Labor monitoring is crucial in modern health care, as it can be used to detect (and help avoid) significant problems with the fetus. In this paper we focus on hypoxia (or oxygen deprivation), a very serious condition that can arise from different pathologies and can lead to life-long disability and death. We present a novel approach to hypoxia detection based on recordings of the uterine pressure and fetal heart rate, which are routinely monitored during labor. The key idea is to learn models of the fetal response to signals from its environment, using time series data recorded during labor. Then, we use the parameters of these models as attributes in a binary classification problem. A majority vote over several periods is taken to provide the current label for the fetus. We use a unique database of real clinical recordings, both from normal and pathological cases. Our approach classifies correctly more than half the pathological cases, 1.5 hours before delivery. These are cases that were missed by clinicians; early detection of this type would have allowed the physician to perform a Caesarean section, possibly avoiding the negative outcome

To meet the growing demands of the National Airspace System (NAS) stakeholders and provide the level of service, safety and security needed to sustain future air transport, the Next Generation Air Transportation System (NextGen) concept calls for technologies and systems offering increasing support from automated systems that provide decision-aiding and optimization capabilities. This is an exciting application for some core aspects of Artificial Intelligence research since the automation must be designed to enable the human operators to access and process a myriad of information sources, understand heightened system complexity, and maximize capacity, throughput and fuel savings in the NAS.. This paper introduces an emerging application of techniques from mixed initiative (adjustable autonomy), multi-agent systems, and task scheduling techniques to the air traffic control domain. Consequently, we have created a testbed for investigating the critical challenges in supporting the early design of systems that allow for optimal, context-sensitive function (role) allocation between air traffic controller and automated agents. A pilot study has been conducted with the testbed and preliminary results show a marked qualitative improvement in using dynamic function allocation optimization versus static function allocation.

Human experts in scientific fields routinely work with evidence that is noisy and untrustworthy, heuristics that are unproven, and possible conclusions that are contradictory. We present a fully implemented AI system, Calvin, for cosmogenic isotope dating, a domain that is fraught with these difficult issues. Calvin solves these problems using an argumentation framework and a system of confidence that uses two-dimensional vectors to express the quality of heuristics and the applicability of evidence. The arguments it produces are strikingly similar to published expert arguments. Calvin is in daily use by isotope dating experts.

Non-invasive identity inference in the home environment is a very challenging problem. A practical solution to the problem could have far reaching implications in many industries, such as home entertainment. In this work, we consider the problem of identity inference using a TV remote control. In particular, we address two challenges that have so far prevented the work of Chang et al. (2009) from being applied in a home entertainment system. First, we show how to learn the patterns of TV remote controls incrementally and online. Second, we generalize our results to partially labeled data. To achieve our goal, we use state-of-the-art methods for max-margin learning and online convex programming. Our solution is efficient, runs in real time, and comes with theoretical guarantees. It performs well in practice and we demonstrate this on 4 datasets of 2 to 4 people.

Wikis are being increasingly used as a tool for conducting colla-borative writing assignments in today’s classrooms. However, Wikis in general (1) do not provide group formation methods to more specifically facilitate collaborative learning of the students and (2) suffer from typical problems of collaborative learning like detection of free-riding (earning credit without contribution). To improve the state of the art of the use of Wikis as a collaborative writing tool, we have designed and implemented ClassroomWiki - a Web-based collaborative Wiki that utilizes a set of learner pedagogy theories to provide multiagent-based tracking, modeling, and group formation functionalities. For the students, ClassroomWiki provides a Web interface for writing and revising their group’s Wiki and a topic-based forum for discussing their ideas during collaboration. When the students collaborate, ClassroomWiki’s agents track all student activities to learn a model of the students and use a Bayesian Network to learn a probabilistic mapping that describes the ability of a group of students with a specific set of models to work together. For the teacher, Clas-sroomWiki provides a framework that uses the learned student models and the mapping to form student groups to improve the collaborative learning of students. ClassroomWiki was deployed in three university-level courses and the results suggest that ClassroomWiki can (1) form better student groups that improve stu-dent learning and collaboration and (2) alleviate free-riding and allow the instructor to provide scaffolding by its multiagent-based tracking and modeling.

Graphic design is the process of creating graphics to meet specific commercial needs based on knowledge of layout principles and esthetic concepts. This is usually an iterative trial and error process which requires a lot of time even for expert designers. This expert knowledge can be modelled, represented and used by a computer to perform design activities. This paper describes a novel approach named Gaudii (standing for "Intelligent Automated Graphic Design Generator") which utilizes principles and techniques known from the fields of Evolutionary Computation and Fuzzy Logic to automatically obtain design elements. Experimental results that demonstrate the potential of the proposed approach are presented in the area of poster design.

Network management is a complex task involving various challenges, such as the heterogeneity of the infrastructure or the information flood caused by billions of log messages from different systems and operated by different organiza- tional units. All of these messages and systems may contain information relevant to other operational units. For example, in order to ensure reliable DSL connections for IPTV cus- tomers, optimal customer traffic path assignments for the current network state and traffic demands need to be evalu- ated. Currently reassignments are only manually performed during routine maintenance or as a response to reported problems. In this paper we present a decision support sys- tem for this task. In addition, the system predicts future pos- sible demands and allows reconfigurations of a DSL access network before congestions may occur.

Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The application of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company during a period of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we have compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called Team Software Process (TSP). Our results show that defect predictors can be used as supportive tools during a new process implementation, predict 75% of code defects, and decrease the testing time compared with 25% of the code defects detected through more labor-intensive strategies such as code reviews and formal checklists.

Parimutuel wagering is a significant source of revenue for many state governments. MonitorPlus is a surveillance system for parimutuel operators and regulators. Using industry expertise and best practices, MonitorPlus examines each and every wager and account transaction for evidence of fraud, crime, and money laundering. Alerts are generated in real-time. In forensic discovery mode, MonitorPlus is designed to collaborate with skilled analysts to discover more complex suspicious wagering patterns. MonitorPlus utilizes machine learning, so its risk profiles are current: its knowledge base improves with time. Each alert is accompanied by an automatically generated, rule-based explanation. This is critically important if an event rises to the level where legal action is required. Our development and deployment strategy is based on a new paradigm of a secure surveillance utility, where real-time alerts and dataintensive forensics support multiple regulatory jurisdictions. We believe this surveillance paradigm can be applied to other application domains such as lotteries, casinos, online gaming, and financial services.

Evolutionary trees of species can be reconstructed by pairwise comparison of their entire genomes. Such a comparison can be quantified by determining the number of events that change the order of genes in a genome. Earlier Erdem and Tillier formulated the pairwise comparison of entire genomes as the problem of planning rearrangement events that transform one genome to the other. We reformulate this problem as a planning problem to extend its applicability to genomes with multiple copies of genes and with unequal gene content, and illustrate its applicability and effectiveness on three real datasets: mitochondrial genomes of Metazoa, chloroplast genomes of Campanulaceae, chloroplast genomes of various land plants and green algae.