OpenRaiser/Intern-Atlas huggingface dataset:
Grounded Discovery of Coordinate Term Relationships between Software Entities
Venue: arXiv cs.CL 2015
Year: 2015

We present an approach for the detection of coordinate-term relationships between entities from the software domain, that refer to Java classes. Usually, relations are found by examining corpus statistics associated with text entities. In some technical domains, however, we have access to additional information about the real-world objects named by the entities, suggesting that coupling information about the "grounded" entities with corpus statistics might lead to improved methods for relation discovery. To this end, we develop a similarity measure for Java classes using distributional information about how they are used in software, which we combine with corpus statistics on the distribution of contexts in which the classes appear in text. Using our approach, cross-validation accuracy on this dataset can be improved dramatically, from around 60% to 88%. Human labeling results show that our classifier has an F1 score of 86% over the top 1000 predicted pairs.
Deep Remix: Remixing Musical Mixtures Using a Convolutional Deep Neural Network
Venue: arXiv cs.SD 2015
Year: 2015

Audio source separation is a difficult machine learning problem and performance is measured by comparing extracted signals with the component source signals. However, if separation is motivated by the ultimate goal of re-mixing then complete separation is not necessary and hence separation difficulty and separation quality are dependent on the nature of the re-mix. Here, we use a convolutional deep neural network (DNN), trained to estimate 'ideal' binary masks for separating voice from music, to perform re-mixing of the vocal balance by operating directly on the individual magnitude components of the musical mixture spectrogram. Our results demonstrate that small changes in vocal gain may be applied with very little distortion to the ultimate re-mix. Our method may be useful for re-mixing existing mixes.
Deconstructing Principal Component Analysis Using a Data Reconciliation Perspective
Venue: arXiv cs.LG 2015
Year: 2015

Data reconciliation (DR) and Principal Component Analysis (PCA) are two popular data analysis techniques in process industries. Data reconciliation is used to obtain accurate and consistent estimates of variables and parameters from erroneous measurements. PCA is primarily used as a method for reducing the dimensionality of high dimensional data and as a preprocessing technique for denoising measurements. These techniques have been developed and deployed independently of each other. The primary purpose of this article is to elucidate the close relationship between these two seemingly disparate techniques. This leads to a unified framework for applying PCA and DR. Further, we show how the two techniques can be deployed together in a collaborative and consistent manner to process data. The framework has been extended to deal with partially measured systems and to incorporate partial knowledge available about the process model.
Using PCA to Efficiently Represent State Spaces
Venue: arXiv cs.LG 2015
Year: 2015

Reinforcement learning algorithms need to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces. This is known as the curse of dimensionality. By projecting the agent's state onto a low-dimensional manifold, we can represent the state space in a smaller and more efficient representation. By using this representation during learning, the agent can converge to a good policy much faster. We test this approach in the Mario Benchmarking Domain. When using dimensionality reduction in Mario, learning converges much faster to a good policy. But, there is a critical convergence-performance trade-off. By projecting onto a low-dimensional manifold, we are ignoring important data. In this paper, we explore this trade-off of convergence and performance. We find that learning in as few as 4 dimensions (instead of 9), we can improve performance past learning in the full dimensional space at a faster convergence rate.
Server component installation and testing of the university information and educational environment on the Moodle LMS platform
Venue: arXiv cs.OH 2015
Year: 2015

The informational educational environment (IEE) of an institution is a complex multilevel system which, along with methodical, organizational and cultural resources, accumulates the intellectual and technical potential of a university, as well as the informative and activity components of the learners and teachers. In practice, the formation of IEE is actually based on the creation of information technologies and their integration into the existing educational environment of the institution. The management of this system is carried out using specialized equipment and software. For the successful formation and operation of IEE, in the present work we review software products that form the basis of the organization of interactive and web interactions between students, teachers and all participants of the educational process. We analyse the technical capabilities that have provided users with IEE services such as the Apache web server with connected modules PHP, MySQL, the Java virtual machine and the Red5 server. We demonstrate the possibility of obtaining results from the interaction of these products, and reports on users' work in webinars, video conferences and web conferences.
Optimal Time-Series Motifs
Venue: arXiv cs.AI 2015
Year: 2015

