Experience and Educationis the best concise statement on education ever published by John Dewey, the man acknowledged to be the pre-eminent educational theorist of the twentieth century. Written more than two decades after Democracy and Education(Dewey's most comprehensive statement of his position in educational philosophy), this book demonstrates how Dewey reformulated his ideas as a result of his intervening experience with the progressive schools and in the light of the criticisms his theories had received. Analysing both "traditional" and "progressive" education, Dr. Dewey here insists that neither the old nor the new education is adequate and that each is miseducative because neither of them applies the principles of a carefully developed philosophy of experience. Many pages of this volume illustrate Dr. Dewey's ideas for a philosophy of experience and its relation to education. He particularly urges that all teachers and educators looking for a new movement in education should think in terms of the deeped and larger issues of education rather than in terms of some divisive "ism" about education, even such an "ism" as "progressivism." His philosophy, here expressed in its most essential, most readable form, predicates an American educational system that respects all sources of experience, on that offers a true learning situation that is both historical and social, both orderly and dynamic.

经验与教育 = Experience and education

Data Mining - Concepts and Techniques.

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

An Introduction to MultiAgent Systems.

Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

Computational Intelligence in Wireless Sensor Networks: A Survey

Software Defined Networking Technology (SDN) provides a prospect to effectively detect and monitor network security problems ascribing to the emergence of the programmable features. Recently, Machine Learning (ML) approaches have been implemented in the SDN-based Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. A stream of advanced machine learning approaches – the deep learning technology (DL) commences to emerge in the SDN context. In this survey, we reviewed various recent works on machine learning (ML) methods that leverage SDN to implement NIDS. More specifically, we evaluated the techniques of deep learning in developing SDN-based NIDS. In the meantime, in this survey, we covered tools that can be used to develop NIDS models in SDN environment. This survey is concluded with a discussion of ongoing challenges in implementing NIDS using ML/DL and future works.

Survey on SDN based network intrusion detection system using machine learning approaches

Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading

Australia is facing a severe water shortage due to below-average rainfall received over the past decade. The agricultural industry is significantly affected by this shortage due to its high water demands. It is important to adopt changes in agricultural practices and employ innovative ideas for the agricultural industry to maintain its current rate of production. Sensor technology can be used to study soil dynamics based on information gathered at regular intervals, and the data collected can be used as feedback to improve irrigation efficiency. In this paper, we describe our experiences in the design, development and deployment of a wireless sensor network to improve water use efficiency for pasture production. Sensor nodes, called sensor pods, were developed using off the shelf components. The design of the sensor pod was a challenging task as the installation has to withstand seasonal weather changes, and be resistant to damages that may be inflicted by cattle in the field. Each sensor pod measures soil moisture, temperature and humidity. Granular matrix sensors are used to measure soil moisture at three different ground depths. Temperature and humidity are measured using the Tmote Sky's on-board sensors. 70 sensor pods were deployed at the TIAR (Tasmanian Institute for Agricultural Research) Elliott Research Farm near Burnie, in the North West of Tasmania, Australia, at the end of December 2007. Preliminary results are now available. The data gathered will be used to develop efficient data evaluation techniques so that irrigation regimes can be automated. This will lead to precision agricultural techniques involving the close monitoring of the field state, and the use of real time data to drive more efficient irrigation practices.

Wireless sensor network deployment for water use efficiency in irrigation

This paper describes a Markov chain analysis of the behaviors of a constructive memory agent. It shows that a constructive memory agent behaves based on the knowledge structures that it has learned from its interaction with the environment. The internal reasoning moves from reactive and reflective to reflexive as the agent acquires more experiences that are increasingly grounded.

Understanding behaviors of a constructive memory agent: A markov chain analysis

We propose a novel approach to automating soil texture classification from in situ sensors in the field. This approach exploits the features of a soil water retention model using machine learning algorithms. Knowledge of the soil textures is then used to learn the composition of the field and its soil horizons. We discuss the role of soil texture classification within our multi-agent irrigation control system and then conduct a preliminary experiment with soil water retention data from the UNSODA database. The system is evaluated with respect to six classifiers. A maximum classification rate of 85.11% was achieved with a MLP neural network, although performance was relatively consistent across all classifiers.

Wei Peng

Papers

Survey on SDN based network intrusion detection system using machine learning approaches

Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading

Wireless sensor network deployment for water use efficiency in irrigation

Understanding behaviors of a constructive memory agent: A markov chain analysis

Machine learning approaches for soil classification in a multi-agent deficit irrigation control system