nils-herrmann/clean_scirepeval huggingface dataset:
Rao-Blackwellised Particle Filtering via Data Augmentation

In this paper, we extend the Rao-Blackwellised particle filtering method to more complex hybrid models consisting of Gaussian latent variables and discrete observations. This is accomplished by augmenting the models with artificial variables that enable us to apply Rao-Blackwellisation. Other improvements include the design of an optimal importance proposal distribution and being able to swap the sampling an selection steps to handle outliers. We focus on sequential binary classifiers that consist of linear combinations of basis functions, whose coefficients evolve according to a Gaussian smoothness prior. Our results show significant improvements.
Object Recognition as Machine Translation – Part 2: Exploiting Image Database Clustering Models

We treat object recognition as a process of attaching words to images and image regions. To accomplish this we exploit clustering methods which learn the joint statistics of words and image regions. We show how these models can then be used to attach words to images outside the training set. This “auto-annotation” process has applications such as image indexing, as well as being related to object recognition. Predicted words can be compared to actual words associated with images in a held out set, and we introduce several performance measures based on this observation. These measures are then used to make principled comparisons of model variants, and proposed enhancements. Word prediction is most simply done as a function of the entire image. However, for recognition we need to learn the correspondence between words and specific image regions. Here we first show that the existing models can be used for this purpose, and then we propose modifications to improve performance based on this goal. Finally, we propose word prediction performance as a segmentation measure and report the results for two segmentation approaches.
Sequential Monte Carlo Methods for Neural Networks

Many problems, arising in science and engineering, require the estimation of nonlinear, time-varying functions that map a set of input signals to a corresponding set of output signals. Some examples include: finding the relation between an input pressure signal and the movement of a pneumatic control valve; using past observations in a time series to predict future events; and using a group of biomedical signals to carry out diagnoses and prognoses. These problems can be reformulated in terms of a generic one of estimating the parameters of a suitable neural network on-line as the input-output data becomes available.
Robust Full Bayesian Learning for Radial Basis Networks

We propose a hierarchical full Bayesian model for radial basis networks. This model treats the model dimension (number of neurons), model parameters, regularization parameters, and noise parameters as unknown random variables. We develop a reversible-jump Markov chain Monte Carlo (MCMC) method to perform the Bayesian computation. We find that the results obtained using this method are not only better than the ones reported previously, but also appear to be robust with respect to the prior specification. In addition, we propose a novel and computationally efficient reversible-jump MCMC simulated annealing algorithm to optimize neural networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima to a large extent. We show that by calibrating the full hierarchical Bayesian prior, we can obtain the classical Akaike information criterion, Bayesian information criterion, and minimum description length model selection criteria within a penalized likelihood framework. Finally, we present a geometric convergence theorem for the algorithm with homogeneous transition kernel and a convergence theorem for the reversible-jump MCMC simulated annealing method.
Sequential Monte Carlo for model selection and estimation of neural networks

We address the complex problem of sequential Bayesian learning and model selection for neural networks. This problem does not usually admit any type of closed-form analytical solution and, as a result, one has to resort to numerical methods. We propose here an original sequential simulation-based strategy to perform the necessary computations. It combines sequential importance sampling, a selection procedure, variance reduction techniques and reversible jump Markov chain Monte Carlo (MCMC) moves. We demonstrate the effectiveness of the method by applying it to radial basis function networks. The approach can be easily extended to other interesting on-line model selection problems.
The Unscented Particle Filter

In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standard particle filtering and other nonlinear filtering methods very substantially. This experimental finding is in agreement with the theoretical convergence proof for the algorithm. The algorithm also includes resampling and (possibly) Markov chain Monte Carlo (MCMC) steps.
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and non-stationarity. They have appeared in several fields under such names as "condensation", "sequential Monte Carlo" and "survival of the fittest". In this paper, we show how we can exploit the structure of the DBN to increase the efficiency of particle filtering, using a technique known as Rao-Blackwellisation. Essentially, this samples some of the variables, and marginalizes out the rest exactly, using the Kalman filter, HMM filter, junction tree algorithm, or any other finite dimensional optimal filter. We show that Rao-Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs. We demonstrate RBPFs on two problems, namely non-stationary online regression with radial basis function networks and robot localization and map building. We also discuss other potential application areas and provide references to some finite dimensional optimal filters.
Reversible Jump MCMC Simulated Annealing for Neural Networks

