Bio: Sam Hsu is an academic researcher from Florida Atlantic University. The author has contributed to research in topics: Distance education & The Internet. The author has an hindex of 10, co-authored 40 publications receiving 259 citations.
TL;DR: In this article, the process of conceiving, planning, designing, implementing, and maintaining a virtual classroom in ten easy-to-follow steps is summarized.
Abstract: Before attempting to describe the steps toward a successful implementation of a virtual classroom, let us start with a few basic and important definitions. A classroom can be defined as a communication system that makes it possible for a group of people/users to come together to dialogue about something they want to learn, and to look at visuals (pictures, diagrams) and text that might aid them in understanding. The conventional classroom is surrounded by walls that provide protection from outside noise and interference, contributing to a more effective learning process (Tiffin and Rajasingham 1995). A virtual classroom, on the other hand, is a system that provides the same opportunities for the teaching and learning process, beyond the physical limits of the traditional classroom's walls, thanks to the use of computer communication networks. Due to the ubiquity and popularity of the Internet -- particularly the World Wide Web -- most virtual classroom implementations are Web-based. Some of the benefits of a Web-based classroom are its geographic, temporal and platform independence, and its simple, familiar and consistent interface. Some of the drawbacks are: limited access to the Internet worldwide; resistance to shift to new and alternative teaching and learning paradigms or methodologies; privacy, security, copyright and related issues; and a lack of uniform quality (McCormack and Jones 1998). In this article we summarize the process of conceiving, planning, designing, implementing, and maintaining a virtual classroom in ten easy-to-follow steps. The article is aimed at the reader who has a previous understanding of the basics of Web-based education and wants a structured simplified view of the steps that ought to be taken in order to successfully implement a virtual classroom. Also, the authors have created the V-model (Fig. 1) for the convenience of accessing the information in this article to graphically communicate the concept and any needed information. [Figure 1 ILLUSTRATION OMITTED] STEP 1 Assess the needs and the necessary conditions to satisfy them. The main purpose of this step is to assure the existence of a need for the proposed virtual classroom and the basic infrastructure to develop it. Put simply, you assess "what is," or the current state of conditions, available system, etc., and "what ought to be," or the desired output. By assessing what is and what ought to be, you have assessed the need and figured out a gap to be bridged (Hamza and Alhalabi 1999). Thus, some questions that should be answered at this stage are: Are there remote students for that course? This is a simple, though essential, question of economics. Unless there is a minimum number of (remote) students who will benefit from the virtual classroom implementation, the initiative will be seen by the upper management as a waste of time, money and resources. Will they be able to access the course site and perform all the necessary interactions? Web-based education assumes the remote students will have the necessary technical conditions to access the course contents from their personal computer. The minimal hardware, software and Internet connection requirements must be assessed in advance, and their costs estimated. Is there institutional support and interest? A virtual classroom is normally too big of a task to be carried out alone without the explicit support of the institution. Support must be present in terms of funding, time allocation, technical resources, and investing in a well-trained staff. Are there administrative policies and procedures for these cases? Implementing an online version of an existing course or creating a new, Web-based course will probably require changes in some administrative policies and procedures. Some of these are registration, admission, fees, prerequisites for taking the course and withdrawal from the course, to name a few. …
••22 Apr 2009
TL;DR: A systematic study to investigate the impact of gene selection on imbalanced microarray data and apply five gene selection measures to eleven microarray datasets, and employ four learning methods to build classification models from the data containing selected genes only.
Abstract: Microarray experiments usually output small volumes but high dimensional data Selecting a number of genes relevant to the tasks at hand is usually one of the most important steps for the expression data analysis While numerous researches have demonstrated the effectiveness of gene selection from different perspectives, existing endeavors, unfortunately, ignore the data imbalance reality, where one type of samples (eg, cancer tissues) may be significantly fewer than the other (eg, normal tissues) In this paper, we carry out a systematic study to investigate the impact of gene selection on imbalanced microarray data Our objective is to understand that if gene selection is applied to imbalanced expression data, what kind of consequences it may bring to the final results? For this purpose, we apply five gene selection measures to eleven microarray datasets, and employ four learning methods to build classification models from the data containing selected genes only Our study will bring important findings and draw numerous conclusions on (1) the impact of gene selection on imbalanced data, and (2) behaviors of different learning methods on the selected data
••10 Oct 2001
TL;DR: The design and development of a Web-based advising system that supplements the conventional advising process is described, to minimize repetitive tasks performed by advisors, to encourage students to adopt a proactive attitude towards advising, and to make advising-related information available to remote students in a single place, in electronic format.
