Automatic analysis of fuselages and problems of conditioning

Imperial Users onl

The main factors behind first generation college students\u27 retention and dropout

Research has shown that first-generation college students face many hurdles until they graduate if they did not drop out. Limited financial resources, lack of personal skills, and social supports cause many first-generation college students to struggle as they strive to achieve their academic aspirations and receive a degree. This study was developed to locate the main factors behind first-generation college students\u27 retention and dropout. For the purpose of forming this study 84 college students were surveyed to collect data from college students themselves. The short-term study found that first-generation college students have more challenges finishing their studies and getting a degree, than having advantages to do so. The study offered some suggestions to what can be done to help this population of students. If these offered suggestions are adopted, it will help reduce first-generation college students\u27 dropout. The study recommended further examination and research concerning first-generation college students\u27 retention and dropout issues

Deep learning based approaches for imitation learning.

Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable

Interconnection of Green Marketing and Green Human Resource Management Functions

Over the globe, we are moving to industrialization, which expands business creation, innovation, and different business exercises. This also expands human comfort, yet in addition, builds its way of life. In any case, on the opposite side, it likewise increments environmental danger that came about biological dangers to person. This article highlights the importance of green HRM practices for effective green marketing. The green human asset (the executives) has to make green mindfulness among the new ability and the current representative working for the association, energize their workers for helping the association to lessen the reasons for ecological debasement through green development, green projects and practices, hold the assets for the group of people yet to come. Based on extensive literature review, it is found that green HRM can create readiness, motivation and pledge to workers to contribute their endeavors and thoughts to the greening of their association for real reflection to the consumers in endeavors to cope with green skepticism and greenwash for effective green marketing and corporate social identity

Low-complex Bayesian estimator for imperfect channels in massive muti-input multi-output system

Motivated by the fact that the complexity of the computations is one of the main challenges in large multiple input multiple output systems, known as massive multiple-input multiple-output (MIMO) systems, this article proposes a low-complex minimum mean squared error (MMSE) Bayesian channel estimator for uplink channels of such systems. First, we have discussed the necessity of the covariance information for the MMSE estimator and how their imperfection knowledge can affect its accuracy. Then, two reduction phases in dimension and floating-point operations have been suggested to reduce its complexity: in phase 1, eigenstructure reduction for channel covariance matrices is implemented based on some truncation rules, while in phase 2, arithmetic operations reduction for matrix multiplications in the MMSE equation is followed. The proposed procedure has significantly reduced the complexity of the MMSE estimator to the first order O(M), which is less than that required for the conventional MMSE with O(M3) in terms of matrix dimension. It has been shown that the estimated channels using our proposed procedure are asymptotically aligned and serve the same quality as the full-rank estimated channels. Our results are validated by averaging the normalized mean squared error (NMSE) over a length of 500 sample realizations through a Monte Carlo simulation using MATLAB R2020a

Organization of Multi-Agent Systems: An Overview

In complex, open, and heterogeneous environments, agents must be able to reorganize towards the most appropriate organizations to adapt unpredictable environment changes within Multi-Agent Systems (MAS). Types of reorganization can be seen from two different levels. The individual agents level (micro-level) in which an agent changes its behaviors and interactions with other agents to adapt its local environment. And the organizational level (macro-level) in which the whole system changes it structure by adding or removing agents. This chapter is dedicated to overview different aspects of what is called MAS Organization including its motivations, paradigms, models, and techniques adopted for statically or dynamically organizing agents in MAS.Comment: 12 page

The role of attitudes and motivation in teaching and learning foreign languages : a theoretical and empirical investigation into the teaching and learning of English in Iraqi preparatory schools

Attitude and motivation, two central concepts in the domain of educational psychology, have not been attended to as required in the literature on English language teaching and learning in Iraq. Consequently, the current study aims at launching a theoretical and empirical investigation into the role of both concepts in bringing about the current discouraging situation of teaching and learning English as a foreign language in Iraq. The theoretical part of the work subsumes the first four chapters. Chapter One is the introduction where the problem to be investigated, the hypotheses, the aims of the research, and the reasons behind the choice of this topic for research have been stated. Chapter Two describes the educational system and the current situation of English language teaching and learning in Iraq. Worth mentioning in this respect are the different pre- and in-service training establishments, English textbooks and tests, and the supervision of teachers of English. Chapter Three is on attitude. The concept has been initially considered from a purely psychological viewpoint with focus on the historical review of attitude development, definition, basic components, main characteristics, formation, and change. Attitude in education forms a second point of departure with emphasis being laid on the role of the concept in teaching and learning foreign languages. Chapter Three ends with attitude measurement. Motivation, the topic of study of Chapter Four, is tackled in terms of its historical development, definition, and different theories. Reference is also made to the role of motivation in education in general, and in foreign language teaching and learning in particular. Accordingly, types of motivation, factors affecting pupils' and teachers' motivation, and teachers' role in motivating pupils form main subjects of discussion. Chapter Four ends with two sections; the first of which tackles the facets of difference between attitude and motivation, while the second deals with the differences between interest on the one hand, and attitude and motivation on the other. Chapter Five is on the method of research adopted to gather the data for the current study. It also contains the analysis of the Pupils' and Teachers' Attitudes and Motivation Questionnaires. Finally, some general remarks about the empirical part of the work are also made. Chapter Six presents the statistical analysis and survey results. It also contains some hypotheses on pupils' and teachers' attitudes and motivation. There is further analysis of some responses made by pupils and teachers which could not be hypothesized. This chapter ends with the analysis of headteachers' and supervisors' perceptions of English language teaching and learning in Iraq. The final chapter titled 'conclusion' contains the general conclusions arrived at by the researcher, followed by some implications for future work