OpenRaiser/Intern-Atlas huggingface dataset:
An OMNeT++ based Framework for Mobility-aware Routing in Mobile Robotic Networks
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we propose a cross-layer extension for the INETMANET framework of OMNeT++, which utilizes mobility control knowledge to enhance the forwarding of routing messages. The well-known mobility meta-model from Reynolds is used to provide a realistic representation of the mobility behavior of autonomous agents with respect to the various influences those agents have to face in real-world applications. Knowledge about the current mobility properties and its predicted development is used by mobility-aware routing mechanisms in order to optimize routing decisions and avoid path losses at runtime. In a proof of concept evaluation, we show that our proposed methods can achieve significant improvements to the robustness of communication paths.
Study of Buffer-Aided Distributed Space-Time Coding for Cooperative Wireless Networks
Venue: arXiv cs.IT 2016
Year: 2016

This work proposes adaptive buffer-aided distributed space-time coding schemes and algorithms with feedback for wireless networks equipped with buffer-aided relays. The proposed schemes employ a maximum likelihood receiver at the destination and adjustable codes subject to a power constraint with an amplify-and-forward cooperative strategy at the relays. Each relay is equipped with a buffer and is capable of storing blocks of received symbols and forwarding the data to the destination if selected. Different antenna configurations and wireless channels, such as static block fading channels, are considered. The effects of using buffer-aided relays to improve the bit error rate (BER) performance are also studied. Adjustable relay selection and optimization algorithms that exploit the extra degrees of freedom of relays equipped with buffers are developed to improve the BER performance. We also analyze the pairwise error probability and diversity of the system when using the proposed schemes and algorithms in a cooperative network. Simulation results show that the proposed schemes and algorithms obtain performance gains over previously reported techniques.
Describing upper body motions based on the Labanotation for learning-from-observation robots
Venue: arXiv cs.RO 2016
Year: 2016

We have been developing a paradigm, which we refer to as Learning-from-observation, for a robot to automatically acquire what-to-do through observation of human performance. Since a simple mimicking method to repeat exact joint angles does not work due to the kinematic and dynamic difference between a human and a robot, the method introduces an intermediate symbolic representation, task models, to conceptually represent what-to-do through observation. Then, these task models are mapped appropriate robot motions depending on each robot hardware. This paper presents task models, designed based on the Labanotation, for upper body movements of humanoid robots. Given a human motion sequence, we first analyze the motions of the upper body, and extract certain fixed poses at certain key frames. These key poses are translated into states represented by Labanotation symbols. Then, task models, identified from the state transitions, are mapped to robot movements on a particular robot hardware. Since the task models based on Labanotation are independent from different robot hardware, we can share the same observation module; we only need task mapping modules depending on different robot hardware. The system was implemented and demonstrated that three different robots can automatically mimic human upper body motions with satisfactory level of resemblance.
Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective
Venue: arXiv cs.LG 2016
Year: 2016

Ensemble methods for classification and clustering have been effectively used for decades, while ensemble learning for outlier detection has only been studied recently. In this work, we design a new ensemble approach for outlier detection in multi-dimensional point data, which provides improved accuracy by reducing error through both bias and variance. Although classification and outlier detection appear as different problems, their theoretical underpinnings are quite similar in terms of the bias-variance trade-off [1], where outlier detection is considered as a binary classification task with unobserved labels but a similar bias-variance decomposition of error. In this paper, we propose a sequential ensemble approach called CARE that employs a two-phase aggregation of the intermediate results in each iteration to reach the final outcome. Unlike existing outlier ensembles which solely incorporate a parallel framework by aggregating the outcomes of independent base detectors to reduce variance, our ensemble incorporates both the parallel and sequential building blocks to reduce bias as well as variance by ($i$) successively eliminating outliers from the original dataset to build a better data model on which outlierness is estimated (sequentially), and ($ii$) combining the results from individual base detectors and across iterations (parallelly). Through extensive experiments on sixteen real-world datasets mainly from the UCI machine learning repository [2], we show that CARE performs significantly better than or at least similar to the individual baselines. We also compare CARE with the state-of-the-art outlier ensembles where it also provides significant improvement when it is the winner and remains close otherwise.
Negative Results for Software Effort Estimation
Venue: arXiv cs.SE 2016
Year: 2016

