nils-herrmann/clean_scirepeval huggingface dataset:
An entropy-based objective evaluation method for image segmentation

Accurate image segmentation is important for many image, video and computer vision applications. Over the last few decades, many image segmentation methods have been proposed. However, the results of these segmentation methods are usually evaluated only visually, qualitatively, or indirectly by the effectiveness of the segmentation on the subsequent processing steps. Such methods are either subjective or tied to particular applications. They do not judge the performance of a segmentation method objectively, and cannot be used as a means to compare the performance of different segmentation techniques. A few quantitative evaluation methods have been proposed, but these early methods have been based entirely on empirical analysis and have no theoretical grounding. In this paper, we propose a novel objective segmentation evaluation method based on information theory. The new method uses entropy as the basis for measuring the uniformity of pixel characteristics (luminance is used in this paper) within a segmentation region. The evaluation method provides a relative quality score that can be used to compare different segmentations of the same image. This method can be used to compare both various parameterizations of one particular segmentation method as well as fundamentally different segmentation techniques. The results from this preliminary study indicate that the proposed evaluation method is superior to the prior quantitative segmentation evaluation techniques, and identify areas for future research in objective segmentation evaluation.
Delaying acknowledgements in tcp and multiple-instance learning in theory and practice

In this thesis, the problems of deciding when to acknowledge packets in the Transmission Control Protocol and learning in the multiple-instance setting are considered, both under theoretical models and using empirical testing. 
In most TCP implementations, there is a mechanism for delaying acknowledgments to reduce bandwidth usage. The theoretical model we introduce and study considers the cost of an acknowledgment as a linear combination of the cost of the number of ACKs sent and the cost of the additional latency introduced by the delays. The goal is to reduce the total cost during the life of a connection. The empirical study presents and compares modifications to the TCP receiver to determine which mechanisms perform best in practice. 
The problem of learning from multiple-instance examples has applications in the development of pharmaceutical drugs. A drug molecule can be considered as a set of points in a weighted high-dimensional feature space, where each point corresponds to a different shape the drug is likely to take. In almost all prior work, the labels have been Boolean. We extend this problem to the setting in which the labels are real values. In this setting, we prove hardness results for the PAC model and present an algorithm that exactly learns a target point when provided with a newly defined multiple-instance membership query (MI-MQ) oracle. We also empirically compare the performance of several algorithms on real and artificial data.
Multiple-Instance Learning of Real-Valued Data

The multiple-instance learning model has received much attention recently with a primary application area being that of drug activity prediction. Most prior work on multiple-instance learning has been for concept learning, yet for drug activity prediction, the label is a real-valued affinity measurement giving the binding strength. We present extensions of k-nearest neighbors (k-NN), Citation-kNN, and the diverse density algorithm for the real-valued setting and study their performance on Boolean and real-valued data. We also provide a method for generating chemically realistic artificial data.
Agnostic Learning of Geometric Patterns

P. W. Goldberg, S. A. Goldman, and S. D. Scott (Mach. Learning25, No. 1 (1996), 51?70) discussed how the problem of recognizing a landmark from a one-dimensional visual image might be mapped to that of learning a one-dimensional geometric pattern and gave a PAC algorithm to learn that class. In this paper, we present an efficient online agnostic learning algorithm for learning the class of constant-dimensional geometric patterns. Our algorithm can tolerate both classification and attribute noise. By working in higher dimensional spaces we can represent more features from the visual image in the geometric pattern. Our mapping of the data to a geometric pattern and, hence, our learning algorithm are applicable to any data representable as a constant-dimensional array of values, e.g., sonar data, temporal difference information, amplitudes of a waveform, or other pattern recognition data. To our knowledge, these classes of patterns are more complex than any class of geometric patterns previously studied. Also, our results are easily adapted to learn the union of fixed-dimensional boxes from multiple-instance examples. Finally, our algorithms are tolerant of concept shift, where the target concept that labels the examples can change over time.
On-line analysis of the TCP acknowledgment delay problem

We study an on-line problem that is motivated by the networking problem of dynamically adjusting of acknowledgments in the Transmission Control Protocol (TCP). We provide a theoretical model for this problem in which the goal is to send acks at a time that minimize a linear combination of the cost for the number of acknowledgments sent and the cost for the additional latency introduced by delaying acknowledgments. To study the usefulness of applying packet arrival time prediction to this problem, we assume there is an oracle that provides the algorithm with the times of the next L arrivals, for some L ≥ 0.
We give two different objective functions for measuring the cost of a solution, each with its own measure of latency cost. For each objective function we first give an O(n2)-time dynamic programming algorithm for optimally solving the off-line problem. Then we describe an on-line algorithm that greedily acknowledges exactly when the cost for an acknowledgment is less than the latency cost incurred by not acknowledging. We show that for this algorithm there is a sequence of n packet arrivals for which it is &OHgr; (***)-competitive for the first objective function, 2-competitive for the second function for L = 0, and 1-competitivefor the second function for L = 1. Next we present a second on-line algorithm which is a slight modification of the first, and we prove that it is 2-competitive for both objective functions for all L. We also give lower bounds on the competitive ratio for any deterministic on-line algorithm. These results show that for each objective function, at least one of our algorithms is optimal.
Finally, we give some initial empirical results using arrival sequences from real network traffic where we compare the two methods used in TCP for acknowledgment delay with our two on-line algorithms. In all cases we examine performance with L = 0 and L = 1.
Real-Valued Multiple-Instance Learning with Queries