We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated annealing algorithm to optimize radial basis function (RBF) networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis functions. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. We also show that by calibrating a Bayesian model, we can obtain the classical AIC, BIC and MDL model selection criteria within a penalized likelihood framework. Finally, we show theoretically and empirically that the algorithm converges to the modes of the full posterior distribution in an efficient way.
Chasing errors through the network stack: a testbed for investigating errors in real traffic on optical networks

A testbed is described that allows both physical layer errors to be observed and analyzed, as well as monitoring network performance via frame loss. Real network traffic loads can be used for testing so that all measurements taken are representative of what would be seen in a deployed system. We illustrate our testbed with an examination of the behavior of a well-known networking standard, Gigabit Ethernet, in conditions of reduced receiver power on optical fiber. Our testbed results show that the line codes used to represent the data in the network affect the bit error rate for that data. Along with the previously reported result that bit error rate and packet error rate have only a weakly deterministic relationship, this highlights the need for testing of all network layers within a complete system carrying real-world traffic.
Toward the Accurate Identification of Network Applications

Well-known port numbers can no longer be used to reliably identify network applications. There is a variety of new Internet applications that either do not use well-known port numbers or use other protocols, such as HTTP, as wrappers in order to go through firewalls without being blocked. One consequence of this is that a simple inspection of the port numbers used by flows may lead to the inaccurate classification of network traffic. In this work, we look at these inaccuracies in detail. Using a full payload packet trace collected from an Internet site we attempt to identify the types of errors that may result from port-based classification and quantify them for the specific trace under study. To address this question we devise a classification methodology that relies on the full packet payload. We describe the building blocks of this methodology and elaborate on the complications that arise in that context. A classification technique approaching 100% accuracy proves to be a labor-intensive process that needs to test flow-characteristics against multiple classification criteria in order to gain sufficient confidence in the nature of the causal application. Nevertheless, the benefits gained from a content-based classification approach are evident. We are capable of accurately classifying what would be otherwise classified as unknown as well as identifying traffic flows that could otherwise be classified incorrectly. Our work opens up multiple research issues that we intend to address in future work.
Optical network packet error rate due to physical layer coding

A physical layer coding scheme is designed to make optimal use of the available physical link, providing functionality to higher components in the network stack. This paper presents results of an exploration of the errors observed when an optical gigabit Ethernet link is subject to attenuation. The results show that some data symbols suffer from a far higher probability of error than others. This effect is caused by an interaction between the physical layer and the 8B/10B block coding scheme. The authors illustrate how the application of a scrambler, performing data whitening, restores content-independent uniformity of packet loss. They also note the implications of their work for other (N, K) block-coded systems and discuss how this effect will manifest itself in a scrambler-based system. A conjecture is made that there is a need to build converged systems with the combinations of physical, data link, and network layers optimized to interact correctly. In the meantime, what will become increasingly necessary is both an identification of the potential for failure and the need to plan around it.
Internet traffic classification using bayesian analysis techniques

Accurate traffic classification is of fundamental importance to numerous other network activities, from security monitoring to accounting, and from Quality of Service to providing operators with useful forecasts for long-term provisioning. We apply a Naïve Bayes estimator to categorize traffic by application. Uniquely, our work capitalizes on hand-classified network data, using it as input to a supervised Naïve Bayes estimator. In this paper we illustrate the high level of accuracy achievable with the \Naive Bayes estimator. We further illustrate the improved accuracy of refined variants of this estimator.Our results indicate that with the simplest of Naïve Bayes estimator we are able to achieve about 65% accuracy on per-flow classification and with two powerful refinements we can improve this value to better than 95%; this is a vast improvement over traditional techniques that achieve 50--70%. While our technique uses training data, with categories derived from packet-content, all of our training and testing was done using header-derived discriminators. We emphasize this as a powerful aspect of our approach: using samples of well-known traffic to allow the categorization of traffic using commonly available information alone.
Explaining Structured Errors in Gigabit Ethernet

