OpenRaiser/Intern-Atlas huggingface dataset:
Towards Predictions of the Image Quality of Experience for Augmented Reality Scenarios
Venue: arXiv cs.MM 2017
Year: 2017

Augmented Reality (AR) devices are commonly head-worn to overlay context-dependent information into the field of view of the device operators. One particular scenario is the overlay of still images, either in a traditional fashion, or as spherical, i.e., immersive, content. For both media types, we evaluate the interplay of user ratings as Quality of Experience (QoE) with (i) the non-referential BRISQUE objective image quality metric and (ii) human subject dry electrode EEG signals gathered with a commercial device. Additionally, we employ basic machine learning approaches to assess the possibility of QoE predictions based on rudimentary subject data. Corroborating prior research for the overall scenario, we find strong correlations for both approaches with user ratings as Mean Opinion Scores, which we consider as QoE metric. In prediction scenarios based on data subsets, we find good performance for the objective metric as well as the EEG-based approach. While the objective metric can yield high QoE prediction accuracies overall, it is limited i its application for individual subjects. The subject-based EEG approach, on the other hand, enables good predictability of the QoE for both media types, but with better performance for regular content. Our results can be employed in practical scenarios by content and network service providers to optimize the user experience in augmented reality scenarios.
The x86isa Books: Features, Usage, and Future Plans
Venue: arXiv cs.PL 2017
Year: 2017

The x86isa library, incorporated in the ACL2 community books project, provides a formal model of the x86 instruction-set architecture and supports reasoning about x86 machine-code programs. However, analyzing x86 programs can be daunting -- even for those familiar with program verification, in part due to the complexity of the x86 ISA. Furthermore, the x86isa library is a large framework, and using and/or contributing to it may not seem straightforward. We present some typical ways of working with the x86isa library, and describe some of its salient features that can make the analysis of x86 machine-code programs less arduous. We also discuss some capabilities that are currently missing from these books -- we hope that this will encourage the community to get involved in this project.
Proof Reduction of Fair Stuttering Refinement of Asynchronous Systems and Applications
Venue: arXiv cs.LO 2017
Year: 2017

We present a series of definitions and theorems demonstrating how to reduce the requirements for proving system refinements ensuring containment of fair stuttering runs. A primary result of the work is the ability to reduce the requisite proofs on runs of a system of interacting state machines to a set of definitions and checks on single steps of a small number of state machines corresponding to the intuitive notions of freedom from starvation and deadlock. We further refine the definitions to afford an efficient explicit-state checking procedure in certain finite state cases. We demonstrate the proof reduction on versions of the Bakery Algorithm.
A Content-based Centrality Metric for Collaborative Caching in Information-Centric Fogs
Venue: arXiv cs.NI 2017
Year: 2017

Information-Centric Fog Computing enables a multitude of nodes near the end-users to provide storage, communication, and computing, rather than in the cloud. In a fog network, nodes connect with each other directly to get content locally whenever possible. As the topology of the network directly influences the nodes' connectivity, there has been some work to compute the graph centrality of each node within that network topology. The centrality is then used to distinguish nodes in the fog network, or to prioritize some nodes over others to participate in the caching fog. We argue that, for an Information-Centric Fog Computing approach, graph centrality is not an appropriate metric. Indeed, a node with low connectivity that caches a lot of content may provide a very valuable role in the network. To capture this, we introduce acontent-based centrality (CBC) metric which takes into account how well a node is connected to the content the network is delivering, rather than to the other nodes in the network. To illustrate the validity of considering content-based centrality, we use this new metric for a collaborative caching algorithm. We compare the performance of the proposed collaborative caching with typical centrality based, non-centrality based, and non-collaborative caching mechanisms. Our simulation implements CBC on three instances of large scale realistic network topology comprising 2,896 nodes with three content replication levels. Results shows that CBC outperforms benchmark caching schemes and yields a roughly 3x improvement for the average cache hit rate.
Going Wider: Recurrent Neural Network With Parallel Cells
Venue: arXiv cs.CL 2017
Year: 2017

