OpenRaiser/Intern-Atlas huggingface dataset:
Identifying missing dictionary entries with frequency-conserving context models
Venue: arXiv cs.CL 2015
Year: 2015

In an effort to better understand meaning from natural language texts, we explore methods aimed at organizing lexical objects into contexts. A number of these methods for organization fall into a family defined by word ordering. Unlike demographic or spatial partitions of data, these collocation models are of special importance for their universal applicability. While we are interested here in text and have framed our treatment appropriately, our work is potentially applicable to other areas of research (e.g., speech, genomics, and mobility patterns) where one has ordered categorical data, (e.g., sounds, genes, and locations). Our approach focuses on the phrase (whether word or larger) as the primary meaning-bearing lexical unit and object of study. To do so, we employ our previously developed framework for generating word-conserving phrase-frequency data. Upon training our model with the Wiktionary---an extensive, online, collaborative, and open-source dictionary that contains over 100,000 phrasal-definitions---we develop highly effective filters for the identification of meaningful, missing phrase-entries. With our predictions we then engage the editorial community of the Wiktionary and propose short lists of potential missing entries for definition, developing a breakthrough, lexical extraction technique, and expanding our knowledge of the defined English lexicon of phrases.
To NACK or not to NACK? Negative Acknowledgments in Information-Centric Networking
Venue: arXiv cs.NI 2015
Year: 2015

Information-Centric Networking (ICN) is an internetworking paradigm that offers an alternative to the current IP\nobreakdash-based Internet architecture. ICN's most distinguishing feature is its emphasis on information (content) instead of communication endpoints. One important open issue in ICN is whether negative acknowledgments (NACKs) at the network layer are useful for notifying downstream nodes about forwarding failures, or requests for incorrect or non-existent information. In benign settings, NACKs are beneficial for ICN architectures, such as CCNx and NDN, since they flush state in routers and notify consumers. In terms of security, NACKs seem useful as they can help mitigating so-called Interest Flooding attacks. However, as we show in this paper, network-layer NACKs also have some unpleasant security implications. We consider several types of NACKs and discuss their security design requirements and implications. We also demonstrate that providing secure NACKs triggers the threat of producer-bound flooding attacks. Although we discuss some potential countermeasures to these attacks, the main conclusion of this paper is that network-layer NACKs are best avoided, at least for security reasons.
Tradeoff for Heterogeneous Distributed Storage Systems between Storage and Repair Cost
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we consider heterogeneous distributed storage systems (DSSs) having flexible reconstruction degree, where each node in the system has dynamic repair bandwidth and dynamic storage capacity. In particular, a data collector can reconstruct the file at time $t$ using some arbitrary nodes in the system and for a node failure the system can be repaired by some set of arbitrary nodes. Using $min$-$cut$ bound, we investigate the fundamental tradeoff between storage and repair cost for our model of heterogeneous DSS. In particular, the problem is formulated as bi-objective optimization linear programing problem. For an arbitrary DSS, it is shown that the calculated $min$-$cut$ bound is tight.
GOptimaEmbed: A SmartSMS-SQLDatabaseManagementSystem for Low-Cost Microcontrollers
Venue: arXiv cs.DB 2015
Year: 2015

The era of the Internet of things, machine-to-machine and human to machine computing has heralded the development of a modern-day smart industry in which humanoids can co-operate,co-exist and interact seamlessly.Currently, there are many projects in this area of smart communication and thus giving rise to an industry electrified by smart things.In this paper we present a novel smart database management system (dbms),GOptimaEmbed, for intelligent querying of databases in device constrained embedded systems. The system uses genetic algorithms as main search engine and simplifies the query process using stored in-memory model based on an invented device dependent Short-messaging-Structured Query Language SMS SQL schema translator. In addition, querying is done over the air using integrated GSM module in the smart space. The system has been applied to querying a plant database and results were quite satisfactory. Keywords. GOptimaEmbed,smart dbms, genetic algorithms, SMS SQL
Robust Anomaly Detection in Dynamic Networks
Venue: arXiv cs.NI 2015
Year: 2015