A physical layer coding scheme is designed to make optimal use of the available physical link, providing functionality to higher components in the network stack. This paper presents results of an exploration of the errors observed when an optical Gigabit Ethernet link is subject to attenuation. In the unexpected results, highlighted by some data payloads suffering from far higher probability of error than others, an analysis of the coding scheme combined with an analysis of the physical light path leads us to conclude that the cause is an interaction between the physical layer and the 8B/10B block coding scheme. We consider the physics of a common type of laser used in optical communications, and how the frequency characteristics of data may affect performance. A conjecture is made that there is a need to build converged systems, with the combinations of physical, data-link, and network layers optimised to interact correctly. In the mean time, what will become increasingly necessary is both an identification of the potential for failure and the need to plan around it.
Como: An open infrastructure for network monitoring-research agenda

Our idea is to define a per-query accuracy metric and maximize the aggregate accuracy of all query results given the available resources. We argue that in network monitoring it is always possible to regulate the accuracy of a query (that will include the measurement error as well as the timeliness of the results). For a given set of queries we then need to define an aggregate utility function and be able to exercise a fine-grained control over it. To overcome this challenge we need to address two orthogonal sub-challenges. The first has to do with allowing users to post any query to the systems in a secure fashion. The second is about the definition and regulation of queries’ accuracy in a distributed monitoring infrastructure. We now describe the two sub-challenges in detail and introduce our ideas to overcome them.
An implementation-based comparison of Measurement-Based Admission Control algorithms

In this paper we present an implementation-based comparison of Measurement-based Admission Control algorithms. Through the use of a special purpose environment, a performance and behaviour comparison is made. The results of this paper illustrate the independence of traffic from admission control behaviour in the homogeneous traffic environment. While illustrating the impact the admission decision will make upon heterogenous traffic systems. These results highlight the importance of estimators being robust to statistical variation in measurements and offering calibrated controls. While based upon a comparison of measurement-based admission control algorithms — these conclusions are applicable to any application of measurement-based estimation.
The CoMo white paper

CoMo (Continuous Monitoring) is a passive monitoring system. CoMo has been designed to be the basic building block of an open network monitoring infrastructure that would allow researchers and network operators to easily process and share network traffic statistics over multiple sites. This paper identifies the challenges that lie ahead in the deployment of such an open infrastructure. These main challenges are: (1) the system must allow any generic metric to be computed on the incoming traffic stream, (2) it must provide privacy and security guarantees to the owner of the monitored link, the network users and the CoMo users, and (3) it must be robust in the face of anomalous traffic patterns. We describe the high-level architecture of CoMo and, in greater detail, the resource management, query processing and security aspects.
The effect of early packet loss on Web page download times

Identification of the various elements contributing to the download time of Web objects has been the subject of much research. It is, however, the download time of a set of objects comprising an entire page which is of critical importance to the user — particularly in the presence of delay leading to subjectively long waits. Because browsers typically use parallel TCP connections to download the set of objects neither the overall page download time, or the contribution of delay, are simply the sum of the individual object download times or delays. We present a technique for identifying and examining the complete set of connections involved in a page download which then enables us to fully analyse the contributory time components and to assess the potentially serious delays which arise from loss early in the connections’ lifetimes.
Network Monitoring with Nprobe

This paper presents an architecture for monitoring 10 Gbps networks, drawing on experience from a current 1 Gbps implementation. The architecture performs full line-rate capture and implements on-line analysis and compression to record interesting data without loss. The use of the monitoring and analysis system is demonstrated along with the presentation of several initial findings made possible with the tool.  Passive network monitoring, full line-rate capture, multi-protocol analysis
An experimental configuration for the evaluation of CAC algorithms

Interest in Connection Admission Control (CAC) algorithms stems from the need for a network user and a network provider to forge an agreement on the Quality of Service (QoS) for a new network connection. Traditional evaluation of CAC algorithms has been through simulation studies. We present an alternative approach: an evaluation environment for CAC algorithms that is based around an experimental test-rig. This paper presents the architecture of the test-rig and an evaluation of its performance.
A comparison of system monitoring methods, passive network monitoring and kernel instrumentation

