OpenRaiser/Intern-Atlas huggingface dataset:
Structured Compressive Sensing Based Superimposed Pilot Design in Downlink Large-Scale MIMO Systems
Venue: arXiv cs.IT 2015
Year: 2015

Large-scale multiple-input multiple-output (MIMO) with high spectrum and energy efficiency is a very promising key technology for future 5G wireless communications. For large-scale MIMO systems, accurate channel state information (CSI) acquisition is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink. Unlike the conventional orthogonal pilots whose pilot overhead prohibitively increases with the number of transmit antennas, we propose a spectrum-efficient superimposed pilot design for downlink large-scale MIMO scenarios, where frequency-domain pilots of different transmit antennas occupy the completely same subcarriers in the freqency domain. Meanwhile, spatial-temporal common sparsity of large-scale MIMO channels motivates us to exploit the emerging theory of structured compressive sensing (CS) for reliable MIMO channel estimation, which is realized by the proposed structured subspace pursuit (SSP) algorithm to simultaneously recover multiple channels with low pilot overhead. Simulation results demonstrate that the proposed scheme performs well and can approach the performance bound.
Capacity Bounds for Discrete-Time, Amplitude-Constrained, Additive White Gaussian Noise Channels
Venue: arXiv cs.IT 2015
Year: 2015

The capacity-achieving input distribution of the discrete-time, additive white Gaussian noise (AWGN) channel with an amplitude constraint is discrete and seems difficult to characterize explicitly. A dual capacity expression is used to derive analytic capacity upper bounds for scalar and vector AWGN channels. The scalar bound improves on McKellips' bound and is within 0.1 bits of capacity for all signal-to-noise ratios (SNRs). The two-dimensional bound is within 0.15 bits of capacity provably up to 4.5 dB, and numerical evidence suggests a similar gap for all SNRs.
Multiagent Cooperation and Competition with Deep Reinforcement Learning
Venue: arXiv cs.AI 2015
Year: 2015

Multiagent systems appear in most social, economical, and political situations. In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments and investigate how two agents controlled by independent Deep Q-Networks interact in the classic videogame Pong. By manipulating the classical rewarding scheme of Pong we demonstrate how competitive and collaborative behaviors emerge. Competitive agents learn to play and score efficiently. Agents trained under collaborative rewarding schemes find an optimal strategy to keep the ball in the game as long as possible. We also describe the progression from competitive to collaborative behavior. The present work demonstrates that Deep Q-Networks can become a practical tool for studying the decentralized learning of multiagent systems living in highly complex environments.
Maintenance Support in Open Source Software Projects
Venue: arXiv cs.SE 2015
Year: 2015

Easy and mostly free access to the internet has resulted in the growing use of open source software (OSS). However, it is a common perception that closed proprietary software is still superior in areas such as software maintenance and management. The research model of this study establishes a relationship between maintenance issues (such as user requests and error handling) and support provided by open source software through project forums, mailing lists and trackers. To conduct this research, we have used a dataset consisting of 120 open source software projects, covering a wide range of categories. The results of the study show that project forums and mailing lists play a significant role in addressing user requests in open source software. However according to the empirical investigation, it has been explored that trackers are used as an effective medium for error reporting as well as user requests.
Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking
Venue: arXiv cs.CV 2015
Year: 2015

Multi-object tracking remains challenging due to frequent occurrence of occlusions and outliers. In order to handle this problem, we propose an Approximation-Shrink Scheme for sequential optimization. This scheme is realized by introducing an Ambiguity-Clearness Graph to avoid conflicts and maintain sequence independent, as well as a sliding window optimization framework to constrain the size of state space and guarantee convergence. Based on this window-wise framework, the states of targets are clustered in a self-organizing manner. Moreover, we show that the traditional online and batch tracking methods can be embraced by the window-wise framework. Experiments indicate that with only a small window, the optimization performance can be much better than online methods and approach to batch methods.
Robotic Search & Rescue via Online Multi-task Reinforcement Learning
Venue: arXiv cs.AI 2015
Year: 2015

Reinforcement learning (RL) is a general and well-known method that a robot can use to learn an optimal control policy to solve a particular task. We would like to build a versatile robot that can learn multiple tasks, but using RL for each of them would be prohibitively expensive in terms of both time and wear-and-tear on the robot. To remedy this problem, we use the Policy Gradient Efficient Lifelong Learning Algorithm (PG-ELLA), an online multi-task RL algorithm that enables the robot to efficiently learn multiple consecutive tasks by sharing knowledge between these tasks to accelerate learning and improve performance. We implemented and evaluated three RL methods--Q-learning, policy gradient RL, and PG-ELLA--on a ground robot whose task is to find a target object in an environment under different surface conditions. In this paper, we discuss our implementations as well as present an empirical analysis of their learning performance.
Long Concept Query on Conceptual Taxonomies
Venue: arXiv cs.IR 2015
Year: 2015

