OpenRaiser/Intern-Atlas huggingface dataset:
Corporate Evidential Decision Making in Performance Prediction Domains
Venue: arXiv cs.AI 2013
Year: 2013

Performance prediction or forecasting sporting outcomes involves a great deal of insight into the particular area one is dealing with, and a considerable amount of intuition about the factors that bear on such outcomes and performances. The mathematical Theory of Evidence offers representation formalisms which grant experts a high degree of freedom when expressing their subjective beliefs in the context of decision-making situations like performance prediction. Furthermore, this reasoning framework incorporates a powerful mechanism to systematically pool the decisions made by individual subject matter experts. The idea behind such a combination of knowledge is to improve the competence (quality) of the overall decision-making process. This paper reports on a performance prediction experiment carried out during the European Football Championship in 1996. Relying on the knowledge of four predictors, Evidence Theory was used to forecast the final scores of all 31 matches. The results of this empirical study are very encouraging.
Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes
Venue: arXiv cs.AI 2013
Year: 2013

We present a method for solving implicit (factored) Markov decision processes (MDPs) with very large state spaces. We introduce a property of state space partitions which we call epsilon-homogeneity. Intuitively, an epsilon-homogeneous partition groups together states that behave approximately the same under all or some subset of policies. Borrowing from recent work on model minimization in computer-aided software verification, we present an algorithm that takes a factored representation of an MDP and an 0<=epsilon<=1 and computes a factored epsilon-homogeneous partition of the state space. This partition defines a family of related MDPs - those MDPs with state space equal to the blocks of the partition, and transition probabilities "approximately" like those of any (original MDP) state in the source block. To formally study such families of MDPs, we introduce the new notion of a "bounded parameter MDP" (BMDP), which is a family of (traditional) MDPs defined by specifying upper and lower bounds on the transition probabilities and rewards. We describe algorithms that operate on BMDPs to find policies that are approximately optimal with respect to the original MDP. In combination, our method for reducing a large implicit MDP to a possibly much smaller BMDP using an epsilon-homogeneous partition, and our methods for selecting actions in BMDPs constitute a new approach for analyzing large implicit MDPs. Among its advantages, this new approach provides insight into existing algorithms to solving implicit MDPs, provides useful connections to work in automata theory and model minimization, and suggests methods, which involve varying epsilon, to trade time and space (specifically in terms of the size of the corresponding state space) for solution quality.
Sequential Update of Bayesian Network Structure
Venue: arXiv cs.AI 2013
Year: 2013

There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains, we cannot afford to ignore the information in new data. While sequential update of parameters for a fixed structure can be accomplished using standard techniques, sequential update of network structure is still an open problem. In this paper, we investigate sequential update of Bayesian networks were both parameters and structure are expected to change. We introduce a new approach that allows for the flexible manipulation of the tradeoff between the quality of the learned networks and the amount of information that is maintained about past observations. We formally describe our approach including the necessary modifications to the scoring functions for learning Bayesian networks, evaluate its effectiveness through an empirical study, and extend it to the case of missing data.
Inference with Idempotent Valuations
Venue: arXiv cs.AI 2013
Year: 2013

Valuation based systems verifying an idempotent property are studied. A partial order is defined between the valuations giving them a lattice structure. Then, two different strategies are introduced to represent valuations: as infimum of the most informative valuations or as supremum of the least informative ones. It is studied how to carry out computations with both representations in an efficient way. The particular cases of finite sets and convex polytopes are considered.
Time-Critical Reasoning: Representations and Application
Venue: arXiv cs.AI 2013
Year: 2013

We review the problem of time-critical action and discuss a reformulation that shifts knowledge acquisition from the assessment of complex temporal probabilistic dependencies to the direct assessment of time-dependent utilities over key outcomes of interest. We dwell on a class of decision problems characterized by the centrality of diagnosing and reacting in a timely manner to pathological processes. We motivate key ideas in the context of trauma-care triage and transportation decisions.
Nested Junction Trees
Venue: arXiv cs.AI 2013
Year: 2013

The efficiency of inference in both the Hugin and, most notably, the Shafer-Shenoy architectures can be improved by exploiting the independence relations induced by the incoming messages of a clique. That is, the message to be sent from a clique can be computed via a factorization of the clique potential in the form of a junction tree. In this paper we show that by exploiting such nested junction trees in the computation of messages both space and time costs of the conventional propagation methods may be reduced. The paper presents a structured way of exploiting the nested junction trees technique to achieve such reductions. The usefulness of the method is emphasized through a thorough empirical evaluation involving ten large real-world Bayesian networks and the Hugin inference algorithm.
Object-Oriented Bayesian Networks
Venue: arXiv cs.AI 2013
Year: 2013

Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian networks begins to resemble the task of programming using logical circuits. In this paper, we describe an object-oriented Bayesian network (OOBN) language, which allows complex domains to be described in terms of inter-related objects. We use a Bayesian network fragment to describe the probabilistic relations between the attributes of an object. These attributes can themselves be objects, providing a natural framework for encoding part-of hierarchies. Classes are used to provide a reusable probabilistic model which can be applied to multiple similar objects. Classes also support inheritance of model fragments from a class to a subclass, allowing the common aspects of related classes to be defined only once. Our language has clear declarative semantics: an OOBN can be interpreted as a stochastic functional program, so that it uniquely specifies a probabilistic model. We provide an inference algorithm for OOBNs, and show that much of the structural information encoded by an OOBN--particularly the encapsulation of variables within an object and the reuse of model fragments in different contexts--can also be used to speed up the inference process.
Incremental Map Generation by Low Cost Robots Based on Possibility/Necessity Grids
Venue: arXiv cs.RO 2013
Year: 2013

In this paper we present some results obtained with a troupe of low-cost robots designed to cooperatively explore and adquire the map of unknown structured orthogonal environments. In order to improve the covering of the explored zone, the robots show different behaviours and cooperate by transferring each other the perceived environment when they meet. The returning robots deliver to a host computer their partial maps and the host incrementally generates the map of the environment by means of apossibility/ necessity grid.
A Target Classification Decision Aid
Venue: arXiv cs.AI 2013
Year: 2013

A submarine's sonar team is responsible for detecting, localising and classifying targets using information provided by the platform's sensor suite. The information used to make these assessments is typically uncertain and/or incomplete and is likely to require a measure of confidence in its reliability. Moreover, improvements in sensor and communication technology are resulting in increased amounts of on-platform and off-platform information available for evaluation. This proliferation of imprecise information increases the risk of overwhelming the operator. To assist the task of localisation and classification a concept demonstration decision aid (Horizon), based on evidential reasoning, has been developed. Horizon is an information fusion software package for representing and fusing imprecise information about the state of the world, expressed across suitable frames of reference. The Horizon software is currently at prototype stage.
Cost-Sharing in Bayesian Knowledge Bases
Venue: arXiv cs.AI 2013
Year: 2013

Bayesian knowledge bases (BKBs) are a generalization of Bayes networks and weighted proof graphs (WAODAGs), that allow cycles in the causal graph. Reasoning in BKBs requires finding the most probable inferences consistent with the evidence. The cost-sharing heuristic for finding least-cost explanations in WAODAGs was presented and shown to be effective by Charniak and Husain. However, the cycles in BKBs would make the definition of cost-sharing cyclic as well, if applied directly to BKBs. By treating the defining equations of cost-sharing as a system of equations, one can properly define an admissible cost-sharing heuristic for BKBs. Empirical evaluation shows that cost-sharing improves performance significantly when applied to BKBs.
Tag-based Semantic Website Recommendation for Turkish Language
Venue: arXiv cs.IR 2013
Year: 2013

With the dramatic increase in the number of websites on the internet, tagging has become popular for finding related, personal and important documents. When the potentially increasing internet markets are analyzed, Turkey, in which most of the people use Turkish language on the internet, found to be exponentially increasing. In this paper, a tag-based website recommendation method is presented, where similarity measures are combined with semantic relationships of tags. In order to evaluate the system, an experiment with 25 people from Turkey is undertaken and participants are firstly asked to provide websites and tags in Turkish and then they are asked to evaluate recommended websites.
Adaptive low rank and sparse decomposition of video using compressive sensing
Venue: arXiv cs.IT 2013
Year: 2013

We address the problem of reconstructing and analyzing surveillance videos using compressive sensing. We develop a new method that performs video reconstruction by low rank and sparse decomposition adaptively. Background subtraction becomes part of the reconstruction. In our method, a background model is used in which the background is learned adaptively as the compressive measurements are processed. The adaptive method has low latency, and is more robust than previous methods. We will present experimental results to demonstrate the advantages of the proposed method.
Comparison of Proposed Data Dissemination Protocols for Sensor Networks Using J-Sim
Venue: arXiv cs.NI 2013
Year: 2013

