OpenRaiser/Intern-Atlas huggingface dataset:
Implementing a Bayesian Scheme for Revising Belief Commitments
Venue: arXiv cs.AI 2013
Year: 2013

Our previous work on classifying complex ship images [1,2] has evolved into an effort to develop software tools for building and solving generic classification problems. Managing the uncertainty associated with feature data and other evidence is an important issue in this endeavor. Bayesian techniques for managing uncertainty [7,12,13] have proven to be useful for managing several of the belief maintenance requirements of classification problem solving. One such requirement is the need to give qualitative explanations of what is believed. Pearl [11] addresses this need by computing what he calls a belief commitment-the most probable instantiation of all hypothesis variables given the evidence available. Before belief commitments can be computed, the straightforward implementation of Pearl's procedure involves finding an analytical solution to some often difficult optimization problems. We describe an efficient implementation of this procedure using tensor products that solves these problems enumeratively and avoids the need for case by case analysis. The procedure is thereby made more practical to use in the general case.
Efficient Inference on Generalized Fault Diagrams
Venue: arXiv cs.AI 2013
Year: 2013

The generalized fault diagram, a data structure for failure analysis based on the influence diagram, is defined. Unlike the fault tree, this structure allows for dependence among the basic events and replicated logical elements. A heuristic procedure is developed for efficient processing of these structures.
Balanced offline allocation of weighted balls into bins
Venue: arXiv cs.DM 2013
Year: 2013

We propose a sorting-based greedy algorithm called SortedGreedy[m] for approximately solving the offline version of the d-choice weighted balls-into-bins problem where the number of choices for each ball is equal to the number of bins. We assume the ball weights to be non-negative. We compare the performance of the sorting-based algorithm with a naive algorithm called Greedy[m]. We show that by sorting the input data according to the weights we are able to achieve an order of magnitude smaller gap (the weight difference between the heaviest and the lightest bin) for small problems (<= 4000 balls), and at least two orders of magnitude smaller gap for larger problems. In practice, SortedGreedy[m] runs almost as fast as Greedy[m]. This makes sorting-based algorithms favorable for solving offline weighted balls-into-bins problems.
Constructing $2m$-variable Boolean functions with optimal algebraic immunity based on polar decomposition of $\mathbb{F}_{2^{2m}}^*$
Venue: arXiv cs.CR 2013
Year: 2013

Constructing $2m$-variable Boolean functions with optimal algebraic immunity based on decomposition of additive group of the finite field $\mathbb{F}_{2^{2m}}$ seems to be a promising approach since Tu and Deng's work. In this paper, we consider the same problem in a new way. Based on polar decomposition of the multiplicative group of $\mathbb{F}_{2^{2m}}$, we propose a new construction of Boolean functions with optimal algebraic immunity. By a slight modification of it, we obtain a class of balanced Boolean functions achieving optimal algebraic immunity, which also have optimal algebraic degree and high nonlinearity. Computer investigations imply that this class of functions also behave well against fast algebraic attacks.
Probabilistic Reasoning About Ship Images
Venue: arXiv cs.AI 2013
Year: 2013

One of the most important aspects of current expert systems technology is the ability to make causal inferences about the impact of new evidence. When the domain knowledge and problem knowledge are uncertain and incomplete Bayesian reasoning has proven to be an effective way of forming such inferences [3,4,8]. While several reasoning schemes have been developed based on Bayes Rule, there has been very little work examining the comparative effectiveness of these schemes in a real application. This paper describes a knowledge based system for ship classification [1], originally developed using the PROSPECTOR updating method [2], that has been reimplemented to use the inference procedure developed by Pearl and Kim [4,5]. We discuss our reasons for making this change, the implementation of the new inference engine, and the comparative performance of the two versions of the system.
Information and Multi-Sensor Coordination
Venue: arXiv cs.SY 2013
Year: 2013