We propose two robust methods for anomaly detection in dynamic networks in which the properties of normal traffic are time-varying. We formulate the robust anomaly detection problem as a binary composite hypothesis testing problem and propose two methods: a model-free and a model-based one, leveraging techniques from the theory of large deviations. Both methods require a family of Probability Laws (PLs) that represent normal properties of traffic. We devise a two-step procedure to estimate this family of PLs. We compare the performance of our robust methods and their vanilla counterparts, which assume that normal traffic is stationary, on a network with a diurnal normal pattern and a common anomaly related to data exfiltration. Simulation results show that our robust methods perform better than their vanilla counterparts in dynamic networks.
Fully Connected Deep Structured Networks
Venue: arXiv cs.CV 2015
Year: 2015

Convolutional neural networks with many layers have recently been shown to achieve excellent results on many high-level tasks such as image classification, object detection and more recently also semantic segmentation. Particularly for semantic segmentation, a two-stage procedure is often employed. Hereby, convolutional networks are trained to provide good local pixel-wise features for the second step being traditionally a more global graphical model. In this work we unify this two-stage process into a single joint training algorithm. We demonstrate our method on the semantic image segmentation task and show encouraging results on the challenging PASCAL VOC 2012 dataset.
Design of a visible-light-communication enhanced WiFi system
Venue: arXiv cs.NI 2015
Year: 2015

Visible light communication (VLC) has wide unlicensed bandwidth, enables communication in radio frequency (RF) sensitive environments, realizes energy-efficient data transmission, and has the potential to boost the capacity of wireless access networks through spatial reuse. On the other hand, WiFi provides more coverage than VLC and does not suffer from the likelihood of blockage due to the light of sight (LOS) requirement of VLC. In order to take the advantages of both WiFi and VLC, we propose and implement two heterogeneous systems with Internet access. One is the hybrid WiFi-VLC system, utilizing unidirectional VLC channel as downlink and reserving the WiFi back-channel as uplink. The asymmetric solution resolves the optical uplink challenges and benefits from the full-duplex communication based on VLC. To further enhance the robustness and increase throughput, the other system is presented, in which we aggregate WiFi and VLC in parallel by leveraging the bonding technique in Linux operating system. Online experiment results reveal that the hybrid system outperforms the conventional WiFi for the crowded environments in terms of throughput and web page loading time; and also demonstrate the further improved performance of the aggregated system when considering the blocking duration and the distance between access point and user device.
Of Two Minds, Multiple Addresses, and One History: Characterizing Opinions, Knowledge, and Perceptions of Bitcoin Across Groups
Venue: arXiv cs.CY 2015
Year: 2015

Digital currencies represent a new method for exchange and investment that differs strongly from any other fiat money seen throughout history. A digital currency makes it possible to perform all financial transactions without the intervention of a third party to act as an arbiter of verification; payments can be made between two people with degrees of anonymity, across continents, at any denomination, and without any transaction fees going to a central authority. The most successful example of this is Bitcoin, introduced in 2008, which has experienced a recent boom of popularity, media attention, and investment. With this surge of attention, we became interested in finding out how people both inside and outside the Bitcoin community perceive Bitcoin -- what do they think of it, how do they feel, and how knowledgeable they are. Towards this end, we conducted the first interview study (N = 20) with participants to discuss Bitcoin and other related financial topics. Some of our major findings include: not understanding how Bitcoin works is not a barrier for entry, although non-user participants claim it would be for them and that user participants are in a state of cognitive dissonance concerning the role of governments in the system. Our findings, overall, contribute to knowledge concerning Bitcoin and attitudes towards digital currencies in general.
Digital Cancelation of Self-Interference for Single-Frequency Full-Duplex Relay Stations via Sampled-Data Control
Venue: arXiv cs.SY 2015
Year: 2015

