OpenRaiser/Intern-Atlas huggingface dataset:
MAP: Microblogging Assisted Profiling of TV Shows
Venue: arXiv cs.IR 2015
Year: 2015

Online microblogging services that have been increasingly used by people to share and exchange information, have emerged as a promising way to profiling multimedia contents, in a sense to provide users a socialized abstraction and understanding of these contents. In this paper, we propose a microblogging profiling framework, to provide a social demonstration of TV shows. Challenges for this study lie in two folds: First, TV shows are generally offline, i.e., most of them are not originally from the Internet, and we need to create a connection between these TV shows with online microblogging services; Second, contents in a microblogging service are extremely noisy for video profiling, and we need to strategically retrieve the most related information for the TV show profiling.To address these challenges, we propose a MAP, a microblogging-assisted profiling framework, with contributions as follows: i) We propose a joint user and content retrieval scheme, which uses information about both actors and topics of a TV show to retrieve related microblogs; ii) We propose a social-aware profiling strategy, which profiles a video according to not only its content, but also the social relationship of its microblogging users and its propagation in the social network; iii) We present some interesting analysis, based on our framework to profile real-world TV shows.
Mathematical Modeling of Insurance Mechanisms for E-commerce Systems
Venue: arXiv cs.MA 2015
Year: 2015

Electronic commerce (a.k.a. E-commerce) systems such as eBay and Taobao of Alibaba are becoming increasingly popular. Having an effective reputation system is critical to this type of internet service because it can assist buyers to evaluate the trustworthiness of sellers, and it can also improve the revenue for reputable sellers and E-commerce operators. We formulate a stochastic model to analyze an eBay-like reputation system and propose four measures to quantify its effectiveness: (1) new seller ramp up time, (2) new seller drop out probability, (3) long term profit gains for sellers, and (4) average per seller transaction gains for the E-commerce operator. Through our analysis, we identify key factors which influence these four measures. We propose a new insurance mechanism which consists of an insurance protocol and a transaction mechanism to improve the above four measures. We show that our insurance mechanism can reduce the ramp up time by around 87.2%, and guarantee new sellers ramp up before the deadline $T_w$ with a high probability (close to 1.0). It also increases the long term profit gains and average per seller transaction gains by at least 95.3%.
Off-Policy Reward Shaping with Ensembles
Venue: arXiv cs.AI 2015
Year: 2015

Potential-based reward shaping (PBRS) is an effective and popular technique to speed up reinforcement learning by leveraging domain knowledge. While PBRS is proven to always preserve optimal policies, its effect on learning speed is determined by the quality of its potential function, which, in turn, depends on both the underlying heuristic and the scale. Knowing which heuristic will prove effective requires testing the options beforehand, and determining the appropriate scale requires tuning, both of which introduce additional sample complexity. We formulate a PBRS framework that reduces learning speed, but does not incur extra sample complexity. For this, we propose to simultaneously learn an ensemble of policies, shaped w.r.t. many heuristics and on a range of scales. The target policy is then obtained by voting. The ensemble needs to be able to efficiently and reliably learn off-policy: requirements fulfilled by the recent Horde architecture, which we take as our basis. We demonstrate empirically that (1) our ensemble policy outperforms both the base policy, and its single-heuristic components, and (2) an ensemble over a general range of scales performs at least as well as one with optimally tuned components.
Structural characterizations of the navigational expressiveness of relation algebras on a tree
Venue: arXiv cs.DB 2015
Year: 2015

