OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Transmission Line Switching under Geomagnetic Disturbances
Venue: arXiv cs.SY 2017
Year: 2017

In recent years, there have been increasing concerns about how geomagnetic disturbances (GMDs) impact electrical power systems. Geomagnetically-induced currents (GICs) can saturate transformers, induce hot spot heating and increase reactive power losses. These effects can potentially cause catastrophic damage to transformers and severely impact the ability of a power system to deliver power. To address this problem, we develop a model of GIC impacts to power systems that includes 1) GIC thermal capacity of transformers as a function of normal Alternating Current (AC) and 2) reactive power losses as a function of GIC. We use this model to derive an optimization problem that protects power systems from GIC impacts through line switching, generator redispatch, and load shedding. We employ state-of-the-art convex relaxations of AC power flow equations to lower bound the objective. We demonstrate the approach on a modified RTS96 system and the UIUC 150-bus system and show that line switching is an effective means to mitigate GIC impacts. We also provide a sensitivity analysis of optimal switching decisions with respect to GMD direction.
Motion Deblurring in the Wild
Venue: arXiv cs.CV 2017
Year: 2017

The task of image deblurring is a very ill-posed problem as both the image and the blur are unknown. Moreover, when pictures are taken in the wild, this task becomes even more challenging due to the blur varying spatially and the occlusions between the object. Due to the complexity of the general image model we propose a novel convolutional network architecture which directly generates the sharp image.This network is built in three stages, and exploits the benefits of pyramid schemes often used in blind deconvolution. One of the main difficulties in training such a network is to design a suitable dataset. While useful data can be obtained by synthetically blurring a collection of images, more realistic data must be collected in the wild. To obtain such data we use a high frame rate video camera and keep one frame as the sharp image and frame average as the corresponding blurred image. We show that this realistic dataset is key in achieving state-of-the-art performance and dealing with occlusions.
Designing a Safe Autonomous Artificial Intelligence Agent based on Human Self-Regulation
Venue: arXiv cs.AI 2017
Year: 2017

There is a growing focus on how to design safe artificial intelligent (AI) agents. As systems become more complex, poorly specified goals or control mechanisms may cause AI agents to engage in unwanted and harmful outcomes. Thus it is necessary to design AI agents that follow initial programming intentions as the program grows in complexity. How to specify these initial intentions has also been an obstacle to designing safe AI agents. Finally, there is a need for the AI agent to have redundant safety mechanisms to ensure that any programming errors do not cascade into major problems. Humans are autonomous intelligent agents that have avoided these problems and the present manuscript argues that by understanding human self-regulation and goal setting, we may be better able to design safe AI agents. Some general principles of human self-regulation are outlined and specific guidance for AI design is given.
Coordinated Autonomous Vehicle Parking for Vehicle-to-Grid Services: Formulation and Distributed Algorithm
Venue: arXiv cs.SY 2017
Year: 2017

Autonomous vehicles (AVs) will revolutionarize ground transport and take a substantial role in the future transportation system. Most AVs are likely to be electric vehicles (EVs) and they can participate in the vehicle-to-grid (V2G) system to support various V2G services. Although it is generally infeasible for EVs to dictate their routes, we can design AV travel plans to fulfill certain system-wide objectives. In this paper, we focus on the AVs looking for parking and study how they can be led to appropriate parking facilities to support V2G services. We formulate the Coordinated Parking Problem (CPP), which can be solved by a standard integer linear program solver but requires long computational time. To make it more practical, we develop a distributed algorithm to address CPP based on dual decomposition. We carry out a series of simulations to evaluate the proposed solution methods. Our results show that the distributed algorithm can produce nearly optimal solutions with substantially less computational time. A coarser time scale can improve computational time but degrade the solution quality resulting in possible infeasible solution. Even with communication loss, the distributed algorithm can still perform well and converge with only little degradation in speed.
High-Assurance Separation Kernels: A Survey on Formal Methods
Venue: arXiv cs.SE 2017
Year: 2017

