OpenRaiser/Intern-Atlas huggingface dataset:
MOO: A Methodology for Online Optimization through Mining the Offline Optimum
Venue: arXiv cs.DS 2000
Year: 2000

Ports, warehouses and courier services have to decide online how an arriving task is to be served in order that cost is minimized (or profit maximized). These operators have a wealth of historical data on task assignments; can these data be mined for knowledge or rules that can help the decision-making? MOO is a novel application of data mining to online optimization. The idea is to mine (logged) expert decisions or the offline optimum for rules that can be used for online decisions. It requires little knowledge about the task distribution and cost structure, and is applicable to a wide range of problems. This paper presents a feasibility study of the methodology for the well-known k-server problem. Experiments with synthetic data show that optimization can be recast as classification of the optimum decisions; the resulting heuristic can achieve the optimum for strong request patterns, consistently outperforms other heuristics for weak patterns, and is robust despite changes in cost model.
About the finding of independent vertices of a graph
Venue: arXiv cs.DS 2000
Year: 2000

We examine the Maximum Independent Set Problem in an undirected graph. The main result is that this problem can be considered as the solving the same problem in a subclass of the weighted normal twin-orthogonal graphs. The problem is formulated which is dual to the problem above. It is shown that, for trivial twin-orthogonal graphs, any of its maximal independent set is also maximum one.
A Theory of Universal Artificial Intelligence based on Algorithmic Complexity
Venue: arXiv cs.AI 2000
Year: 2000

Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameterless theory of universal Artificial Intelligence. We give strong arguments that the resulting AIXI model is the most intelligent unbiased agent possible. We outline for a number of problem classes, including sequence prediction, strategic games, function minimization, reinforcement and supervised learning, how the AIXI model can formally solve them. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXI-tl, which is still effectively more intelligent than any other time t and space l bounded agent. The computation time of AIXI-tl is of the order tx2^l. Other discussed topics are formal definitions of intelligence order relations, the horizon problem and relations of the AIXI theory to other AI approaches.
Scientific Collaboratories as Socio-Technical Interaction Networks: A Theoretical Approach
Venue: arXiv cs.CY 2000
Year: 2000

Collaboratories refer to laboratories where scientists can work together while they are in distant locations from each other and from key equipment. They have captured the interest both of CSCW researchers and of science funders who wish to optimize the use of rare scientific equipment and expertise. We examine the kind of CSCW conceptions that help us best understand the character of working relationships in these scientific collaboratories. Our model, inspired by actor-network theory, considers technologies as Socio-technical Interaction Networks (STINs). This model provides a rich understanding of the scientific collaboratories, and also a more complete understanding of the conditions and activities that support collaborative work in them. We illustrate the significance of STIN models with several cases drawn from the fields of high energy physics and materials science.
Modeling the Uncertainty in Complex Engineering Systems
Venue: arXiv cs.AI 2000
Year: 2000

Existing procedures for model validation have been deemed inadequate for many engineering systems. The reason of this inadequacy is due to the high degree of complexity of the mechanisms that govern these systems. It is proposed in this paper to shift the attention from modeling the engineering system itself to modeling the uncertainty that underlies its behavior. A mathematical framework for modeling the uncertainty in complex engineering systems is developed. This framework uses the results of computational learning theory. It is based on the premise that a system model is a learning machine.
Computational Complexity and Phase Transitions
Venue: arXiv cs.CC 2000
Year: 2000

Phase transitions in combinatorial problems have recently been shown to be useful in locating "hard" instances of combinatorial problems. The connection between computational complexity and the existence of phase transitions has been addressed in Statistical Mechanics and Artificial Intelligence, but not studied rigorously. We take a step in this direction by investigating the existence of sharp thresholds for the class of generalized satisfiability problems defined by Schaefer. In the case when all constraints are clauses we give a complete characterization of such problems that have a sharp threshold. While NP-completeness does not imply (even in this restricted case) the existence of a sharp threshold, it "almost implies" this, since clausal generalized satisfiability problems that lack a sharp threshold are either 1. polynomial time solvable, or 2. predicted, with success probability lower bounded by some positive constant by across all the probability range, by a single, trivial procedure.
A Real-Time Novelty Detector for a Mobile Robot
Venue: arXiv cs.RO 2000
Year: 2000

