OpenRaiser/Intern-Atlas huggingface dataset:
Software effort estimation based on optimized model tree
Venue: arXiv cs.SE 2017
Year: 2017

Background: It is widely recognized that software effort estimation is a regression problem. Model Tree (MT) is one of the Machine Learning based regression techniques that is useful for software effort estimation, but as other machine learning algorithms, the MT has a large space of configuration and requires to carefully setting its parameters. The choice of such parameters is a dataset dependent so no general guideline can govern this process which forms the motivation of this work. Aims: This study investigates the effect of using the most recent optimization algorithm called Bees algorithm to specify the optimal choice of MT parameters that fit a dataset and therefore improve prediction accuracy. Method: We used MT with optimal parameters identified by the Bees algorithm to construct software effort estimation model. The model has been validated over eight datasets come from two main sources: PROMISE and ISBSG. Also we used 3-Fold cross validation to empirically assess the prediction accuracies of different estimation models. As benchmark, results are also compared to those obtained with Stepwise Regression Case-Based Reasoning and Multi-Layer Perceptron. Results: The results obtained from combination of MT and Bees algorithm are encouraging and outperforms other well-known estimation methods applied on employed datasets. They are also interesting enough to suggest the effectiveness of MT among the techniques that are suitable for effort estimation. Conclusions: The use of the Bees algorithm enabled us to automatically find optimal MT parameters required to construct effort estimation models that fit each individual dataset. Also it provided a significant improvement on prediction accuracy.
Anisotropic-Scale Junction Detection and Matching for Indoor Images
Venue: arXiv cs.CV 2017
Year: 2017

Junctions play an important role in the characterization of local geometric structures in images, the detection of which is a longstanding and challenging task. Existing junction detectors usually focus on identifying the junction locations and the orientations of the junction branches while ignoring their scales; however, these scales also contain rich geometric information. This paper presents a novel approach to junction detection and characterization that exploits the locally anisotropic geometries of a junction and estimates the scales of these geometries using an \emph{a contrario} model. The output junctions have anisotropic scales --- i.e., each branch of a junction is associated with an independent scale parameter --- and are thus termed anisotropic-scale junctions (ASJs). We then apply the newly detected ASJs for the matching of indoor images, in which there may be dramatic changes in viewpoint and the detected local visual features, e.g., key-points, are usually insufficiently distinctive. We propose to use the anisotropic geometries of our junctions to improve the matching precision for indoor images. Matching results obtained on sets of indoor images demonstrate that our approach achieves state-of-the-art performance in indoor image matching.
Distributed Mechanism Design with Learning Guarantees
Venue: arXiv cs.GT 2017
Year: 2017

Mechanism design for fully strategic agents commonly assumes broadcast nature of communication between agents of the system. Moreover, for mechanism design, the stability of Nash equilibrium (NE) is demonstrated by showing convergence of specific pre-designed learning dynamics, rather than for a class of learning dynamics. In this paper we consider two common resource allocation problems: sharing $ K $ infinitely divisible resources among strategic agents for their private consumption (private goods), and determining the level for an infinitely divisible public good with $ P $ features, that is shared between strategic agents. For both cases, we present a distributed mechanism for a set of agents who communicate through a given network. In a distributed mechanism, agents' messages are not broadcast to all other agents as in the standard mechanism design framework, but are exchanged only in the local neighborhood of each agent. The presented mechanisms produce a unique NE and fully implement the social welfare maximizing allocation. In addition, the mechanisms are budget-balanced at NE. It is also shown that the mechanisms induce a game with contractive best-response, leading to guaranteed convergence for all learning dynamics within the Adaptive Best-Response dynamics class, including dynamics such as Cournot best-response, $ k- $period best-response and Fictitious Play. We also present a numerically study of convergence under repeated play, for various communication graphs and learning dynamics.
Improving Document Clustering by Eliminating Unnatural Language
Venue: arXiv cs.IR 2017
Year: 2017

Technical documents contain a fair amount of unnatural language, such as tables, formulas, pseudo-codes, etc. Unnatural language can be an important factor of confusing existing NLP tools. This paper presents an effective method of distinguishing unnatural language from natural language, and evaluates the impact of unnatural language detection on NLP tasks such as document clustering. We view this problem as an information extraction task and build a multiclass classification model identifying unnatural language components into four categories. First, we create a new annotated corpus by collecting slides and papers in various formats, PPT, PDF, and HTML, where unnatural language components are annotated into four categories. We then explore features available from plain text to build a statistical model that can handle any format as long as it is converted into plain text. Our experiments show that removing unnatural language components gives an absolute improvement in document clustering up to 15%. Our corpus and tool are publicly available.
The challenge of decentralized marketplaces
Venue: arXiv cs.DC 2017
Year: 2017