Context:More than half the literature on software effort estimation (SEE) focuses on comparisons of new estimation methods. Surprisingly, there are no studies comparing state of the art latest methods with decades-old approaches. Objective:To check if new SEE methods generated better estimates than older methods. Method: Firstly, collect effort estimation methods ranging from "classical" COCOMO (parametric estimation over a pre-determined set of attributes) to "modern" (reasoning via analogy using spectral-based clustering plus instance and feature selection, and a recent "baseline method" proposed in ACM Transactions on Software Engineering).Secondly, catalog the list of objections that lead to the development of post-COCOMO estimation methods.Thirdly, characterize each of those objections as a comparison between newer and older estimation methods.Fourthly, using four COCOMO-style data sets (from 1991, 2000, 2005, 2010) and run those comparisons experiments.Fifthly, compare the performance of the different estimators using a Scott-Knott procedure using (i) the A12 effect size to rule out "small" differences and (ii) a 99% confident bootstrap procedure to check for statistically different groupings of treatments). Results: The major negative results of this paper are that for the COCOMO data sets, nothing we studied did any better than Boehm's original procedure. Conclusions: When COCOMO-style attributes are available, we strongly recommend (i) using that data and (ii) use COCOMO to generate predictions. We say this since the experiments of this paper show that, at least for effort estimation,how data is collected is more important than what learner is applied to that data.
Extending Unification in $\mathcal{EL}$ to Disunification: The Case of Dismatching and Local Disunification
Venue: arXiv cs.LO 2016
Year: 2016

Unification in Description Logics has been introduced as a means to detect redundancies in ontologies. We try to extend the known decidability results for unification in the Description Logic $\mathcal{EL}$ to disunification since negative constraints can be used to avoid unwanted unifiers. While decidability of the solvability of general $\mathcal{EL}$-disunification problems remains an open problem, we obtain NP-completeness results for two interesting special cases: dismatching problems, where one side of each negative constraint must be ground, and local solvability of disunification problems, where we consider only solutions that are constructed from terms occurring in the input problem. More precisely, we first show that dismatching can be reduced to local disunification, and then provide two complementary NP-algorithms for finding local solutions of disunification problems.
TODIM and TOPSIS with Z-numbers
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, we present an approach that is able to handle with Z-numbers in the context of Multi-Criteria Decision Making (MCDM) problems. Z-numbers are composed of two parts, the first one is a restriction on the values that can be assumed, and the second part is the reliability of the information. As human beings we communicate with other people by means of natural language using sentences like: the journey time from home to university takes about half hour, very likely. Firstly, Z-numbers are converted to fuzzy numbers using a standard procedure. Next, the Z-TODIM and Z-TOPSIS are presented as a direct extension of the fuzzy TODIM and fuzzy TOPSIS, respectively. The proposed methods are applied to two case studies and compared with the standard approach using crisp values. Results obtained show the feasibility of the approach. In addition, a graphical interface was built to handle with both methods Z- TODIM and Z-TOPSIS allowing ease of use for user in other areas of knowledge.
Monitoring the energy consumed by a network infrastructure to detect and isolate faults in communication architecture
Venue: arXiv cs.NI 2016
Year: 2016

In recent years, a number of major improvements were introduced in the area of computer networks, energy-efficient network protocols and network management systems. Software Defined Networking (SDN) as a new paradigm for managing complex networks brings a significant opportunity to reduce the energy consumption among ICT. In this paper, we are tackling improvements in the process of monitoring the states of the networking devices and optimizing the existing solutions. We are monitoring the energy consumption of a network architecture and augment the retrieved raw power data to detect changes in the state of the devices. The goal is to benchmark the difference between the power data fetched from the real-measures and the data extracted from the power models, translated as the expected behavior of the devices. An application is designed to monitor and analyze the retrieved power data of a simulated ICT infrastructure composed of Cisco switches and routers, Dell Precision stations and Raritan PDUs. Moreover, smart algorithms are developed which outcome results with detection of changes in the state of the devices, as well as with detection and isolation of possible anomalies and their impact on the energy consumption. The application is envisioned to be an extension to the existing SDN controllers for monitoring and reporting the changes in the state of the ICT devices.
Poisson Noise Reduction with Higher-order Natural Image Prior Model
Venue: arXiv cs.CV 2016
Year: 2016

