OpenRaiser/Intern-Atlas huggingface dataset:
Generic 3D Convolutional Fusion for image restoration
Venue: arXiv cs.CV 2016
Year: 2016

Also recently, exciting strides forward have been made in the area of image restoration, particularly for image denoising and single image super-resolution. Deep learning techniques contributed to this significantly. The top methods differ in their formulations and assumptions, so even if their average performance may be similar, some work better on certain image types and image regions than others. This complementarity motivated us to propose a novel 3D convolutional fusion (3DCF) method. Unlike other methods adapted to different tasks, our method uses the exact same convolutional network architecture to address both image denois- ing and single image super-resolution. As a result, our 3DCF method achieves substantial improvements (0.1dB-0.4dB PSNR) over the state-of-the-art methods that it fuses, and this on standard benchmarks for both tasks. At the same time, the method still is computationally efficient.
Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction
Venue: arXiv cs.CL 2016
Year: 2016

This paper presents a computational model of the processing of dynamic spatial relations occurring in an embodied robotic interaction setup. A complete system is introduced that allows autonomous robots to produce and interpret dynamic spatial phrases (in English) given an environment of moving objects. The model unites two separate research strands: computational cognitive semantics and on commonsense spatial representation and reasoning. The model for the first time demonstrates an integration of these different strands.
Random graph models for dynamic networks
Venue: arXiv cs.SI 2016
Year: 2016

We propose generalizations of a number of standard network models, including the classic random graph, the configuration model, and the stochastic block model, to the case of time-varying networks. We assume that the presence and absence of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. In addition to computing equilibrium properties of these models, we demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data. This allows us, for instance, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate our methods with a selection of applications, both to computer-generated test networks and real-world examples.
Generic Feature Learning for Wireless Capsule Endoscopy Analysis
Venue: arXiv cs.CV 2016
Year: 2016

The interpretation and analysis of the wireless capsule endoscopy recording is a complex task which requires sophisticated computer aided decision (CAD) systems in order to help physicians with the video screening and, finally, with the diagnosis. Most of the CAD systems in the capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, new CAD system has to be designed from scratch. This characteristic makes the design of new CAD systems a very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which avoids the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed by using state of the art hand-crafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase).
Learning Null Space Projections in Operational Space Formulation
Venue: arXiv cs.LG 2016
Year: 2016

In recent years, a number of tools have become available that recover the underlying control policy from constrained movements. However, few have explicitly considered learning the constraints of the motion and ways to cope with unknown environment. In this paper, we consider learning the null space projection matrix of a kinematically constrained system in the absence of any prior knowledge either on the underlying policy, the geometry, or dimensionality of the constraints. Our evaluations have demonstrated the effectiveness of the proposed approach on problems of differing dimensionality, and with different degrees of non-linearity.
Unrelated Machine Scheduling of Jobs with Uniform Smith Ratios
Venue: arXiv cs.DS 2016
Year: 2016

We consider the classic problem of scheduling jobs on unrelated machines so as to minimize the weighted sum of completion times. Recently, for a small constant $\varepsilon >0 $, Bansal et al. gave a $(3/2-\varepsilon)$-approximation algorithm improving upon the natural barrier of $3/2$ which follows from independent randomized rounding. In simplified terms, their result is obtained by an enhancement of independent randomized rounding via strong negative correlation properties. In this work, we take a different approach and propose to use the same elegant rounding scheme for the weighted completion time objective as devised by Shmoys and Tardos for optimizing a linear function subject to makespan constraints. Our main result is a $1.21$-approximation algorithm for the natural special case where the weight of a job is proportional to its processing time (specifically, all jobs have the same Smith ratio), which expresses the notion that each unit of work has the same weight. In addition, as a direct consequence of the rounding, our algorithm also achieves a bi-criteria $2$-approximation for the makespan objective. Our technical contribution is a tight analysis of the expected cost of the solution compared to the one given by the Configuration-LP relaxation - we reduce this task to that of understanding certain worst-case instances which are simple to analyze.
Emotion-Based Crowd Representation for Abnormality Detection
Venue: arXiv cs.CV 2016
Year: 2016

