User-input driven QoS management in ad hoc networks

Though the network quality-of-service (QoS) metrics are defined in terms of technical parameters (e.g., delay, jitter, bandwidth), they are rather subjective when it comes to the end user. Oftentimes, the end user finds it difficult to express his desired QoS in such technical parameters, though he has a fair idea of what QoS he desires. In this paper, we show how translation functions can be devised and used to translate the user inputs to networking parameters that are used by various layers of the protocol stack. In particular, we consider an ad hoc network and show an interface design that uses translation functions to map user supplied inputs to parameters at the medium access control (MAC) and routing layers. These parameters, in turn, choose the right strategies that particular layer functionality can adopt, such that the QoS desired by the user is achieved. We also compute the associated costs due to the different strategies adopted. We implement the interface on ns-2 and conduct simulation experiments with randomly scattered IEEE 802.11 enabled nodes. Results show the functionality of the interface and demonstrate how delay, throughput, and network lifetime are affected when the end user seeks different levels of QoS.     

The design and implementation of a session layer for delay-tolerant networks

The DTN architecture is based around sender-initiated unicast communication that is insufficient or inconvenient to meet the needs of many applications. To address these limitations, we define a DTN session layer and associated extensions to the DTN bundle protocol that more naturally support receiver-driven applications and multicast communication. Within a session, we provide mechanisms allowing applications to convey ordering relationships between successive transmissions that can be used by the network to help ensure a distributed application’s delivery ordering expectations are met. We also extend the bundle protocol’s expiration procedures to support more efficient network utilization by allowing in-network deletion of obsolete messages. We present the design rationale and describe our implementation of these mechanisms and discuss their advantages in meeting the needs of several popular types of applications.     

Exploiting an antivirus interface

We propose a technique for defeating signature-based malware detectors by exploiting information disclosed by antivirus interfaces. This information is leveraged to reverse engineer relevant details of the detector's underlying signature database, revealing binary obfuscations that suffice to conceal malware from the detector. Experiments with real malware and antivirus interfaces on Windows operating systems justify the effectiveness of our approach.     

An approach for community detection in social networks based on cooperative games theory

Detecting communities is of great importance in social network analysis. However it is an issue that has not yet been satisfactorily solved, despite the efforts made by interdisciplinary research communities over the past few years, because of the nature of complexity in deciding how community structures should be recognized. In this paper we propose an approach based on cooperative game theory for community detection in social networks. We regard individuals as players, and regard communities as coalitions formed by players, and model community detection problem as the formation and optimization of coalitions. Furthermore, we define coalition profile for players to indicate coalitions that players joined, the order of a coalition profile is defined as the number of coalitions in a coalition profile, and we introduce a utility function to measure preference of coalition profiles. Accordingly, we propose an algorithm to detect a coalition profile with maximal utility function values. We have implemented the algorithms developed in this study and experimental results demonstrate the effectiveness of our approaches.     

Gambling versus Designing: Organizing for the Design of the Probability Space in the Energy Sector

The objective of this paper is to elucidate an organizational process for the design of generic technologies (GTs). While recognizing the success of GTs, the literature on innovation management generally describes their design according to evolutionary strategies featuring multiple and uncertain trials, resulting in the discovery of common features among multiple applications. This random walk depends on multiple market and technological uncertainties that are considered exogenous: as smart as he can be, the ‘gambler’ must play in a given probability space. However, what happens when the innovator is not a gambler but a designer, i.e., when the actor is able to establish new links between previously independent emerging markets and technologies? Formally speaking, the actor designs a new probability space. Building on a case study of two technological development programmes at the French Center for Atomic Energy, we present cases of GTs that correspond to this logic of designing the probability space, i.e. the logic of intentionally designing common features that bridge the gap between a priori heterogeneous applications and technologies. This study provides another example showing that the usual trial‐and‐learning strategy is not the only strategy to design GTs and that these technologies can be designed by intentionally building new interdependences between markets and technologies. Our main result is that building these interdependences requires organizational patterns that correspond to a ‘design of exploration’ phase in which multiple technology suppliers and application providers are involved in designing both the probability space itself and the instruments to explore and benefit from this new space.     