Online trust systems are playing an important role in to-days world and face various challenges in building them. Billions of dollars of products and services are traded through electronic commerce, files are shared among large peer-to-peer networks and smart contracts can potentially replace paper contracts with digital contracts. These systems rely on trust mechanisms in peer-to-peer networks like reputation systems or a trustless public ledger. In most cases, reputation systems are build to determine the trustworthiness of users and to provide incentives for users to make a fair contribution to the peer-to-peer network. The main challenges are how to set up a good trust system, how to deal with security issues and how to deal with strategic users trying to cheat on the system. The Sybil attack, the most important attack on reputation systems is discussed. At last match making in two sided markets and the strategy proofness of these markets are discussed.
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images
Venue: arXiv cs.CV 2017
Year: 2017

Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in subsequent unsupervised unmixing steps i.e., the derivation of the spectral signatures of the endmembers (endmembers' extraction) and the estimation of the abundance fractions of the pixels. A common practice amply followed in literature is to treat endmembers' number estimation and unmixing, independently as two separate tasks, providing the outcome of the former as input to the latter. In this paper, we go beyond this computationally demanding strategy. More precisely, we set forth a multiple constrained optimization framework, which encapsulates endmembers' number estimation and unsupervised unmixing in a single task. This is attained by suitably formulating the problem via a low-rank and sparse nonnegative matrix factorization rationale, where low-rankness is promoted with the use of a sophisticated $\ell_2/\ell_1$ norm penalty term. An alternating proximal algorithm is then proposed for minimizing the emerging cost function. The results obtained by simulated and real data experiments verify the effectiveness of the proposed approach.
Distributed Scheduling in Wireless Powered Communication Network: Protocol Design and Performance Analysis
Venue: arXiv cs.NI 2017
Year: 2017

Wireless powered communication network (WPCN) is a novel networking paradigm that uses radio frequency (RF) wireless energy transfer (WET) technology to power the information transmissions of wireless devices (WDs). When energy and information are transferred in the same frequency band, a major design issue is transmission scheduling to avoid interference and achieve high communication performance. Commonly used centralized scheduling methods in WPCN may result in high control signaling overhead and thus are not suitable for wireless networks constituting a large number of WDs with random locations and dynamic operations. To tackle this issue, we propose in this paper a distributed scheduling protocol for energy and information transmissions in WPCN. Specifically, we allow a WD that is about to deplete its battery to broadcast an energy request buzz (ERB), which triggers WET from its associated hybrid access point (HAP) to recharge the battery. If no ERB is sent, the WDs contend to transmit data to the HAP using the conventional $p$-persistent CSMA (carrier sensing multiple access). In particular, we propose an energy queueing model based on an energy decoupling property to derive the throughput performance. Our analysis is verified through simulations under practical network parameters, which demonstrate good throughput performance of the distributed scheduling protocol and reveal some interesting design insights that are different from conventional contention-based communication network assuming the WDs are powered with unlimited energy supplies.
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Venue: arXiv cs.CV 2017
Year: 2017

Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Here, we perform unsupervised learning to identify anomalies in imaging data as candidates for markers. We propose AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Applied to new data, the model labels anomalies, and scores image patches indicating their fit into the learned distribution. Results on optical coherence tomography images of the retina demonstrate that the approach correctly identifies anomalous images, such as images containing retinal fluid or hyperreflective foci.
Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images
Venue: arXiv cs.CV 2017
Year: 2017

Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology. However, it is well known that whole-slide scanners can fail in detecting all tissue regions, for example due to the tissue type, or due to weak staining because their tissue detection algorithms are not robust enough. In this paper, we introduce two different convolutional neural network architectures for whole slide image segmentation to accurately identify the tissue sections. We also compare the algorithms to a published traditional method. We collected 54 whole slide images with differing stains and tissue types from three laboratories to validate our algorithms. We show that while the two methods do not differ significantly they outperform their traditional counterpart (Jaccard index of 0.937 and 0.929 vs. 0.870, p < 0.01).
PSF field learning based on Optimal Transport Distances
Venue: arXiv cs.CV 2017
Year: 2017