Motifs are the most repetitive/frequent patterns of a time-series. The discovery of motifs is crucial for practitioners in order to understand and interpret the phenomena occurring in sequential data. Currently, motifs are searched among series sub-sequences, aiming at selecting the most frequently occurring ones. Search-based methods, which try out series sub-sequence as motif candidates, are currently believed to be the best methods in finding the most frequent patterns. However, this paper proposes an entirely new perspective in finding motifs. We demonstrate that searching is non-optimal since the domain of motifs is restricted, and instead we propose a principled optimization approach able to find optimal motifs. We treat the occurrence frequency as a function and time-series motifs as its parameters, therefore we \textit{learn} the optimal motifs that maximize the frequency function. In contrast to searching, our method is able to discover the most repetitive patterns (hence optimal), even in cases where they do not explicitly occur as sub-sequences. Experiments on several real-life time-series datasets show that the motifs found by our method are highly more frequent than the ones found through searching, for exactly the same distance threshold.
A Unified Stability Analysis Approach for a Class of Interconnected System
Venue: arXiv cs.SY 2015
Year: 2015

From the structural perspective, this paper investigates a new formulation of the concept of input-to-state stability (ISS), and based on this formulation, proposes a new stability analysis approach for a class of interconnected system. The new formulation of ISS is better able to reflect the tendency of the state $x(t)$ tracking the input $u(t)$ and weakens the conservative of the original form. The stability analysis method which transforms the interconnected system into the equivalent cascade form, does not depend on the Lyapunov function, breaks through the limitation of the small-gain theorem and extends the application of ISS. As its applications in three typical kinds of interconnected systems, this method is used to prove the small-gain theorem again and analyzes the stability of a class of interconnected system and the consensus of the multi-agent system (MAS).
The Combinatorial World (of Auctions) According to GARP
Venue: arXiv cs.GT 2015
Year: 2015

Revealed preference techniques are used to test whether a data set is compatible with rational behaviour. They are also incorporated as constraints in mechanism design to encourage truthful behaviour in applications such as combinatorial auctions. In the auction setting, we present an efficient combinatorial algorithm to find a virtual valuation function with the optimal (additive) rationality guarantee. Moreover, we show that there exists such a valuation function that both is individually rational and is minimum (that is, it is component-wise dominated by any other individually rational, virtual valuation function that approximately fits the data). Similarly, given upper bound constraints on the valuation function, we show how to fit the maximum virtual valuation function with the optimal additive rationality guarantee. In practice, revealed preference bidding constraints are very demanding. We explain how approximate rationality can be used to create relaxed revealed preference constraints in an auction. We then show how combinatorial methods can be used to implement these relaxed constraints. Worst/best-case welfare guarantees that result from the use of such mechanisms can be quantified via the minimum/maximum virtual valuation function.
Approximation of Capacity for ISI Channels with One-bit Output Quantization
Venue: arXiv cs.IT 2015
Year: 2015

Motivated by recent high bandwidth communication systems, Inter-Symbol Interference (ISI) channels with 1-bit quantized output are considered under an average-power-constrained continuous input. While the exact capacity is difficult to characterize, an approximation that matches with the exact channel output up to a probability of error is provided. The approximation does not have additive noise, but constrains the channel output (without noise) to be above a threshold in absolute value. The capacity under the approximation is computed using methods involving standard Gibbs distributions. Markovian achievable schemes approaching the approximate capacity are provided. The methods used over the approximate ISI channel result in ideas for practical coding schemes for ISI channels with 1-bit output quantization.
Danger is My Middle Name: Experimenting with SSL Vulnerabilities in Android Apps
Venue: arXiv cs.CR 2015
Year: 2015

This paper presents a measurement study of information leakage and SSL vulnerabilities in popular Android apps. We perform static and dynamic analysis on 100 apps, downloaded at least 10M times, that request full network access. Our experiments show that, although prior work has drawn a lot of attention to SSL implementations on mobile platforms, several popular apps (32/100) accept all certificates and all hostnames, and four actually transmit sensitive data unencrypted. We set up an experimental testbed simulating man-in-the-middle attacks and find that many apps (up to 91% when the adversary has a certificate installed on the victim's device) are vulnerable, allowing the attacker to access sensitive information, including credentials, files, personal details, and credit card numbers. Finally, we provide a few recommendations to app developers and highlight several open research problems.
Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks
Venue: arXiv cs.IT 2015
Year: 2015