Separation kernels provide temporal/spatial separation and controlled information flow to their hosted applications. They are introduced to decouple the analysis of applications in partitions from the analysis of the kernel itself. More than 20 implementations of separation kernels have been developed and widely applied in critical domains, e.g., avionics/aerospace, military/defense, and medical devices. Formal methods are mandated by the security/safety certification of separation kernels and have been carried out since this concept emerged. However, this field lacks a survey to systematically study, compare, and analyze related work. On the other hand, high-assurance separation kernels by formal methods still face big challenges. In this paper, an analytical framework is first proposed to clarify the functionalities, implementations, properties and standards, and formal methods application of separation kernels. Based on the proposed analytical framework, a taxonomy is designed according to formal methods application, functionalities, and properties of separation kernels. Research works in the literature are then categorized and overviewed by the taxonomy. In accordance with the analytical framework, a comprehensive analysis and discussion of related work are presented. Finally, four challenges and their possible technical directions for future research are identified, e.g. specification bottleneck, multicore and concurrency, and automation of full formal verification.
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics
Venue: arXiv cs.CV 2017
Year: 2017

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.
Detecting Arbitrary Attacks Using Continuous Secured Side Information in Wireless Networks
Venue: arXiv cs.CR 2017
Year: 2017

This paper focuses on Byzantine attack detection for Gaussian two-hop one-way relay network, where an amplify-and-forward relay may conduct Byzantine attacks by forwarding altered symbols to the destination. For facilitating attack detection, we utilize the openness of wireless medium to make the destination observe some secured signals that are not attacked. Then, a detection scheme is developed for the destination by using its secured observations to statistically check other observations from the relay. On the other hand, notice the Gaussian channel is continuous, which allows the possible Byzantine attacks to be conducted within continuous alphabet(s). The existing work on discrete channel is not applicable for investigating the performance of the proposed scheme. The main contribution of this paper is to prove that if and only if the wireless relay network satisfies a non-manipulable channel condition, the proposed detection scheme achieves asymptotic errorless performance against arbitrary attacks that allow the stochastic distributions of altered symbols to vary arbitrarily and depend on each other. No pre-shared secret or secret transmission is needed for the detection. Furthermore, we also prove that the relay network is non-manipulable as long as all channel coefficients are non-zero, which is not essential restrict for many practical systems.
An anti-incursion algorithm for unknown probabilistic adversaries on connected graphs
Venue: arXiv cs.DM 2017
Year: 2017

A gambler moves on the vertices $1, \ldots, n$ of a graph using the probability distribution $p_{1}, \ldots, p_{n}$. A cop pursues the gambler on the graph, only being able to move between adjacent vertices. What is the expected number of moves that the gambler can make until the cop catches them? Komarov and Winkler proved an upper bound of approximately $1.97n$ for the expected capture time on any connected $n$-vertex graph when the cop does not know the gambler's distribution. We improve this upper bound to approximately $1.95n$ by modifying the cop's pursuit algorithm.
Sharing Means Renting?: An Entire-marketplace Analysis of Airbnb
Venue: arXiv cs.CY 2017
Year: 2017

Airbnb, an online marketplace for accommodations, has experienced a staggering growth accompanied by intense debates and scattered regulations around the world. Current discourses, however, are largely focused on opinions rather than empirical evidences. Here, we aim to bridge this gap by presenting the first large-scale measurement study on Airbnb, using a crawled data set containing 2.3 million listings, 1.3 million hosts, and 19.3 million reviews. We measure several key characteristics at the heart of the ongoing debate and the sharing economy. Among others, we find that Airbnb has reached a global yet heterogeneous coverage. The majority of its listings across many countries are entire homes, suggesting that Airbnb is actually more like a rental marketplace rather than a spare-room sharing platform. Analysis on star-ratings reveals that there is a bias toward positive ratings, amplified by a bias toward using positive words in reviews. The extent of such bias is greater than Yelp reviews, which were already shown to exhibit a positive bias. We investigate a key issue---commercial hosts who own multiple listings on Airbnb---repeatedly discussed in the current debate. We find that their existence is prevalent, they are early-movers towards joining Airbnb, and their listings are disproportionately entire homes and located in the US. Our work advances the current understanding of how Airbnb is being used and may serve as an independent and empirical reference to inform the debate.
Deep Class Aware Denoising
Venue: arXiv cs.CV 2017
Year: 2017

The increasing demand for high image quality in mobile devices brings forth the need for better computational enhancement techniques, and image denoising in particular. At the same time, the images captured by these devices can be categorized into a small set of semantic classes. However simple, this observation has not been exploited in image denoising until now. In this paper, we demonstrate how the reconstruction quality improves when a denoiser is aware of the type of content in the image. To this end, we first propose a new fully convolutional deep neural network architecture which is simple yet powerful as it achieves state-of-the-art performance even without being class-aware. We further show that a significant boost in performance of up to $0.4$ dB PSNR can be achieved by making our network class-aware, namely, by fine-tuning it for images belonging to a specific semantic class. Relying on the hugely successful existing image classifiers, this research advocates for using a class-aware approach in all image enhancement tasks.
Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps
Venue: arXiv cs.CV 2017
Year: 2017

