OpenRaiser/Intern-Atlas huggingface dataset:
EAPESS: An Adaptive Transmission Scheme in Wireless Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

Reduced energy consumption in sensor nodes is one of the major challenges in Wireless Sensor Networks (WSNs) deployment. In this regard, Error Control Coding (ECC) is one of techniques used for energy optimization in WSNs. Similarly, critical distance is another term being used for energy efficiency, when used with ECC provides better results of energy saving. In this paper three different critical distance values are used against different coding gains for sake of energy saving. If distance lies below critical distance values then particular encoders are selected with respect to their particular coding gains. Coding gains are used for critical distances estimation of all encoders. This adaptive encoder and transmit power selection scheme with respect to their coding gain results in a significant energy saving in WSNs environment. Simulations provide better results of energy saving achieved by using this adaptive scheme.
Exploiting Hybrid Channel Information for Downlink Multi-User MIMO Scheduling
Venue: arXiv cs.IT 2013
Year: 2013

We investigate the downlink multi-user MIMO (MU-MIMO) scheduling problem in the presence of imperfect Channel State Information at the transmitter (CSIT) that comprises of coarse and current CSIT as well as finer but delayed CSIT. This scheduling problem is characterized by an intricate `exploitation - exploration tradeoff' between scheduling the users based on current CSIT for immediate gains, and scheduling them to obtain finer albeit delayed CSIT and potentially larger future gains. We solve this scheduling problem by formulating a frame based joint scheduling and feedback approach, where in each frame a policy is obtained as the solution to a Markov Decision Process. We prove that our proposed approach can be made arbitrarily close to the optimal and then demonstrate its significant gains over conventional MU-MIMO scheduling.
Stochastic Modeling of Large-Scale Solid-State Storage Systems: Analysis, Design Tradeoffs and Optimization
Venue: arXiv cs.PF 2013
Year: 2013

Solid state drives (SSDs) have seen wide deployment in mobiles, desktops, and data centers due to their high I/O performance and low energy consumption. As SSDs write data out-of-place, garbage collection (GC) is required to erase and reclaim space with invalid data. However, GC poses additional writes that hinder the I/O performance, while SSD blocks can only endure a finite number of erasures. Thus, there is a performance-durability tradeoff on the design space of GC. To characterize the optimal tradeoff, this paper formulates an analytical model that explores the full optimal design space of any GC algorithm. We first present a stochastic Markov chain model that captures the I/O dynamics of large-scale SSDs, and adapt the mean-field approach to derive the asymptotic steady-state performance. We further prove the model convergence and generalize the model for all types of workload. Inspired by this model, we propose a randomized greedy algorithm (RGA) that can operate along the optimal tradeoff curve with a tunable parameter. Using trace-driven simulation on DiskSim with SSD add-ons, we demonstrate how RGA can be parameterized to realize the performance-durability tradeoff.
Asynchronous Cellular Operations on Gray Images Extracting Topographic Shape Features and Their Relations
Venue: arXiv cs.CV 2013
Year: 2013

A variety of operations of cellular automata on gray images is presented. All operations are of a wave-front nature finishing in a stable state. They are used to extract shape descripting gray objects robust to a variety of pattern distortions. Topographic terms are used: "lakes", "dales", "dales of dales". It is shown how mutual object relations like "above" can be presented in terms of gray image analysis and how it can be used for character classification and for gray pattern decomposition. Algorithms can be realized with a parallel asynchronous architecture. Keywords: Pattern Recognition, Mathematical Morphology, Cellular Automata, Wave-front Algorithms, Gray Image Analysis, Topographical Shape Descriptors, Asynchronous Parallel Processors, Holes, Cavities, Concavities, Graphs.
On whether and how D-RISC and Microgrids can be kept relevant (self-assessment report)
Venue: arXiv cs.AR 2013
Year: 2013