Poisson denoising is an essential issue for various imaging applications, such as night vision, medical imaging and microscopy. State-of-the-art approaches are clearly dominated by patch-based non-local methods in recent years. In this paper, we aim to propose a local Poisson denoising model with both structure simplicity and good performance. To this end, we consider a variational modeling to integrate the so-called Fields of Experts (FoE) image prior, that has proven an effective higher-order Markov Random Fields (MRF) model for many classic image restoration problems. We exploit several feasible variational variants for this task. We start with a direct modeling in the original image domain by taking into account the Poisson noise statistics, which performs generally well for the cases of high SNR. However, this strategy encounters problem in cases of low SNR. Then we turn to an alternative modeling strategy by using the Anscombe transform and Gaussian statistics derived data term. We retrain the FoE prior model directly in the transform domain. With the newly trained FoE model, we end up with a local variational model providing strongly competitive results against state-of-the-art non-local approaches, meanwhile bearing the property of simple structure. Furthermore, our proposed model comes along with an additional advantage, that the inference is very efficient as it is well-suited for parallel computation on GPUs. For images of size $512 \times 512$, our GPU implementation takes less than 1 second to produce state-of-the-art Poisson denoising performance.
Detecting highly cyclic structure with complex eigenpairs
Venue: arXiv cs.SI 2016
Year: 2016

Many large, real-world complex networks have rich community structure that a network scientist seeks to understand. These communities may overlap or have intricate internal structure. Extracting communities with particular topological structure, even when they overlap with other communities, is a powerful capability that would provide novel avenues of focusing in on structure of interest. In this work we consider extracting highly-cyclic regions of directed graphs (digraphs). We demonstrate that embeddings derived from complex-valued eigenvectors associated with stochastic propagator eigenvalues near roots of unity are well-suited for this purpose. We prove several fundamental theoretic results demonstrating the connection between these eigenpairs and the presence of highly-cyclic structure and we demonstrate the use of these vectors on a few real-world examples.
Temporal Logic Programs with Variables
Venue: arXiv cs.AI 2016
Year: 2016

In this note we consider the problem of introducing variables in temporal logic programs under the formalism of "Temporal Equilibrium Logic" (TEL), an extension of Answer Set Programming (ASP) for dealing with linear-time modal operators. To this aim, we provide a definition of a first-order version of TEL that shares the syntax of first-order Linear-time Temporal Logic (LTL) but has a different semantics, selecting some LTL models we call "temporal stable models". Then, we consider a subclass of theories (called "splittable temporal logic programs") that are close to usual logic programs but allowing a restricted use of temporal operators. In this setting, we provide a syntactic definition of "safe variables" that suffices to show the property of "domain independence" -- that is, addition of arbitrary elements in the universe does not vary the set of temporal stable models. Finally, we present a method for computing the derivable facts by constructing a non-temporal logic program with variables that is fed to a standard ASP grounder. The information provided by the grounder is then used to generate a subset of ground temporal rules which is equivalent to (and generally smaller than) the full program instantiation.
Circuit Breakers, Discovery, and API Gateways in Microservices
Venue: arXiv cs.SE 2016
Year: 2016

We review some of the most widely used patterns for the programming of microservices: circuit breaker, service discovery, and API gateway. By systematically analysing different deployment strategies for these patterns, we reach new insight especially for the application of circuit breakers. We also evaluate the applicability of Jolie, a language for the programming of microservices, for these patterns and report on other standard frameworks offering similar solutions. Finally, considerations for future developments are presented.
Cache-Aided Coded Multicast for Correlated Sources
Venue: arXiv cs.IT 2016
Year: 2016

The combination of edge caching and coded multicasting is a promising approach to improve the efficiency of content delivery over cache-aided networks. The global caching gain resulting from content overlap distributed across the network in current solutions is limited due to the increasingly personalized nature of the content consumed by users. In this paper, the cache-aided coded multicast problem is generalized to account for the correlation among the network content by formulating a source compression problem with distributed side information. A correlation-aware achievable scheme is proposed and an upper bound on its performance is derived. It is shown that considerable load reductions can be achieved, compared to state of the art correlation-unaware schemes, when caching and delivery phases specifically account for the correlation among the content files.
On the Phase Transition of Finding a Biclique in a larger Bipartite Graph
Venue: arXiv cs.AI 2016
Year: 2016