This paper studies the problem of finding typical entities when the concept is given as a query. For a short concept such as university, this is a well-studied problem of retrieving knowledge base such as Microsoft's Probase and Google's isA database pre-materializing entities found for the concept in Hearst patterns of the web corpus. However, we find most real-life queries are long concept queries (LCQs), such as top American private university, which cannot and should not be pre-materialized. Our goal is an online construction of entity retrieval for LCQs. We argue a naive baseline of rewriting LCQs into an intersection of an expanded set of composing short concepts leads to highly precise results with extremely low recall. Instead, we propose to augment the concept list, by identifying related concepts of the query concept. However, as such increase of recall often invites false positives and decreases precision in return, we propose the following two techniques: First, we identify concepts with different relatedness to generate linear orderings and pairwise ordering constraints. Second, we rank entities trying to avoid conflicts with these constraints, to prune out lowly ranked one (likely false positives). With these novel techniques, our approach significantly outperforms state-of-the-arts.
The Multiverse Loss for Robust Transfer Learning
Venue: arXiv cs.CV 2015
Year: 2015

Deep learning techniques are renowned for supporting effective transfer learning. However, as we demonstrate, the transferred representations support only a few modes of separation and much of its dimensionality is unutilized. In this work, we suggest to learn, in the source domain, multiple orthogonal classifiers. We prove that this leads to a reduced rank representation, which, however, supports more discriminative directions. Interestingly, the softmax probabilities produced by the multiple classifiers are likely to be identical. Experimental results, on CIFAR-100 and LFW, further demonstrate the effectiveness of our method.
Analysis Traceability and Provenance for HEP
Venue: arXiv cs.DB 2015
Year: 2015

This paper presents the use of the CRISTAL software in the N4U project. CRISTAL was used to create a set of provenance aware analysis tools for the Neuroscience domain. This paper advocates that the approach taken in N4U to build the analysis suite is sufficiently generic to be able to be applied to the HEP domain. A mapping to the PROV model for provenance interoperability is also presented and how this can be applied to the HEP domain for the interoperability of HEP analyses.
Using Cloud-Aware Provenance to Reproduce Scientific Workflow Execution on Cloud
Venue: arXiv cs.DB 2015
Year: 2015

Provenance has been thought of a mechanism to verify a workflow and to provide workflow reproducibility. This provenance of scientific workflows has been effectively carried out in Grid based scientific workflow systems. However, recent adoption of Cloud-based scientific workflows present an opportunity to investigate the suitability of existing approaches or propose new approaches to collect provenance information from the Cloud and to utilize it for workflow repeatability in the Cloud infrastructure. This paper presents a novel approach that can assist in mitigating this challenge. This approach can collect Cloud infrastructure information from an outside Cloud client along with workflow provenance and can establish a mapping between them. This mapping is later used to re-provision resources on the Cloud for workflow execution. The reproducibility of the workflow execution is performed by: (a) capturing the Cloud infrastructure information (virtual machine configuration) along with the workflow provenance, (b) re-provisioning the similar resources on the Cloud and re-executing the workflow on them and (c) by comparing the outputs of workflows. The evaluation of the prototype suggests that the proposed approach is feasible and can be investigated further. Moreover, there is no reference reproducibility model exists in literature that can provide guidelines to achieve this goal in Cloud. This paper also attempts to present a model that is used in the proposed design to achieve workflow reproducibility in the Cloud environment.
Position paper: a general framework for applying machine learning techniques in operating room
Venue: arXiv cs.CY 2015
Year: 2015

In this position paper we describe a general framework for applying machine learning and pattern recognition techniques in healthcare. In particular, we are interested in providing an automated tool for monitoring and incrementing the level of awareness in the operating room and for identifying human errors which occur during the laparoscopy surgical operation. The framework that we present is divided in three different layers: each layer implements algorithms which have an increasing level of complexity and which perform functionality with an higher degree of abstraction. In the first layer, raw data collected from sensors in the operating room during surgical operation, they are pre-processed and aggregated. The results of this initial phase are transferred to a second layer, which implements pattern recognition techniques and extract relevant features from the data. Finally, in the last layer, expert systems are employed to take high level decisions, which represent the final output of the system.
Discontinuous integral control for mechanical systems
Venue: arXiv cs.SY 2015
Year: 2015