Recurrent Neural Network (RNN) has been widely applied for sequence modeling. In RNN, the hidden states at current step are full connected to those at previous step, thus the influence from less related features at previous step may potentially decrease model's learning ability. We propose a simple technique called parallel cells (PCs) to enhance the learning ability of Recurrent Neural Network (RNN). In each layer, we run multiple small RNN cells rather than one single large cell. In this paper, we evaluate PCs on 2 tasks. On language modeling task on PTB (Penn Tree Bank), our model outperforms state of art models by decreasing perplexity from 78.6 to 75.3. On Chinese-English translation task, our model increases BLEU score for 0.39 points than baseline model.
Rotation Averaging and Strong Duality
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally. We show how to circumvent this difficulty through the use of Lagrangian duality. While such an approach is well-known it is normally not guaranteed to provide a tight relaxation. Based on spectral graph theory, we analytically prove that in many cases there is no duality gap unless the noise levels are severe. This allows us to obtain certifiably global solutions to a class of important non-convex problems in polynomial time. We also propose an efficient, scalable algorithm that out-performs general purpose numerical solvers and is able to handle the large problem instances commonly occurring in structure from motion settings. The potential of this proposed method is demonstrated on a number of different problems, consisting of both synthetic and real-world data.
Representing ($q$--)hypergeometric products and mixed versions in difference rings
Venue: arXiv cs.SC 2017
Year: 2017

In recent years, Karr's difference field theory has been extended to the so-called $RΠΣ$-extensions in which one can represent not only indefinite nested sums and products that can be expressed by transcendental ring extensions, but one can also handle algebraic products of the form $α^n$ where $α$ is a root of unity. In this article we supplement this summation theory substantially by the following building block. We provide new algorithms that represent a finite number of hypergeometric or mixed $(q_1,...,q_e)$-multibasic hypergeometric products in such a difference ring. This new insight provides a complete summation machinery that enables one to formulate such products and indefinite nested sums defined over such products in $RΠΣ$-extensions fully automatically. As a side-product, one obtains compactified expressions where the products are algebraically independent among each other, and one can solve the zero-recognition problem for such products.
Testing Core Membership in Public Goods Economies
Venue: arXiv cs.GT 2017
Year: 2017

This paper develops a recent line of economic theory seeking to understand public goods economies using methods of topological analysis. Our first main result is a very clean characterization of the economy's core (the standard solution concept in public goods). Specifically, we prove that a point is in the core iff it is Pareto efficient, individually rational, and the set of points it dominates is path connected. While this structural theorem has a few interesting implications in economic theory, the main focus of the second part of this paper is on a particular algorithmic application that demonstrates its utility. Since the 1960s, economists have looked for an efficient computational process that decides whether or not a given point is in the core. All known algorithms so far run in exponential time (except in some artificially restricted settings). By heavily exploiting our new structure, we propose a new algorithm for testing core membership whose computational bottleneck is the solution of $O(n)$ convex optimization problems on the utility function governing the economy. It is fairly natural to assume that convex optimization should be feasible, as it is needed even for very basic economic computational tasks such as testing Pareto efficiency. Nevertheless, even without this assumption, our work implies for the first time that core membership can be efficiently tested on (e.g.) utility functions that admit "nice" analytic expressions, or that appropriately defined $\varepsilon$-approximate versions of the problem are tractable (by using modern black-box $\varepsilon$-approximate convex optimization algorithms).
Fourth-order Tensors with Multidimensional Discrete Transforms
Venue: arXiv cs.NA 2017
Year: 2017

The big data era is swamping areas including data analysis, machine/deep learning, signal processing, statistics, scientific computing, and cloud computing. The multidimensional feature and huge volume of big data put urgent requirements to the development of multilinear modeling tools and efficient algorithms. In this paper, we build a novel multilinear tensor space that supports useful algorithms such as SVD and QR, while generalizing the matrix space to fourth-order tensors was believed to be challenging. Specifically, given any multidimensional discrete transform, we show that fourth-order tensors are bilinear operators on a space of matrices. First, we take a transform-based approach to construct a new tensor space by defining a new multiplication operation and tensor products, and accordingly the analogous concepts: identity, inverse, transpose, linear combinations, and orthogonality. Secondly, we define the $\mathcal{L}$-SVD for fourth-order tensors and present an efficient algorithm, where the tensor case requires a stronger condition for unique decomposition than the matrix case. Thirdly, we define the tensor $\mathcal{L}$-QR decomposition and propose a Householder QR algorithm to avoid the catastrophic cancellation problem associated with the conventional Gram-Schmidt process. Finally, we validate our schemes on video compression and one-shot face recognition. For video compression, compared with the existing tSVD, the proposed $\mathcal{L}$-SVD achieves $3\sim 10$dB gains in RSE, while the running time is reduced by about $50\%$ and $87.5\%$, respectively. For one-shot face recognition, the recognition rate is increased by about $10\% \sim 20\%$.
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
Venue: arXiv cs.CV 2017
Year: 2017