Recognising new or unusual features of an environment is an ability which is potentially very useful to a robot. This paper demonstrates an algorithm which achieves this task by learning an internal representation of `normality' from sonar scans taken as a robot explores the environment. This model of the environment is used to evaluate the novelty of each sonar scan presented to it with relation to the model. Stimuli which have not been seen before, and therefore have more novelty, are highlighted by the filter. The filter has the ability to forget about features which have been learned, so that stimuli which are seen only rarely recover their response over time. A number of robot experiments are presented which demonstrate the operation of the filter.
Knowledge and common knowledge in a distributed environment
Venue: arXiv cs.DC 2000
Year: 2000

Reasoning about knowledge seems to play a fundamental role in distributed systems. Indeed, such reasoning is a central part of the informal intuitive arguments used in the design of distributed protocols. Communication in a distributed system can be viewed as the act of transforming the system's state of knowledge. This paper presents a general framework for formalizing and reasoning about knowledge in distributed systems. We argue that states of knowledge of groups of processors are useful concepts for the design and analysis of distributed protocols. In particular, distributed knowledge corresponds to knowledge that is ``distributed'' among the members of the group, while common knowledge corresponds to a fact being ``publicly known''. The relationship between common knowledge and a variety of desirable actions in a distributed system is illustrated. Furthermore, it is shown that, formally speaking, in practical systems common knowledge cannot be attained. A number of weaker variants of common knowledge that are attainable in many cases of interest are introduced and investigated.
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
Venue: arXiv cs.CL 2000
Year: 2000

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.
Multiagent Control of Self-reconfigurable Robots
Venue: arXiv cs.RO 2000
Year: 2000

We demonstrate how multiagent systems provide useful control techniques for modular self-reconfigurable (metamorphic) robots. Such robots consist of many modules that can move relative to each other, thereby changing the overall shape of the robot to suit different tasks. Multiagent control is particularly well-suited for tasks involving uncertain and changing environments. We illustrate this approach through simulation experiments of Proteo, a metamorphic robot system currently under development.
Verifying Termination and Error-Freedom of Logic Programs with block Declarations
Venue: arXiv cs.LO 2000
Year: 2000

We present verification methods for logic programs with delay declarations. The verified properties are termination and freedom from errors related to built-ins. Concerning termination, we present two approaches. The first approach tries to eliminate the well-known problem of speculative output bindings. The second approach is based on identifying the predicates for which the textual position of an atom using this predicate is irrelevant with respect to termination. Three features are distinctive of this work: it allows for predicates to be used in several modes; it shows that block declarations, which are a very simple delay construct, are sufficient to ensure the desired properties; it takes the selection rule into account, assuming it to be as in most Prolog implementations. The methods can be used to verify existing programs and assist in writing new programs.
Type Classes and Constraint Handling Rules
Venue: arXiv cs.PL 2000
Year: 2000

Type classes are an elegant extension to traditional, Hindley-Milner based typing systems. They are used in modern, typed languages such as Haskell to support controlled overloading of symbols. Haskell 98 supports only single-parameter and constructor type classes. Other extensions such as multi-parameter type classes are highly desired but are still not officially supported by Haskell. Subtle issues arise with extensions, which may lead to a loss of feasible type inference or ambiguous programs. A proper logical basis for type class systems seems to be missing. Such a basis would allow extensions to be characterised and studied rigorously. We propose to employ Constraint Handling Rules as a tool to study and develop type class systems in a uniform way.
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
Venue: arXiv cs.CL 2000
Year: 2000

A crucial step in processing speech audio data for information extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segmentation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) are absent in spoken language. We investigate the use of prosody (information gleaned from the timing and melody of speech) for these tasks. Using decision tree and hidden Markov modeling techniques, we combine prosodic cues with word-based approaches, and evaluate performance on two speech corpora, Broadcast News and Switchboard. Results show that the prosodic model alone performs on par with, or better than, word-based statistical language models -- for both true and automatically recognized words in news speech. The prosodic model achieves comparable performance with significantly less training data, and requires no hand-labeling of prosodic events. Across tasks and corpora, we obtain a significant improvement over word-only models using a probabilistic combination of prosodic and lexical information. Inspection reveals that the prosodic models capture language-independent boundary indicators described in the literature. Finally, cue usage is task and corpus dependent. For example, pause and pitch features are highly informative for segmenting news speech, whereas pause, duration and word-based cues dominate for natural conversation.
A Decision-Theoretic Approach to Resource Allocation in Wireless Multimedia Networks
Venue: arXiv cs.NI 2000
Year: 2000