The control and integration of distributed, multi-sensor perceptual systems is a complex and challenging problem. The observations or opinions of different sensors are often disparate incomparable and are usually only partial views. Sensor information is inherently uncertain and in addition the individual sensors may themselves be in error with respect to the system as a whole. The successful operation of a multi-sensor system must account for this uncertainty and provide for the aggregation of disparate information in an intelligent and robust manner. We consider the sensors of a multi-sensor system to be members or agents of a team, able to offer opinions and bargain in group decisions. We will analyze the coordination and control of this structure using a theory of team decision-making. We present some new analytic results on multi-sensor aggregation and detail a simulation which we use to investigate our ideas. This simulation provides a basis for the analysis of complex agent structures cooperating in the presence of uncertainty. The results of this study are discussed with reference to multi-sensor robot systems, distributed Al and decision making under uncertainty.
Imprecise Meanings as a Cause of Uncertainty in Medical Knowledge-Based Systems
Venue: arXiv cs.AI 2013
Year: 2013

There has been a considerable amount of work on uncertainty in knowledge-based systems. This work has generally been concerned with uncertainty arising from the strength of inferences and the weight of evidence. In this paper we discuss another type of uncertainty: that which is due to imprecision in the underlying primitives used to represent the knowledge of the system. In particular, a given word may denote many similar but not identical entities. Such words are said to be lexically imprecise. Lexical imprecision has caused widespread problems in many areas. Unless this phenomenon is recognized and appropriately handled, it can degrade the performance of knowledge-based systems. In particular, it can lead to difficulties with the user interface, and with the inferencing processes of these systems. Some techniques are suggested for coping with this phenomenon.
Evidence as Opinions of Experts
Venue: arXiv cs.AI 2013
Year: 2013

We describe a viewpoint on the Dempster/Shafer 'Theory of Evidence', and provide an interpretation which regards the combination formulas as statistics of the opinions of "experts". This is done by introducing spaces with binary operations that are simpler to interpret or simpler to implement than the standard combination formula, and showing that these spaces can be mapped homomorphically onto the Dempster/Shafer theory of evidence space. The experts in the space of "opinions of experts" combine information in a Bayesian fashion. We present alternative spaces for the combination of evidence suggested by this viewpoint.
Bayesian Inference for Radar Imagery Based Surveillance
Venue: arXiv cs.AI 2013
Year: 2013

We are interested in creating an automated or semi-automated system with the capability of taking a set of radar imagery, collection parameters and a priori map and other tactical data, and producing likely interpretations of the possible military situations given the available evidence. This paper is concerned with the problem of the interpretation and computation of certainty or belief in the conclusions reached by such a system.
Decomposing Petri nets
Venue: arXiv cs.LO 2013
Year: 2013

In recent work, the second and third authors introduced a technique for reachability checking in 1-bounded Petri nets, based on wiring decompositions, which are expressions in a fragment of the compositional algebra of nets with boundaries. Here we extend the technique to the full algebra and introduce the related structural property of decomposition width on directed hypergraphs. Small decomposition width is necessary for the applicability of the reachability checking algorithm. We give examples of families of nets with constant decomposition width and develop the underlying theory of decompositions.
On PROGRESS Operation. How to Make Object-Oriented Programming System More Object-Oriented (DRAFT)
Venue: arXiv cs.PL 2013
Year: 2013

A system, which implements persistent objects, has to provide different opportunities to change the objects in arbitrary ways during their existence. A traditional realization of OO paradigm in modern programming systems has fundamental drawbacks which complicate an implementation of persistent modifiable objects considerably. There is alternative realization that does not have these drawbacks. In the article the PROGRESS operation is offered, which modify existing object within an existing inheritance hierarchy.
From Constraints to Resolution Rules, Part I: Conceptual Framework
Venue: arXiv cs.AI 2013
Year: 2013