For mechanical systems we present a controller able to track an unknown smooth signal, converging in finite time and by means of a continuous control signal. The control scheme is insensitive against unknown perturbations with bounded derivative. The controller consists of a non locally Lipschitz state feedback control law, and a discontinuous integral controller, that is able to estimate the unknown perturbation and to compensate for it. To complete an output feedback control a continuous observer for the velocity is added. It is shown that the closed loop consisting of state feedback, state observer and discontinuous integral controller has an equilibrium point that is globally, finite time stable, despite of perturbations with bounded derivative. The proof is based on a new smooth Lyapunov function.
Sentiment Analysis on YouTube: A Brief Survey
Venue: arXiv cs.SI 2015
Year: 2015

Sentiment analysis or opinion mining is the field of study related to analyze opinions, sentiments, evaluations, attitudes, and emotions of users which they express on social media and other online resources. The revolution of social media sites has also attracted the users towards video sharing sites, such as YouTube. The online users express their opinions or sentiments on the videos that they watch on such sites. This paper presents a brief survey of techniques to analyze opinions posted by users about a particular video.
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2015
Year: 2015

We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification problem is characterised by large intra-class variations and small inter-class variations. To overcome these problems our proposed MixDCNN system partitions images into K subsets of similar images and learns an expert DCNN for each subset. The output from each of the K DCNNs is combined to form a single classification decision. In contrast to previous techniques, we provide a formulation to perform joint end-to-end training of the K DCNNs simultaneously. Extensive experiments, on three datasets using two network structures (AlexNet and GoogLeNet), show that the proposed MixDCNN system consistently outperforms other methods. It provides a relative improvement of 12.7% and achieves state-of-the-art results on two datasets.
Efficient Deterministic Single Round Document Exchange for Edit Distance
Venue: arXiv cs.DS 2015
Year: 2015

Suppose that we have two parties that possess each a binary string. Suppose that the length of the first string (document) is $n$ and that the two strings (documents) have edit distance (minimal number of deletes, inserts and substitutions needed to transform one string into the other) at most $k$. The problem we want to solve is to devise an efficient protocol in which the first party sends a single message that allows the second party to guess the first party's string. In this paper we show an efficient deterministic protocol for this problem. The protocol runs in time $O(n\cdot \mathtt{polylog}(n))$ and has message size $O(k^2+k\log^2n)$ bits. To the best of our knowledge, ours is the first efficient deterministic protocol for this problem, if efficiency is measured in both the message size and the running time. As an immediate application of our new protocol, we show a new error correcting code that is efficient even for large numbers of (adversarial) edit errors.
A neurodynamic framework for local community extraction in networks
Venue: arXiv cs.SI 2015
Year: 2015

To understand the structure and organization of a large-scale social, biological or technological network, it can be helpful to describe and extract local communities or modules of the network. In this article, we develop a neurodynamic framework to describe the local communities which correspond to the stable states of a neuro-system built based on the network. The quantitative criteria to describe the neurodynamic system can cover a large range of objective functions. The resolution limit of these functions enable us to propose a generic criterion to explore multi-resolution local communities. We explain the advantages of this framework and illustrate them by testing on a number of model and real-world networks.
Enhancing Infrastructure Security in Real Estate
Venue: arXiv cs.CY 2015
Year: 2015

As a result of the increased dependency on obtaining information and connecting each computer together for ease of access or communication, organizations risk being attacked and losing private information through breaches or insecure business activities. To help protect organizations and their assets, companies need to develop a strong understanding of the risks imposed on their company and the security solutions designed to prevent or minimize vulnerabilities. To reduce the impact threats have on a network, organizations need to: design a defense layer system that provides multiple instances of protection to prevent unauthorized access to core information, implement a strong network hardware/intrusion prevention system, and create all-inclusive network or security policies that detail user rules and company rights. In order to enhance the overall security of a basic infrastructure, this paper will provide a detailed look into gathering the organizational requirements, designing and implementing a secure physical network layout, and selecting the standards needed to prevent unauthorized access
Inferring Interpersonal Relations in Narrative Summaries
Venue: arXiv cs.CL 2015
Year: 2015

Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. We formulate the problem as a joint structured prediction for each narrative, and present a model that combines evidence from linguistic and semantic features, as well as features based on the structure of the social community in the text. We also provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation.
Online Budgeted Repeated Matching
Venue: arXiv cs.DS 2015
Year: 2015

