Evaluating the effect of structural dimensions on the successful implementation of strategies in Payam-e-noor University of Iran

This study aims to assess the relationship between structural dimensions of organization including centralization, complexity and formalization on one side and strategy effectiveness on the other side. Structural dimensions are determined based on Robbins theory, and each of them is considered as independent variables of research. Strategy effectiveness, which includes achieving strategic goals or successful implementation of strategies are the independent variable of the research, based on Noble’s strategy implementation model. One primary thesis and three secondary these are defined. This is a descriptive research of two variable correlation. The target population includes 600 senior managers of Payam-e-noor university around the country, including staff administrative managers, province and unit headmasters, of Iran at the time of data collection. Simple random sampling is used, with sample size of 120. Library resources are used for theoretical foundation data collection and note-taking. Questionnaires are used to collect data and evaluate research theses. Inferential Statistics and Pearson correlation coefficient are used to analyze the research theses. The first two theses are confirmed, at 95% and 99% respectively, but the third thesis is rejected based on the collected data. Therefore, based on this study, complexity and formalization have respectively inverse and direct effect on strategy effectiveness, but centralization does not affect strategy effectiveness in Payam-e-noor University

Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing

Health monitoring applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings. Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time. However, resource constraints on most of these wearable devices prevent the ability to perform online learning on them. To address this issue, it is required to rethink the machine learning models from the algorithmic perspective to be suitable to run on wearable devices. Hyperdimensional computing (HDC) offers a well-suited on-device learning solution for resource-constrained devices and provides support for privacy-preserving personalization. Our HDC-based method offers flexibility, high efficiency, resilience, and performance while enabling on-device personalization and privacy protection. We evaluate the efficacy of our approach using three case studies and show that our system improves the energy efficiency of training by up to $45.8\times$ compared with the state-of-the-art Deep Neural Network (DNN) algorithms while offering a comparable accuracy

Edge-centric Optimization of Multi-modal ML-driven eHealth Applications

Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics. Smart eHealth applications sense input data from multiple modalities, transmit the data to edge and/or cloud nodes, and process the data with compute intensive machine learning (ML) algorithms. Run-time variations with continuous stream of noisy input data, unreliable network connection, computational requirements of ML algorithms, and choice of compute placement among sensor-edge-cloud layers affect the efficiency of ML-driven eHealth applications. In this chapter, we present edge-centric techniques for optimized compute placement, exploration of accuracy-performance trade-offs, and cross-layered sense-compute co-optimization for ML-driven eHealth applications. We demonstrate the practical use cases of smart eHealth applications in everyday settings, through a sensor-edge-cloud framework for an objective pain assessment case study