In this article, we propose sampled-data design of digital filters that cancel the continuous-time effect of coupling waves in a single-frequency full-duplex relay station. In this study, we model a relay station as a continuoustime system while conventional researches treat it as a discrete-time system. For a continuous-time model, we propose digital feedback canceler based on the sampled-data H-infinity control theory to cancel coupling waves taking intersample behavior into account. We also propose robust control against unknown multipath interference. Simulation results are shown to illustrate the effectiveness of the proposed method.
Learning Co-Sparse Analysis Operators with Separable Structures
Venue: arXiv cs.LG 2015
Year: 2015

In the co-sparse analysis model a set of filters is applied to a signal out of the signal class of interest yielding sparse filter responses. As such, it may serve as a prior in inverse problems, or for structural analysis of signals that are known to belong to the signal class. The more the model is adapted to the class, the more reliable it is for these purposes. The task of learning such operators for a given class is therefore a crucial problem. In many applications, it is also required that the filter responses are obtained in a timely manner, which can be achieved by filters with a separable structure. Not only can operators of this sort be efficiently used for computing the filter responses, but they also have the advantage that less training samples are required to obtain a reliable estimate of the operator. The first contribution of this work is to give theoretical evidence for this claim by providing an upper bound for the sample complexity of the learning process. The second is a stochastic gradient descent (SGD) method designed to learn an analysis operator with separable structures, which includes a novel and efficient step size selection rule. Numerical experiments are provided that link the sample complexity to the convergence speed of the SGD algorithm.
Deep Learning and the Information Bottleneck Principle
Venue: arXiv cs.LG 2015
Year: 2015

Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual information between the layers and the input and output variables. Using this representation we can calculate the optimal information theoretic limits of the DNN and obtain finite sample generalization bounds. The advantage of getting closer to the theoretical limit is quantifiable both by the generalization bound and by the network's simplicity. We argue that both the optimal architecture, number of layers and features/connections at each layer, are related to the bifurcation points of the information bottleneck tradeoff, namely, relevant compression of the input layer with respect to the output layer. The hierarchical representations at the layered network naturally correspond to the structural phase transitions along the information curve. We believe that this new insight can lead to new optimality bounds and deep learning algorithms.
Modified Dijkstra Algorithm with Invention Hierarchies Applied to a Conic Graph
Venue: arXiv cs.DS 2015
Year: 2015

A modified version of the Dijkstra algorithm using an inventive contraction hierarchy is proposed. The algorithm considers a directed acyclic graph with a conical or semi-circular structure for which a pair of edges is chosen iteratively from multi-sources. The algorithm obtains minimum paths by using a comparison process. The comparison process follows a mathematical construction routine that considers a forward and backward check such that only paths with minimum lengths are selected. In addition, the algorithm automatically invents a new path by computing the absolute edge difference for the minimum edge pair and its succeeding neighbour in O (n) time. The invented path is approximated to the hidden path using a fitness criterion. The proposed algorithm extends the multi-source multi-destination problem to include those paths for which a path mining redirection from multi-sources to multi-destinations is a minimum. The algorithm has been applied to a hospital locator path finding system and the results were quite satisfactory.
Global 6DOF Pose Estimation from Untextured 2D City Models
Venue: arXiv cs.CV 2015
Year: 2015

We propose a method for estimating the 3D pose for the camera of a mobile device in outdoor conditions, using only an untextured 2D model. Previous methods compute only a relative pose using a SLAM algorithm, or require many registered images, which are cumbersome to acquire. By contrast, our method returns an accurate, absolute camera pose in an absolute referential using simple 2D+height maps, which are broadly available, to refine a first estimate of the pose provided by the device's sensors. We show how to first estimate the camera absolute orientation from straight line segments, and then how to estimate the translation by aligning the 2D map with a semantic segmentation of the input image. We demonstrate the robustness and accuracy of our approach on a challenging dataset.
Detecting Incompleteness, Conflicting and Unreachability XACML Policies using Answer Set Programming
Venue: arXiv cs.CR 2015
Year: 2015