Given a document D in the form of an unordered node-labeled tree, we study the expressiveness on D of various basic fragments of XPath, the core navigational language on XML documents. Working from the perspective of these languages as fragments of Tarski's relation algebra, we give characterizations, in terms of the structure of D, for when a binary relation on its nodes is definable by an expression in these algebras. Since each pair of nodes in such a relation represents a unique path in D, our results therefore capture the sets of paths in D definable in each of the fragments. We refer to this perspective on language semantics as the "global view." In contrast with this global view, there is also a "local view" where one is interested in the nodes to which one can navigate starting from a particular node in the document. In this view, we characterize when a set of nodes in D can be defined as the result of applying an expression to a given node of D. All these definability results, both in the global and the local view, are obtained by using a robust two-step methodology, which consists of first characterizing when two nodes cannot be distinguished by an expression in the respective fragments of XPath, and then bootstrapping these characterizations to the desired results.
Construction and impromptu repair of an MST in a distributed network with o(m) communication
Venue: arXiv cs.DC 2015
Year: 2015

In the CONGEST model, a communications network is an undirected graph whose $n$ nodes are processors and whose $m$ edges are the communications links between processors. At any given time step, a message of size $O(\log n)$ may be sent by each node to each of its neighbors. We show for the synchronous model: If all nodes start in the same round, and each node knows its ID and the ID's of its neighbors, or in the case of MST, the distinct weights of its incident edges and knows $n$, then there are Monte Carlo algorithms which succeed w.h.p. to determine a minimum spanning forest (MST) and a spanning forest (ST) using $O(n \log^2 n/\log\log n)$ messages for MST and $O(n \log n )$ messages for ST, resp. These results contradict the "folk theorem" noted in Awerbuch, et.al., JACM 1990 that the distributed construction of a broadcast tree requires $Ω(m)$ messages. This lower bound has been shown there and in other papers for some CONGEST models; our protocol demonstrates the limits of these models. A dynamic distributed network is one which undergoes online edge insertions or deletions. We also show how to repair an MST or ST in a dynamic network with asynchronous communication. An edge deletion can be processed in $O(n\log n /\log \log n)$ expected messages in the MST, and $O(n)$ expected messages for the ST problem, while an edge insertion uses $O(n)$ messages in the worst case. We call this "impromptu" updating as we assume that between processing of edge updates there is no preprocessing or storage of additional information. Previous algorithms for this problem that use an amortized $o(m)$ messages per update require substantial preprocessing and additional local storage between updates.
Large-Scale Deep Learning on the YFCC100M Dataset
Venue: arXiv cs.LG 2015
Year: 2015

We present a work-in-progress snapshot of learning with a 15 billion parameter deep learning network on HPC architectures applied to the largest publicly available natural image and video dataset released to-date. Recent advancements in unsupervised deep neural networks suggest that scaling up such networks in both model and training dataset size can yield significant improvements in the learning of concepts at the highest layers. We train our three-layer deep neural network on the Yahoo! Flickr Creative Commons 100M dataset. The dataset comprises approximately 99.2 million images and 800,000 user-created videos from Yahoo's Flickr image and video sharing platform. Training of our network takes eight days on 98 GPU nodes at the High Performance Computing Center at Lawrence Livermore National Laboratory. Encouraging preliminary results and future research directions are presented and discussed.
Weakening the Isolation Assumption of Tamper-proof Hardware Tokens
Venue: arXiv cs.CR 2015
Year: 2015

Recent results have shown the usefulness of tamper-proof hardware tokens as a setup assumption for building UC-secure two-party computation protocols, thus providing broad security guarantees and allowing the use of such protocols as buildings blocks in the modular design of complex cryptography protocols. All these works have in common that they assume the tokens to be completely isolated from their creator, but this is a strong assumption. In this work we investigate the feasibility of cryptographic protocols in the setting where the isolation of the hardware token is weakened. We consider two cases: (1) the token can relay messages to its creator, or (2) the creator can send messages to the token after it is sent to the receiver. We provide a detailed characterization for both settings, presenting both impossibilities and information-theoretically secure solutions.
Locally-Oriented Programming: A Simple Programming Model for Stencil-Based Computations on Multi-Level Distributed Memory Architectures
Venue: arXiv cs.PL 2015
Year: 2015