Context: in astronomy, observing large fractions of the sky within a reasonable amount of time implies using large field-of-view (fov) optical instruments that typically have a spatially varying Point Spread Function (PSF). Depending on the scientific goals, galaxies images need to be corrected for the PSF whereas no direct measurement of the PSF is available. Aims: given a set of PSFs observed at random locations, we want to estimate the PSFs at galaxies locations for shapes measurements correction. Contributions: we propose an interpolation framework based on Sliced Optimal Transport. A non-linear dimension reduction is first performed based on local pairwise approximated Wasserstein distances. A low dimensional representation of the unknown PSFs is then estimated, which in turn is used to derive representations of those PSFs in the Wasserstein metric. Finally, the interpolated PSFs are calculated as approximated Wasserstein barycenters. Results: the proposed method was tested on simulated monochromatic PSFs of the Euclid space mission telescope (to be launched in 2020). It achieves a remarkable accuracy in terms of pixels values and shape compared to standard methods such as Inverse Distance Weighting or Radial Basis Function based interpolation methods.
Performance Analysis of Ultra-Dense Networks with Elevated Base Stations
Venue: arXiv cs.IT 2017
Year: 2017

This paper analyzes the downlink performance of ultra-dense networks with elevated base stations (BSs). We consider a general dual-slope pathloss model with distance-dependent probability of line-of-sight (LOS) transmission between BSs and receivers. Specifically, we consider the scenario where each link may be obstructed by randomly placed buildings. Using tools from stochastic geometry, we show that both coverage probability and area spectral efficiency decay to zero as the BS density grows large. Interestingly, we show that the BS height alone has a detrimental effect on the system performance even when the standard single-slope pathloss model is adopted.
Modelling politics in requirements engineering: adding emoji to existing notations
Venue: arXiv cs.SE 2017
Year: 2017

Notwithstanding several authors have recognised the conceptual key of "politics" as an important component in any Require-ments Engineering (RE) process, practitioners still lack a prag-matic answer on how to deal with the political dimension: such an ability has become a mostly desirable but totally undetailed part of what we usually and vaguely refer to as "professional experience". Nor were practitioners given any suitable tool or method to easily detect, represent, control and if possible leverage politics. Authors argue that this issue could be successfully ad-dressed and resolved if, when we map organisations against the system to be developed, we include power and politics in their "too human" and even emotional dimension. A simple way to do so is to use emoji pictograms: most of them are part of a universal language, which requirements engineers could easily adopt and exploit to assess and produce models that include an extra layer of "political" information, without the need to actually introduce any new notation. A few examples of emoji-aware UML and organisational charts are hereby proposed, more as a platform to support communication and share reflections on how to deal with politics than as an actual technology to be adopted.
Unpacking Blockchains
Venue: arXiv cs.CY 2017
Year: 2017

The Bitcoin digital currency appeared in 2009. Since this time, researchers and practitioners have looked under the hood of the open source Bitcoin currency, and discovered that Bitcoins Blockchain software architecture is useful for non-monetary purposes too. By coalescing the research and practice on Blockchains, this work begins to unpack Blockchains as a general phenomenon, therein, arguing that all Blockchain phenomena can be conceived as being comprised of transaction platforms and digital ledgers, and illustrating where public key encryption plays a differential role in facilitating these features of Blockchains.
First- and Second-Order Hypothesis Testing for Mixed Memoryless Sources with General Mixture
Venue: arXiv cs.IT 2017
Year: 2017

The first- and second-order optimum achievable exponents in the simple hypothesis testing problem are investigated. The optimum achievable exponent for type II error probability, under the constraint that the type I error probability is allowed asymptotically up to epsilon, is called the epsilon-optimum exponent. In this paper, we first give the second-order epsilon-exponent in the case where the null hypothesis and the alternative hypothesis are a mixed memoryless source and a stationary memoryless source, respectively. We next generalize this setting to the case where the alternative hypothesis is also a mixed memoryless source. We address the first-order epsilon-optimum exponent in this setting. In addition, an extension of our results to more general setting such as the hypothesis testing with mixed general source and the relationship with the general compound hypothesis testing problem are also discussed.
Internet of Things: An Overview
Venue: arXiv cs.DC 2017
Year: 2017

As technology proceeds and the number of smart devices continues to grow substantially, need for ubiquitous context-aware platforms that support interconnected, heterogeneous, and distributed network of devices has given rise to what is referred today as Internet-of-Things. However, paving the path for achieving aforementioned objectives and making the IoT paradigm more tangible requires integration and convergence of different knowledge and research domains, covering aspects from identification and communication to resource discovery and service integration. Through this chapter, we aim to highlight researches in topics including proposed architectures, security and privacy, network communication means and protocols, and eventually conclude by providing future directions and open challenges facing the IoT development.
On Optimal 2- and 3-Planar Graphs
Venue: arXiv cs.CG 2017
Year: 2017