A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced. The proposed approach develops a hyperspectral-appropriate version of the SLIC superpixel segmentation algorithm, leverages map information to guide segmentation, and incorporates the semi-supervised Partial Membership Latent Dirichlet Allocation (sPM-LDA) to obtain a final superpixel segmentation. The proposed method is applied to two real hyperspectral data sets and quantitative cluster validity metrics indicate that the proposed approach outperforms existing hyperspectral superpixel segmentation methods.
Estimation of Graphlet Statistics
Venue: arXiv cs.SI 2017
Year: 2017

Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been severely limited to applications and domains with relatively small graphs. Most previous work has focused on exact algorithms, however, it is often too expensive to compute graphlets exactly in massive networks with billions of edges, and finding an approximate count is usually sufficient for many applications. In this work, we propose an unbiased graphlet estimation framework that is (a) fast with significant speedups compared to the state-of-the-art, (b) parallel with nearly linear-speedups, (c) accurate with <1% relative error, (d) scalable and space-efficient for massive networks with billions of edges, and (e) flexible for a variety of real-world settings, as well as estimating macro and micro-level graphlet statistics (e.g., counts) of both connected and disconnected graphlets. In addition, an adaptive approach is introduced that finds the smallest sample size required to obtain estimates within a given user-defined error bound. On 300 networks from 20 domains, we obtain <1% relative error for all graphlets. This is significantly more accurate than existing methods while using less data. Moreover, it takes a few seconds on billion edge graphs (as opposed to days/weeks). These are by far the largest graphlet computations to date.
A probabilistic framework for the control of systems with discrete states and stochastic excitation
Venue: arXiv cs.SY 2017
Year: 2017

A probabilistic framework is proposed for the optimization of efficient switched control strategies for physical systems dominated by stochastic excitation. In this framework, the equation for the state trajectory is replaced with an equivalent equation for its probability distribution function in the constrained optimization setting. This allows for a large class of control rules to be considered, including hysteresis and a mix of continuous and discrete random variables. The problem of steering atmospheric balloons within a stratified flowfield is a motivating application; the same approach can be extended to a variety of mixed-variable stochastic systems and to new classes of control rules.
CrowdTone: Crowd-powered tone feedback and improvement system for emails
Venue: arXiv cs.HC 2017
Year: 2017

In this paper, we present CrowdTone, a system designed to help people set the appropriate tone in their email communication. CrowdTone utilizes the context and content of an email message to identify and set the appropriate tone through a consensus-building process executed by crowd workers. We evaluated CrowdTone with 22 participants, who provided a total of 29 emails that they had received in the past, and ran them through CrowdTone. Participants and professional writers assessed the quality of improvements finding a substantial increase in the percentage of emails deemed "appropriate" or "very appropriate" - from 25% to more than 90% by recipients, and from 45% to 90% by professional writers. Additionally, the recipients' feedback indicated that more than 90% of the CrowdTone processed emails showed improvement.
Automatic Wikipedia Link Generation Based On Interlanguage Links
Venue: arXiv cs.SI 2017
Year: 2017

This paper presents a new way to increase interconnectivity in small Wikipedias (fewer than a 100,000 articles), by automatically linking articles based on interlanguage links. Many small Wikipedias have many articles with very few links, this is mainly due to the short article length. This makes it difficult to navigate between the articles. In many cases the article does exist for a small Wikipedia, however the article is just missing a link. Due to the fact that Wikipedias are translated in to many languages, it allows us to generate new links for small Wikipedias using the links from a large Wikipedia (more than a 100,000 articles).
Sign Language Recognition Using Temporal Classification
Venue: arXiv cs.CV 2017
Year: 2017

Devices like the Myo armband available in the market today enable us to collect data about the position of a user's hands and fingers over time. We can use these technologies for sign language translation since each sign is roughly a combination of gestures across time. In this work, we utilize a dataset collected by a group at the University of South Wales, which contains parameters, such as hand position, hand rotation, and finger bend, for 95 unique signs. For each input stream representing a sign, we predict which sign class this stream falls into. We begin by implementing baseline SVM and logistic regression models, which perform reasonably well on high quality data. Lower quality data requires a more sophisticated approach, so we explore different methods in temporal classification, including long short term memory architectures and sequential pattern mining methods.
Differential Dynamic Programming for time-delayed systems
Venue: arXiv cs.SY 2017
Year: 2017