In crowd behavior understanding, a model of crowd behavior need to be trained using the information extracted from video sequences. Since there is no ground-truth available in crowd datasets except the crowd behavior labels, most of the methods proposed so far are just based on low-level visual features. However, there is a huge semantic gap between low-level motion/appearance features and high-level concept of crowd behaviors. In this paper we propose an attribute-based strategy to alleviate this problem. While similar strategies have been recently adopted for object and action recognition, as far as we know, we are the first showing that the crowd emotions can be used as attributes for crowd behavior understanding. The main idea is to train a set of emotion-based classifiers, which can subsequently be used to represent the crowd motion. For this purpose, we collect a big dataset of video clips and provide them with both annotations of "crowd behaviors" and "crowd emotions". We show the results of the proposed method on our dataset, which demonstrate that the crowd emotions enable the construction of more descriptive models for crowd behaviors. We aim at publishing the dataset with the article, to be used as a benchmark for the communities.
Context-based Pseudonym Changing Scheme for Vehicular Adhoc Networks
Venue: arXiv cs.CR 2016
Year: 2016

Vehicular adhoc networks allow vehicles to share their information for safety and traffic efficiency. However, sharing information may threaten the driver privacy because it includes spatiotemporal information and is broadcast publicly and periodically. In this paper, we propose a context-adaptive pseudonym changing scheme which lets a vehicle decide autonomously when to change its pseudonym and how long it should remain silent to ensure unlinkability. This scheme adapts dynamically based on the density of the surrounding traffic and the user privacy preferences. We employ a multi-target tracking algorithm to measure privacy in terms of traceability in realistic vehicle traces. We use Monte Carlo analysis to estimate the quality of service (QoS) of a forward collision warning application when vehicles apply this scheme. According to the experimental results, the proposed scheme provides a better compromise between traceability and QoS than a random silent period scheme.
A Model of Pathways to Artificial Superintelligence Catastrophe for Risk and Decision Analysis
Venue: arXiv cs.AI 2016
Year: 2016

An artificial superintelligence (ASI) is artificial intelligence that is significantly more intelligent than humans in all respects. While ASI does not currently exist, some scholars propose that it could be created sometime in the future, and furthermore that its creation could cause a severe global catastrophe, possibly even resulting in human extinction. Given the high stakes, it is important to analyze ASI risk and factor the risk into decisions related to ASI research and development. This paper presents a graphical model of major pathways to ASI catastrophe, focusing on ASI created via recursive self-improvement. The model uses the established risk and decision analysis modeling paradigms of fault trees and influence diagrams in order to depict combinations of events and conditions that could lead to AI catastrophe, as well as intervention options that could decrease risks. The events and conditions include select aspects of the ASI itself as well as the human process of ASI research, development, and management. Model structure is derived from published literature on ASI risk. The model offers a foundation for rigorous quantitative evaluation and decision making on the long-term risk of ASI catastrophe.
New security notions and feasibility results for authentication of quantum data
Venue: arXiv cs.CR 2016
Year: 2016

We give a new class of security definitions for authentication in the quantum setting. These definitions capture and strengthen existing definitions of security against quantum adversaries for both classical message authentication codes (MACs) and well as full quantum state authentication schemes. The main feature of our definitions is that they precisely characterize the effective behavior of any adversary when the authentication protocol accepts, including correlations with the key. Our definitions readily yield a host of desirable properties and interesting consequences; for example, our security definition for full quantum state authentication implies that the entire secret key can be re-used if the authentication protocol succeeds. Next, we present several protocols satisfying our security definitions. We show that the classical Wegman-Carter authentication scheme with 3-universal hashing is secure against superposition attacks, as well as adversaries with quantum side information. We then present conceptually simple constructions of full quantum state authentication. Finally, we prove a lifting theorem which shows that, as long as a protocol can securely authenticate the maximally entangled state, it can securely authenticate any state, even those that are entangled with the adversary. Thus, this shows that protocols satisfying a fairly weak form of authentication security automatically satisfy a stronger notion of security (in particular, the definition of Dupuis, et al (2012)).
Energy-Efficient Real-Time Scheduling for Two-Type Heterogeneous Multiprocessors
Venue: arXiv cs.DC 2016
Year: 2016

We propose three novel mathematical optimization formulations that solve the same two-type heterogeneous multiprocessor scheduling problem for a real-time taskset with hard constraints. Our formulations are based on a global scheduling scheme and a fluid model. The first formulation is a mixed-integer nonlinear program, since the scheduling problem is intuitively considered as an assignment problem. However, by changing the scheduling problem to first determine a task workload partition and then to find the execution order of all tasks, the computation time can be significantly reduced. Specifically, the workload partitioning problem can be formulated as a continuous nonlinear program for a system with continuous operating frequency, and as a continuous linear program for a practical system with a discrete speed level set. The task ordering problem can be solved by an algorithm with a complexity that is linear in the total number of tasks. The work is evaluated against existing global energy/feasibility optimal workload allocation formulations. The results illustrate that our algorithms are both feasibility optimal and energy optimal for both implicit and constrained deadline tasksets. Specifically, our algorithm can achieve up to 40% energy saving for some simulated tasksets with constrained deadlines. The benefit of our formulation compared with existing work is that our algorithms can solve a more general class of scheduling problems due to incorporating a scheduling dynamic model in the formulations and allowing for a time-varying speed profile. Moreover, our algorithms can be applied to both online and offline scheduling schemes.
A Step towards Advanced Metering for the Smart Grid: A Survey of Energy Monitors
Venue: arXiv cs.OH 2016
Year: 2016