We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. Our method combines a new convolutional neural network (CNN) based pose regressor with kinematic skeleton fitting. Our novel fully-convolutional pose formulation regresses 2D and 3D joint positions jointly in real time and does not require tightly cropped input frames. A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton. This makes our approach the first monocular RGB method usable in real-time applications such as 3D character control---thus far, the only monocular methods for such applications employed specialized RGB-D cameras. Our method's accuracy is quantitatively on par with the best offline 3D monocular RGB pose estimation methods. Our results are qualitatively comparable to, and sometimes better than, results from monocular RGB-D approaches, such as the Kinect. However, we show that our approach is more broadly applicable than RGB-D solutions, i.e. it works for outdoor scenes, community videos, and low quality commodity RGB cameras.
Combinatorial Auctions Do Need Modest Interaction
Venue: arXiv cs.GT 2017
Year: 2017

We study the necessity of interaction for obtaining efficient allocations in subadditive combinatorial auctions. This problem was originally introduced by Dobzinski, Nisan, and Oren (STOC'14) as the following simple market scenario: $m$ items are to be allocated among $n$ bidders in a distributed setting where bidders valuations are private and hence communication is needed to obtain an efficient allocation. The communication happens in rounds: in each round, each bidder, simultaneously with others, broadcasts a message to all parties involved and the central planner computes an allocation solely based on the communicated messages. Dobzinski et.al. showed that no non-interactive ($1$-round) protocol with polynomial communication (in the number of items and bidders) can achieve approximation ratio better than $Ω(m^{{1}/{4}})$, while for any $r \geq 1$, there exists $r$-round protocols that achieve $\widetilde{O}(r \cdot m^{{1}/{r+1}})$ approximation with polynomial communication; in particular, $O(\log{m})$ rounds of interaction suffice to obtain an (almost) efficient allocation. A natural question at this point is to identify the "right" level of interaction (i.e., number of rounds) necessary to obtain an efficient allocation. In this paper, we resolve this question by providing an almost tight round-approximation tradeoff for this problem: we show that for any $r \geq 1$, any $r$-round protocol that uses polynomial communication can only approximate the social welfare up to a factor of $Ω(\frac{1}{r} \cdot m^{{1}/{2r+1}})$. This in particular implies that $Ω(\frac{\log{m}}{\log\log{m}})$ rounds of interaction are necessary for obtaining any efficient allocation in these markets. Our work builds on the recent multi-party round-elimination technique of Alon, Nisan, Raz, and Weinstein (FOCS'15) and settles an open question posed by Dobzinski et.al. and Alon et. al.
Cross-Layer Optimization of Wireless Links under Reliability and Energy Constraints
Venue: arXiv cs.IT 2017
Year: 2017

The vision of connecting billions of battery operated devices to be used for diverse emerging applications calls for a wireless communication system that can support stringent reliability and latency requirements. Both reliability and energy efficiency are critical for many of these applications that involve communication with short packets which undermine the coding gain achievable from large packets. In this paper, we study a cross-layer approach to optimize the performance of low-power wireless links. At first, we derive a simple and accurate packet error rate (PER) expression for uncoded schemes in block fading channels based on a new proposition that shows that the waterfall threshold in the PER upper bound in Nakagami-m fading channels is tightly approximated by the m-th moment of an asymptotic distribution of PER in AWGN channel. The proposed PER approximation establishes an explicit connection between the physical and link layers parameters, and the packet error rate. We exploit this connection for cross-layer design and optimization of communication links. To this end, we propose a semi-analytic framework to jointly optimize signal-to-noise ratio (SNR) and modulation order at physical layer, and the packet length and number of retransmissions at link layer with respect to distance under the prescribed delay and reliability constraints.
Adaptive Pressure Control for Use in Variable-Thrust Rocket Development
Venue: arXiv cs.SY 2017
Year: 2017

The precise control of gas generator pressure is important to obtain variable thrust in ducted rockets. A delay resistant closed loop reference model adaptive control is proposed in this paper to address this problem. The proposed controller combines delay compensation and adaptation with improved transient response. The controller is successfully implemented using an industrial grade cold air test setup, which is a milestone towards obtaining a fully developed throttleable rocket gas generator controller. Simulation and experimental comparisons with alternative adaptive approaches and a fixed controller demonstrate improved performance and effective handling of time delays and uncertainties. A step by step design methodology, covering robustfying schemes, selection of adaptation rates and initial controller parameters, is also provided to facilitate implementations.
A National Research Agenda for Intelligent Infrastructure
Venue: arXiv cs.CY 2017
Year: 2017

