Computing repairs for constraint violations in UML/OCL conceptual schemas

Updating the contents of an information base may violate some of the constraints defined over the schema. The classical way to deal with this problem has been to reject the requested update when its application would lead to some constraint violation. We follow here an alternative approach aimed at automatically computing the repairs of an update, i.e., the minimum additional changes that, when applied together with the requested update, bring the information base to a new state where all constraints are satisfied. Our approach is independent of the language used to define the schema and the constraints, since it is based on a logic formalization of both, although we apply it to UML and OCL because they are widely used in the conceptual modeling community. Our method can be used for maintaining the consistency of an information base after the application of some update, and also for dealing with the problem of fixing up non-executable operations. The fragment of OCL that we use to define the constraints has the same expressiveness as relational algebra and we also identify a subset of it which provides some nice properties in the repair-computation process. Experiments are conducted to analyze the efficiency of our approach.Peer ReviewedPostprint (author's final draft

CURRENT STATUS AND FUTURE GOALS OF THE GLOBAL CC2020 PROJECT: INTERACTIVE TUTORIAL

The purpose of this tutorial is to give the conference participants an update on the current status and future goals of the global CC2020 project. It will also provide the SIGED community with an opportunity to participate in a discussion that gives the CC2020 steering committee qualitative feedback, contributing directly to the outcomes of the project. The tutorial will actively solicit participant contributions and serve as an important mechanism for interaction between the project and the SIGED community. The topics will include a) general introduction to the project and its goals; b) use of competencies as common currency for curriculum analysis; c) use of visualization to compare computing degree programs; and d) lessons for the information systems discipline from the CC2020 project

Gradient-based Bi-level Optimization for Deep Learning: A Survey

Bi-level optimization, especially the gradient-based category, has been widely used in the deep learning community including hyperparameter optimization and meta-knowledge extraction. Bi-level optimization embeds one problem within another and the gradient-based category solves the outer-level task by computing the hypergradient, which is much more efficient than classical methods such as the evolutionary algorithm. In this survey, we first give a formal definition of the gradient-based bi-level optimization. Next, we delineate criteria to determine if a research problem is apt for bi-level optimization and provide a practical guide on structuring such problems into a bi-level optimization framework, a feature particularly beneficial for those new to this domain. More specifically, there are two formulations: the single-task formulation to optimize hyperparameters such as regularization parameters and the distilled data, and the multi-task formulation to extract meta-knowledge such as the model initialization. With a bi-level formulation, we then discuss four bi-level optimization solvers to update the outer variable including explicit gradient update, proxy update, implicit function update, and closed-form update. Finally, we wrap up the survey by highlighting two prospective future directions: (1) Effective Data Optimization for Science examined through the lens of task formulation. (2) Accurate Explicit Proxy Update analyzed from an optimization standpoint.Comment: AI4Science; Bi-level Optimization; Hyperparameter Optimization; Meta Learning; Implicit Functio

Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm

We introduce a community detection algorithm (Fluid Communities) based on the idea of fluids interacting in an environment, expanding and contracting as a result of that interaction. Fluid Communities is based on the propagation methodology, which represents the state-of-the-art in terms of computational cost and scalability. While being highly efficient, Fluid Communities is able to find communities in synthetic graphs with an accuracy close to the current best alternatives. Additionally, Fluid Communities is the first propagation-based algorithm capable of identifying a variable number of communities in network. To illustrate the relevance of the algorithm, we evaluate the diversity of the communities found by Fluid Communities, and find them to be significantly different from the ones found by alternative methods.Comment: Accepted at the 6th International Conference on Complex Networks and Their Application

MSIS 2006: Model Curriculum and Guidelines for Graduate Degree Programs in Information Systems

This article presents the MSIS 2006 Model Curriculum and Guidelines for Graduate Degree Programs in Information Systems. As with MSIS 2000 and its predecessors, the objective is to create a model for schools designing or revising an MS curriculum in Information Systems. The curriculum was designed by a joint committee of the Association for Information Systems and the Association for Computing Machinery. MSIS2006 is a major update of MSIS 2000. Features include increasing the number of required courses from 10 to 12 while revising prerequisites, introducing new courses and revising existing courses to modernize the curriculum, and alternatives for phased upgrading from MSIS2000 to MSIS 2006. As with the previous curriculum, it is the product of detailed consultation with the IS community. The curriculum received the endorsement of 8 major IS professional groups

A Subset of the CERN Virtual Machine File System: Fast Delivering of Complex Software Stacks for Supercomputing Resources

Delivering a reproducible environment along with complex and up-to-date software stacks on thousands of distributed and heterogeneous worker nodes is a critical task. The CernVM-File System (CVMFS) has been designed to help various communities to deploy software on worldwide distributed computing infrastructures by decoupling the software from the Operating System. However, the installation of this file system depends on a collaboration with system administrators of the remote resources and an HTTP connectivity to fetch dependencies from external sources. Supercomputers, which offer tremendous computing power, generally have more restrictive policies than grid sites and do not easily provide the mandatory conditions to exploit CVMFS. Different solutions have been developed to tackle the issue, but they are often specific to a scientific community and do not deal with the problem in its globality. In this paper, we provide a generic utility to assist any community in the installation of complex software dependencies on supercomputers with no external connectivity. The approach consists in capturing dependencies of applications of interests, building a subset of dependencies, testing it in a given environment, and deploying it to a remote computing resource. We experiment this proposal with a real use case by exporting Gauss-a Monte-Carlo simulation program from the LHCb experiment-on Mare Nostrum, one of the top supercomputers of the world. We provide steps to encapsulate the minimum required files and deliver a light and easy-to-update subset of CVMFS: 12.4 Gigabytes instead of 5.2 Terabytes for the whole LHCb repository