The smart grid initiative has encouraged utility companies worldwide to rollout new and smarter versions of energy meters. Before an extensive rollout, which is both labor-intensive and incurs high capital costs, consumers need to be incentivized to reap the long-term benefits of smart grid. Off-the-shelf energy monitors can provide consumers with an insight of such potential benefits. Since energy monitors are owned by the consumer, the consumer has greater control over data which significantly reduces privacy and data confidentiality concerns. We evaluate several existing energy monitors using an online technical survey and online product literature. For consumers, the use of different off-the-shelf energy monitors can help demonstrate the potential gains of smart grid. Our survey indicates a trend towards incorporation of state-of-the-art capabilities, like appliance level monitoring through load disaggregation in energy monitors, which can encourage effective consumer participation. Multiple sensor types and ratings allow some monitors to operate in various configurations and environments.
Towards Controllability of Wireless Network Quality using Mobile Robotic Routers
Venue: arXiv cs.RO 2016
Year: 2016

We consider a problem of robotic router placement and mobility control with the objective of formation and maintenance of an optimal communication network between a set of transmitter-receiver pairs. In this scenario, the communication path between any transmitter-receiver pair contains a predetermined set of mobile robotic routers nodes. The goal of this work is to design an algorithm to optimize the positions of the robotic nodes to improve the overall performance of the network. We define the optimization metric to be the minimum of the Signal to Interference plus Noise Ratios (SINR) over all the links. In this manuscript, we propose two optimization algorithms to solve this problem in a centralized and a decentralized manner, respectively.We also demonstrate the performances of both algorithms based on a set of simulation experiments.
Facing ADAS validation complexity with usage oriented testing
Venue: arXiv cs.CY 2016
Year: 2016

Validating Advanced Driver Assistance Systems (ADAS) is a strategic issue, since such systems are becoming increasingly widespread in the automotive field. ADAS bring extra comfort to drivers, and this has become a selling point. But these functions, while useful, must not affect the general safety of the vehicle which is the manufacturer's responsibility. A significant number of current ADAS are based on vision systems, and applications such as obstacle detection and detection of pedestrians have become essential components of functions such as automatic emergency braking. These systems that preserve and protect road users take on even more importance with the arrival of the new Euro NCAP protocols. Therefore the robustness and reliability of ADAS functions cannot be neglected and car manufacturers need to have tools to ensure that the ADAS functions running on their vehicles operate with the utmost safety. Furthermore, the complexity of these systems in conjunction with the nearly infinite number of parameter combinations related to the usage profile of functions based on image sensors push us to think about testing optimization methods and tool standards to support the design and validation phases of ADAS systems. The resources required for the validation using current methods make them actually less and less adapted to new active safety features, which induce very strong dependability requirements. Today, to test the camera-based ADAS, test vehicles are equipped with these systems and are performing long hours of driving that can last for years. These tests are used to validate the use of the function and to verify its response to the requirements described in the specifications without considering the functional safety standard ISO26262.
Beyond Movie Recommendations: Solving the Continuous Cold Start Problem in E-commerceRecommendations
Venue: arXiv cs.IR 2016
Year: 2016

Many e-commerce websites use recommender systems or personalized rankers to personalize search results based on their previous interactions. However, a large fraction of users has no prior inter-actions, making it impossible to use collaborative filtering or rely on user history for personalization. Even the most active users mayvisit only a few times a year and may have volatile needs or different personas, making their personal history a sparse and noisy signal at best. This paper investigates how, when we cannot rely on the user history, the large scale availability of other user interactions still allows us to build meaningful profiles from the contextual data and whether such contextual profiles are useful to customize the ranking, exemplified by data from a major online travel agentBooking.com.Our main findings are threefold: First, we characterize the Continuous Cold Start Problem(CoCoS) from the viewpoint of typical e-commerce applications. Second, as explicit situational con-text is not available in typical real world applications, implicit cues from transaction logs used at scale can capture essential features of situational context. Third, contextual user profiles can be created offline, resulting in a set of smaller models compared to a single huge non-contextual model, making contextual ranking available with negligible CPU and memory footprint. Finally we conclude that, in an online A/B test on live users, our contextual ranker in-creased user engagement substantially over a non-contextual base-line, with click-through-rate (CTR) increased by 20%. This clearly demonstrates the value of contextual user profiles in a real world application.
Approximation and Parameterized Complexity of Minimax Approval Voting
Venue: arXiv cs.DS 2016
Year: 2016