Emerging hybrid accelerator architectures for high performance computing are often suited for the use of a data-parallel programming model. Unfortunately, programmers of these architectures face a steep learning curve that frequently requires learning a new language (e.g., OpenCL). Furthermore, the distributed (and frequently multi-level) nature of the memory organization of clusters of these machines provides an additional level of complexity. This paper presents preliminary work examining how programming with a local orientation can be employed to provide simpler access to accelerator architectures. A locally-oriented programming model is especially useful for the solution of algorithms requiring the application of a stencil or convolution kernel. In this programming model, a programmer codes the algorithm by modifying only a single array element (called the local element), but has read-only access to a small sub-array surrounding the local element. We demonstrate how a locally-oriented programming model can be adopted as a language extension using source-to-source program transformations.
On Synchronous and Asynchronous Monitor Instrumentation for Actor-based systems
Venue: arXiv cs.LO 2015
Year: 2015

We study the impact of synchronous and asynchronous monitoring instrumentation on runtime overheads in the context of a runtime verification framework for actor-based systems. We show that, in such a context, asynchronous monitoring incurs substantially lower overhead costs. We also show how, for certain properties that require synchronous monitoring, a hybrid approach can be used that ensures timely violation detections for the important events while, at the same time, incurring lower overhead costs that are closer to those of an asynchronous instrumentation.
Lower Bounds for Cover-Free Families
Venue: arXiv cs.DM 2015
Year: 2015

Let ${\cal F}$ be a set of blocks of a $t$-set $X$. $(X,{\cal F})$ is called $(w,r)$-cover-free family ($(w,r)-$CFF) provided that, the intersection of any $w$ blocks in ${\cal F}$ is not contained in the union of any other $r$ blocks in ${\cal F}$. We give new asymptotic lower bounds for the number of minimum points $t$ in a $(w,r)$-CFF when $w\le r=|{\cal F}|^ε$ for some constant $ε\ge 1/2$.
Simulation of Color Blindness and a Proposal for Using Google Glass as Color-correcting Tool
Venue: arXiv cs.HC 2015
Year: 2015

The human visual color response is driven by specialized cells called cones, which exist in three types, viz. R, G, and B. Software is developed to simulate how color images are displayed for different types of color blindness. Specified the default color deficiency associated with a user, it generates a preview of the rainbow (in the visible range, from red to violet) and shows up, side by side with a colorful image provided as input, the display correspondent colorblind. The idea is to provide an image processing after image acquisition to enable a better perception ofcolors by the color blind. Examples of pseudo-correction are shown for the case of Protanopia (red blindness). The system is adapted into a screen of an i-pad or a cellphone in which the colorblind observe the camera, the image processed with color detail previously imperceptible by his naked eye. As prospecting, wearable computer glasses could be manufactured to provide a corrected image playback. The approach can also provide augmented reality for human vision by adding the UV or IR responses as a new feature of Google Glass.
On the Entropy Computation of Large Complex Gaussian Mixture Distributions
Venue: arXiv cs.IT 2015
Year: 2015

The entropy computation of Gaussian mixture distributions with a large number of components has a prohibitive computational complexity. In this paper, we propose a novel approach exploiting the sphere decoding concept to bound and approximate such entropy terms with reduced complexity and good accuracy. Moreover, we propose an SNR region based enhancement of the approximation method to reduce the complexity even further. Using Monte-Carlo simulations, the proposed methods are numerically demonstrated for the computation of the mutual information including the entropy term of various channels with finite constellation modulations such as binary and quadratic amplitude modulation (QAM) inputs for communication applications.
Variable and value elimination in binary constraint satisfaction via forbidden patterns
Venue: arXiv cs.CC 2015
Year: 2015

