We describe a crowdsourcing system, called SmartNotes, which detects security threats related to web browsing, such as Internet scams, deceptive sales of substandard products, and websites with intentionally misleading information. It combines automatically collected data about websites with user votes and comments, and uses them to identify potential threats. We have implemented it as a browser extension, which is available for free public use.

Internet scam is fraudulent or intentionally misleading information posted on the web, usually with the intent of tricking people into sending money or disclosing sensitive information. We describe an application of logistic regression to detection of Internet scam. The developed system automatically collects 43 characteristic statistics about websites from 11 online sources and computes the probability that a given website is malicious. We present its empirical evaluation, which shows that its precision and recall are about 98%.

We formalise the notion of important extrema of a time series, that is, its major minima and maxima; analyse the basic mathematical properties of important extrema; and apply these results to the problem of time-series compression. First, we define the numeric importance levels of extrema in a series, and present algorithms for identifying major extrema and computing their importances. Then, we give a procedure for fast lossy compression of a time series at a given rate, by extracting its most important minima and maxima, and discarding the other points.

DiscFinder is a scalable approach for identifying large-scale astronomical structures, such as galaxy clusters, in massive observation and simulation astrophysics datasets. It is designed to operate on datasets with tens of billions of astronomical objects, even in the case when the dataset is much larger than the aggregate memory of compute cluster used for the processing.

While many cybersecurity tools are available to computer users, their default configurations often do not match needs of specific users. Since most modern users are not computer experts, they are often unable to customize these tools, thus getting either insufficient or excessive security. To address this problem, we are developing an automated assistant that learns security needs of the user and helps customize available tools.

We describe a system for scheduling a conference based on incomplete information about available resources and scheduling constraints. We explain the representation of uncertain knowledge and related common-sense rules, which allow reasoning based on uncertain and partially missing data.

We consider the problem of converting a set of numeric data points into a smoothed approximation of the underlying probability distribution. We describe a representation of distributions by histograms with variable-width bars, and give a greedy smoothing algorithm based on this representation.

Fuzzy classification ranks items by degree rather than assigning them either within or without of a category. The novelty of our work is in integrating fuzzy classification algorithms with an interface to visualize fuzzy results. An advantage of our algorithms' `fuzziness' is that it provides additional information per retrieved result that helps in deciding whether to drill down to the document or skip it. An advantage of our interface is that it allows users to visualize those differences quickly. We have created a prototype that allows the retrieval of journal articles by content word or by ontology-supported browse categories that can be selected independently or in tandem. Journal articles in our digital library pertain to paleontology, but techniques demonstrated viable in indexing and ranking paleo-journal literature should apply to other knowledge domains with little modification.

We consider the problem of gathering data for evaluation of given hypotheses, and describe a method for analyzing tradeoffs between the expected utility and the cost of data collection.

We consider the problem of heuristic evaluation of given hypotheses based on limited observations, in situations when available data are insufficient for rigorous statistical analysis.

We consider the task of scheduling a conference based on incomplete information about resources and constraints, which requires elicitation of additional data, and describe a learning procedure that improves elicitation strategies. We outline the representation of incomplete knowledge, and then describe an adaptive elicitation procedure, which learns to identify critical missing data.

We present initial work on a general-purpose system for the analysis of incomplete and uncertain data, integrated with Excel. We explain the representation of main types of uncertainty, and outline tools for the analysis of uncertain data and planning of additional data collection.

We consider the task of scheduling a conference based on incomplete information about resources and constraints, and describe a mechanism for the dynamic learning of related default assumptions, which enable the scheduling system to make reasonable guesses about missing data. We outline the representation of incomplete knowledge, describe the learning procedure, and demonstrate that the learned knowledge improves the scheduling results.

We formalize the concept of domain representation in artificial intelligence, and propose a framework for evaluating representation and comparing alternative representations.

We describe a technique for fast lossy compression of a time series based on the assignment of importance levels to its minima and maxima

The Internet has led to the development of online markets, and computer scientists have designed various auction algorithms, as well as automated exchanges for standardized commodities; however, they have done little work on exchanges for complex non-standard goods. We present an exchange system for trading complex goods, such as used cars or non-standard financial securities. The system allows traders to represent their buy and sell orders by multiple attributes; for example, a car buyer can specify a model, options, colour, and other desirable features. Traders can also provide complex price constraints, along with preferences among acceptable trades; for instance, a car buyer can specify dependency of an acceptable price on the model, year of production, and mileage. We describe the representation and indexing of orders, and give algorithms for fast identification of matches between buy and sell orders. The system identifies the most preferable matches, which maximize trader satisfaction, and it allows control over the trade-off between speed and optimality of matching. It supports markets with up to 300 000 orders, and processes hundreds of new orders per second.

