Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

A prevalent and pervasive view of designing is that it can be modeled using variables and decisions made about what values should be taken by these variables. The activity of designing is carried out with the expectation that the designed artifact will operate in the natural world and the social world. These worlds impose constraints on the variables and their values; so, design could be described as a goal-oriented, constrained, decision- making activity. However, design distinguish- es itself from other similarly described activities not only by its domain but also by additional necessary features. Designing involves exploration, exploring what variables might be appropriate. The process of explo- ration involves both goal variables and deci- sion variables. In addition, designing involves learning: Part of the exploration activity is learning about emerging features as a design proceeds. Finally, design activity occurs within two contexts: the context within which the designer operates and the context produced by the developing design itself. The designer’s perception of what the context is affects the implication of the context on the design. The context shifts as the designer’s perceptions change. Design activity can be now characterized as a goal-oriented, con- strained, decision-making, exploration, and learning activity that operates within a con- text that depends on the designer’s percep- tion of the context.

Design Prototypes: A Knowledge Representation Schema for Design

Methods and systems consistent with the present invention provide a data processing system having a business object model reflecting the data used during a business transaction. Consistent interfaces are generated from the business object model. These interfaces are suitable for use across industries, across businesses, and across different departments within a business during a business transaction.

Consistent set of interfaces derived from a business object model

House of Quality

In engineering design, all products and artifacts have some intended reason behind their existence: the product or artifact function. Functional modeling provides an abstract, yet direct, method for understanding and representing an overall product or artifact function. Functional modeling also strategically guides design activities such as problem decomposition, physical modeling, product architecting, concept generation, and team organization. A formal function representation is needed to support functional modeling, and a standardized set of function-related terminology leads to repeatable and meaningful results from such a representation. We refer to this representation as a functional basis; in this paper, we seek to reconcile and integrate two independent research efforts into a significantly evolved functional basis. These efforts include research from the National Institute of Standards and Technology and two US universities, and their industrial partners. The overall approach for integrating the functional representations and the final results are presented. This approach also provides a mechanism for evaluating whether future revisions are needed to the functional basis and, if so, how to proceed. The integration process is discussed relative to differences, similarities, insights into the representations, and product validation. Based on the results, a more versatile and comprehensive design vocabulary emerges. This vocabulary will greatly enhance and expand the frontiers of research in design repositories, product architecture, design synthesis, and general product modeling.

/pdf/a-functional-basis-for-engineering-design-reconciling-and-2i115zzaxr.pdf

A Functional Basis for Engineering Design: Reconciling and Evolving Previous Efforts

United States industry spends billions of dollars as a result of poor interoperability between computer-aided engineering software tools. While ongoing standards development efforts are attempting to address this problem in today's tools, the more significant demand in next-generation tools will be for representations that allow information used or generated during various product development activities to feed forward and backward into others by way of direct electronic interchange. Although the next generation of tools has the potential for greatly increased benefits, there is also a potential for the cost of poor interoperability to multiply. The goal of this work is to develop representations of information that are unavailable in traditional computer-aided engineering tools to support the exchange of product information in a distributed product development environment. This paper develops a vision of next-generation product development systems and provides a core representation for product development information on which future systems can be built.

https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=821599

A foundation for interoperability in next-generation product development systems

Disclosed is a web page development system and method utilizing a common data core (vertical structure and correlated trees for representing web pages) to generate virtual applications based upon the user's interaction, environment (log in IP, stated location, type of device, type of bandwidth, etc.), and pre-set configuration/ rights. The present invention provides a multi-stage processing pipeline with data components at the input end and generated web pages at the output end. Different processing engines act at specific stages in the pipeline. These engines provide the functionalities needed in a web application and include security, localization/personalization, page templating and caching. An asynchronous engine enables limiting of processing to only certain (pertinent) portions of a web page. A static adaptation engine integrates dynamic behavior (functionalities) into static web pages. An assembly member assembles a requested web page based on output from the engines.

Computer method and apparatus for developing web pages and applications

The complexity of modern products and design tools has made the exchange of design information more complex. It is widely recognised that the seamless capture, storage, and retrieval of design information is one of the major challenges for the next generation of computer aided design tools. This paper presents a model for the flow of design information that is sufficiently formal to eventually support a semantics-based approach for developing information exchange standards. The model classifies design information into various types, organises these types into information states and levels of abstraction, and identifies the various transformations that operate within and between the information states. The model’s ability to support a variety of design process models is illustrated by applying it to the Systems Integration of Manufacturing Applications (SIMA) design process model, and the model is then applied to a design example.

A Model for the Flow of Design Information in Product Development

Glucose is a simple sugar that plays an essential role in many basic metabolic and signaling pathways. Many proteins have binding sites that are highly specific to glucose. The exponential increase of genomic data has revealed the identity of many proteins that seem to be central to biological processes, but whose exact functions are unknown. Many of these proteins seem to be associated with disease processes. Being able to predict glucose-specific binding sites in these proteins will greatly enhance our ability to annotate protein function and may significantly contribute to drug design. We hereby present the first glucose-binding site classifier algorithm. We consider the sugar-binding pocket as a spherical spatio-chemical environment and represent it as a vector of geometric and chemical features. We then perform Random Forests feature selection to identify key features and analyze them using support vector machines classification. Our work shows that glucose binding sites can be modeled effectively using a limited number of basic chemical and residue features. Using a leave-one-out cross-validation method, our classifier achieves a 8.11% error, a 89.66% sensitivity and a 93.33% specificity over our dataset. From a biochemical perspective, our results support the relevance of ordered water molecules and ions in determining glucose specificity. They also reveal the importance of carboxylate residues in glucose binding and the high concentration of negatively charged atoms in direct contact with the bound glucose molecule. Proteins 2009. © 2009 Wiley-Liss, Inc.

Prediction of protein-glucose binding sites using support vector machines

This paper presents a new and extensible method for information retrieval and content analysis in natural languages (NL). The proposed method is stem-based; stems are extracted based on a set of language dependent rules that are interpreted by a rule engine. The rule engine allows the system to be adapted to any natural language by modifying the NL semantic rules and grammar. The system has been fully tested using Arabic, and partially using English, Hebrew, and Persian. We validate our approach using a database-based prototype.

Walid Keirouz

Papers

A foundation for interoperability in next-generation product development systems

Computer method and apparatus for developing web pages and applications

A Model for the Flow of Design Information in Product Development

Prediction of protein-glucose binding sites using support vector machines

A Rule-Based Extensible Stemmer for Information Retrieval with Application to Arabic