A basic combinatorial online resource allocation problem is considered, where multiple servers have individual capacity constraints, and at each time slot, a set of jobs arrives, that have potentially different weights to different servers. At each time slot, a one-to-one matching has to be found between jobs and servers, subject to individual capacity constraints, in an online manner. The objective is to maximize the aggregate weight of jobs allotted to servers, summed across time slots and servers, subject to individual capacity constraints. This problem generalizes the well known adwords problem, and is also relevant for various other modern applications. A simple greedy algorithm is shown to be 3-competitive, whenever the weight of any edge is at most half of the corresponding server capacity. Moreover, a randomized version of the greedy algorithm is shown to be 6-competitive for the unrestricted edge weights case. For parallel servers with small-weight jobs, we show that a load-balancing algorithm is near-optimal.
Augmenting Phrase Table by Employing Lexicons for Pivot-based SMT
Venue: arXiv cs.CL 2015
Year: 2015

Pivot language is employed as a way to solve the data sparseness problem in machine translation, especially when the data for a particular language pair does not exist. The combination of source-to-pivot and pivot-to-target translation models can induce a new translation model through the pivot language. However, the errors in two models may compound as noise, and still, the combined model may suffer from a serious phrase sparsity problem. In this paper, we directly employ the word lexical model in IBM models as an additional resource to augment pivot phrase table. In addition, we also propose a phrase table pruning method which takes into account both of the source and target phrasal coverage. Experimental result shows that our pruning method significantly outperforms the conventional one, which only considers source side phrasal coverage. Furthermore, by including the entries in the lexicon model, the phrase coverage increased, and we achieved improved results in Chinese-to-Japanese translation using English as pivot language.
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
Venue: arXiv cs.CV 2015
Year: 2015

Fisher Vector classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms for solving image classification problems. However, both are generally considered `black box' predictors as the non-linear transformations involved have so far prevented transparent and interpretable reasoning. Recently, a principled technique, Layer-wise Relevance Propagation (LRP), has been developed in order to better comprehend the inherent structured reasoning of complex nonlinear classification models such as Bag of Feature models or DNNs. In this paper we (1) extend the LRP framework also for Fisher Vector classifiers and then use it as analysis tool to (2) quantify the importance of context for classification, (3) qualitatively compare DNNs against FV classifiers in terms of important image regions and (4) detect potential flaws and biases in data. All experiments are performed on the PASCAL VOC 2007 data set.
Fast and High Quality Highlight Removal from A Single Image
Venue: arXiv cs.CV 2015
Year: 2015

Specular reflection exists widely in photography and causes the recorded color deviating from its true value, so fast and high quality highlight removal from a single nature image is of great importance. In spite of the progress in the past decades in highlight removal, achieving wide applicability to the large diversity of nature scenes is quite challenging. To handle this problem, we propose an analytic solution to highlight removal based on an L2 chromaticity definition and corresponding dichromatic model. Specifically, this paper derives a normalized dichromatic model for the pixels with identical diffuse color: a unit circle equation of projection coefficients in two subspaces that are orthogonal to and parallel with the illumination, respectively. In the former illumination orthogonal subspace, which is specular-free, we can conduct robust clustering with an explicit criterion to determine the cluster number adaptively. In the latter illumination parallel subspace, a property called pure diffuse pixels distribution rule (PDDR) helps map each specular-influenced pixel to its diffuse component. In terms of efficiency, the proposed approach involves few complex calculation, and thus can remove highlight from high resolution images fast. Experiments show that this method is of superior performance in various challenging cases.
Science of Cyber Security as a System of Models and Problems
Venue: arXiv cs.CR 2015
Year: 2015

Terms like "Science of Cyber" or "Cyber Science" have been appearing in literature with growing frequency, and influential organizations initiated research initiatives toward developing such a science even though it is not clearly defined. We propose to define the domain of the science of cyber security by noting the most salient artifact within cyber security -- malicious software -- and defining the domain as comprised of phenomena that involve malicious software (as well as legitimate software and protocols used maliciously) used to compel a computing device or a network of computing devices to perform actions desired by the perpetrator of malicious software (the attacker) and generally contrary to the intent (the policy) of the legitimate owner or operator (the defender) of the computing device(s). We further define the science of cyber security as the study of relations -- preferably expressed as theoretically-grounded models -- between attributes, structures and dynamics of: violations of cyber security policy; the network of computing devices under attack; the defenders' tools and techniques; and the attackers' tools and techniques where malicious software plays the central role. We offer a simple formalism of these key objects within cyber science and systematically derive a classification of primary problem classes within cyber science.