The multiple-instance model was motivated by the drug activity prediction problem where each example is a possible configuration for a molecule and each bag contains all likely configurations for the molecule. While there has been a significant amount of theoretical and empirical research directed towards this problem, most research performed under the multiple-instance model is for concept learning. However, binding affinity between molecules and receptors is quantitative and hence a real-valued classification is preferable.In this paper we initiate a theoretical study of real-valued multiple instance learning. We prove that the problem of finding a target point consistent with a set of labeled multiple-instance examples (or bags) is NP-complete. We also prove that the problem of learning from realvalued multiple-instance examples is as hard as learning DNF. Another contribution of our work is in defining and studying a multiple-instance membership query (MI-MQ). We give a positive result on exactly learning the target point for a multiple-instance problem in which the learner is provided with a MI-MQ oracle and a single adversarially selected bag.
EM-DD: An Improved Multiple-Instance Learning Technique

We present a new multiple-instance (MI) learning technique (EM-DD) that combines EM with the diverse density (DD) algorithm. EM-DD is a general-purpose MI algorithm that can be applied with boolean or real-value labels and makes real-value predictions. On the boolean Musk benchmarks, the EM-DD algorithm without any tuning significantly outperforms all previous algorithms. EM-DD is relatively insensitive to the number of relevant attributes in the data set and scales up well to large bag sizes. Furthermore, EM-DD provides a new framework for MI learning, in which the MI problem is converted to a single-instance setting by using EM to estimate the instance responsible for the label of the bag.
Online Scheduling with Hard Deadlines

We study non-preemptive, online admission control in the hard deadline model: each job must either be serviced prior to its deadline or be rejected. Our setting consists of a single resource that services an online sequence of jobs; each job has a length indicating the length of time for which it needs the resource and a delay indicating the maximum time it can wait for the service to be started. The goal is to maximize total resource utilization. The jobs are non-preemptive and exclusive, meaning once a job begins, it runs to completion, and at most one job can use the resource at any time. We obtain a series of results, under varying assumptions of job lengths and delays.
Enhancing Supervised Learning with Unlabeled Data

In a wide variety of supervised learning scenarios, there is a small set of labeled data, along with a large pool of unlabeled data. In this thesis, we present a new semi-supervised learning method called co-learning that is designed to use unlabeled data to enhance standard supervised learning algorithms. The idea is that two or more standard supervised learning algorithms can leverage off the fact that they have different representations of the hypotheses and they are likely to detect different patterns in labeled data. We also design an active co-learning strategy to bootstrap our co-leaning procedure when the originally labeled data set is too small to provide accurate confidence estimate for the learned hypotheses. We provide a priority sampling technique as the selection component in our active co-learning method. We evaluate our co-learning algorithms on several datasets from a commonly used data repository in the machine learning community. We also test our co-learning method on text categorization. The contribution of this research is to put forward a new semi-supervised learning approach for learning with a small number of labeled examples, and explore the applicability of our co-learning strategy in real world applications.
On Learning Unions of Pattern Languages and Tree Patterns

We present efficient on-line algorithms for learning unions of a constant number of tree patterns, unions of a constant number of one-variable pattern languages, and unions of a constant number of pattern languages with fixed length substitutions. By fixed length substitutions we mean that each occurence of variable xi must be substituted by terminal strings of fixed length l(xi). We prove that if an arbitrary unions of pattern languages with fixed length substitutions can be learned efficiently then DNFs are efficiently learnable in the mistake bound model. Since we use a reduction to Winnow, our algorithms are robust against attribute noise. Furthermore, they can be modified to handle concept drift. Also, our approach is quite general and may be applicable to learning other pattern related classes. For example, we could learn a more general pattern language class in which a penalty (i.e. weight) is assigned to each violation of the rule that a terminal symbol cannot be changed or that a pair of variable symbols, of the same variable, must be substituted by the same terminal string. An instance is positive iff the penalty incurred for violating these rules is below a given tolerable threshold.
TCP Dynamic Acknowledgment Delay: Theory and Practice (Extended Abstract)