We present three results on the complexity of Minimax Approval Voting. First, we study Minimax Approval Voting parameterized by the Hamming distance $d$ from the solution to the votes. We show Minimax Approval Voting admits no algorithm running in time $\mathcal{O}^\star(2^{o(d\log d)})$, unless the Exponential Time Hypothesis (ETH) fails. This means that the $\mathcal{O}^\star(d^{2d})$ algorithm of Misra et al. [AAMAS 2015] is essentially optimal. Motivated by this, we then show a parameterized approximation scheme, running in time $\mathcal{O}^\star(\left({3}/ε\right)^{2d})$, which is essentially tight assuming ETH. Finally, we get a new polynomial-time randomized approximation scheme for Minimax Approval Voting, which runs in time $n^{\mathcal{O}(1/ε^2 \cdot \log(1/ε))} \cdot \mathrm{poly}(m)$, almost matching the running time of the fastest known PTAS for Closest String due to Ma and Sun [SIAM J. Comp. 2009].
Synthetic Language Generation and Model Validation in BEAST2
Venue: arXiv cs.CL 2016
Year: 2016

Generating synthetic languages aids in the testing and validation of future computational linguistic models and methods. This thesis extends the BEAST2 phylogenetic framework to add linguistic sequence generation under multiple models. The new plugin is then used to test the effects of the phenomena of word borrowing on the inference process under two widely used phylolinguistic models.
How2Sketch: Generating Easy-To-Follow Tutorials for Sketching 3D Objects
Venue: arXiv cs.GR 2016
Year: 2016

Accurately drawing 3D objects is difficult for untrained individuals, as it requires an understanding of perspective and its effects on geometry and proportions. Step-by-step tutorials break the complex task of sketching an entire object down into easy-to-follow steps that even a novice can follow. However, creating such tutorials requires expert knowledge and is a time-consuming task. As a result, the availability of tutorials for a given object or viewpoint is limited. How2Sketch addresses this problem by automatically generating easy-to-follow tutorials for arbitrary 3D objects. Given a segmented 3D model and a camera viewpoint,it computes a sequence of steps for constructing a drawing scaffold comprised of geometric primitives, which helps the user draw the final contours in correct perspective and proportion. To make the drawing scaffold easy to construct, the algorithm solves for an ordering among the scaffolding primitives and explicitly makes small geometric modifications to the size and location of the object parts to simplify relative positioning. Technically, we formulate this scaffold construction as a single selection problem that simultaneously solves for the ordering and geometric changes of the primitives. We demonstrate our algorithm for generating tutorials on a variety of man-made objects and evaluate how easily the tutorials can be followed with a user study.
LWIP and Wi-Fi Boost Link Management
Venue: arXiv cs.NI 2016
Year: 2016

3GPP LWIP Release 13 technology and its prestandard version Wi-Fi Boost have recently emerged as an efficient LTE and Wi-Fi integration at the IP layer, allowing uplink on LTE and downlink on Wi-Fi. This solves all the contention problems of Wi-Fi and allows an optimum usage of the unlicensed band for downlink. In this paper, we present a new feature of Wi-Fi Boost, its radio link management, which allows to steer the downlink traffic between both LTE and Wi-Fi upon congestion detection in an intelligent manner. This customised congestion detection algorithm is based on IP probing, and can work with any Wi-Fi access point. Simulation results in a typical enterprise scenario show that LWIP R13 and Wi-Fi Boost can enhance network performance up to 5x and 6x over LTE-only, and 4x and 5x over Wi-Fi only networks, respectively, and that the the proposed radio link management can further improve Wi-Fi Boost performance over LWIP R13 up to 19 %. Based on the promising results, this paper suggests to enhance LWIP R13 user feedback in future LTE releases.
Assisting Drivers During Overtaking Using Car-2-Car Communication and Multi-Agent Systems
Venue: arXiv cs.AI 2016
Year: 2016

A warning system for assisting drivers during overtaking maneuvers is proposed. The system relies on Car-2-Car communication technologies and multi-agent systems. A protocol for safety overtaking is proposed based on ACL communicative acts. The mathematical model for safety overtaking used Kalman filter to minimize localization error.