OpenRaiser/Intern-Atlas huggingface dataset:
Determining the Characteristic Vocabulary for a Specialized Dictionary using Word2vec and a Directed Crawler
Venue: arXiv cs.CL 2016
Year: 2016

Specialized dictionaries are used to understand concepts in specific domains, especially where those concepts are not part of the general vocabulary, or having meanings that differ from ordinary languages. The first step in creating a specialized dictionary involves detecting the characteristic vocabulary of the domain in question. Classical methods for detecting this vocabulary involve gathering a domain corpus, calculating statistics on the terms found there, and then comparing these statistics to a background or general language corpus. Terms which are found significantly more often in the specialized corpus than in the background corpus are candidates for the characteristic vocabulary of the domain. Here we present two tools, a directed crawler, and a distributional semantics package, that can be used together, circumventing the need of a background corpus. Both tools are available on the web.
On level-transitivity and exponential growth
Venue: arXiv cs.FL 2016
Year: 2016

We prove that if the group generated by a Mealy automaton acts level-transitively on a regular rooted tree, then the semigroup generated by the dual automaton has exponential growth, hence giving a decision procedure of exponential growth for a restricted family of automaton semigroups.
Caching Improvement Using Adaptive User Clustering
Venue: arXiv cs.NI 2016
Year: 2016

In this article we explore one of the most promising technologies for 5G wireless networks using an underlay small cell network, namely proactive caching. Using the increase in storage technologies and through studying the users behavior, peak traffic can be reduced through proactive caching of the content that is most probable to be requested. We propose a new method, in which, instead of caching the most popular content, the users within the network are clustered according to their content popularity and the caching is done accordingly. We present also a method for estimating the number of clusters within the network based on the Akaike information criterion. We analytically derive a closed form expression of the hit probability and we propose an optimization problem in which the small base stations association with clusters is optimized.
Analysis of Key Generation Rate from Wireless Channel in In-Band Full-Duplex Communications
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, the impact of in-band full-duplex (IBFD) wireless communications on secret key generation via physical layer channel state information is investigated. A key generation strategy for IBFD wireless devices to increase the rate of generated secret keys over multipath fading channels is proposed. Conventionally, due to the half-duplex (HD) constraint on wireless transmissions, sensing simultaneous reciprocal channel measurements is not possible, which leads to a degraded key generation rate. However, with the advent of IBFD wireless devices, the legitimate nodes can sense the shared wireless link simultaneously at the possible cost of a self-interference (SI) channel estimation and some residual self-interference (RSI). As we demonstrate, with HD correlated observations the key rate is upper bounded by a constant, while with IBFD the key rate is only limited by the SI cancellation performance and is in general greater than that of its HD counterpart. Our analysis shows that with reasonable levels of SI cancellation, in the high SNR regime the key rate of IBFD is much higher, while in low SNRs, the HD system performs better. Finally, the key rate loss due to the overhead imposed by the SI channel estimation phase is discussed.
3D Printed Stencils for Texturing Flat Surfaces
Venue: arXiv cs.GR 2016
Year: 2016

We address the problem of texturing flat surfaces by spray-painting through 3D printed stencils. We propose a system that (1) decomposes an image into alpha-blended layers; (2) computes a stippling given a transparency channel; (3) generates a 3D printed stencil given a stippling and (4) simulates the effects of spray-painting through the stencil.
ObliviSync: Practical Oblivious File Backup and Synchronization
Venue: arXiv cs.CR 2016
Year: 2016

Oblivious RAM (ORAM) protocols are powerful techniques that hide a client's data as well as access patterns from untrusted service providers. We present an oblivious cloud storage system, ObliviSync, that specifically targets one of the most widely-used personal cloud storage paradigms: synchronization and backup services, popular examples of which are Dropbox, iCloud Drive, and Google Drive. This setting provides a unique opportunity because the above privacy properties can be achieved with a simpler form of ORAM called write-only ORAM, which allows for dramatically increased efficiency compared to related work. Our solution is asymptotically optimal and practically efficient, with a small constant overhead of approximately 4x compared with non-private file storage, depending only on the total data size and parameters chosen according to the usage rate, and not on the number or size of individual files. Our construction also offers protection against timing-channel attacks, which has not been previously considered in ORAM protocols. We built and evaluated a full implementation of ObliviSync that supports multiple simultaneous read-only clients and a single concurrent read/write client whose edits automatically and seamlessly propagate to the readers. We show that our system functions under high work loads, with realistic file size distributions, and with small additional latency (as compared to a baseline encrypted file system) when paired with Dropbox as the synchronization service.
Fast approximate furthest neighbors with data-dependent hashing
Venue: arXiv cs.DS 2016
Year: 2016