Fluorinated allylisocyanurates are extremely efficient gelling agents for fluoroelastomers. Blends of fluorinated allylisocyanurate gelled fluoroelastomers with non-gelled fluoroelastomers have improved processability as compared with non-gelled fluoroelastomers and blends of other gelled fluoroelastomers with non-gelled fluoroelastomers.
Learning with Unreliable Boundary Queries

We introduce a model for learning from examples and membership queries in situations where the boundary between positive and negative examples is somewhat ill-defined. In our model, queries near the boundary of a target concept may receive incorrect or “don't care” responses, and the distribution of examples has zero probability mass on the boundary region. The motivation behind our model is that in many cases the boundary between positive and negative examples is complicated or “fuzzy.” However, one may still hope to learn successfully, because the typical examples that one sees do not come from that region. We present several positive results in this new model. We show how to learn the intersection of two arbitrary halfspaces when membership queries near the boundary may be answered incorrectly. Our algorithm is an extension of an algorithm of Baum (1990, 1991) that learns the intersection of two halfspaces whose bounding planes pass through the origin in the PAC-with-membership-queries model. We also describe algorithms for learning several subclasses of monotone DNF formulas.
Exploring applications of learning theory to pattern matching and dynamic adjustment of tcp acknowledgement delays

This student's dissertation, entitled Exploring Applications of Learning Theory to Pattern Matching and Dynamic Adjustment of TCP Acknowledgment Delays has been examined by the undersigned committee of ve faculty members and has received full approval for acceptance in partial fullllment of the requirements for the degree Doctor of Science. Learning theory is a subarea of machine learning that applies results from theoretical computer science, including complexity theory and algorithm design and analysis. In learning theory we rigorously study the eeciency and predictive performance of learning algorithms. While applications of machine learning are well-known, results from learning theory are just beginning to enter the practical arena. The purpose of this dissertation is to develop learning theory algorithms and take initial steps toward modifying them and applying them to practical problems. Here we look at two problems. One is learning geometric patterns, with a preliminary investigation of the learning algorithms' applicability to pattern matching, particularly landmark matching for robot navigation. The other area we study is dynamically adjusting acknowledgment delays in the transmission control protocol (TCP). Here the learning comes in when we attempt to predict TCP packet arrivals to enhance the performance of the algorithms that are adjusting the delays. For the problem of learning geometric patterns, we present three algorithms, all provably eecient and with guaranteed bounds on their error. Two of the algorithms are batch-style algorithms that learn one-dimensional patterns and one algorithm is on-line and can eeciently learn patterns in any xed dimension. Simulation results for the rst two algorithms are also presented, including some runs on real robot vision data. For the acknowledgment delay problem, we rst give two objective functions for measuring an algorithm's performance. These functions measure performance by taking a weighted average of the number of acknowledgments sent and the amount of extra latency introduced by delaying the acknowledgments. We then present two on-line algorithms for this problem, and prove that under certain circumstances they are guaranteed to work well. Simulation results are also provided, contrasting our algorithms with ones currently used in practice. We then perform a preliminary investigation of applying tools from learning theory to the problem of predicting TCP packet arrivals, in hopes of improving the performance of our algorithms on real network traac.
Exact Learning of Discretized Geometric Concepts

We first present an algorithm that uses membership and equivalence queries to exactly identify a discretized geometric concept defined by the union of m axis-parallel boxes in d-dimensional discretized Euclidean space where each coordinate can have n discrete values. This algorithm receives at most md counterexamples and uses time and membership queries polynomial in m and log n for any constant d. Furthermore, all equivalence queries can be formulated as the union of O(md log m) axis-parallel boxes. 
Next, we show how to extend our algorithm to efficiently learn, from only equivalence queries, any discretized geometric concept generated from any number of halfspaces with any number of known (to the learner) slopes in a constant dimensional space. In particular, our algorithm exactly learns (from equivalence queries only) unions of discretized axis-parallel boxes in constant dimensional space in polynomial time. Furthermore, this equivalence query only algorithm can be modified to handle a polynomial number of lies in the counterexamples provided by the environment. 
Finally, we introduce a new complexity measure that better captures the complexity of the union of $m$ boxes than simply the number of boxes and the dimension. Our new measure, $\sigma$, is the number of segments in the target, where a segment is a maximum portion of one of the sides of the target that lies entirely inside or entirely outside each of the other halfspaces defining the target. We present a modification of our first algorithm that uses time and queries polynomial in $\sigma$ and log n. In fact, the time and queries (both membership and equivalence) used by this single algorithm are polynomial for either m or d constant.
TCP dynamic acknowledgment delay (extended abstract): theory and practice