Many real world problems naturally appear as constraints satisfaction problems (CSP), for which very efficient algorithms are known. Most of these involve the combination of two techniques: some direct propagation of constraints between variables (with the goal of reducing their sets of possible values) and some kind of structured search (depth-first, breadth-first,...). But when such blind search is not possible or not allowed or when one wants a 'constructive' or a 'pattern-based' solution, one must devise more complex propagation rules instead. In this case, one can introduce the notion of a candidate (a 'still possible' value for a variable). Here, we give this intuitive notion a well defined logical status, from which we can define the concepts of a resolution rule and a resolution theory. In order to keep our analysis as concrete as possible, we illustrate each definition with the well known Sudoku example. Part I proposes a general conceptual framework based on first order logic; with the introduction of chains and braids, Part II will give much deeper results.
Effect of the Feedback Function on the QoS in a Time Space Priority with Active Queue Management
Venue: arXiv cs.NI 2013
Year: 2013

The basic idea behind an active queue management (AQM) is to sense the congestion level within the network and inform the packet sources about, so that they reduce their sending rate. In literature a lot off mechanisms of AQM are studied. But there are not used in the context of the DiffServ architecture where different types of packet with different requirements of QoS share the same link. In this paper, we study an access control mechanism for RT and NRT packets arriving in a buffer implemented at an end user in HSDPA. The mechanism uses thresholds to mange access in the buffer and gives access priority to RT packets. In order to control the arrival rate of the NRT packets in the buffer an active queue management is used. We study the effect of the feedback function on the QoS parameters for both kinds of packets .Mathematical description and analytical results are given, and numerical results show that the proposed function achieves higher QoS for the NRT packets in the system.
The Removal of Numerical Drift from Scientific Models
Venue: arXiv cs.SE 2013
Year: 2013

Computer programs often behave differently under different compilers or in different computing environments. Relative debugging is a collection of techniques by which these differences are analysed. Differences may arise because of different interpretations of errors in the code, because of bugs in the compilers or because of numerical drift, and all of these were observed in the present study. Numerical drift arises when small and acceptable differences in values computed by different systems are integrated, so that the results drift apart. This is well understood and need not degrade the validity of the program results. Coding errors and compiler bugs may degrade the results and should be removed. This paper describes a technique for the comparison of two program runs which removes numerical drift and therefore exposes coding and compiler errors. The procedure is highly automated and requires very little intervention by the user. The technique is applied to the Weather Research and Forecasting model, the most widely used weather and climate modelling code.
Principal ideal languages and synchronizing automata
Venue: arXiv cs.FL 2013
Year: 2013

We study ideal languages generated by a single word. We provide an algorithm to construct a strongly connected synchronizing automaton for which such a language serves as the language of synchronizing words. Also we present a compact formula to calculate the syntactic complexity of this language.
Probabilistic Classification using Fuzzy Support Vector Machines
Venue: arXiv cs.LG 2013
Year: 2013

In medical applications such as recognizing the type of a tumor as Malignant or Benign, a wrong diagnosis can be devastating. Methods like Fuzzy Support Vector Machines (FSVM) try to reduce the effect of misplaced training points by assigning a lower weight to the outliers. However, there are still uncertain points which are similar to both classes and assigning a class by the given information will cause errors. In this paper, we propose a two-phase classification method which probabilistically assigns the uncertain points to each of the classes. The proposed method is applied to the Breast Cancer Wisconsin (Diagnostic) Dataset which consists of 569 instances in 2 classes of Malignant and Benign. This method assigns certain instances to their appropriate classes with probability of one, and the uncertain instances to each of the classes with associated probabilities. Therefore, based on the degree of uncertainty, doctors can suggest further examinations before making the final diagnosis.
Evolution of Swarm Robotics Systems with Novelty Search
Venue: arXiv cs.NE 2013
Year: 2013