This report lays flat my personal views on D-RISC and Microgrids as of March 2013. It reflects the opinions and insights that I have gained from working on this project during the period 2008-2013. This report is structed in two parts: deconstruction and reconstruction. In the deconstruction phase, I review what I believe are the fundamental motivation and goals of the D-RISC/Microgrids enterprise, and identify what I judge are shortcomings: that the project did not deliver on its expectations, that fundamental questions are left unanswered, and that its original motivation may not even be relevant in scientific research any more in this day and age. In the reconstruction phase, I start by identifying the merits of the current D-RISC/Microgrids technology and know-how taken at face value, re-motivate its existence from a different angle, and suggest new, relevant research questions that could justify continued scientific investment.
Parameter identification in large kinetic networks with BioPARKIN
Venue: arXiv cs.MS 2013
Year: 2013

Modelling, parameter identification, and simulation play an important role in systems biology. Usually, the goal is to determine parameter values that minimise the difference between experimental measurement values and model predictions in a least-squares sense. Large-scale biological networks, however, often suffer from missing data for parameter identification. Thus, the least-squares problems are rank-deficient and solutions are not unique. Many common optimisation methods ignore this detail because they do not take into account the structure of the underlying inverse problem. These algorithms simply return a "solution" without additional information on identifiability or uniqueness. This can yield misleading results, especially if parameters are co-regulated and data are noisy.
FreeIMU: An Open Hardware Framework for Orientation and Motion Sensing
Venue: arXiv cs.ET 2013
Year: 2013

Orientation and Motion Sensing are widely implemented on various consumer products, such as mobile phones, tablets and cameras as they enable immediate interaction with virtual information. The prototyping phase of any orientation and motion sensing capable device is however a quite difficult process as it may involve complex hardware designing, math algorithms and programming. In this paper, we present FreeIMU, an Open Hardware Framework for prototyping orientation and motion sensing capable devices. The framework consists in a small circuit board containing various sensors and a software library, built on top of the Arduino platform. Both the hardware and library are released under open licences and supported by an active community allowing to be implemented into research and commercial projects.
Combinatorial Analysis of Multiple Networks
Venue: arXiv cs.SI 2013
Year: 2013

The study of complex networks has been historically based on simple graph data models representing relationships between individuals. However, often reality cannot be accurately captured by a flat graph model. This has led to the development of multi-layer networks. These models have the potential of becoming the reference tools in network data analysis, but require the parallel development of specific analysis methods explicitly exploiting the information hidden in-between the layers and the availability of a critical mass of reference data to experiment with the tools and investigate the real-world organization of these complex systems. In this work we introduce a real-world layered network combining different kinds of online and offline relationships, and present an innovative methodology and related analysis tools suggesting the existence of hidden motifs traversing and correlating different representation layers. We also introduce a notion of betweenness centrality for multiple networks. While some preliminary experimental evidence is reported, our hypotheses are still largely unverified, and in our opinion this calls for the availability of new analysis methods but also new reference multi-layer social network data.
Which research in design creativity and innovation? Let us not forget the reality of companies
Venue: arXiv cs.CY 2013
Year: 2013

Studying design creativity and innovation from practical perspectives for companies requires both a good understanding of the company ecosystem and its inner processes contributing to delivered innovations and a rigorous design research methodology to provide effective design models, methods, platforms that are truly effective in the context of company. Working in an Industrial Engineering laboratory, we advocate a more systemic vision of design creativity and innovation in company ecosystems. We present in this paper an attempt to develop and make professional an innovation engineering. Our research works are illustrated along the different research topics of an innovation process. We start by a recent survey on innovation practice and organizational models led in 28 large companies. The lessons learned about this survey reinforce our belief that there is a need for a new method in agile management of radical innovation projects in company contexts. We currently develop, test and apply such a methodology named: Radical Innovation Design(RID). Its effectiveness has been evaluated through a large scale evaluation of the project outcomes for the company. Two extensions of RID have been proposed and deployed in company contexts: a selection procedure for innovation clusters and a value-driven process for airplane development projects.
On the optimality of a L1/L1 solver for sparse signal recovery from sparsely corrupted compressive measurements
Venue: arXiv cs.IT 2013
Year: 2013