Abstract: Academic advising is an important and time-consuming task and different tools and techniques can be used to make it an effective and efficient process. This paper describes the design and development of a Web-based advising system that supplements the conventional advising process. The system's goals include: to minimize repetitive tasks performed by advisors, to encourage students to adopt a proactive attitude towards advising, to make advising-related information available to remote students in a single place, in electronic format, and to minimize inconsistencies in the advising process. The system supports three different types of users (students, advisors, and secretaries), each of which has different privileges and allowed operations. Student users may use the system to find relevant advising-related information, such as course descriptions and advising FAQs. They can also ask the system which course(s) to take next, based on the classes they have already taken.
03 Nov 1997
TL;DR: The WINDMIL architecture harnesses the computing capability of the managed network elements to prevent data overloading by processing management data locally while also using Web based data storage techniques to provide universal access to management data and functions.
Abstract: Current network management architectures do not provide a scaleable end-to-end solution for managing heterogeneous high speed distributed networks. We propose a new management architecture, "Web integrated Network for Distributed Management Including Logic" (WINDMIL) in order to address the challenges of managing such complex networks. The WINDMIL architecture harnesses the computing capability of the managed network elements to prevent data overloading by processing management data locally while also using Web based data storage techniques to provide universal access to management data and functions. The performance of the WINDMIL architecture in the Internet environment is analyzed using three types of data storage models: centralized, partially distributed and fully distributed.
••04 Nov 1998
TL;DR: A pilot project that integrates conventional classroom teaching and Web-based distance learning technologies to form a hybrid instruction model for a teaching paradigm that can be easily applied toward learner-centered education is proposed.
Abstract: In recent years, many universities have sought to develop distance learning courses and programs that are delivered through the Web. The promising results achieved by many of these projects lead to a question: how can we integrate the best features of Web-based learning into a conventional classroom-based model of instruction? To answer this question, the Department of Computer Science and Engineering and the Department of Languages and Linguistics at Florida Atlantic University, USA, are jointly working on a pilot project that integrates conventional classroom teaching and Web-based distance learning technologies to form a hybrid instruction model for a teaching paradigm that can be easily applied toward learner-centered education. This paper explains the motivation for this project and describes its main technical aspects. A number of the pedagogical and practical issues related to the proposed model are reviewed and the results obtained in its first trial are discussed. Conclusions drawn from a student exit survey and from classroom experience are presented at the end of the article with recommendations for future implementations of a hybrid instructional model.
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
TL;DR: This paper provides a structured and comprehensive overview of various facets of network anomaly detection so that a researcher can become quickly familiar with every aspect of network anomalies detection.
Abstract: Network anomaly detection is an important and dynamic research area. Many network intrusion detection methods and systems (NIDS) have been proposed in the literature. In this paper, we provide a structured and comprehensive overview of various facets of network anomaly detection so that a researcher can become quickly familiar with every aspect of network anomaly detection. We present attacks normally encountered by network intrusion detection systems. We categorize existing network anomaly detection methods and systems based on the underlying computational techniques used. Within this framework, we briefly describe and compare a large number of network anomaly detection methods and systems. In addition, we also discuss tools that can be used by network defenders and datasets that researchers in network anomaly detection can use. We also highlight research directions in network anomaly detection.
•18 May 1999
TL;DR: In this paper, a method is provided for remotely managing a plurality of network elements of a telecommunications network through a special communication link including a computer internet such as a local area network, the world wide web or the Internet.
Abstract: In accordance with the invention, a method is provided for remotely managing a plurality of network element of a telecommunications network through a special communication link including a computer internet such as a local area network, the world wide web or the Internet. A management computer is connected to an element management system server through a communication link including the computer internet. At least one of the plurality of network elements is also coupled to the element management server through the computer internet and the at least one of the plurality of network elements is managed via communications conveyed through the element management server between the management computer and the at least one network element.
TL;DR: In order to build a reliable smart grid, an overview of relevant cyber security and privacy issues is presented and several potential research fields are discussed at the end of this paper.
Abstract: Smart grid is a promising power delivery infrastructure integrated with communication and information technologies. Its bi-directional communication and electricity flow enable both utilities and customers to monitor, predict, and manage energy usage. It also advances energy and environmental sustainability through the integration of vast distributed energy resources. Deploying such a green electric system has enormous and far-reaching economic and social benefits. Nevertheless, increased interconnection and integration also introduce cyber-vulnerabilities into the grid. Failure to address these problems will hinder the modernization of the existing power system. In order to build a reliable smart grid, an overview of relevant cyber security and privacy issues is presented. Based on current literatures, several potential research fields are discussed at the end of this paper.