Recently, XACML is a popular access control policy language that is used widely in many applications. Policies in XACML are built based on many components over distributed resources. Due to the expressiveness of XACML, it is not trivial for policy administrators to understand the overall effect and consequences of XACML policies they have written. In this paper we show a mechanism and a tool how to analyses big access control policies sets such as (i) incompleteness policies, (ii) conflicting policies, and (iii) unreachable policies. To detect these problems we present a method using Answer Set Programming (ASP) in the context of XACML 3.0.
Rate Selection for Cooperative HARQ-CC Systems over Time-Correlated Nakagami-m Fading Channels
Venue: arXiv cs.IT 2015
Year: 2015

This paper addresses the problem of rate selection for the cooperative hybrid automatic repeat request with chase combination (HARQ-CC) system, where time correlated Nakagami-m fading channels are considered. To deal with this problem, the closed-form cumulative distribution function (CDF) for the combine SNRs through maximal ratio combining (MRC) is first derived as a generalized Fox's $\bar H$ function. By using this result, outage probability and delay-limited throughput (DLT) are derived in closed forms, which then enables the rate selection for maximum DLT. These analytical results are validated via Monte Carlo simulations. The impacts of time correlation and channel fading-order parameter $m$ upon outage probability, DLT and the optimal rate are investigated thoroughly. It is found that the system can achieve more diversity gain from less correlated channels, and the outage probability of cooperative HARQ-CC system decreases with the increase of $m$, and etc. Furthermore, the optimal rate increases with the number of retransmissions, while it decreases with the increase of the channel time correlation.
Simpler, Linear-Time Transitive Orientation via Lexicographic Breadth-First Search
Venue: arXiv cs.DS 2015
Year: 2015

Comparability graphs are the undirected graphs whose edges can be directed so that the resulting directed graph is transitive. They are related to posets and have applications in scheduling theory. This paper considers the problem of finding a transitive orientation of a comparability graph, a requirement for many of its applications. A linear-time algorithm is presented based on an elegant partition refinement scheme developed elsewhere for the problem. The algorithm is intended as a simpler and more practical alternative to the existing lineartime solution, which is commonly understood to be difficult and mainly of theoretical value. It accomplishes this by using Lexicographic Breadth-First Search to achieve the same effect as produced by modular decomposition in the earlier linear-time algorithm.
Reduced Complexity Calculation of LMMSE Filter Coefficients for GFDM
Venue: arXiv cs.IT 2015
Year: 2015

A low-complexity algorithm for calculation of the LMMSE filter coefficients for GFDM in a block-fading multipath environment is derived in this letter. The simplification is based on the block circularity of the involved matrices. The proposal reduces complexity from cubic to squared order. The proposed approach can be generalized to other waveforms with circular pulse shaping.
A Case Based Reasoning Approach for Answer Reranking in Question Answering
Venue: arXiv cs.AI 2015
Year: 2015

In this document I present an approach to answer validation and reranking for question answering (QA) systems. A cased-based reasoning (CBR) system judges answer candidates for questions from annotated answer candidates for earlier questions. The promise of this approach is that user feedback will result in improved answers of the QA system, due to the growing case base. In the paper, I present the adequate structuring of the case base and the appropriate selection of relevant similarity measures, in order to solve the answer validation problem. The structural case base is built from annotated MultiNet graphs, which provide representations for natural language expressions, and corresponding graph similarity measures. I cover a priori relations to experienced answer candidates for former questions. I compare the CBR System results to current approaches in an experiment integrating CBR into an existing framework for answer validation and reranking. This integration is achieved by adding CBR-related features to the input of a learned ranking model that determines the final answer ranking. In the experiments based on QA@CLEF questions, the best learned models make heavy use of CBR features. Observing the results with a continually growing case base, I present a positive effect of the size of the case base on the accuracy of the CBR subsystem.
On the role of the plasmodial cytoskeleton in facilitating intelligent behaviour in slime mould Physarum polycephalum
Venue: arXiv cs.ET 2015
Year: 2015

