Wrapper Maintenance: A Machine Learning Approach

The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and efficient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an efficient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year. The verification algorithm correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes, resulting in precision of 0.73 and recall of 0.95. We validated the reinduction algorithm on ten Web sources. We were able to successfully reinduce the wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data extraction task

Design of an instrumented smart cutting tool and its implementation and application perspectives

This paper presents an innovative design of a smart cutting tool, using two surface acoustic wave (SAW) strain sensors mounted onto the top and the side surface of the tool shank respectively, and its implementation and application perspectives. This surface acoustic wave-based smart cutting tool is capable of measuring the cutting force and the feed force in a real machining environment, after a calibration process under known cutting conditions. A hybrid dissimilar workpiece is then machined using the SAW-based smart cutting tool. The hybrid dissimilar material is made of two different materials, NiCu alloy (Monel) and steel, welded together to form a single bar; this can be used to simulate an abrupt change in material properties. The property transition zone is successfully detected by the tool; the sensor feedback can then be used to initiate a change in the machining parameters to compensate for the altered material properties.The UK Technology Strategy Board (TSB) for supporting this research (SEEM Project, contract No. BD266E

Displacement and Diffusion: Rural Hotspot Policing and Drug Arrests in Southwest Virginia

Drug production, distribution, and trafficking is a growing problem in Southwest Virginia. Routine activity theory and situational crime prevention theory focus on the specific characteristics and have led to many policing initiatives such as hotspot policing. Many policing approaches, including hotspot policing, have positively impacted the production, distribution, and trafficking of methamphetamine, cocaine, heroin, and fentanyl in rural Virginia. However, there is little known about the impact these approaches have had on the displacement and diffusion of these drugs in the areas where the biggest law enforcement operations have taken place. Displacement and diffusion are common consequences of any drug initiatives. It cannot be assumed that rural hotspots follow the same patterns as urban hotspots. It also cannot be assumed that situational changes will affect crime patterns. Studying a rural area rather than an urban location gives greater insight into the effectiveness of hotspot policing in rural areas. This study aimed to show if major drug operations, considered hotspot policing for the purpose of this study, have an impact on the arrest rates of methamphetamine, cocaine, heroin, and fentanyl. Data were analyzed through a paired t-test and an ANOVA to determine the impact each operation (Operation Trap Door, Operation Pandemic, and Operation Appalachian Action) had on the county in which the operation occurred and the surrounding counties. Operations Trap Door, Pandemic, and Appalachian Action did not initiate statistically significant displacement or diffusion of benefits

Active Learning with Multiple Views

Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce Co-Testing, which is the first approach to multi-view active learning. Second, we extend the multi-view learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that Co-Testing outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing

International Space Station ECLSS Technical Task Agreement Summary Report

A summary of work accomplished under Technical Task Agreement by the Marshall Space Flight Center (MSFC) documents activities regarding the Environmental Control and Life Support Systems (ECLSS) of the International Space Station (ISS) program. These MSFC activities were in-line to the designing, the development, the testing, and the flight of ECLSS equipment. MSFC's unique capabilities for performing integrated system testing and analyses, and its ability to perform some tasks cheaper and faster to support ISS program needs are the basis for the Technical Task Agreement activities. Tasks were completed in the Water Recovery Systems, Air Revitalization Systems, and microbiology areas. The results of each task is described in this summary report

Gas-Surface Dynamics and Profile Evolution during Etching of Silicon

Scattering of energetic F atoms on a fluorinated Si surface is studied by molecular beam methods. The energy transfer closely follows hard-sphere collision kinematics. Energy and angular distributions of unreacted F atoms suggest significant multiple-bounce scattering in addition to single-bounce scattering and trapping desorption. An empirical model of the atom-surface interaction dynamics is used in a Monte Carlo simulation of topography evolution during neutral beam etching of Si. Model predictions of profile phenomena are validated by experiments