WQ study an on-line problem that is motivated by the networking problem of dynamically adjusting delays of acknowlcdgmcnts in the Transmission Control Protocol (TCP). The thcoroticnl problem we study is the following. There is a scqucnco of n packet arrival times .4 = (or,. . . , an) and a look-nhoad coefficient L, The goal is to partition A into 1; subsequences orroe,. . . , a~: (where a subsequence end is deilncd by an acknowledgment) that minimizes a linear combination of the cost for the number of acknowledgments sent and the cost for the additional latency introduced by delaylng acknowledgments. At each arrival, an oracle provides the algorithm with the times of the next L arrivals. First we give an 0(n*) dynamic programming algorithm for optimally solving the off-line problem. Then we deacribc an on-line algorithm that greedily acknowledges exactly when the cost for an acknowledgment is less than the latency cost obtained by not acknowledging. We show that for this algorithm there ls a sequence of n packet arrivals for which it is S-l (fi) -competitive. Next we present a second on-line algorithm which is a slight modification of the first that we prove is 2-competitive. Let Copt be the cost of the optimal solution and let CA be the cost of the solution produced by algorithm A. We then show that for any on-line algorithm A with any constant look-ahead L, CA > SC+-c where c is a factor that can be made arbitrarily small with rcnpcct to Capt. Thus, in the worst case, our result for L = 0 IQ the hcst possible even for on-line algorithms that can use nny constant look-ahead. We then give some initial empirical results using arrival sequences from real network traffic where we compare the two methods used in TCP for acknowledgment delay with our two on-line algorithms. In all cases we examine performance mitli L = 0 and L = 1, Finally, we consider an alternate dcilnition for the latency cost in our objective function, *Supported in part by NSF NY1 Grant CC%9357707 with mntching funds provfdcd by WUTA.
Noise-tolerant distribution-free learning of general geometric concepts

We present an efficient algorithm for PAC-learning a very general class of geometric concepts over <inline-equation><f><sc>R</sc></f> </inline-equation><supscrpt>d</supscrpt> for fixed <italic>d</italic>. More specifically, let <inline-equation><f><sc>T</sc></f> </inline-equation> be any set of <italic>s</italic> halfspaces. Let <italic>x</italic> =(x<subscrpt>1</subscrpt>, …, <italic>x</italic><subscrpt>d</subscrpt>) be an arbitrary point in <inline-equation><f><sc>R</sc></f> </inline-equation><supscrpt>d</supscrpt>. With each <italic>t</italic><inline-equation><f>∈<sc>T</sc></f> </inline-equation> we associate a boolean indicator function <italic>I<subscrpt>t</subscrpt>(x)</italic> which is 1 if and only if <italic>x</italic> is in the halfspace <italic>t</italic>. The  concept class, <inline-equation><f><sc>C</sc><sup>d</sup><inf>s</inf></f> </inline-equation>, that we study consists of all  concepts formed by any Boolean function over <italic>I<subscrpt>t1</subscrpt>, …, I<subscrpt>ts</subscrpt></italic> for   <italic>t<subscrpt>i</subscrpt></italic> <inline-equation><f>∈<sc>T</sc></f> </inline-equation>. This class is much more general than any geometric concept class known to be PAC-learnable. Our results can be extended easily to learn efficiently any Boolean combination of a polynomial number of concepts selected from any concept class <inline-equation><f><sc>C</sc></f> </inline-equation> over <inline-equation><f><sc>R</sc></f> </inline-equation> given that the VC-dimension of <inline-equation><f><sc>C</sc></f> </inline-equation> has dependence only on <italic>d</italic> and there is a polynomial time algorithm to determine if there is a concept from  <inline-equation><f><sc>C</sc></f> </inline-equation> consistent with a given set of labeled examples. We also present a statistical query version of our algorithm that can tolerate random classification noise. Finally we present a generalization of the  standard ε-net result of Haussler and Welzl [1987] and apply it to give an alternative noise-tolerant algorithm for  <italic>d</italic> = 2 based on geometric subdivisions.
TCP Dynamic Acknowledgment Delay: Theory and Practice

We study an on-line problem that is motivated by the networking problem of dynamically adjusting delays of acknowledgments in the Transmission Control Protocol (TCP). The theoretical problem we study is the following. There is a sequence of n packet arrival times A = and a look-ahead coefficient L. The goal is to partition A into k subsequences sigma1, sigma2, ...,sigmak (where a subsequence end is defined by an acknowledgment) that minimizes a linear combination of the cost for the number of acknowledgments sent and the cost for the additional latency introduced by delaying acknowledgments. At each arrival, an oracle provides the algorithm with the times of the next L arrivals. For all the results of our paper, we describe how to incorporate other contraints to better match the true acknowledgment delay problem. We first define two different objective functions for measuring the cost of a solution, each with its own measure of latency cost. For each objective function we first given an O (nsquared)-time dynamic programming algorithm for optimally solbing the off-line problem. Then... Read complete abstract on page 2.