The plasmodium of slime mould Physarum polycephalum behaves as an amorphous reaction-diffusion computing substrate and is capable of apparently intelligent behaviour. But how does intelligence emerge in an acellular organism? Through a range of laboratory experiments, we visualise the plasmodial cytoskeleton, a ubiquitous cellular protein scaffold whose functions are manifold and essential to life, and discuss its putative role as a network for transducing, transmitting and structuring data streams within the plasmodium. Through a range of computer modelling techniques, we demonstrate how emergent behaviour, and hence computational intelligence, may occur in cytoskeletal communications networks. Specifically, we model the topology of both the actin and tubulin cytoskeletal networks and discuss how computation may occur therein. Furthermore, we present bespoke cellular automata and particle swarm models for the computational process within the cytoskeleton and observe the incidence of emergent patterns in both. Our work grants unique insight into the origins of natural intelligence; the results presented here are therefore readily transferable to the fields of natural computation, cell biology and biomedical science. We conclude by discussing how our results may alter our biological, computational and philosophical understanding of intelligence and consciousness.
Hardware Probing Interface and Test Robustness
Venue: arXiv cs.OH 2015
Year: 2015

Computerized integrity test of an electronic product hardware interface and product probing validation are considered. Integrity testing is based on a current voltage characteristic measurement, when a small voltage and/or current stimuli are applied to the product pads including power supply circuitry pads, so that the product is not normally powered on. Test fixture needles validation is a part of a self test maintenance scenario designed to predict deterioration of product probing.
Discovering functional zones using bus smart card data and points of interest in Beijing
Venue: arXiv cs.CY 2015
Year: 2015

Cities comprise various functional zones, including residential, educational, commercial zones, etc. It is important for urban planners to identify different functional zones and understand their spatial structure within the city in order to make better urban plans. In this research, we used 77976010 bus smart card records of Beijing City in one week in April 2008 and converted them into two-dimensional time series data of each bus platform, Then, through data mining in the big database system and previous studies on citizens' trip behavior, we established the DZoF (discovering zones of different functions) model based on SCD (smart card Data) and POIs (points of interest), and pooled the results at the TAZ (traffic analysis zone) level. The results suggested that DzoF model and cluster analysis based on dimensionality reduction and EM (expectation-maximization) algorithm can identify functional zones that well match the actual land uses in Beijing. The methodology in the present research can help urban planners and the public understand the complex urban spatial structure and contribute to the academia of urban geography and urban planning.
Benchmarking NLopt and state-of-art algorithms for Continuous Global Optimization via Hybrid IACO$_\mathbb{R}$
Venue: arXiv cs.NE 2015
Year: 2015

This paper presents a comparative analysis of the performance of the Incremental Ant Colony algorithm for continuous optimization ($IACO_\mathbb{R}$), with different algorithms provided in the NLopt library. The key objective is to understand how the various algorithms in the NLopt library perform in combination with the Multi Trajectory Local Search (Mtsls1) technique. A hybrid approach has been introduced in the local search strategy by the use of a parameter which allows for probabilistic selection between Mtsls1 and a NLopt algorithm. In case of stagnation, the algorithm switch is made based on the algorithm being used in the previous iteration. The paper presents an exhaustive comparison on the performance of these approaches on Soft Computing (SOCO) and Congress on Evolutionary Computation (CEC) 2014 benchmarks. For both benchmarks, we conclude that the best performing algorithm is a hybrid variant of Mtsls1 with BFGS for local search.