The allocation of scarce spectral resources to support as many user applications as possible while maintaining reasonable quality of service is a fundamental problem in wireless communication. We argue that the problem is best formulated in terms of decision theory. We propose a scheme that takes decision-theoretic concerns (like preferences) into account and discuss the difficulties and subtleties involved in applying standard techniques from the theory of Markov Decision Processes (MDPs) in constructing an algorithm that is decision-theoretically optimal. As an example of the proposed framework, we construct such an algorithm under some simplifying assumptions. Additionally, we present analysis and simulation results that show that our algorithm meets its design goals. Finally, we investigate how far from optimal one well-known heuristic is. The main contribution of our results is in providing insight and guidance for the design of near-optimal admission-control policies.
Approximation and Exactness in Finite State Optimality Theory
Venue: arXiv cs.CL 2000
Year: 2000

Previous work (Frank and Satta 1998; Karttunen, 1998) has shown that Optimality Theory with gradient constraints generally is not finite state. A new finite-state treatment of gradient constraints is presented which improves upon the approximation of Karttunen (1998). The method turns out to be exact, and very compact, for the syllabification analysis of Prince and Smolensky (1993).
Data sonification and sound visualization
Venue: arXiv cs.SD 2000
Year: 2000

This article describes a collaborative project between researchers in the Mathematics and Computer Science Division at Argonne National Laboratory and the Computer Music Project of the University of Illinois at Urbana-Champaign. The project focuses on the use of sound for the exploration and analysis of complex data sets in scientific computing. The article addresses digital sound synthesis in the context of DIASS (Digital Instrument for Additive Sound Synthesis) and sound visualization in a virtual-reality environment by means of M4CAVE. It describes the procedures and preliminary results of some experiments in scientific sonification and sound visualization.
Efficient cache use for stencil operations on structured discretization grids
Venue: arXiv cs.PF 2000
Year: 2000

We derive tight bounds on cache misses for evaluation of explicit stencil operators on structured grids. Our lower bound is based on the isoperimetrical property of the discrete octahedron. Our upper bound is based on good surface to volume ratio of a parallelepiped spanned by a reduced basis of the inter- ference lattice of a grid. Measurements show that our algorithm typically reduces the number of cache misses by factor of three relative to a compiler optimized code. We show that stencil calculations on grids whose interference lattice have a short vector feature abnormally high numbers of cache misses. We call such grids unfavorable and suggest to avoid these in computations by appropriate padding. By direct measurements on MIPS R10000 we show a good correlation of abnormally high cache misses and unfavorable three-dimensional grids.
To Preference via Entrenchment
Venue: arXiv cs.LO 2000
Year: 2000

We introduce a simple generalization of Gardenfors and Makinson's epistemic entrenchment called partial entrenchment. We show that preferential inference can be generated as the sceptical counterpart of an inference mechanism defined directly on partial entrenchment.
Boolean Satisfiability with Transitivity Constraints
Venue: arXiv cs.LO 2000
Year: 2000

We consider a variant of the Boolean satisfiability problem where a subset E of the propositional variables appearing in formula Fsat encode a symmetric, transitive, binary relation over N elements. Each of these relational variables, e[i,j], for 1 <= i < j <= N, expresses whether or not the relation holds between elements i and j. The task is to either find a satisfying assignment to Fsat that also satisfies all transitivity constraints over the relational variables (e.g., e[1,2] & e[2,3] ==> e[1,3]), or to prove that no such assignment exists. Solving this satisfiability problem is the final and most difficult step in our decision procedure for a logic of equality with uninterpreted functions. This procedure forms the core of our tool for verifying pipelined microprocessors. To use a conventional Boolean satisfiability checker, we augment the set of clauses expressing Fsat with clauses expressing the transitivity constraints. We consider methods to reduce the number of such clauses based on the sparse structure of the relational variables. To use Ordered Binary Decision Diagrams (OBDDs), we show that for some sets E, the OBDD representation of the transitivity constraints has exponential size for all possible variable orderings. By considering only those relational variables that occur in the OBDD representation of Fsat, our experiments show that we can readily construct an OBDD representation of the relevant transitivity constraints and thus solve the constrained satisfiability problem.
Algorithms for Analysing Firewall and Router Access Lists
Venue: arXiv cs.NI 2000
Year: 2000