Variable or value elimination in a constraint satisfaction problem (CSP) can be used in preprocessing or during search to reduce search space size. A variable elimination rule (value elimination rule) allows the polynomial-time identification of certain variables (domain elements) whose elimination, without the introduction of extra compensatory constraints, does not affect the satisfiability of an instance. We show that there are essentially just four variable elimination rules and three value elimination rules defined by forbidding generic sub-instances, known as irreducible existential patterns, in arc-consistent CSP instances. One of the variable elimination rules is the already-known Broken Triangle Property, whereas the other three are novel. The three value elimination rules can all be seen as strict generalisations of neighbourhood substitution.
A Constant Factor Approximation for Orthogonal Order Preserving Layout Adjustment
Venue: arXiv cs.CG 2015
Year: 2015

Given an initial placement of a set of rectangles in the plane, we consider the problem of finding a disjoint placement of the rectangles that minimizes the area of the bounding box and preserves the orthogonal order i.e.\ maintains the sorted ordering of the rectangle centers along both $x$-axis and $y$-axis with respect to the initial placement. This problem is known as Layout Adjustment for Disjoint Rectangles(LADR). It was known that LADR is $\mathbb{NP}$-hard, but only heuristics were known for it. We show that a certain decision version of LADR is $\mathbb{APX}$-hard, and give a constant factor approximation for LADR.
Decision Maker using Coupled Incompressible-Fluid Cylinders
Venue: arXiv cs.AI 2015
Year: 2015

The multi-armed bandit problem (MBP) is the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards by referring to past experiences. Inspired by fluctuated movements of a rigid body in a tug-of-war game, we formulated a unique search algorithm that we call the `tug-of-war (TOW) dynamics' for solving the MBP efficiently. The cognitive medium access, which refers to multi-user channel allocations in cognitive radio, can be interpreted as the competitive multi-armed bandit problem (CMBP); the problem is to determine the optimal strategy for allocating channels to users which yields maximum total rewards gained by all users. Here we show that it is possible to construct a physical device for solving the CMBP, which we call the `TOW Bombe', by exploiting the TOW dynamics existed in coupled incompressible-fluid cylinders. This analog computing device achieves the `socially-maximum' resource allocation that maximizes the total rewards in cognitive medium access without paying a huge computational cost that grows exponentially as a function of the problem size.
Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we propose and study the effectiveness of customer engagement plans that clearly specify the amount of intervention in customer's load settings by the grid operator for peak load reduction. We suggest two different types of plans, including Constant Deviation Plans (CDPs) and Proportional Deviation Plans (PDPs). We define an adjustable reference temperature for both CDPs and PDPs to limit the output temperature of each thermostat load and to control the number of devices eligible to participate in Demand Response Program (DRP). We model thermostat loads as power throttling devices and design algorithms to evaluate the impact of power throttling states and plan parameters on peak load reduction. Based on the simulation results, we recommend PDPs to the customers of a residential community with variable thermostat set point preferences, while CDPs are suitable for customers with similar thermostat set point preferences. If thermostat loads have multiple power throttling states, customer engagement plans with less temperature deviations from thermostat set points are recommended. Contrary to classical ON/OFF control, higher temperature deviations are required to achieve similar amount of peak load reduction. Several other interesting tradeoffs and useful guidelines for designing mutually beneficial incentives for both the grid operator and customers can also be identified.
Communication Efficient Algorithms for Top-k Selection Problems
Venue: arXiv cs.DS 2015
Year: 2015

We present scalable parallel algorithms with sublinear per-processor communication volume and low latency for several fundamental problems related to finding the most relevant elements in a set, for various notions of relevance: We begin with the classical selection problem with unsorted input. We present generalizations with locally sorted inputs, dynamic content (bulk-parallel priority queues), and multiple criteria. Then we move on to finding frequent objects and top-k sum aggregation. Since it is unavoidable that the output of these algorithms might be unevenly distributed over the processors, we also explain how to redistribute this data with minimal communication.
A local strategy for cleaning expanding cellular domains by simple robots
Venue: arXiv cs.DS 2015
Year: 2015