This short note proves the $\ell_2-\ell_1$ instance optimality of a $\ell_1/\ell_1$ solver, i.e a variant of \emph{basis pursuit denoising} with a $\ell_1$ fidelity constraint, when applied to the estimation of sparse (or compressible) signals observed by sparsely corrupted compressive measurements. The approach simply combines two known results due to Y. Plan, R. Vershynin and E. Candès.
Joint Power Adjustment and Receiver Design for Distributed Space-Time Coded in Cooperative MIMO Systems
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, a joint power allocation algorithm with minimum mean-squared error (MMSE) receiver for a cooperative Multiple-Input and Multiple-Output (MIMO) network which employs multiple relays and a Decode-and-Forward (DF) strategy is proposed. A Distributed Space-Time Coding (DSTC) scheme is applied in each relay node. We present a joint constrained optimization algorithm to determine the power allocation parameters and the MMSE receive filter parameter vectors for each transmitted symbol in each link, as well as the channel coefficients matrix. A Stochastic Gradient (SG) algorithm is derived for the calculation of the joint optimization in order to release the receiver from the massive calculation complexity for the MMSE receive filter and power allocation parameters. The simulation results indicate that the proposed algorithm obtains gains compared to the equal power allocation system.
Low-Rank STAP Algorithm for Airborne Radar Based on Basis-Function Approximation
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we develop a novel reduced-rank space-time adaptive processing (STAP) algorithm based on adaptive basis function approximation (ABFA) for airborne radar applications. The proposed algorithm employs the well-known framework of the side-lobe canceller (SLC) structure and consists of selected sets of basis functions that perform dimensionality reduction and an adaptive reduced-rank filter. Compared to traditional reduced-rank techniques, the proposed scheme works on an instantaneous basis, selecting the best suited set of basis functions at each instant to minimize the squared error. Furthermore, we derive stochastic gradient (SG) and recursive least squares (RLS) algorithm for efficiently implementing the proposed ABFA scheme. Simulations for a clutter-plus-jamming suppression application show that the proposed STAP algorithm outperforms the state-of-the-art reduced-rank schemes in convergence and tracking at significantly lower complexity.
Discovery of Convoys in Network Proximity Log
Venue: arXiv cs.DB 2013
Year: 2013

This paper describes an algorithm for discovery of convoys in database with proximity log. Traditionally, discovery of convoys covers trajectories databases. This paper presents a model for context-aware browsing application based on the network proximity. Our model uses mobile phone as proximity sensor and proximity data replaces location information. As per our concept, any existing or even especially created wireless network node could be used as presence sensor that can discover access to some dynamic or user-generated content. Content revelation in this model depends on rules based on the proximity. Discovery of convoys in historical user's logs provides a new class of rules for delivering local content to mobile subscribers.
Q-LEACH: A New Routing Protocol for WSNs
Venue: arXiv cs.NI 2013
Year: 2013

Wireless Sensor Networks (WSNs) with their dynamic applications gained a tremendous attention of researchers. Constant monitoring of critical situations attracted researchers to utilize WSNs at vast platforms. The main focus in WSNs is to enhance network life-time as much as one could, for efficient and optimal utilization of resources. Different approaches based upon clustering are proposed for optimum functionality. Network life-time is always related with energy of sensor nodes deployed at remote areas for constant and fault tolerant monitoring. In this work, we propose Quadrature-LEACH (Q-LEACH) for homogenous networks which enhances stability period, network life-time and throughput quiet significantly.
Iterative Expectation for Multi Period Information Retrieval
Venue: arXiv cs.IR 2013
Year: 2013

