Competency Assurance Management System: Enhancement of Assessment and Verification Process

The paper addresses Competency Assurance Management Systems (CAMS): design, developing and implementing of learning programs. Competency Development Framework (DFW) skills profile of a specific job consists of set of components: competencies, levels of proficiency, and Performance Criteria -Behavior Indicators. To implement CAMS DFWs successfully, the learning development programs need to meet the SMART principles. One of which is to be measurable through mapping real work tasks to a specific Key performance Indicator(s) and achievable through mapping them to a specific competence(s). Competencies achievement may be ensured though conducting assessments. The paper explores the quality of assessment and verification (A&V) process that make CAMS DFW implementation more effective and reliable. It focuses on technical coaching as a main stage of the A&V process and how to enhance its quality and the outcome of the CAMS system accordingly

Smart cards: State-of-the-art to future directions

The evolution of smart card technology provides an interesting case study of the relationship and interactions between security and business requirements. This paper maps out the milestones for smart card technology, discussing at each step the opportunities and challenges. The paper reviews recently proposed innovative ownership/management models and the security challenges associated with them. The paper concludes with a discussion of possible future directions for the technology, and the challenges these present

MOSAIC vision and scenarios for mobile collaborative work related to health and wellbeing

The main objective of the MOSAIC project is to accelerate innovation in Mobile Worker Support Environments by shaping future research and innovation activities in Europe. The modus operandi of MOSAIC is to develop visions and illustrative scenarios for future collaborative workspaces involving mobile and location-aware working. Analysis of the scenarios is input to the process of road mapping with the purpose of developing strategies for R&D leading to deployment of innovative mobile work technologies and applications across different domains. This paper relates to one specific domain, that of Health and Wellbeing. The focus is therefore is on mobile working environments which enable mobile collaborative working related to the domain of healthcare and wellbeing services for citizens. This paper reports the work of MOSAIC T2.2 on the vision and scenarios for mobile collaborative work related to this domain. This work was also an input to the activity of developing the MOSAIC roadmap for future research and development targeted at realization of the future Health and Wellbeing vision. The MOSAIC validation process for the Health and Wellbeing scenarios is described and one scenario – the Major Incident Scenario - is presented in detail

Capturing Regular Human Activity through a Learning Context Memory

A learning context memory consisting of two main parts is presented. The first part performs lossy data compression, keeping the amount of stored data at a minimum by combining similar context attributes — the compression rate for the presented GPS data is 150:1 on average. The resulting data is stored in an appropriate data structure highlighting the level of compression. Elements with a high level of compression are used in the second part to form the start and end points of episodes capturing common activity consisting of consecutive events. The context memory is used to investigate how little context data can be stored containing still enough information to capture regular human activity

Class-Weighted Convolutional Features for Visual Instance Search

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable. Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors for image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. In this paper, we go beyond this spatial information and propose a local-aware encoding of convolutional features based on semantic information predicted in the target image. To this end, we obtain the most discriminative regions of an image using Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the network and therefore, our approach, has the additional advantage of not requiring external information. In addition, we use CAMs to generate object proposals during an unsupervised re-ranking stage after a first fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate the competitiveness of our approach outperforming the current state-of-the-art when using off-the-shelf models trained on ImageNet. The source code and model used in this paper are publicly available at http://imatge-upc.github.io/retrieval-2017-cam/.Comment: To appear in the British Machine Vision Conference (BMVC), September 201

The Portrayal of Complementary and Alternative Medicine in Mass Print Magazines Since 1980

Objectives: The objectives of this study were to examine and describe the portrayal of complementary and alternative medicine (CAM) in mass print media magazines. Design: The sample included all 37 articles found in magazines with circulation rates of greater than 1 million published in the United States and Canada from 1980 to 2005. The analysis was quantitative and qualitative and included investigation of both manifest and latent magazine story messages. Results: Manifest analysis noted that CAM was largely represented as a treatment for a patient with a medically diagnosed illness or specific symptoms. Discussions used biomedical terms such as patient rather than consumer and disease rather than wellness. Latent analysis revealed three themes: (1) CAMs were described as good but not good enough; (2) individualism and consumerism were venerated; and (3) questions of costs were raised in the context of confusion and ambivalence

Security and Power Aware IPV6 Programming in Internet of Things Using CONTIKI and COOJA

The current era is surrounded with enormous devices and gadgets connected with each other using high performance technologies. Such type of technology loaded object communication is treated under the aegis of Internet of Things (IoT). A number of applications are using IoT based communication whether it is related to defense equipments, smart cities, smart offices, highway patrolling, smart toll collections, business communications, satellite televisions, traffic systems or interconnected web cams for social security. IoT is also known and associated with other terms including Ubiquitous Computing (UbiComp), Pervasive Computing or Ambient Computing in which number of devices and objects are virtually connected for remote monitoring and decision making. This manuscript underlines the security and power aware programming in IoT for higher performance in Cooja

DeWitt Wallace Library Annual Report 2016-2017

Summary of library and media services activities for 2016-201