A distinguishing characteristic of wireless sensor networks is the opportunity to exploit characteristics of the application at lower layers. This paper reports on the results of a simulation comparison of proposed data dissemination protocols using the J-Sim simulator for the WSN protocols: Forwarding Diffusion Data Dissemination(FDDDP), Decentralized Data Dissemination(DDDP), Credit Broadcast Data Dissemination (CBDDP), Energy Aware & Geographical Data Dissemination (EAGDDP) .Our performance provides useful insights for the network designer such as which protocols (and design choices) scale control traffic well, improve data delivery or reduce overall energy consumption,improves routing overhead and maximizes the bandwidth utilization. The static pre configuration of the cell size in DDDP, is one of the reasons why DDDP exhibits larger routing overhead than FDDDP by 74.2% on average. Although CBDDP produces approximately 94.6% smaller overhead than DDDP and 90.7% smaller than FDDDP, because of statically configured amount credit CBDDP delivers on average 7.5 times more of the redundant data packets than DDDP and FDDDP.EAGDDP improves the delivery by 80% on average and makes a balance of energy consumption .We suggest that making these protocols truly self-learning can significantly improve their performance.
Kleene Algebra with Tests and Coq Tools for While Programs
Venue: arXiv cs.LO 2013
Year: 2013

We present a Coq library about Kleene algebra with tests, including a proof of their completeness over the appropriate notion of languages, a decision procedure for their equational theory, and tools for exploiting hypotheses of a particular shape in such a theory. Kleene algebra with tests make it possible to represent if-then-else statements and while loops in most imperative programming languages. They were actually introduced by Kozen as an alternative to propositional Hoare logic. We show how to exploit the corresponding Coq tools in the context of program verification by proving equivalences of while programs, correctness of some standard compiler optimisations, Hoare rules for partial correctness, and a particularly challenging equivalence of flowchart schemes.
Adaptive Compressive Spectrum Sensing for Wideband Cognitive Radios
Venue: arXiv cs.IT 2013
Year: 2013

This letter presents an adaptive spectrum sensing algorithm that detects wideband spectrum using sub-Nyquist sampling rates. By taking advantage of compressed sensing (CS), the proposed algorithm reconstructs the wideband spectrum from compressed samples. Furthermore, an l2 norm validation approach is proposed that enables cognitive radios (CRs) to automatically terminate the signal acquisition once the current spectral recovery is satisfactory, leading to enhanced CR throughput. Numerical results show that the proposed algorithm can not only shorten the spectrum sensing interval, but also improve the throughput of wideband CRs.
Relaying Technologies for Smart Grid Communications
Venue: arXiv cs.IT 2013
Year: 2013

Wireless technologies can support a broad range of smart grid applications including advanced metering infrastructure (AMI) and demand response (DR). However, there are many formidable challenges when wireless technologies are applied to the smart gird, e.g., the tradeoffs between wireless coverage and capacity, the high reliability requirement for communication, and limited spectral resources. Relaying has emerged as one of the most promising candidate solutions for addressing these issues. In this article, an introduction to various relaying strategies is presented, together with a discussion of how to improve spectral efficiency and coverage in relay-based information and communications technology (ICT) infrastructure for smart grid applications. Special attention is paid to the use of unidirectional relaying, collaborative beamforming, and bidirectional relaying strategies.
Editorial: Making GIScience Research More Open Access
Venue: arXiv cs.DL 2013
Year: 2013

This is the editorial for the special issue on "data-intensive geospatial computing", which I guest edited with the International Journal of Geographical Information Science (Taylor & Francis). As remarked in the editorial, the special issue is particularly special in the sense that all source and data are published together with the published papers. This editorial elaborates on scholarly communication, with particular attention to publishing data alongside papers and the emergence of open access journals, in order to make our research more open access.
Cooperative Environmental Monitoring for PTZ Visual Sensor Networks: A Payoff-based Learning Approach
Venue: arXiv cs.SY 2013
Year: 2013

This paper investigates cooperative environmental monitoring for Pan-Tilt-Zoom (PTZ) visual sensor networks. We first present a novel formulation of the optimal environmental monitoring problem, whose objective function is intertwined with the uncertain state of the environment. In addition, due to the large volume of vision data, it is desired for each sensor to execute processing through local computation and communication. To address the issues, we present a distributed solution to the problem based on game theoretic cooperative control and payoff-based learning. At the first stage, a utility function is designed so that the resulting game constitutes a potential game with potential function equal to the group objective function, where the designed utility is shown to be computable through local image processing and communication. Then, we present a payoff-based learning algorithm so that the sensors are led to the global objective function maximizers without using any prior information on the environmental state. Finally, we run experiments to demonstrate the effectiveness of the present approach.
An LMP O(log n)-Approximation Algorithm for Node Weighted Prize Collecting Steiner Tree
Venue: arXiv cs.DS 2013
Year: 2013