Many Information Retrieval (IR) models make use of offline statistical techniques to score documents for ranking over a single period, rather than use an online, dynamic system that is responsive to users over time. In this paper, we explicitly formulate a general Multi Period Information Retrieval problem, where we consider retrieval as a stochastic yet controllable process. The ranking action during the process continuously controls the retrieval system's dynamics, and an optimal ranking policy is found in order to maximise the overall users' satisfaction over the multiple periods as much as possible. Our derivations show interesting properties about how the posterior probability of the documents relevancy evolves from users feedbacks through clicks, and provides a plug-in framework for incorporating different click models. Based on the Multi-Armed Bandit theory, we propose a simple implementation of our framework using a dynamic ranking rule that takes rank bias and exploration of documents into account. We use TREC data to learn a suitable exploration parameter for our model, and then analyse its performance and a number of variants using a search log data set; the experiments suggest an ability to explore document relevance dynamically over time using user feedback in a way that can handle rank bias.
Divide-and-Rule Scheme for Energy Efficient Routing in Wireless Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

From energy conservation perspective inWireless Sensor Networks (WSNs), clustering of sensor nodes is a challenging task. Clustering technique in routing protocols play a key role to prolong the stability period and lifetime of the network. In this paper, we propose and evaluate a new routing protocol for WSNs. Our protocol; Divide-and-Rule (DR) is based upon static clustering and dynamic Cluster Head (CH) selection technique. This technique selects fixed number of CHs in each round instead of probabilistic selection of CH. Simulation results show that DR protocol outperform its counterpart routing protocols.
EDDEEC: Enhanced Developed Distributed Energy-Efficient Clustering for Heterogeneous Wireless Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

Wireless Sensor Networks (WSNs) consist of large number of randomly deployed energy constrained sensor nodes. Sensor nodes have ability to sense and send sensed data to Base Station (BS). Sensing as well as transmitting data towards BS require high energy. In WSNs, saving energy and extending network lifetime are great challenges. Clustering is a key technique used to optimize energy consumption in WSNs. In this paper, we propose a novel clustering based routing technique: Enhanced Developed Distributed Energy Efficient Clustering scheme (EDDEEC) for heterogeneous WSNs. Our technique is based on changing dynamically and with more efficiency the Cluster Head (CH) election probability. Simulation results show that our proposed protocol achieves longer lifetime, stability period and more effective messages to BS than Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC) and Enhanced DEEC (EDEEC) in heterogeneous environments.
Modal Logics for Qualitative Possibility and Beliefs
Venue: arXiv cs.AI 2013
Year: 2013

Possibilistic logic has been proposed as a numerical formalism for reasoning with uncertainty. There has been interest in developing qualitative accounts of possibility, as well as an explanation of the relationship between possibility and modal logics. We present two modal logics that can be used to represent and reason with qualitative statements of possibility and necessity. Within this modal framework, we are able to identify interesting relationships between possibilistic logic, beliefs and conditionals. In particular, the most natural conditional definable via possibilistic means for default reasoning is identical to Pearl's conditional for e-semantics.
Lattice-Based Graded Logic: a Multimodal Approach
Venue: arXiv cs.AI 2013
Year: 2013

Experts do not always feel very, comfortable when they have to give precise numerical estimations of certainty degrees. In this paper we present a qualitative approach which allows for attaching partially ordered symbolic grades to logical formulas. Uncertain information is expressed by means of parameterized modal operators. We propose a semantics for this multimodal logic and give a sound and complete axiomatization. We study the links with related approaches and suggest how this framework might be used to manage both uncertain and incomplere knowledge.
Dynamic Network Models for Forecasting
Venue: arXiv cs.AI 2013
Year: 2013

We have developed a probabilistic forecasting methodology through a synthesis of belief network models and classical time-series analysis. We present the dynamic network model (DNM) and describe methods for constructing, refining, and performing inference with this representation of temporal probabilistic knowledge. The DNM representation extends static belief-network models to more general dynamic forecasting models by integrating and iteratively refining contemporaneous and time-lagged dependencies. We discuss key concepts in terms of a model for forecasting U.S. car sales in Japan.
A Symbolic Approach to Reasoning with Linguistic Quantifiers
Venue: arXiv cs.AI 2013
Year: 2013

