A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.     

Lagrangean relaxation with clusters for point-feature cartographic label placement problems

This paper presents two new mathematical formulations for the point-feature cartographic label placement problem (PFCLP) and a new Lagrangean relaxation with clusters (LagClus) to provide bounds to these formulations. The PFCLP can be represented by a conflict graph and the relaxation divides the graph in small subproblems (clusters) that are easily solved. The edges connecting clusters are relaxed in a Lagrangean way and a subgradient algorithm improves the bounds. The LagClus was successfully applied to a set of instances up to 1000 points providing the best results of those reported in the literature.     

Malicious uses of blockchains by malware: from the analysis to Smart-Zephyrus

The permanent availability and relative obscurity of blockchains is the perfect ground for using them for malicious purposes. However, the use of blockchains by malwares has not been characterized yet. This paper analyses the current state of the art in this area. One of the lessons learned is that covert communications for malware have received little attention. To foster further defence-oriented research, a novel mechanism (dubbed Smart-Zephyrus) is built leveraging smart contracts written in Solidity. Our results show that it is possible to hide 4 Kb of secret in 41 s. While being expensive (around USD 1.82 per bit), the provided stealthiness might be worth the price for attackers.

     

Relating instance hardness to classification performance in a dataset: a visual approach

Machine Learning - Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The...     

Effect of warm mix additives on the interfacial bonding characteristics of asphalt binders

The quality of the interfacial bonding between asphalt binder and aggregates plays a significant role in determining the durability of asphalt mixtures. Warm mix asphalt (WMA) modifiers have been used extensively in the last decade primarily to reduce production and compaction temperatures as well as to improve workability of asphalt mixtures. This study aimed to provide better understanding of the effects of these WMA modifiers on the interfacial bonding between asphalt binders and aggregates. The evaluation focused on measuring surface energy of binders in unaged and aged states and aggregates and then calculating energy parameters that describe the potential of a given asphalt-aggregate combination to resist fatigue cracking and moisture damage. Results show that the combination of asphalt-WMA additive, as well as the content applied of WMA additive has a significant impact on the fatigue cracking and moisture damage resistance. The results suggest that it is poor practice to use a given type and percentage of WMA modifier without regard for binder type. Instead, test methods are recommended to evaluate the compatibility of asphalt binder, WMA additive type/content, and aggregates for improved performance at different conditions.     

Supporting Unified Distributed Management and Autonomic Decisions: Design,Implementation and Deployment

Nowadays, the prevailing use of networks based on traditional centralized management systems reflects on a fast increase of the management costs. The growth in the number of network equipments and services reinforces the need to distribute the management responsibilities throughout the network devices. In this approach, each device executes common network management functionalities, being part of the overall network management platform. In this paper, we present a Unified Distributed Network Management (UDNM) framework that provides a unified (wired and wireless) management network solution, where further different network services can take part of this infrastructure, e.g., flow monitoring, accurate routing decisions, distributed policies dissemination, etc. This framework is divided in two main components: (A) Situation awareness, which sets up initial information through bootstrapping, discovery, fault-management process and exchange of management information; (B) Autonomic Decision System (ADS) that performs distributed decisions in the network with incomplete information. We deploy the UDNM framework in a testbed which involves two cities (\(\approx\)250 km between), different standards (IEEE 802.3, IEEE 802.11 and IEEE 802.16e) and network technologies, such as, wired virtual grid, wireless ad-hoc gateways, ad-hoc mobile access devices. The UDNM framework integrates management functionalities into the managed devices, proving to be a lightweight and easy-respond framework. The performance analysis shows that the UDNM framework is feasible to unify devices management functionalities and to take accurate decisions on top of a real network.     

A short numerical study on the optimization methods influence on topology optimization

Structural and Multidisciplinary Optimization - Structural topology optimization problems are commonly defined using continuous design variables combined with material interpolation schemes. One of...     

RFuzzy: Syntax, semantics and implementation details of a simple and expressive fuzzy tool over Prolog

We present the RFuzzy framework, a Prolog-based tool for representing and reasoning with fuzzy information. The advantages of our framework in comparison to previous tools along this line of research are its easy, user-friendly syntax, and its expressivity through the availability of default values and types.In this approach we describe the formal syntax, the operational semantics and the declarative semantics of RFuzzy (based on a lattice). A least model semantics, a least fixpoint semantics and an operational semantics are introduced and their equivalence is proven. We provide a real implementation that is free and available. (It can be downloaded from http://babel.ls.fi.upm.es/software/rfuzzy/.) Besides implementation details, we also discuss some actual applications using RFuzzy.     

Adaptive inter-layer message routing in 3D networks-on-chip

Existing routing algorithms for 3D deal with regular mesh/torus 3D topologies. Today 3D NoCs are quite irregular, especially those with heterogeneous layers. In this paper, we present a routing algorithm targeting 3D networks-on-chip (NoCs) with incomplete sets of vertical links between adjacent layers. The routing algorithm tolerates multiple link and node failures, in the case of absence of NoC partitioning. In addition, it deals with congestion. The routing algorithm for 3D NoCs preserves the deadlock-free propriety of the chosen 2D routing algorithms. It is also scalable and supports a local reconfiguration that complements the reconfiguration of the 2D routing algorithms in case of failures of nodes or links. The algorithm incurs a small overhead in terms of exchanged messages for reconfiguration and does not introduce significant additional complexity in the routers. Theoretical analysis of the 3D routing algorithm is provided and validated by simulations for different traffic loads and failure rates.