Trajectory optimization considers the problem of deciding how to control a dynamical system to move along a trajectory which minimizes some cost function. Differential Dynamic Programming (DDP) is an optimal control method which utilizes a second-order approximation of the problem to find the control. It is fast enough to allow real-time control and has been shown to work well for trajectory optimization in robotic systems. Here we extend classic DDP to systems with multiple time-delays in the state. Being able to find optimal trajectories for time-delayed systems with DDP opens up the possibility to use richer models for system identification and control, including recurrent neural networks with multiple timesteps in the state. We demonstrate the algorithm on a two-tank continuous stirred tank reactor. We also demonstrate the algorithm on a recurrent neural network trained to model an inverted pendulum with position information only.
Group Visual Sentiment Analysis
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we introduce a framework for classifying images according to high-level sentiment. We subdivide the task into three primary problems: emotion classification on faces, human pose estimation, and 3D estimation and clustering of groups of people. We introduce novel algorithms for matching body parts to a common individual and clustering people in images based on physical location and orientation. Our results outperform several baseline approaches.
Rapid, User-Transparent, and Trustworthy Device Pairing for D2D-Enabled Mobile Crowdsourcing
Venue: arXiv cs.NI 2017
Year: 2017

Mobile Crowdsourcing is a promising service paradigm utilizing ubiquitous mobile devices to facilitate largescale crowdsourcing tasks (e.g. urban sensing and collaborative computing). Many applications in this domain require Device-to-Device (D2D) communications between participating devices for interactive operations such as task collaborations and file transmissions. Considering the private participating devices and their opportunistic encountering behaviors, it is highly desired to establish secure and trustworthy D2D connections in a fast and autonomous way, which is vital for implementing practical Mobile Crowdsourcing Systems (MCSs). In this paper, we develop an efficient scheme, Trustworthy Device Pairing (TDP), which achieves user-transparent secure D2D connections and reliable peer device selections for trustworthy D2D communications. Through rigorous analysis, we demonstrate the effectiveness and security intensity of TDP in theory. The performance of TDP is evaluated based on both real-world prototype experiments and extensive trace-driven simulations. Evaluation results verify our theoretical analysis and show that TDP significantly outperforms existing approaches in terms of pairing speed, stability, and security.
Joint Optimization of Power Splitting and Allocation for SWIPT in Interference Alignment Networks
Venue: arXiv cs.IT 2017
Year: 2017

Interference alignment (IA) is a promising solution for interference management in wireless networks. On the other hand, simultaneous wireless information and power transfer (SWIPT) has become an emerging technique. Although some works have been done on IA and SWIPT, these two important areas have traditionally been addressed separately in the literature. In this paper, we propose to use a common framework to jointly study IA and SWIPT. We analyze the performance of SWIPT in IA networks. Specifically, we derive the upper bound of the power that can be harvested in IA networks. In addition, we show that, to improve the performance of wireless power transfer and information transmission, users should be dynamically selected as energy harvesting (EH) or information decoding (ID) terminals. Furthermore, we design two easy-implemented SWIPT-user selection (SWIPT-US) algorithms in IA networks. To optimize the ID and EH performance of SWIPT in IA networks, a power-splitting optimization (PSO) algorithm is proposed when power splitters are available, and its closed-form optimal solutions are derived. Power allocation in the PSO algorithm is also studied to further optimize the performance. Simulation results are presented to show the effectiveness of the proposed algorithms.
An open shop approach in approximating optimal data transmission duration in WDM networks
Venue: arXiv cs.NI 2017
Year: 2017

In the past decade Optical WDM Networks (Wavelength Division Multiplexing) are being used quite often and especially as far as broadband applications are concerned. Message packets transmitted through such networks can be interrupted using time slots in order to maximize network usage and minimize the time required for all messages to reach their destination. However, preempting a packet will result in time cost. The problem of scheduling message packets through such a network is referred to as PBS and is known to be NP-Hard. In this paper we have reduced PBS to Open Shop Scheduling and designed variations of polynomially solvable instances of Open Shop to approximate PBS. We have combined these variations and called the induced algorithm HSA (Hybridic Scheduling Algorithm). We ran experiments to establish the efficiency of HSA and found that in all datasets used it produces schedules very close to the optimal. To further establish HSAs efficiency we ran tests to compare it to SGA, another algorithm which when tested in the past has yielded excellent results.