Machine-to-machine (M2M) communication's severe power limitations challenge the interconnectivity, access management, and reliable communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the {\em energy density} of an M2M communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.
Dynamic Consistency of Conditional Simple Temporal Networks via Mean Payoff Games: a Singly-Exponential Time DC-Checking
Venue: arXiv cs.DS 2015
Year: 2015

Conditional Simple Temporal Network (CSTN) is a constraint-based graph-formalism for conditional temporal planning. It offers a more flexible formalism than the equivalent CSTP model of Tsamardinos, Vidal and Pollack, from which it was derived mainly as a sound formalization. Three notions of consistency arise for CSTNs and CSTPs: weak, strong, and dynamic. Dynamic consistency is the most interesting notion, but it is also the most challenging and it was conjectured to be hard to assess. Tsamardinos, Vidal and Pollack gave a doubly-exponential time algorithm for deciding whether a CSTN is dynamically-consistent and to produce, in the positive case, a dynamic execution strategy of exponential size. In the present work we offer a proof that deciding whether a CSTN is dynamically-consistent is coNP-hard and provide the first singly-exponential time algorithm for this problem, also producing a dynamic execution strategy whenever the input CSTN is dynamically-consistent. The algorithm is based on a novel connection with Mean Payoff Games, a family of two-player combinatorial games on graphs well known for having applications in model-checking and formal verification. The presentation of such connection is mediated by the Hyper Temporal Network model, a tractable generalization of Simple Temporal Networks whose consistency checking is equivalent to determining Mean Payoff Games. In order to analyze the algorithm we introduce a refined notion of dynamic-consistency, named ε-dynamic-consistency, and present a sharp lower bounding analysis on the critical value of the reaction time \hat{\varepsilon} where the CSTN transits from being, to not being, dynamically-consistent. The proof technique introduced in this analysis of \hat{\varepsilon} is applicable more in general when dealing with linear difference constraints which include strict inequalities.
A novel plasticity rule can explain the development of sensorimotor intelligence
Venue: arXiv cs.RO 2015
Year: 2015

Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, the self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system specific modifications of the DEP rule but arise rather from the underlying mechanism of spontaneous symmetry breaking due to the tight brain-body-environment coupling. The new synaptic rule is biologically plausible and it would be an interesting target for a neurobiolocal investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.
A new intrinsic way to measure IXP performance: an experience in Bolivia
Venue: arXiv cs.NI 2015
Year: 2015

Bolivia, a landlocked emerging country in South America, has one of the smallest networks in the whole Internet. Before the IXP implementation, delivering packets between national ISPs had to be sent them through international transit links. Being aware of this situation and looking for increasing the number of users, Bolivian government enacted a law to gather all national ISPs on a single IXP in 2013. In spite of several articles have researched about this topic, no one before has set the focus on measuring the evolution of end-users parameters in a South American developing country, moreover after a significant changing on the topology. For the current work, we have mainly studied hop, latency, traffic and route variation, a long a seven months. Topology have not been studied because Bolivian ISPs must be connected each others under legal obligation. To achieve our measurement goals, and under absence of global-scale measuring projects in this country, we have developed our own active-measurement platform among local ASes. During the platform development we had to deal with local ISP fears, governmental agencies and regulation pressures. We also survey the main previous papers on IXP analysis, and we classfied them on obtained data and their sources.
Harvesting Entities from the Web Using Unique Identifiers -- IBEX
Venue: arXiv cs.DB 2015
Year: 2015

In this paper we study the prevalence of unique entity identifiers on the Web. These are, e.g., ISBNs (for books), GTINs (for commercial products), DOIs (for documents), email addresses, and others. We show how these identifiers can be harvested systematically from Web pages, and how they can be associated with human-readable names for the entities at large scale. Starting with a simple extraction of identifiers and names from Web pages, we show how we can use the properties of unique identifiers to filter out noise and clean up the extraction result on the entire corpus. The end result is a database of millions of uniquely identified entities of different types, with an accuracy of 73--96% and a very high coverage compared to existing knowledge bases. We use this database to compute novel statistics on the presence of products, people, and other entities on the Web.
Uplink Energy-Delay Trade-off under Optimized Relay Placement in Cellular Networks
Venue: arXiv cs.NI 2015
Year: 2015

