Validation study of the Chinese Early Development Instrument (CEDI)

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Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches

Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements

Demonstrating Controlled Change for Autonomous Space Vehicles

Recent research discusses concepts of infield changes to overcome the drawbacks of conventional lab-based system design processes. In this paper, we evaluate the concept of controlled change by applying it to a demonstration of a potential future space exploration scenario with mobile robots. The robots are capable of executing several image computations for exploration, object detection and pose estimation, which can be allocated to both FPGA-and processor resources of a System-on-Chip. The demonstrator addresses three scenarios which cover application-, environment-, and platform change. The system adapts itself to any of the named changes. This capability can increase the autonomy of future space missions. Exemplary, the demonstrator executes adaption of applications during operation to fulfill the mission goals, adaption of reliability under changing environment conditions, and adaption to sensor failure

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed

Refinement of the English language teaching textbook evaluation checklist

The English Language Teaching (ELT) Textbook Evaluation Checklist was developed in response to the need for a reliable, valid and practical instrument to evaluate English language teaching textbooks. The checklist was qualitatively developed by a review of the literature (Mukundan & Ahour, 2010; Mukundan et al., 2011a) and was refined through qualitative (Mukundan et al., 2011b) and quantitative (Mukundan & Nimehchisalem, 2012a) methods. As the validation test results of the checklist (Mukundan & Nimehchisalem, 2012b; Nimehchisalem & Mukundan, 2013) indicated, it could be refined further to improve its validity, reliability and practicality. The present study discusses the modifications made to the checklist following the comments of a panel of experts (n=3), who were sent a copy of the old version of the checklist. They commented on the comprehensiveness, importance and clarity of the domains and items of the checklist independently. The qualitative method was used to collect and analyse the data. The checklist was refined based on the experts' comments; problematic items were removed or revised and a scoring guide was added to it. The refined instrument is more economical than its previous version, and yet further research is required to test its validity empirically

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Perception of the Body in Space: Mechanisms

The principal topic is the perception of body orientation and motion in space and the extent to which these perceptual abstraction can be related directly to the knowledge of sensory mechanisms, particularly for the vestibular apparatus. Spatial orientation is firmly based on the underlying sensory mechanisms and their central integration. For some of the simplest situations, like rotation about a vertical axis in darkness, the dynamic response of the semicircular canals furnishes almost enough information to explain the sensations of turning and stopping. For more complex conditions involving multiple sensory systems and possible conflicts among their messages, a mechanistic response requires significant speculative assumptions. The models that exist for multisensory spatial orientation are still largely of the non-rational parameter variety. They are capable of predicting relationships among input motions and output perceptions of motion, but they involve computational functions that do not now and perhaps never will have their counterpart in central nervous system machinery. The challenge continues to be in the iterative process of testing models by experiment, correcting them where necessary, and testing them again

Small and Medium Enterprises in the Sustainable Supply Chain: A Review

The aim of this paper is to provide a literature review and a summary of the role of small- and medium sized enterprises (SME) in green- and sustainable supply chains using a comprehensive review of the literature. After introducing the most important notions relating to sustainable supply chains and the survey methodology, a detailed analysis of scientific publications describing various issues related to SMEs and their role in green/sustainable supply chains will be presented. Results show the most important focus areas addressed and methodologies used in the literature, as well as a list of potential research questions still unanswered in this important topic

Power-Performance Modeling and Adaptive Management of Heterogeneous Mobile Platforms​

abstract: Nearly 60% of the world population uses a mobile phone, which is typically powered by a system-on-chip (SoC). While the mobile platform capabilities range widely, responsiveness, long battery life and reliability are common design concerns that are crucial to remain competitive. Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful SoC with numerous other resources, including display, memory, power management IC, battery and wireless modems. Furthermore, the SoC itself is a heterogeneous resource that integrates many processing elements, such as CPU cores, GPU, video, image, and audio processors. Therefore, CPU cores do not dominate the platform power consumption under many application scenarios. Competitive performance requires higher operating frequency, and leads to larger power consumption. In turn, power consumption increases the junction and skin temperatures, which have adverse effects on the device reliability and user experience. As a result, allocating the power budget among the major platform resources and temperature control have become fundamental consideration for mobile platforms. Dynamic thermal and power management algorithms address this problem by putting a subset of the processing elements or shared resources to sleep states, or throttling their frequencies. However, an adhoc approach could easily cripple the performance, if it slows down the performance-critical processing element. Furthermore, mobile platforms run a wide range of applications with time varying workload characteristics, unlike early generations, which supported only limited functionality. As a result, there is a need for adaptive power and performance management approaches that consider the platform as a whole, rather than focusing on a subset. Towards this need, our specific contributions include (a) a framework to dynamically select the Pareto-optimal frequency and active cores for the heterogeneous CPUs, such as ARM big.Little architecture, (b) a dynamic power budgeting approach for allocating optimal power consumption to the CPU and GPU using performance sensitivity models for each PE, (c) an adaptive GPU frame time sensitivity prediction model to aid power management algorithms, and (d) an online learning algorithm that constructs adaptive run-time models for non-stationary workloads.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201