This paper presents the comparison of two methods of system monitoring, passive network monitoring and kernel instrumentation. The comparison is made on the basis of passive network monitoring being used as a replacement for kernel instrumentation in some situations. Despite the fact that the passive network monitoring technique is shown to perform poorly as a direct replacement for kernel instrumentation, this paper indicates the areas where passive network monitoring could be used to the greatest advantage and presents methods by which the discrepancies between results of the two techniques could be minimised.
Ideal Observers for Detecting Motion: Correspondence Noise

We derive a Bayesian Ideal Observer (BIO) for detecting motion and solving the correspondence problem. We obtain Barlow and Tripathy's classic model as an approximation. Our psychophysical experiments show that the trends of human performance are similar to the Bayesian Ideal, but overall human performance is far worse. We investigate ways to degrade the Bayesian Ideal but show that even extreme degradations do not approach human performance. Instead we propose that humans perform motion tasks using generic, general purpose, models of motion. We perform more psychophysical experiments which are consistent with humans using a Slow-and-Smooth model and which rule out an alternative model using Slowness.
Augmented Rescorla-Wagner and Maximum Likelihood Estimation

We show that linear generalizations of Rescorla-Wagner can perform Maximum Likelihood estimation of the parameters of all generative models for causal reasoning. Our approach involves augmenting variables to deal with conjunctions of causes, similar to the agumented model of Rescorla. Our results involve genericity assumptions on the distributions of causes. If these assumptions are violated, for example for the Cheng causal power theory, then we show that a linear Rescorla-Wagner can estimate the parameters of the model up to a nonlinear transformtion. Moreover, a nonlinear Rescorla-Wagner is able to estimate the parameters directly to within arbitrary accuracy. Previous results can be used to determine convergence and to estimate convergence rates.
The DLR Hierarchy of Approximate Inference

We propose a hierarchy for approximate inference based on the Dobrushin, Lanford, Ruelle (DLR) equations. This hierarchy includes existing algorithms, such as belief propagation, and also motivates novel algorithms such as factorized neighbors (FN) algorithms and variants of mean field (MF) algorithms. In particular, we show that extrema of the Bethe free energy correspond to approximate solutions of the DLR equations. In addition, we demonstrate a close connection between these approximate algorithms and Gibbs sampling. Finally, we compare and contrast various of the algorithms in the DLR hierarchy on spin-glass problems. The experiments show that algorithms higher up in the hierarchy give more accurate results when they converge but tend to be less stable.
A Hierarchical Compositional System for Rapid Object Detection

We describe a hierarchical compositional system for detecting de- formable objects in images. Objects are represented by graphical models. The algorithm uses a hierarchical tree where the root of the tree corre- sponds to the full object and lower-level elements of the tree correspond to simpler features. The algorithm proceeds by passing simple messages up and down the tree. The method works rapidly, in under a second, on 320 × 240 images. We demonstrate the approach on detecting cat- s, horses, and hands. The method works in the presence of background clutter and occlusions. Our approach is contrasted with more traditional methods such as dynamic programming and belief propagation.
Image Parsing: Unifying Segmentation, Detection, and Recognition

In this paper we present a Bayesian framework for parsing images into their constituent visual patterns. The parsing algorithm optimizes the posterior probability and outputs a scene representation as a “parsing graph”, in a spirit similar to parsing sentences in speech and natural language. The algorithm constructs the parsing graph and re-configures it dynamically using a set of moves, which are mostly reversible Markov chain jumps. This computational framework integrates two popular inference approaches—generative (top-down) methods and discriminative (bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on a sequence (cascade) of bottom-up tests/filters. In our Markov chain algorithm design, the posterior probability, defined by the generative models, is the invariant (target) probability for the Markov chain, and the discriminative probabilities are used to construct proposal probabilities to drive the Markov chain. Intuitively, the bottom-up discriminative probabilities activate top-down generative models. In this paper, we focus on two types of visual patterns—generic visual patterns, such as texture and shading, and object patterns including human faces and text. These types of patterns compete and cooperate to explain the image and so image parsing unifies image segmentation, object detection, and recognition (if we use generic visual patterns only then image parsing will correspond to image segmentation (Tu and Zhu, 2002. ). We illustrate our algorithm on natural images of complex city scenes and show examples where image segmentation can be improved by allowing object specific knowledge to disambiguate low-level segmentation cues, and conversely where object detection can be improved by using generic visual patterns to explain away shadows and occlusions.