Relay nodes-enhanced architectures are deemed a viable solution to enhance coverage and capacity of nowadays cellular networks. Besides a number of desirable features, these architectures reduce the average distance between users and network nodes, thus allowing for battery savings for users transmitting on the uplink. In this paper, we investigate the extent of these savings, by optimizing relay nodes deployment in terms of uplink energy consumption per transmitted bit, while taking into account a minimum uplink average user delay that has to be guaranteed. A novel performance evaluation framework for uplink relay networks is first proposed to study this energy-delay trade-off. A simulated annealing is then run to find an optimized relay placement solution under a delay constraint; exterior penalty functions are used in order to deal with a difficult energy landscape, in particular when the constraint is tight. Finally, results show that relay nodes deployment consistently improve users uplink energy efficiency, under a wide range of traffic conditions and that relays are particularly efficient in non-uniform traffic scenarios.
Fast Guided Filter
Venue: arXiv cs.CV 2015
Year: 2015

The guided filter is a technique for edge-aware image filtering. Because of its nice visual quality, fast speed, and ease of implementation, the guided filter has witnessed various applications in real products, such as image editing apps in phones and stereo reconstruction, and has been included in official MATLAB and OpenCV. In this note, we remind that the guided filter can be simply sped up from O(N) time to O(N/s^2) time for a subsampling ratio s. In a variety of applications, this leads to a speedup of >10x with almost no visible degradation. We hope this acceleration will improve performance of current applications and further popularize this filter. Code is released.
Preprint Virtual Reality GIS and Cloud Service Based Traffic Analysis Platform
Venue: arXiv cs.OH 2015
Year: 2015

This is the preprint version of our paper on The 23rd International Conference on Geoinformatics (Geoinformatics2015). City traffic data has several characteristics, such as large scale, diverse predictable and real-time, which falls in the range of definition of Big Data. This paper proposed a cloud service platform which targets for wise transportation is to carry out unified management and mining analysis of the huge number of the multivariate and heterogeneous dynamic transportation information, provides real-time transportation information, increase the utilization efficiency of transportation, promote transportation management and service level of travel information and provide decision support of transportation management by virtual reality as visual.
The BiPublishers ranking: Main results and methodological problems when constructing rankings of academic publishers
Venue: arXiv cs.DL 2015
Year: 2015

We present the results of the Bibliometric Indicators for Publishers project (also known as BiPublishers). This project represents the first attempt to systematically develop bibliometric publisher rankings. The data for this project was derived from the Book Citation Index, and the study time period was 2009-2013. We have developed 42 rankings: 4 for by fields and 38 by disciplines. We display six indicators by publisher divided into three types: output, impact and publisher's profile. The aim is to capture different characteristics of the research performance of publishers. 254 publishers were processed and classified according to publisher type: commercial publishers and university presses. We present the main publishers by fields. Then, we discuss the main challenges presented when developing this type of tools. The BiPublishers ranking is an on-going project which aims to develop and explore new data sources and indicators to better capture and define the research impact of publishers.
Energetic Galerkin Projection of Electromagnetic Fields between Different Meshes
Venue: arXiv cs.CE 2015
Year: 2015

In order to project electromagnetic fields between different meshes with respect to the conservation of energetic values, Galerkin projection formulations based on the energetic norm are developed in this communication. The proposed formulations are applied to an academic example.
On the Convergence of the Pairwise Comparisons Inconsistency Reduction Process
Venue: arXiv cs.DM 2015
Year: 2015

This study investigates a powerful model, targeted to subjective assessments, based on pairwise comparisons. It provides a proof that a distance-based inconsistency reduction transforms an inconsistent pairwise comparisons (PC) matrix into a consistent PC matrix which is generated by the geometric means of rows of a given inconsistent PC matrix. The distance-based inconsistency indicator was defined in 1993 for pairwise comparisons. Its convergence was analyzed in 1996 (regretfully, with an incomplete proof; finally completed in 2010). However, there was no clear interpretation of the convergence limit which is of considerable importance for applications and this study does so.
A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models
Venue: arXiv cs.NE 2015
Year: 2015

In this paper, we propose the new fixed-size ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation. FOFE can model the word order in a sequence using a simple ordinally-forgetting mechanism according to the positions of words. In this work, we have applied FOFE to feedforward neural network language models (FNN-LMs). Experimental results have shown that without using any recurrent feedbacks, FOFE based FNN-LMs can significantly outperform not only the standard fixed-input FNN-LMs but also the popular RNN-LMs.