We present a novel hashing strategy for approximate furthest neighbor search that selects projection bases using the data distribution. This strategy leads to an algorithm, which we call DrusillaHash, that is able to outperform existing approximate furthest neighbor strategies. Our strategy is motivated by an empirical study of the behavior of the furthest neighbor search problem, which lends intuition for where our algorithm is most useful. We also present a variant of the algorithm that gives an absolute approximation guarantee; to our knowledge, this is the first such approximate furthest neighbor hashing approach to give such a guarantee. Performance studies indicate that DrusillaHash can achieve comparable levels of approximation to other algorithms while giving up to an order of magnitude speedup. An implementation is available in the mlpack machine learning library (found at http://www.mlpack.org).
Enabling Distributed Optimization in Large-Scale Power Systems
Venue: arXiv cs.DC 2016
Year: 2016

Distributed optimization for solving non-convex Optimal Power Flow (OPF) problems in power systems has attracted tremendous attention in the last decade. Most studies are based on the geographical decomposition of IEEE test systems for verifying the feasibility of the proposed approaches. However, it is not clear if one can extrapolate from these studies that those approaches can be applied to very large-scale real-world systems. In this paper, we show, for the first time, that distributed optimization can be effectively applied to a large-scale real transmission network, namely, the Polish 2383-bus system for which no pre-defined partitions exist, by using a recently developed partitioning technique. More specifically, the problem solved is the AC OPF problem with geographical decomposition of the network using the Alternating Direction Method of Multipliers (ADMM) method in conjunction with the partitioning technique. Through extensive experimental results and analytical studies, we show that with the presented partitioning technique the convergence performance of ADMM can be improved substantially, which enables the application of distributed approaches on very large-scale systems.
The Exception that Improves the Rule
Venue: arXiv cs.DB 2016
Year: 2016

The database community has developed numerous tools and techniques for data curation and exploration, from declarative languages, to specialized techniques for data repair, and more. Yet, there is currently no consensus on how to best expose these powerful tools to an analyst in a simple, intuitive, and above all, flexible way. Thus, analysts continue to rely on tools such as spreadsheets, imperative languages, and notebook style programming environments like Jupyter for data curation. In this work, we explore the integration of spreadsheets, notebooks, and relational databases. We focus on a key advantage that both spreadsheets and imperative notebook environments have over classical relational databases: ease of exception. By relying on set-at-a-time operations, relational databases sacrifice the ability to easily define singleton operations, exceptions to a normal data processing workflow that affect query processing for a fixed set of explicitly targeted records. In comparison, a spreadsheet user can easily change the formula for just one cell, while a notebook user can add an imperative operation to her notebook that alters an output 'view'. We believe that enabling such idiosyncratic manual transformations in a classical relational database is critical for curation, as curation operations that are easy to declare for individual values can often be extremely challenging to generalize. We explore the challenges of enabling singletons in relational databases, propose a hybrid spreadsheet/relational notebook environment for data curation, and present our vision of Vizier, a system that exposes data curation through such an interface.
A Survey of Qualitative Spatial and Temporal Calculi -- Algebraic and Computational Properties
Venue: arXiv cs.AI 2016
Year: 2016

Qualitative Spatial and Temporal Reasoning (QSTR) is concerned with symbolic knowledge representation, typically over infinite domains. The motivations for employing QSTR techniques range from exploiting computational properties that allow efficient reasoning to capture human cognitive concepts in a computational framework. The notion of a qualitative calculus is one of the most prominent QSTR formalisms. This article presents the first overview of all qualitative calculi developed to date and their computational properties, together with generalized definitions of the fundamental concepts and methods, which now encompass all existing calculi. Moreover, we provide a classification of calculi according to their algebraic properties.
Constructions of Good Entanglement-Assisted Quantum Error Correcting Codes
Venue: arXiv cs.IT 2016
Year: 2016

Entanglement-assisted quantum error correcting codes (EAQECCs) are a simple and fundamental class of codes. They allow for the construction of quantum codes from classical codes by relaxing the duality condition and using pre-shared entanglement between the sender and receiver. However, in general it is not easy to determine the number of shared pairs required to construct an EAQECC. In this paper, we show that this number is related to the hull of the classical code. Using this fact, we give methods to construct EAQECCs requiring desirable amount of entanglement. This leads to design families of EAQECCs with good error performance. Moreover, we construct maximal entanglement EAQECCs from LCD codes. Finally, we prove the existence of asymptotically good EAQECCs in the odd characteristic case.
Hyperspectral Subspace Identification Using SURE
Venue: arXiv cs.CV 2016
Year: 2016