This paper investigates the possibility of performing automated reasoning in probabilistic logic when probabilities are expressed by means of linguistic quantifiers. Each linguistic term is expressed as a prescribed interval of proportions. Then instead of propagating numbers, qualitative terms are propagated in accordance with the numerical interpretation of these terms. The quantified syllogism, modelling the chaining of probabilistic rules, is studied in this context. It is shown that a qualitative counterpart of this syllogism makes sense, and is relatively independent of the threshold defining the linguistically meaningful intervals, provided that these threshold values remain in accordance with the intuition. The inference power is less than that of a full-fledged probabilistic con-quaint propagation device but better corresponds to what could be thought of as commonsense probabilistic reasoning.
Some Problems for Convex Bayesians
Venue: arXiv cs.AI 2013
Year: 2013

We discuss problems for convex Bayesian decision making and uncertainty representation. These include the inability to accommodate various natural and useful constraints and the possibility of an analog of the classical Dutch Book being made against an agent behaving in accordance with convex Bayesian prescriptions. A more general set-based Bayesianism may be as tractable and would avoid the difficulties we raise.
The Bounded Bayesian
Venue: arXiv cs.AI 2013
Year: 2013

The ideal Bayesian agent reasons from a global probability model, but real agents are restricted to simplified models which they know to be adequate only in restricted circumstances. Very little formal theory has been developed to help fallibly rational agents manage the process of constructing and revising small world models. The goal of this paper is to present a theoretical framework for analyzing model management approaches. For a probability forecasting problem, a search process over small world models is analyzed as an approximation to a larger-world model which the agent cannot explicitly enumerate or compute. Conditions are given under which the sequence of small-world models converges to the larger-world probabilities.
Empirical Probabilities in Monadic Deductive Databases
Venue: arXiv cs.AI 2013
Year: 2013

We address the problem of supporting empirical probabilities in monadic logic databases. Though the semantics of multivalued logic programs has been studied extensively, the treatment of probabilities as results of statistical findings has not been studied in logic programming/deductive databases. We develop a model-theoretic characterization of logic databases that facilitates such a treatment. We present an algorithm for checking consistency of such databases and prove its total correctness. We develop a sound and complete query processing procedure for handling queries to such databases.
R&D Analyst: An Interactive Approach to Normative Decision System Model Construction
Venue: arXiv cs.AI 2013
Year: 2013

This paper describes the architecture of R&D Analyst, a commercial intelligent decision system for evaluating corporate research and development projects and portfolios. In analyzing projects, R&D Analyst interactively guides a user in constructing an influence diagram model for an individual research project. The system's interactive approach can be clearly explained from a blackboard system perspective. The opportunistic reasoning emphasis of blackboard systems satisfies the flexibility requirements of model construction, thereby suggesting that a similar architecture would be valuable for developing normative decision systems in other domains. Current research is aimed at extending the system architecture to explicitly consider of sequential decisions involving limited temporal, financial, and physical resources.
Expressing Relational and Temporal Knowledge in Visual Probabilistic Networks
Venue: arXiv cs.AI 2013
Year: 2013

Bayesian networks have been used extensively in diagnostic tasks such as medicine, where they represent the dependency relations between a set of symptoms and a set of diseases. A criticism of this type of knowledge representation is that it is restricted to this kind of task, and that it cannot cope with the knowledge required in other artificial intelligence applications. For example, in computer vision, we require the ability to model complex knowledge, including temporal and relational factors. In this paper we extend Bayesian networks to model relational and temporal knowledge for high-level vision. These extended networks have a simple structure which permits us to propagate probability efficiently. We have applied them to the domain of endoscopy, illustrating how the general modelling principles can be used in specific cases.