A graph is $k$-planar if it can be drawn in the plane such that no edge is crossed more than $k$ times. While for $k=1$, optimal $1$-planar graphs, i.e., those with $n$ vertices and exactly $4n-8$ edges, have been completely characterized, this has not been the case for $k \geq 2$. For $k=2,3$ and $4$, upper bounds on the edge density have been developed for the case of simple graphs by Pach and Tóth, Pach et al. and Ackerman, which have been used to improve the well-known "Crossing Lemma". Recently, we proved that these bounds also apply to non-simple $2$- and $3$-planar graphs without homotopic parallel edges and self-loops. In this paper, we completely characterize optimal $2$- and $3$-planar graphs, i.e., those that achieve the aforementioned upper bounds. We prove that they have a remarkably simple regular structure, although they might be non-simple. The new characterization allows us to develop notable insights concerning new inclusion relationships with other graph classes.
Quantized output feedback stabilization by Luenberger observers
Venue: arXiv cs.SY 2017
Year: 2017

We study a stabilization problem for systems with quantized output feedback. The state estimate from a Luenberger observer is used for control inputs and quantization centers. First we consider the case when only the output is quantized and provide data-rate conditions for stabilization. We next generalize the results to the case where both of the plant input and output are quantized and where controllers send the quantized estimate of the plant output to encoders as quantization centers. Finally, we present the numerical comparison of the derived data-rate conditions with those in the earlier studies and a time response of an inverted pendulum.
Formalizing Memory Accesses and Interrupts
Venue: arXiv cs.OS 2017
Year: 2017

The hardware/software boundary in modern heterogeneous multicore computers is increasingly complex, and diverse across different platforms. A single memory access by a core or DMA engine traverses multiple hardware translation and caching steps, and the destination memory cell or register often appears at different physical addresses for different cores. Interrupts pass through a complex topology of interrupt controllers and remappers before delivery to one or more cores, each with specific constraints on their configurations. System software must not only correctly understand the specific hardware at hand, but also configure it appropriately at runtime. We propose a formal model of address spaces and resources in a system that allows us to express and verify invariants of the system's runtime configuration, and illustrate (and motivate) it with several real platforms we have encountered in the process of OS implementation.
Modelling of Autosar Libraries for Large Scale Testing
Venue: arXiv cs.SE 2017
Year: 2017

We demonstrate a specific method and technology for model-based testing of large software projects with the QuickCheck tool using property-based specifications. Our specifications are very precise, state-full models of the software under test (SUT). In our approach we define (a) formal descriptions of valid function call sequences (public API), (b) postconditions that check the validity of each call, and (c) call-out specifications that define and validate external system interactions (SUT calling external API). The QuickCheck tool automatically generates and executes tests from these specifications. Commercially, this method and tool have been used to test large parts of the industrially developed automotive libraries based on the Autosar standard. In this paper, we exemplify our approach with a circular buffer specified by Autosar, to demonstrate the capabilities of the model-based testing method of QuickCheck. Our example is small compared to the commercial QuickCheck models, but faithfully addresses many of the same challenges.
The Unheralded Value of the Multiway Rendezvous: Illustration with the Production Cell Benchmark
Venue: arXiv cs.PL 2017
Year: 2017

The multiway rendezvous introduced in Theoretical CSP is a powerful paradigm to achieve synchronization and communication among a group of (possibly more than two) processes. We illustrate the advantages of this paradigm on the production cell benchmark, a model of a real metal processing plant, for which we propose a compositional software controller, which is written in LNT and LOTOS, and makes intensive use of the multiway rendezvous.
Automatic Text Summarization Approaches to Speed up Topic Model Learning Process
Venue: arXiv cs.IR 2017
Year: 2017

The number of documents available into Internet moves each day up. For this reason, processing this amount of information effectively and expressibly becomes a major concern for companies and scientists. Methods that represent a textual document by a topic representation are widely used in Information Retrieval (IR) to process big data such as Wikipedia articles. One of the main difficulty in using topic model on huge data collection is related to the material resources (CPU time and memory) required for model estimate. To deal with this issue, we propose to build topic spaces from summarized documents. In this paper, we present a study of topic space representation in the context of big data. The topic space representation behavior is analyzed on different languages. Experiments show that topic spaces estimated from text summaries are as relevant as those estimated from the complete documents. The real advantage of such an approach is the processing time gain: we showed that the processing time can be drastically reduced using summarized documents (more than 60\% in general). This study finally points out the differences between thematic representations of documents depending on the targeted languages such as English or latin languages.