Remote sensing hyperspectral sensors collect large volumes of high dimensional spectral and spatial data. However, due to spectral and spatial redundancy the true hyperspectral signal lies on a subspace of much lower dimension than the original data. The identification of the signal subspace is a very important first step for most hyperspectral algorithms. In this paper we investigate the important problem of identifying the hyperspectral signal subspace by minimizing the mean squared error (MSE) between the true signal and an estimate of the signal. Since the MSE is uncomputable in practice, due to its dependency on the true signal, we propose a method based on the Stein's unbiased risk estimator (SURE) that provides an unbiased estimate of the MSE. The resulting method is simple and fully automatic and we evaluate it using both simulated and real hyperspectral data sets. Experimental results shows that our proposed method compares well to recent state-of-the-art subspace identification methods.
Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth
Venue: arXiv cs.DC 2016
Year: 2016

Stochastic surface growth models aid in studying properties of universality classes like the Kardar--Paris--Zhang class. High precision results obtained from large scale computational studies can be transferred to many physical systems. Many properties, such as roughening and some two-time functions can be studied using stochastic cellular automaton (SCA) variants of stochastic models. Here we present a highly efficient SCA implementation of a surface growth model capable of simulating billions of lattice sites on a single GPU. We also provide insight into cases requiring arbitrary random probabilities which are not accessible through bit-vectorization.
Beyond Counting: New Perspectives on the Active IPv4 Address Space
Venue: arXiv cs.NI 2016
Year: 2016

In this study, we report on techniques and analyses that enable us to capture Internet-wide activity at individual IP address-level granularity by relying on server logs of a large commercial content delivery network (CDN) that serves close to 3 trillion HTTP requests on a daily basis. Across the whole of 2015, these logs recorded client activity involving 1.2 billion unique IPv4 addresses, the highest ever measured, in agreement with recent estimates. Monthly client IPv4 address counts showed constant growth for years prior, but since 2014, the IPv4 count has stagnated while IPv6 counts have grown. Thus, it seems we have entered an era marked by increased complexity, one in which the sole enumeration of active IPv4 addresses is of little use to characterize recent growth of the Internet as a whole. With this observation in mind, we consider new points of view in the study of global IPv4 address activity. Our analysis shows significant churn in active IPv4 addresses: the set of active IPv4 addresses varies by as much as 25% over the course of a year. Second, by looking across the active addresses in a prefix, we are able to identify and attribute activity patterns to network restructurings, user behaviors, and, in particular, various address assignment practices. Third, by combining spatio-temporal measures of address utilization with measures of traffic volume, and sampling-based estimates of relative host counts, we present novel perspectives on worldwide IPv4 address activity, including empirical observation of under-utilization in some areas, and complete utilization, or exhaustion, in others.
Conversational Contextual Cues: The Case of Personalization and History for Response Ranking
Venue: arXiv cs.CL 2016
Year: 2016

We investigate the task of modeling open-domain, multi-turn, unstructured, multi-participant, conversational dialogue. We specifically study the effect of incorporating different elements of the conversation. Unlike previous efforts, which focused on modeling messages and responses, we extend the modeling to long context and participant's history. Our system does not rely on handwritten rules or engineered features; instead, we train deep neural networks on a large conversational dataset. In particular, we exploit the structure of Reddit comments and posts to extract 2.1 billion messages and 133 million conversations. We evaluate our models on the task of predicting the next response in a conversation, and we find that modeling both context and participants improves prediction accuracy.
Scalable and Passive Wireless Network Clock Synchronization
Venue: arXiv cs.NI 2016
Year: 2016

Clock synchronization is ubiquitous in wireless systems for communication, sensing and control. In this paper we design a scalable system in which an indefinite number of passively receiving wireless units can synchronize to a single master clock at the level of discrete clock ticks. Accurate synchronization requires an estimate of the node positions. If such information is available the framework developed here takes position uncertainties into account. In the absence of such information we propose a mechanism which enables simultaneous synchronization and positioning. Furthermore we derive the Cramer-Rao bounds for the system which show that it enables synchronization accuracy at sub-nanosecond levels. Finally, we develop and evaluate an online estimation method which is statistically efficient.
New model for datasets citation and extraction reproducibility in VAMDC
Venue: arXiv cs.DL 2016
Year: 2016