Team THOR's Entry in the DARPA Robotics Challenge Finals 2015

This paper describes Team THOR's approach to human‐in‐the‐loop disaster response robotics for the 2015 DARPA Robotics Challenge (DRC) Finals. Under the duress of unpredictable networking and terrain, fluid operator interactions and dynamic disturbance rejection become major concerns for effective teleoperation. We present a humanoid robot designed to effectively traverse a disaster environment while allowing for a wide range of manipulation abilities. To complement the robot hardware, a hierarchical software foundation implements network strategies that provide real‐time feedback to an operator under restricted bandwidth using layered user interfaces. Our strategy for humanoid locomotion includes a backward‐facing knee configuration paired with specialized toe and heel lifting strategies that allow the robot to traverse difficult surfaces while rejecting external perturbations. With an upper body planner that encodes operator preferences, predictable motion plans are executed in unforeseen circumstances. These plans are critical for manipulation in unknown environments. Our approach was validated during the DRC Finals competition, where Team THOR scored three points in 18 min of operation time, and the results are presented along with an analysis of each task.     

Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain

Practical and financial constraints associated with traditional field-based lithological mapping are often responsible for the generation of maps with insufficient detail and inaccurately located contacts. In arid areas with well exposed rocks and soils, high-resolution multi- and hyperspectral imagery is a valuable mapping aid as lithological units can be readily discriminated and mapped by automatically matching image pixel spectra to a set of reference spectra. However, the use of spectral imagery in all but the most barren terrain is problematic because just small amounts of vegetation cover can obscure or mask the spectra of underlying geological substrates. The use of ancillary information may help to improve lithological discrimination, especially where geobotanical relationships are absent or where distinct lithologies exhibit inherent spectral similarity. This study assesses the efficacy of airborne multispectral imagery for detailed lithological mapping in a vegetated section of the Troodos ophiolite (Cyprus), and investigates whether the mapping performance can be enhanced through the integration of LiDAR-derived topographic data. In each case, a number of algorithms involving different combinations of input variables and classification routine were employed to maximise the mapping performance. Despite the potential problems posed by vegetation cover, geobotanical associations aided the generation of a lithological map - with a satisfactory overall accuracy of 65.5% and Kappa of 0.54 - using only spectral information. Moreover, owing to the correlation between topography and lithology in the study area, the integration of LiDAR-derived topographic variables led to significant improvements of up to 22.5% in the overall mapping accuracy compared to spectral-only approaches. The improvements were found to be considerably greater for algorithms involving classification with an artificial neural network (the Kohonen Self-Organizing Map) than the parametric Maximum Likelihood Classifier. The results of this study demonstrate the enhanced capability of data integration for detailed lithological mapping in areas where spectral discrimination is complicated by the presence of vegetation or inherent spectral similarities.     

Satellite observations of circulation features associated with a bowhead whale feeding ‘hotspot’ near Barrow, Alaska

Satellite images, along with oceanographic, meteorological, and whale aerial survey data, are used to illustrate aspects of ocean circulation associated with a bowhead whale feeding ‘hotspot’ near Barrow, Alaska. In response to weak winds, a strong front forms near the shelf-break along the southern edge of Barrow Canyon when the Alaska Coastal Current flows adjacent to the southern flank of Barrow Canyon or intrudes onto the western Beaufort shelf. This front is of particular local interest because it is indicative of aggregation and retention of zooplankton on the western Beaufort shelf and, as a result, is a locus for bowhead whales pausing to feed during their westward fall migration. Groups (4 or more individuals) of bowhead whales are primarily seen on the western Beaufort shelf following wind conditions that promote the formation of this front.     

Efficiently Testing Sparse <Emphasis Type="Italic">GF</Emphasis>(2) Polynomials

We give the first algorithm that is both query-efficient and time-efficient for testing whether an unknown function f:{0,1} ⁿ →{−1,1} is an s-sparse GF(2) polynomial versus ε-far from every such polynomial. Our algorithm makes poly(s,1/ε) black-box queries to f and runs in time n⋅poly(s,1/ε). The only previous algorithm for this testing problem (Diakonikolas et al. in Proceedings of the 48th Annual Symposium on Foundations of Computer Science, FOCS, pp. 549–558, 2007) used poly(s,1/ε) queries, but had running time exponential in s and super-polynomial in 1/ε.     

Genetic adaptive state estimation

A genetic algorithm (GA) uses the principles of evolution, natural selection, and genetics to offer a method for parallel search of complex spaces. This paper describes a GA that can perform on-line adaptive state estimation for linear and nonlinear systems. First, it shows how to construct a genetic adaptive state estimator where a GA evolves the model in a state estimator in real time so that the state estimation error is driven to zero. Next, several examples are used to illustrate the operation and performance of the genetic adaptive state estimator. Its performance is compared to that of the conventional adaptive Luenberger observer for two linear system examples. Next, a genetic adaptive state estimator is used to predict when surge and stall occur in a nonlinear jet engine. Our main conclusion is that the genetic adaptive state estimator has the potential to offer higher performance estimators for nonlinear systems over current methods.