We report on the phase transition of finding a complete subgraph, of specified dimensions, in a bipartite graph. Finding a complete subgraph in a bipartite graph is a problem that has growing attention in several domains, including bioinformatics, social network analysis and domain clustering. A key step for a successful phase transition study is identifying a suitable order parameter, when none is known. To this purpose, we have applied a decision tree classifier to real-world instances of this problem, in order to understand what problem features separate an instance that is hard to solve from those that is not. We have successfully identified one such order parameter and with it the phase transition of finding a complete bipartite subgraph of specified dimensions. Our phase transition study shows an easy-to-hard-to-easy-to-hard-to-easy pattern. Further, our results indicate that the hardest instances are in a region where it is more likely that the corresponding bipartite graph will have a complete subgraph of specified dimensions, a positive answer. By contrast, instances with a negative answer are more likely to appear in a region where the computational cost is negligible. This behaviour is remarkably similar for problems of a number of different sizes.
Online Output-Feedback Parameter and State Estimation for Second Order Linear Systems
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, a concurrent learning based adaptive observer is developed for a class of second-order linear time-invariant systems with uncertain system matrices. The developed technique yields an exponentially convergent state estimator and an exponentially convergent parameter estimator. As opposed to persistent excitation required for parameter convergence in traditional adaptive methods, excitation over a finite time-interval is sufficient for the developed technique to achieve exponential convergence. Simulation results in both noise-free and noisy environments are presented to validate the design.
A picture is worth a thousand words: an empirical study on the influence of content visibility on diffusion processes within a virtual world
Venue: arXiv cs.SI 2016
Year: 2016

Studying information diffusion and the spread of goods in the real world and in many digital services can be extremely difficult since information about the information flows is challenging to accurately track. How information spreads has commonly been analysed from the perspective of homophily, social influence, and initial seed selection. However, in virtual worlds and virtual economies, the movements of information and goods can be precisely tracked. Therefore, these environments create laboratories for the accurate study of information diffusion characteristics that have been difficult to study in prior research. In this paper, we study how content visibility as well as sender and receiver characteristics, the relationship between them, and the types of multilayer social network layers affect content absorption and diffusion in virtual world. The results show that prior visibility of distributed content is the strongest predictor of content adoption and its further spread across networks. Among other analysed factors, the mechanics of diffusion, content quality, and content adoption by users neighbours on the social activity layer had very strong influences on the adoption of new content.
Macro-optimization of email recommendation response rates harnessing individual activity levels and group affinity trends
Venue: arXiv cs.AI 2016
Year: 2016

Recommendation emails are among the best ways to re-engage with customers after they have left a website. While on-site recommendation systems focus on finding the most relevant items for a user at the moment (right item), email recommendations add two critical additional dimensions: who to send recommendations to (right person) and when to send them (right time). It is critical that a recommendation email system not send too many emails to too many users in too short of a time-window, as users may unsubscribe from future emails or become desensitized and ignore future emails if they receive too many. Also, email service providers may mark such emails as spam if too many of their users are contacted in a short time-window. Optimizing email recommendation systems such that they can yield a maximum response rate for a minimum number of email sends is thus critical for the long-term performance of such a system. In this paper, we present a novel recommendation email system that not only generates recommendations, but which also leverages a combination of individual user activity data, as well as the behavior of the group to which they belong, in order to determine each user's likelihood to respond to any given set of recommendations within a given time period. In doing this, we have effectively created a meta-recommendation system which recommends sets of recommendations in order to optimize the aggregate response rate of the entire system. The proposed technique has been applied successfully within CareerBuilder's job recommendation email system to generate a 50\% increase in total conversions while also decreasing sent emails by 72%
Robust Fusion of Multi-Band Images with Different Spatial and Spectral Resolutions for Change Detection
Venue: arXiv cs.CV 2016
Year: 2016

Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through different kinds of sensors. More precisely, this paper addresses the problem of detecting changes between two multi-band optical images characterized by different spatial and spectral resolutions. This sensor dissimilarity introduces additional issues in the context of operational change detection. To alleviate these issues, classical change detection methods are applied after independent preprocessing steps (e.g., resampling) used to get the same spatial and spectral resolutions for the pair of observed images. Nevertheless, these preprocessing steps tend to throw away relevant information. Conversely, in this paper, we propose a method that more effectively uses the available information by modeling the two observed images as spatial and spectral versions of two (unobserved) latent images characterized by the same high spatial and high spectral resolutions. As they cover the same scene, these latent images are expected to be globally similar except for possible changes in sparse spatial locations. Thus, the change detection task is envisioned through a robust multi-band image fusion method which enforces the differences between the estimated latent images to be spatially sparse. This robust fusion problem is formulated as an inverse problem which is iteratively solved using an efficient block-coordinate descent algorithm. The proposed method is applied to real panchormatic/multispectral and hyperspectral images with simulated realistic changes. A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed strategy.
Time Derivative of Rotation Matrices: A Tutorial
Venue: arXiv cs.RO 2016
Year: 2016

The time derivative of a rotation matrix equals the product of a skew-symmetric matrix and the rotation matrix itself. This article gives a brief tutorial on the well-known result.