We describe the representation of uncertain knowledge in a conference-scheduling system, which may include incomplete information about available resources, conference events, and scheduling constraints. We then explain the use of this incomplete knowledge in the evaluation of schedule quality.

We consider the task of scheduling a conference based on incomplete data about available resource and scheduling constraints, and describe a procedure for automated elicitation of additional data. This procedure is part of an interactive system for scheduling under uncertainty, which identifies critical missing information, generates related questions to the human administrator, and uses answers to improve the schedule.

We describe a scheduling system that supports collaboration between the user and automated optimizer. It enables the user to monitor the optimizer decisions, make any of the decisions manually, and leave the other decisions to the system. Furthermore, it identifies the tasks that require the user's participation, and asks for assistance with these tasks.

We describe a system for scheduling a conference based on incomplete information about available resources and scheduling constraints. We explain the representation of uncertain knowledge, describe a local-search algorithm for generating near-optimal schedules, and give empirical results of automated scheduling under uncertainty.

The PRODIGY system is based on bidirectional planning, which is a combination of goal-directed reasoning with simulated execution. Researchers have implemented a series of planners that utilize this search strategy, and demonstrated that it is an efficient technique, a fair match to other successful planners; however, they have provided few formal results on the common principles underlying the developed algorithms. We formalize bidirectional planning, elucidate some techniques for improving its efficiency and show how different strategies for controlling search complexity give rise to different versions of PRODIGY. In particular, we demonstrate that PRODIGY is not complete and discuss advantages and drawbacks of its incompleteness. We then develop a complete bidirectional planner and compare it experimentally with PRODIGY. We show that the complete planner is almost as fast as PRODIGY and solves a wider range of problems.

A clinical trial is a study conducted on a group of patients to evaluate a new treatment procedure. Usually, clinicians manually select patients for a clinical trial; the choice of eligible patients is a labor-intensive process, and clinicians are often unable to identify sufficient number of patients, which delays the evaluation of new treatments. We have developed a Web-based system that helps clinicians to determine the eligibility of patients for multiple clinical trials. It uses probabilistic techniques that minimize the amount of manual data entry, by ordering the related data-entry steps. We describe the developed system and give the results of applying it to retrospective data of breast cancer patients at the Moffitt Cancer Center.

Choosing the right problem-solving method from available methods is a crucial skill for experts in many areas. We present a technique for automatic selection among methods based on analysis of their past performances. We formalize the statistical problem involved in choosing an efficient method, derive a solution to this problem, and describe a selection algorithm. The algorithm not only chooses among available methods, but also decides when to abandon the chosen method if it takes too much time.

We present an automated exchange for trading complex goods, such as used cars, which allows traders to describe desirable purchases and sales by multiple attributes. The developed exchange system supports markets with up to 300,000 orders.

When clinicians test a new treatment procedure, they need to identify and recruit patients with appropriate medical conditions. We have developed an expert system that helps clinicians select patients for experimental treatments, and to reduce the number and overall cost of related medical tests. We describe experiments on selecting patients for new treatments at the Moffitt Cancer Center. The experiments have shown that the system can increase the number of selected patients by a factor of three, and that it can also reduce the cost of the selection process.

We present an exchange system for trading nonstandard goods, such as used cars. We explain the representation of desirable purchases and sales, describe data structures for fast identification of matches between buyers and sellers, and give experiments on the system's performance.

We describe a technique for fast compression of time series and indexing of compressed series. We have tested it on three data sets: stock prices, air and sea temperatures, and wind speeds.

Courses on algorithm analysis often include little programming, and do not emphasize application of advanced techniques to practical problems. On the other hand, students usually prefer hands-on learning, and may lack motivation to study theory. We augmented an algorithms course with a series of programming tasks, which involved application of the course material to image processing. These tasks motivated the students, and led to better understanding and retention of theoretical material. They also enabled the students to learn the basics of representing and manipulating images, along with the algorithm theory.

Planning is a complex reasoning task that is well suited for the study of improving performance and knowledge by learning, i.e. by accumulation and interpretation of planning experience. PRODIGY is an architecture that integrates planning with multiple learning mechanisms. Learning occurs at the planner's decision points and integration in PRODIGY is achieved via mutually interpretable knowledge structures. This article describes the PRODIGY planner, briefly reports on several learning modules developed earlier along the project, and presents in more detail two recently explored methods to learn to generate plans of better quality. We introduce the techniques, illustrate them with comprehensive examples, and show preliminary empirical results. The article also includes a retrospective discussion of the characteristics of the overall PRODIGY architecture and discusses their evolution within the goal of the project of building a large and robust integrated planning and learning system.