Information filtering via preferential diffusion

Recommender systems have shown great potential to address information overload problem, namely to help users in finding interesting and relevant objects within a huge information space. Some physical dynamics, including heat conduction process and mass or energy diffusion on networks, have recently found applications in personalized recommendation. Most of the previous studies focus overwhelmingly on recommendation accuracy as the only important factor, while overlook the significance of diversity and novelty which indeed provide the vitality of the system. In this paper, we propose a recommendation algorithm based on the preferential diffusion process on user-object bipartite network. Numerical analyses on two benchmark datasets, MovieLens and Netflix, indicate that our method outperforms the state-of-the-art methods. Specifically, it can not only provide more accurate recommendations, but also generate more diverse and novel recommendations by accurately recommending unpopular objects.Comment: 12 pages, 10 figures, 2 table

Operational framework based on modeling languages to support product repository implementation.

Part 3: Tools and MethodologiesInternational audienceEmbracing Product Lifecycle Management approach involves integrating a product repository in the company information system. From customer's needs to disposal stage, several product representations exist. The product repository purpose is to secure consistency of one product representation with the others. This paper presents an operational modeling framework that supports product repository implementation. In order to ensure consistency, this framework identifies correspondences between entities of languages (“trade” languages and standard languages). The presented concepts are illustrated with correspondences between language entities of product designed and productplanned to be built Bills of Materials

Unification of multiple models for complex system development

In the design of automotive product, the constant evolution of customer requirements and international regulations leads to new considerations of the sys-tem design process. The authors propose a modeling approach for complex system design based on the coupling of collaborative models and heterogeneous experts’ (i.e. authoring) models used for product behavior assessment. The approach aims at modeling a system at different systemic and temporal levels in the design pro-cess and allows a flexible navigation with the possibility of changing or adding models in the design space. The purpose behind the use of this approach is to lead to an optimal design solution in the context of innovative design for complex sys-tem

Deep Topology Classification: A New Approach for Massive Graph Classification

The classification of graphs is a key challenge within many scientific fields using graphs to represent data and is an active area of research. Graph classification can be critical in identifying and labelling unknown graphs within a dataset and has seen application across many scientific fields. Graph classification poses two distinct problems: the classification of elements within a graph and the classification of the entire graph. Whilst there is considerable work on the first problem, the efficient and accurate classification of massive graphs into one or more classes has, thus far, received less attention. In this paper we propose the Deep Topology Classification (DTC) approach for global graph classification. DTC extracts both global and vertex level topological features from a graph to create a highly discriminate representation in feature space. A deep feed-forward neural network is designed and trained to classify these graph feature vectors. This approach is shown to be over 99% accurate at discerning graph classes over two datasets. Additionally, it is shown to be more accurate than current state of the art approaches both in binary and multi-class graph classification tasks

Biodegradative mechanism of the brown rot basidiomycete Gloeophyllum trabeum: evidence for an extracellular hydroquinone-driven fenton reaction

AbstractWe have identified key components of the extracellular oxidative system that the brown rot fungus Gloeophyllum trabeum uses to degrade a recalcitrant polymer, polyethylene glycol, via hydrogen abstraction reactions. G. trabeum produced an extracellular metabolite, 2,5-dimethoxy-1,4-benzoquinone, and reduced it to 2,5-dimethoxyhydroquinone. In the presence of 2,5-dimethoxy-1,4-benzoquinone, the fungus also reduced extracellular Fe3+ to Fe2+ and produced extracellular H2O2. Fe3+ reduction and H2O2 formation both resulted from a direct, non-enzymatic reaction between 2,5-dimethoxyhydroquinone and Fe3+. polyethylene glycol depolymerization by G. trabeum required both 2,5-dimethoxy-1,4-benzoquinone and Fe3+ and was completely inhibited by catalase. These results provide evidence that G. trabeum uses a hydroquinone-driven Fenton reaction to cleave polyethylene glycol. We propose that similar reactions account for the ability of G. trabeum to attack lignocellulose

Unambiguous detection of nitrated explosive vapours by fluorescence quenching of dendrimer films

Unambiguous and selective standoff (non-contact) infield detection of nitro-containingexplosives and taggants is an important goal but difficult to achieve with standard analyticaltechniques. Oxidative fluorescence quenching is emerging as a high sensitivity method fordetecting such materials but is prone to false positives—everyday items such as perfumeselicit similar responses. Here we report thin films of light-emitting dendrimers that detectvapours of explosives and taggants selectively—fluorescence quenching is not observed for arange of common interferents. Using a combination of neutron reflectometry, quartz crystalmicrobalance and photophysical measurements we show that the origin of the selectivity isprimarily electronic and not the diffusion kinetics of the analyte or its distribution in the film.The results are a major advance in the development of sensing materials for the standoffdetection of nitro-based explosive vapours, and deliver significant insights into the physicalprocesses that govern the sensing efficacy

Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams

Ministry of Education, Singapore under its Academic Research Funding Tier

Unified representation of fixtures: Clamping, locating and supporting elements in CNC manufacture

International audienceA CNC machining operation is the outcome of the application of the integrated capabilities of various resources within the CNC machining centre. Part fixtures, clamping and other location mechanisms are essential subsets of CNC machining resources. Today, various fixturing techniques and attachments available on the market allow manufacturers to enhance their production capability without buying expensive machine tools. This technology rich fixturing domain is detached while representing and exchanging machine tool resource information for making manufacturing decisions. The research work in this paper utilizes the STEP-NC compliant Unified Manufacturing Resource Model (UMRM) for representing fixtures in conjunction with the parent CNC machining centre. Thus UMRM is enhanced in this context to represent various fixtures such as universal vises, chucks, pallets, auxiliary rotary tables among others. The major contribution of this paper is the application of the extension of the UMRM approach for representing fixturing domain, which allows generic modelling of fixtures and loading devices in addition to machine workpiece and process modelling. This would enable the stage of automated process planning and manufacturing. The universal approach in representing resource information allows the data to be utilised for making a wide variety of manufacturing decisions

Enriching STEP product model with geometric dimension and tolerance information for one-dimensional tolerance analysis

Information exchange and sharing become a necessity for digital factory but they have been more challenging as the industry is computerized more. This is mainly because the capabilities of computerized systems have grown significantly in a very rapid pace in their own information structure, and they require to retrieve various data from different computer systems. ISO 10303-STEP has been developed to provide a neutral format for exchanging product data. However, implementation of STEP has several issues, including the following two: (1) the complete STEP file should be processed even for querying a small set of data, and (2) information required for realizing any functional activity (e.g., any analysis on any part of a product) is not explicitly identified. Hence, in this study, functionality-based conformance classes (FCCs) are developed to organize the current conformance classes (CCs) (which are the classes required to be implemented fully in order to be conformant to any particular STEP standard) for supporting different functional activities. Following the concept of data exchange specification (DEX)/template, several templates that are repeatedly used small information groups are introduced in order to create manageable sets of data constructs. In this study, the FCCs for 1D tolerance analysis are developed by enriching the available STEP information models with GD&T. The use of extended STEP models is illustrated with a case study. Copyright © 2017 by ASME