, indicating that the public within these countries may be ignoring their country-specific news outlets and seeking other online sources to address their media needs and desires.
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability
Venue: arXiv cs.LG 2018
Year: 2018

We study the detailed path-wise behavior of the discrete-time Langevin algorithm for non-convex Empirical Risk Minimization (ERM) through the lens of metastability, adopting some techniques from Berglund and Gentz (2003. For a particular local optimum of the empirical risk, with an arbitrary initialization, we show that, with high probability, at least one of the following two events will occur: (1) the Langevin trajectory ends up somewhere outside the $\varepsilon$-neighborhood of this particular optimum within a short recurrence time; (2) it enters this $\varepsilon$-neighborhood by the recurrence time and stays there until a potentially exponentially long escape time. We call this phenomenon empirical metastability. This two-timescale characterization aligns nicely with the existing literature in the following two senses. First, the effective recurrence time (i.e., number of iterations multiplied by stepsize) is dimension-independent, and resembles the convergence time of continuous-time deterministic Gradient Descent (GD). However unlike GD, the Langevin algorithm does not require strong conditions on local initialization, and has the possibility of eventually visiting all optima. Second, the scaling of the escape time is consistent with the Eyring-Kramers law, which states that the Langevin scheme will eventually visit all local minima, but it will take an exponentially long time to transit among them. We apply this path-wise concentration result in the context of statistical learning to examine local notions of generalization and optimality.
Reducing Initial Cell-search Latency in mmWave Networks
Venue: arXiv cs.IT 2018
Year: 2018

Millimeter-wave (mmWave) networks rely on directional transmissions, in both control plane and data plane, to overcome severe path-loss. Nevertheless, the use of narrow beams complicates the initial cell-search procedure where we lack sufficient information for beamforming. In this paper, we investigate the feasibility of random beamforming for cell-search. We develop a stochastic geometry framework to analyze the performance in terms of failure probability and expected latency of cell-search. Meanwhile, we compare our results with the naive, but heavily used, exhaustive search scheme. Numerical results show that, for a given discovery failure probability, random beamforming can substantially reduce the latency of exhaustive search, especially in dense networks. Our work demonstrates that developing complex cell-discovery algorithms may be unnecessary in dense mmWave networks and thus shed new lights on mmWave system design.