Our infrastructure touches the day-to-day life of each of our fellow citizens, and its capabilities, integrity and sustainability are crucial to the overall competitiveness and prosperity of our country. Unfortunately, the current state of U.S. infrastructure is not good: the American Society of Civil Engineers' latest report on America's infrastructure ranked it at a D+ -- in need of $3.9 trillion in new investments. This dire situation constrains the growth of our economy, threatens our quality of life, and puts our global leadership at risk. The ASCE report called out three actions that need to be taken to address our infrastructure problem: 1) investment and planning in the system; 2) bold leadership by elected officials at the local and federal state; and 3) planning sustainability and resiliency in our infrastructure. While our immediate infrastructure needs are critical, it would be shortsighted to simply replicate more of what we have today. By doing so, we miss the opportunity to create Intelligent Infrastructure that will provide the foundation for increased safety and resilience, improved efficiencies and civic services, and broader economic opportunities and job growth. Indeed, our challenge is to proactively engage the declining, incumbent national infrastructure system and not merely repair it, but to enhance it; to create an internationally competitive cyber-physical system that provides an immediate opportunity for better services for citizens and that acts as a platform for a 21st century, high-tech economy and beyond.
Intelligent Infrastructure for Smart Agriculture: An Integrated Food, Energy and Water System
Venue: arXiv cs.CY 2017
Year: 2017

Agriculture provides economic opportunity through innovation; helps rural America to thrive; promotes agricultural production that better nourishes Americans; and aims to preserve natural resources through healthy private working lands, conservation, improved watersheds, and restored forests. From agricultural production to food supply, agriculture supports rural and urban economies across the U.S. It accounts for 10% of U.S. jobs and is currently creating new jobs in the growing field of data-driven farming. However, U.S. global competitiveness associated with food and nutrition security is at risk because of accelerated investments by many other countries in agriculture, food, energy, and resource management. To ensure U.S. global competitiveness and long-term food security, it is imperative that we build sustainable physical and cyber infrastructures to enable self-managing and sustainable farming. Such infrastructures should enable next generation precision-farms by harnessing modern and emerging technologies such as small satellites, broadband Internet, tele-operation, augmented reality, advanced data analytics, sensors, and robotics.
Machine Comprehension by Text-to-Text Neural Question Generation
Venue: arXiv cs.CL 2017
Year: 2017

We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question generation as a means to improve the performance of question answering systems. Our model is trained and evaluated on the recent question-answering dataset SQuAD.
A Systematic Approach for Exploring Tradeoffs in Predictive HVAC Control Systems for Buildings
Venue: arXiv cs.SY 2017
Year: 2017

Heating, Ventilation, and Cooling (HVAC) systems are often the most significant contributor to the energy usage, and the operational cost, of large office buildings. Therefore, to understand the various factors affecting the energy usage, and to optimize the operational efficiency of building HVAC systems, energy analysts and architects often create simulations (e.g., EnergyPlus or DOE-2), of buildings prior to construction or renovation to determine energy savings and quantify the Return-on-Investment (ROI). While useful, these simulations usually use static HVAC control strategies such as lowering room temperature at night, or reactive control based on simulated room occupancy. Recently, advances have been made in HVAC control algorithms that predict room occupancy. However, these algorithms depend on costly sensor installations and the tradeoffs between predictive accuracy, energy savings, comfort and expenses are not well understood. Current simulation frameworks do not support easy analysis of these tradeoffs. Our contribution is a simulation framework that can be used to explore this design space by generating objective estimates of the energy savings and occupant comfort for different levels of HVAC prediction and control performance. We validate our framework on a real-world occupancy dataset spanning 6 months for 235 rooms in a large university office building. Using the gold standard of energy use modeling and simulation (Revit and Energy Plus), we compare the energy consumption and occupant comfort in 29 independent simulations that explore our parameter space. Our results highlight a number of potentially useful tradeoffs with respect to energy savings, comfort, and algorithmic performance among predictive, reactive, and static schedules, for a stakeholder of our building.
Steal Your Life Using 5 Cents: Hacking Android Smartphones with NFC Tags
Venue: arXiv cs.CR 2017
Year: 2017

Nowadays privacy in the connected world is a big user's concern. The ubiquity of mobile devices permits billions of users browse the web at anytime, anywhere. Near Field Communication (NFC) appeared as a seamlessly and simply communication protocol between devices. Commercial services such as Android Pay, and Apple Pay offer contactless payment methods that are spreading in more and more scenarios. However, we take risks while using NFC on Android devices, we can be hacked, and our privacy can be affected. In this paper we study the current vulnerabilities in the NFC-Android ecosystem. We conduct a series of experiments and we expose that with NFC and Android devices are vulnerable to URL/URI spoofing, Bank/social network information hacking, and user's device tracking via fingerprint and geo-location. It is important for the community to understand the problem and come up solution that can tackle these issues and inform the users about privacy awareness and risks on using these contactless services.