In this paper we present a new paradigm for the identification of datasets extracted from the Virtual Atomic and Molecular Data Centre (VAMDC) e-science infrastructure. Such identification includes information on the origin and version of the datasets, references associated to individual data in the datasets, as well as timestamps linked to the extraction procedure. This paradigm is described through the modifications of the language used to exchange data within the VAMDC and through the services that will implement those modifications. This new paradigm should enforce traceability of datasets, favour reproducibility of datasets extraction, and facilitate the systematic citation of the authors having originally measured and/or calculated the extracted atomic and molecular data.
Multi-pretrained Deep Neural Network
Venue: arXiv cs.NE 2016
Year: 2016

Pretraining is widely used in deep neutral network and one of the most famous pretraining models is Deep Belief Network (DBN). The optimization formulas are different during the pretraining process for different pretraining models. In this paper, we pretrained deep neutral network by different pretraining models and hence investigated the difference between DBN and Stacked Denoising Autoencoder (SDA) when used as pretraining model. The experimental results show that DBN get a better initial model. However the model converges to a relatively worse model after the finetuning process. Yet after pretrained by SDA for the second time the model converges to a better model if finetuned.
Death and Suicide in Universal Artificial Intelligence
Venue: arXiv cs.AI 2016
Year: 2016

Reinforcement learning (RL) is a general paradigm for studying intelligent behaviour, with applications ranging from artificial intelligence to psychology and economics. AIXI is a universal solution to the RL problem; it can learn any computable environment. A technical subtlety of AIXI is that it is defined using a mixture over semimeasures that need not sum to 1, rather than over proper probability measures. In this work we argue that the shortfall of a semimeasure can naturally be interpreted as the agent's estimate of the probability of its death. We formally define death for generally intelligent agents like AIXI, and prove a number of related theorems about their behaviour. Notable discoveries include that agent behaviour can change radically under positive linear transformations of the reward signal (from suicidal to dogmatically self-preserving), and that the agent's posterior belief that it will survive increases over time.
Implications of Environmental Uncertainty for Business-IT Alignment: A Comparative Study of SMEs and Large Organizations
Venue: arXiv cs.CY 2016
Year: 2016

This paper presents a comprehensive study of the influence of environmental uncertainty on business-IT alignment. The existing literature postulates environmental uncertainty as a key challenge to achieving business-IT alignment. Hence, the first objective of this study is to identify the extent of the impact of environmental uncertainty on business-IT alignment, and to determine its relative impact in the light of the other antecedents. Furthermore, small and medium sized enterprises (SMEs) differ fundamentally from large firms in many ways. Thus this paper also aims to investigate the variation between SMEs and large firms with regard to the antecedents for strategic alignment. Based on data collected from 212 firms, a conceptual model is tested against the research objectives. The findings provide important contributions to both research and practice by demonstrating the relative impact of environmental uncertainty, and showing how the antecedents of alignment vary between SMEs and large firms.
The Influence of Trust and subjective Norms on Citizens Intentions to Engage in E-participation on E-government Websites
Venue: arXiv cs.CY 2016
Year: 2016

Advancements in web technology have revolutionised the way citizens interact with governments. Unlike traditional methods of communication between citizens and governments, E-Participation via e-government websites enhances communication and enables citizens to become actively involved in the policy-making process. Despite the growing importance of e-participation, the potential factors influencing citizens engagement in e-participation have not been the subject of investigation. Using responses obtained from Saudi citizens, this study examines a number of factors that may influence the intentions of citizens to engage in e-participation activities on e-government websites. The results suggest that the factors of trust and subjective norms have a significant impact on the intentions of citizens to engage in e-participation activities.
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
Venue: arXiv cs.CL 2016
Year: 2016

We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens. There are many ways to estimate or learn the high-level coarse tokens, but we argue that a simple extraction procedure is sufficient to capture a wealth of high-level discourse semantics. Such procedure allows training the multiresolution recurrent neural network by maximizing the exact joint log-likelihood over both sequences. In contrast to the standard log- likelihood objective w.r.t. natural language tokens (word perplexity), optimizing the joint log-likelihood biases the model towards modeling high-level abstractions. We apply the proposed model to the task of dialogue response generation in two challenging domains: the Ubuntu technical support domain, and Twitter conversations. On Ubuntu, the model outperforms competing approaches by a substantial margin, achieving state-of-the-art results according to both automatic evaluation metrics and a human evaluation study. On Twitter, the model appears to generate more relevant and on-topic responses according to automatic evaluation metrics. Finally, our experiments demonstrate that the proposed model is more adept at overcoming the sparsity of natural language and is better able to capture long-term structure.