Novelty search is a recent artificial evolution technique that challenges traditional evolutionary approaches. In novelty search, solutions are rewarded based on their novelty, rather than their quality with respect to a predefined objective. The lack of a predefined objective precludes premature convergence caused by a deceptive fitness function. In this paper, we apply novelty search combined with NEAT to the evolution of neural controllers for homogeneous swarms of robots. Our empirical study is conducted in simulation, and we use a common swarm robotics task - aggregation, and a more challenging task - sharing of an energy recharging station. Our results show that novelty search is unaffected by deception, is notably effective in bootstrapping the evolution, can find solutions with lower complexity than fitness-based evolution, and can find a broad diversity of solutions for the same task. Even in non-deceptive setups, novelty search achieves solution qualities similar to those obtained in traditional fitness-based evolution. Our study also encompasses variants of novelty search that work in concert with fitness-based evolution to combine the exploratory character of novelty search with the exploitatory character of objective-based evolution. We show that these variants can further improve the performance of novelty search. Overall, our study shows that novelty search is a promising alternative for the evolution of controllers for robotic swarms.
A Framework for Comparing Uncertain Inference Systems to Probability
Venue: arXiv cs.AI 2013
Year: 2013

Several different uncertain inference systems (UISs) have been developed for representing uncertainty in rule-based expert systems. Some of these, such as Mycin's Certainty Factors, Prospector, and Bayes' Networks were designed as approximations to probability, and others, such as Fuzzy Set Theory and DempsterShafer Belief Functions were not. How different are these UISs in practice, and does it matter which you use? When combining and propagating uncertain information, each UIS must, at least by implication, make certain assumptions about correlations not explicily specified. The maximum entropy principle with minimum cross-entropy updating, provides a way of making assumptions about the missing specification that minimizes the additional information assumed, and thus offers a standard against which the other UISs can be compared. We describe a framework for the experimental comparison of the performance of different UISs, and provide some illustrative results.
An Odds Ratio Based Inference Engine
Venue: arXiv cs.AI 2013
Year: 2013

Expert systems applications that involve uncertain inference can be represented by a multidimensional contingency table. These tables offer a general approach to inferring with uncertain evidence, because they can embody any form of association between any number of pieces of evidence and conclusions. (Simpler models may be required, however, if the number of pieces of evidence bearing on a conclusion is large.) This paper presents a method of using these tables to make uncertain inferences without assumptions of conditional independence among pieces of evidence or heuristic combining rules. As evidence is accumulated, new joint probabilities are calculated so as to maintain any dependencies among the pieces of evidence that are found in the contingency table. The new conditional probability of the conclusion is then calculated directly from these new joint probabilities and the conditional probabilities in the contingency table.
Developing and Analyzing Boundary Detection Operators Using Probabilistic Models
Venue: arXiv cs.CV 2013
Year: 2013

Most feature detectors such as edge detectors or circle finders are statistical, in the sense that they decide at each point in an image about the presence of a feature, this paper describes the use of Bayesian feature detectors.
Concurrent learning-based approximate optimal regulation
Venue: arXiv cs.SY 2013
Year: 2013

In deterministic systems, reinforcement learning-based online approximate optimal control methods typically require a restrictive persistence of excitation (PE) condition for convergence. This paper presents a concurrent learning-based solution to the online approximate optimal regulation problem that eliminates the need for PE. The development is based on the observation that given a model of the system, the Bellman error, which quantifies the deviation of the system Hamiltonian from the optimal Hamiltonian, can be evaluated at any point in the state space. Further, a concurrent learning-based parameter identifier is developed to compensate for parametric uncertainty in the plant dynamics. Uniformly ultimately bounded (UUB) convergence of the system states to the origin, and UUB convergence of the developed policy to the optimal policy are established using a Lyapunov-based analysis, and simulations are performed to demonstrate the performance of the developed controller.
Hardware Acceleration of the Gipps Model for Real-Time Traffic Simulation
Venue: arXiv cs.OH 2013
Year: 2013

