The influencing mechanism of manufacturing scene change on process domain knowledge reuse

It is necessary for a enterprise to reuse outside process domain knowledge to develop intelligent manufacturing technology. The key factors influencing knowledge reuse in digital manufacturing scene are manufacturing activities and PPR (Products, Processes and Resources) related to knowledge modeling, enterprise and integrated systems related to knowledge utilizing. How these factors influence knowledge modeling and utilizing is analyzed. Process domain knowledge reuse across the enterprises consists of knowledge reconfiguration and integrated application with CAx systems. The module-based knowledge model and loosely-coupled integration application of process domain knowledge are proposed. The aircraft sheet metal process domain knowledge reuse is taken as an example, and it shows that the knowledge reuse process can be made flexible and rapid

State Case Studies of Infant and Early Childhood Mental Health Systems: Strategies for Change

Profiles efforts to develop mental health identification and intervention systems for children up to age 5 in Colorado, Indiana, Massachusetts, and Rhode Island. Examines hurdles, reform potentials, and lessons learned, including the role of partnerships

Establishing a resource center: A guide for organizations supporting community foundations

Maintaining a resource center such as a library is a central tasks of an association to serve its members, though one of the first to be neglected. WINGS-CF commissioned this guide to assist organizations supporting community foundations to review and organize their resource items, and to propose several classification systems / taxonomies

Current Developments in Services for People with Intellectual Disabilities

[Taken from Executive Summary] This literature review is the culmination of the Saskatchewan Community Living Division jurisdictional study which began in the autumn of 2003. Following a brief survey of developments in providing services to people with intellectual disabilities (hitherto the People) for creating the questionnaire for this study, information was gleaned from the provinces and territories on their services. The CLD Jurisdictional Project was completed in the spring of 2005. Subsequently, a thorough search and examination of pertinent resources for serving this People and for policy development was conducted. From over 800 documents about 350 were selected for this literature review. The material is recorded in the following chapters: Public Consultation and Policy Development; Social Philosophy: the philosophical influence on contemporary social issues; Definition of disabilities; Needs assessment systems; Human Rights; Advocacy; Community services & Deinstitutionalization; Issues and Influences; Citizenship; Inclusion; Self-determination; Person-centered planning; Supports; Respite; Individualized funding; Canadian governmental initiatives; Provincial Services

MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query it with their data via an API. However, if the user's input is sensitive, sending it to the server is undesirable and sometimes even legally not possible. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model. In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service. MLCapsule executes the model locally on the user's side and therefore the data never leaves the client. Meanwhile, MLCapsule offers the service provider the same level of control and security of its model as the commonly used server-side execution. In addition, MLCapsule is applicable to offline applications that require local execution. Beyond protecting against direct model access, we couple the secure offline deployment with defenses against advanced attacks on machine learning models such as model stealing, reverse engineering, and membership inference