Network firewalls and routers use a rule database to decide which packets will be allowed from one network onto another. By filtering packets the firewalls and routers can improve security and performance. However, as the size of the rule list increases, it becomes difficult to maintain and validate the rules, and lookup latency may increase significantly. Ordered binary decision diagrams (BDDs) - a compact method of representing and manipulating boolean expressions - are a potential method of representing the rules. This paper presents a new algorithm for representing such lists as a BDD and then shows how the resulting boolean expression can be used to analyse rule sets.
Tagger Evaluation Given Hierarchical Tag Sets
Venue: arXiv cs.CL 2000
Year: 2000

We present methods for evaluating human and automatic taggers that extend current practice in three ways. First, we show how to evaluate taggers that assign multiple tags to each test instance, even if they do not assign probabilities. Second, we show how to accommodate a common property of manually constructed ``gold standards'' that are typically used for objective evaluation, namely that there is often more than one correct answer. Third, we show how to measure performance when the set of possible tags is tree-structured in an IS-A hierarchy. To illustrate how our methods can be used to measure inter-annotator agreement, we show how to compute the kappa coefficient over hierarchical tag sets.
Flipturning polygons
Venue: arXiv cs.CG 2000
Year: 2000

A flipturn is an operation that transforms a nonconvex simple polygon into another simple polygon, by rotating a concavity 180 degrees around the midpoint of its bounding convex hull edge. Joss and Shannon proved in 1973 that a sequence of flipturns eventually transforms any simple polygon into a convex polygon. This paper describes several new results about such flipturn sequences. We show that any orthogonal polygon is convexified after at most n-5 arbitrary flipturns, or at most 5(n-4)/6 well-chosen flipturns, improving the previously best upper bound of (n-1)!/2. We also show that any simple polygon can be convexified by at most n^2-4n+1 flipturns, generalizing earlier results of Ahn et al. These bounds depend critically on how degenerate cases are handled; we carefully explore several possibilities. We describe how to maintain both a simple polygon and its convex hull in O(log^4 n) time per flipturn, using a data structure of size O(n). We show that although flipturn sequences for the same polygon can have very different lengths, the shape and position of the final convex polygon is the same for all sequences and can be computed in O(n log n) time. Finally, we demonstrate that finding the longest convexifying flipturn sequence of a simple polygon is NP-hard.
The Bisimulation Problem for equational graphs of finite out-degree
Venue: arXiv cs.LO 2000
Year: 2000

The "bisimulation problem" for equational graphs of finite out-degree is shown to be decidable. We reduce this problem to the bisimulation problem for deterministic rational (vectors of) boolean series on the alphabet of a dpda M. We then exhibit a complete formal system for deducing equivalent pairs of such vectors.
A Learning Approach to Shallow Parsing
Venue: arXiv cs.LG 2000
Year: 2000

A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject-Verb (SV) phrases that compare favorably with the best published results are presented. In doing that, we compare two ways of modeling the problem of learning to recognize patterns and suggest that shallow parsing patterns are better learned using open/close predictors than using inside/outside predictors.
Noun-phrase co-occurrence statistics for semi-automatic semantic lexicon construction
Venue: arXiv cs.CL 2000
Year: 2000

Generating semantic lexicons semi-automatically could be a great time saver, relative to creating them by hand. In this paper, we present an algorithm for extracting potential entries for a category from an on-line corpus, based upon a small set of exemplars. Our algorithm finds more correct terms and fewer incorrect ones than previous work in this area. Additionally, the entries that are generated potentially provide broader coverage of the category than would occur to an individual coding them by hand. Our algorithm finds many terms not included within Wordnet (many more than previous algorithms), and could be viewed as an ``enhancer'' of existing broad-coverage resources.
Introduction to the CoNLL-2000 Shared Task: Chunking
Venue: arXiv cs.CL 2000
Year: 2000

We describe the CoNLL-2000 shared task: dividing text into syntactically related non-overlapping groups of words, so-called text chunking. We give background information on the data sets, present a general overview of the systems that have taken part in the shared task and briefly discuss their performance.
Computing Crossing Numbers in Quadratic Time
Venue: arXiv cs.DS 2000
Year: 2000

We show that for every fixed non-negative integer k there is a quadratic time algorithm that decides whether a given graph has crossing number at most k and, if this is the case, computes a drawing of the graph in the plane with at most k crossings.