We present a strategy SEP for finite state machines tasked with cleaning a cellular environment in which a contamination spreads. Initially, the contaminated area is of height $h$ and width $w$. It may be bounded by four monotonic chains, and contain rectangular holes. The robot does not know the initial contamination, sensing only the eight cells in its neighborhood. It moves from cell to cell, $d$ times faster than the contamination spreads, and is able to clean its current cell. A speed of $d<\sqrt{2}(h+w)$ is in general not sufficient to contain the contamination. Our strategy SEP succeeds if $d \geq 3(h+w)$ holds. It ensures that the contaminated cells stay connected. Greedy strategies violating this principle need speed at least $d \geq 4(h+w)$; all bounds are up to small additive constants.
Finding Structural Information of RF Power Amplifiers using an Orthogonal Non-Parametric Kernel Smoothing Estimator
Venue: arXiv cs.IT 2015
Year: 2015

A non-parametric technique for modeling the behavior of power amplifiers is presented. The proposed technique relies on the principles of density estimation using the kernel method and is suited for use in power amplifier modeling. The proposed methodology transforms the input domain into an orthogonal memory domain. In this domain, non-parametric static functions are discovered using the kernel estimator. These orthogonal, non-parametric functions can be fitted with any desired mathematical structure, thus facilitating its implementation. Furthermore, due to the orthogonality, the non-parametric functions can be analyzed and discarded individually, which simplifies pruning basis functions and provides a tradeoff between complexity and performance. The results show that the methodology can be employed to model power amplifiers, therein yielding error performance similar to state-of-the-art parametric models. Furthermore, a parameter-efficient model structure with 6 coefficients was derived for a Doherty power amplifier, therein significantly reducing the deployment's computational complexity. Finally, the methodology can also be well exploited in digital linearization techniques.
Decentralized Trusted Timestamping using the Crypto Currency Bitcoin
Venue: arXiv cs.CR 2015
Year: 2015

Trusted timestamping is a process for proving that certain information existed at a given point in time. This paper presents a trusted timestamping concept and its implementation in form of a web-based service that uses the decentralized Bitcoin block chain to store anonymous, tamper-proof timestamps for digital content. The service allows users to hash files, such as text, photos or videos, and store the created hashes in the Bitcoin block chain. Users can then retrieve and verify the timestamps that have been committed to the block chain. The non-commercial service enables anyone, e.g., researchers, authors, journalists, students, or artists, to prove that they were in possession of certain information at a given point in time. Common use cases include proving that a contract has been signed, a photo taken, a video recorded, or a task completed prior to a certain date. All procedures maintain complete privacy of the user's data.
Inducing Approximately Optimal Flow Using Truthful Mediators
Venue: arXiv cs.GT 2015
Year: 2015

We revisit a classic coordination problem from the perspective of mechanism design: how can we coordinate a social welfare maximizing flow in a network congestion game with selfish players? The classical approach, which computes tolls as a function of known demands, fails when the demands are unknown to the mechanism designer, and naively eliciting them does not necessarily yield a truthful mechanism. Instead, we introduce a weak mediator that can provide suggested routes to players and set tolls as a function of reported demands. However, players can choose to ignore or misreport their type to this mediator. Using techniques from differential privacy, we show how to design a weak mediator such that it is an asymptotic ex-post Nash equilibrium for all players to truthfully report their types to the mediator and faithfully follow its suggestion, and that when they do, they end up playing a nearly optimal flow. Notably, our solution works in settings of incomplete information even in the absence of a prior distribution on player types. Along the way, we develop new techniques for privately solving convex programs which may be of independent interest.
On Projection Based Operators in Lp space for Exact Similarity Search
Venue: arXiv cs.IR 2015
Year: 2015

We investigate exact indexing for high dimensional Lp norms based on the 1-Lipschitz property and projection operators. The orthogonal projection that satisfies the 1-Lipschitz property for the Lp norm is described. The adaptive projection defined by the first principal component is introduced.