In the node-weighted prize-collecting Steiner tree problem (NW-PCST) we are given an undirected graph $G=(V,E)$, non-negative costs $c(v)$ and penalties $π(v)$ for each $v \in V$. The goal is to find a tree $T$ that minimizes the total cost of the vertices spanned by $T$ plus the total penalty of vertices not in $T$. This problem is well-known to be set-cover hard to approximate. Moss and Rabani (STOC'01) presented a primal-dual Lagrangean-multiplier-preserving $O(\ln |V|)$-approximation algorithm for this problem. We show a serious problem with the algorithm, and present a new, fundamentally different primal-dual method achieving the same performance guarantee. Our algorithm introduces several novel features to the primal-dual method that may be of independent interest.
Non-Asymptotic Analysis of an Optimal Algorithm for Network-Constrained Averaging with Noisy Links
Venue: arXiv cs.DC 2013
Year: 2013

The problem of network-constrained averaging is to compute the average of a set of values distributed throughout a graph G using an algorithm that can pass messages only along graph edges. We study this problem in the noisy setting, in which the communication along each link is modeled by an additive white Gaussian noise channel. We propose a two-phase decentralized algorithm, and we use stochastic approximation methods in conjunction with the spectral graph theory to provide concrete (non-asymptotic) bounds on the mean-squared error. Having found such bounds, we analyze how the number of iterations T_G(n; δ) required to achieve mean-squared error δ scales as a function of the graph topology and the number of nodes n. Previous work provided guarantees with the number of iterations scaling inversely with the second smallest eigenvalue of the Laplacian. This paper gives an algorithm that reduces this graph dependence to the graph diameter, which is the best scaling possible.
Passive Learning with Target Risk
Venue: arXiv cs.LG 2013
Year: 2013

In this paper we consider learning in passive setting but with a slight modification. We assume that the target expected loss, also referred to as target risk, is provided in advance for learner as prior knowledge. Unlike most studies in the learning theory that only incorporate the prior knowledge into the generalization bounds, we are able to explicitly utilize the target risk in the learning process. Our analysis reveals a surprising result on the sample complexity of learning: by exploiting the target risk in the learning algorithm, we show that when the loss function is both strongly convex and smooth, the sample complexity reduces to $Ø(\log (\frac{1}ε))$, an exponential improvement compared to the sample complexity $Ø(\frac{1}ε)$ for learning with strongly convex loss functions. Furthermore, our proof is constructive and is based on a computationally efficient stochastic optimization algorithm for such settings which demonstrate that the proposed algorithm is practically useful.
Circumstantial-Evidence-Based Judgment for Software Effort Estimation
Venue: arXiv cs.SE 2013
Year: 2013

Expert judgment for software effort estimation is oriented toward direct evidences that refer to actual effort of similar projects or activities through experts' experiences. However, the availability of direct evidences implies the requirement of suitable experts together with past data. The circumstantial-evidence-based judgment proposed in this paper focuses on the development experiences deposited in human knowledge, and can then be used to qualitatively estimate implementation effort of different proposals of a new project by rational inference. To demonstrate the process of circumstantial-evidence-based judgment, this paper adopts propositional learning theory based diagnostic reasoning to infer and compare different effort estimates when implementing a Web service composition project with some different techniques and contexts. The exemplar shows our proposed work can help determine effort tradeoff before project implementation. Overall, circumstantial-evidence-based judgment is not an alternative but complementary to expert judgment so as to facilitate and improve software effort estimation.
Software Cost Estimation Framework for Service-Oriented Architecture Systems using Divide-and-Conquer Approach
Venue: arXiv cs.SE 2013
Year: 2013

Due to the complexity of Service-Oriented Architecture (SOA), cost and effort estimation for SOA-based software development is more difficult than that for traditional software development. Unfortunately, there is a lack of published work about cost and effort estimation for SOA-based software. Existing cost estimation approaches are inadequate to address the complex service-oriented systems. This paper proposes a novel framework based on Divide-and-Conquer (D&C) for cost estimation for building SOA-based software. By dealing with separately development parts, the D&C framework can help organizations simplify and regulate SOA implementation cost estimation. Furthermore, both cost estimation modeling and software sizing work can be satisfied respectively by switching the corresponding metrics within this framework. Given the requirement of developing these metrics, this framework also defines the future research in four different directions according to the separate cost estimation sub-problems.
Performance Evaluation of IPTV over WiMAX Networks Under Different Terrain Environments
Venue: arXiv cs.NI 2013
Year: 2013

Deployment Video on Demand (VoD) over the next generation (WiMAX) has become one of the intense interest subjects in the research these days, and is expected to be the main revenue generators in the near future. In this paper, the performance of Quilty of Service of video streaming (IPTV) over fixed mobile WiMax network is investigated under different terrain environments, namely Free Space, Outdoor to Indoor and Pedestrian. OPNET is used to investigate the performance of VoD over WiMAX. Our findings analyzing different network statistics such as packet lost, path loss, delay, network throughput.