Traffic simulation software is becoming increasingly popular as more cities worldwide use it to better manage their crowded traffic networks. An important requirement for such software is the ability to produce accurate results in real time, requiring great computation resources. This work proposes an ASIC-based hardware accelerated approach for the AIMSUN traffic simulator, taking advantage of repetitive tasks in the algorithm. Different system configurations using this accelerator are also discussed. Compared with the traditional software simulator, it has been found to improve the performance by as much as 9x when using a single processing element approach, or more depending on the chosen hardware configuration.
Eat the Cake and Have It Too: Privacy Preserving Location Aggregates in Geosocial Networks
Venue: arXiv cs.CR 2013
Year: 2013

Geosocial networks are online social networks centered on the locations of subscribers and businesses. Providing input to targeted advertising, profiling social network users becomes an important source of revenue. Its natural reliance on personal information introduces a trade-off between user privacy and incentives of participation for businesses and geosocial network providers. In this paper we introduce location centric profiles (LCPs), aggregates built over the profiles of users present at a given location. We introduce PROFILR, a suite of mechanisms that construct LCPs in a private and correct manner. We introduce iSafe, a novel, context aware public safety application built on PROFILR . Our Android and browser plugin implementations show that PROFILR is efficient: the end-to-end overhead is small even under strong correctness assurances.
Survey of Server Virtualization
Venue: arXiv cs.OS 2013
Year: 2013

Virtualization is a term that refers to the abstraction of computer resources. The purpose of virtual computing environment is to improve resource utilization by providing a unified integrated operating platform for users and applications based on aggregation of heterogeneous and autonomous resources. More recently, virtualization at all levels (system, storage, and network) became important again as a way to improve system security, reliability and availability, reduce costs, and provide greater flexibility. Virtualization has rapidly become a go-to technology for increasing efficiency in the data center. With virtualization technologies providing tremendous flexibility, even disparate architectures may be deployed on a single machine without interference This paper explains the basics of server virtualization and addresses pros and cons of virtualization
Simulation of Obstruction Avoidance Generously Mobility (OAGM) Model using Graph-theory Technique
Venue: arXiv cs.NI 2013
Year: 2013

An Obstruction Avoidance Generously Mobility (OAGM) model has been introduced for controlling ad-hoc sensor networks and thereby operating emerging fields like military and healthcare services. According to this model, the ability to send a message to a group of users simultaneously, based solely on their geographic location, is desirable by using Mission Critical Mobility model that assumes the obstacle shapes like rectangle or square in the simulation terrain. The OAGM model is developed by grasping the critical situations of military and healthcare services by incorporating the node movement model, hierarchical node organization, placement of obstacle that affect the movement of nodes and also signal propagation. Graph theory technique is used to find the shortest path of the node movement process. The varying number of parameter sets with DSR protocol is analyzed for MCM and OAGM mobility model. The results show OAGM performance is better than MCM.
A notion of graph likelihood and an infinite monkey theorem
Venue: arXiv cs.DM 2013
Year: 2013

We play with a graph-theoretic analogue of the folklore infinite monkey theorem. We define a notion of graph likelihood as the probability that a given graph is constructed by a monkey in a number of time steps equal to the number of vertices. We present an algorithm to compute this graph invariant and closed formulas for some infinite classes. We have to leave the computational complexity of the likelihood as an open problem.
A Novel Metaheuristics To Solve Mixed Shop Scheduling Problems
Venue: arXiv cs.NE 2013
Year: 2013

This paper represents the metaheuristics proposed for solving a class of Shop Scheduling problem. The Bacterial Foraging Optimization algorithm is featured with Ant Colony Optimization algorithm and proposed as a natural inspired computing approach to solve the Mixed Shop Scheduling problem. The Mixed Shop is the combination of Job Shop, Flow Shop and Open Shop scheduling problems. The sample instances for all mentioned Shop problems are used as test data and Mixed Shop survive its computational complexity to minimize the makespan. The computational results show that the proposed algorithm is gentler to solve and performs better than the existing algorithms.