bluuebunny/crossref_metadata_2025 huggingface dataset:
Positive Unlabelled Learning for Document Classification
Authors: Li Xiao-Li
Published: May 2011

In traditional supervised learning, a large number of labeled positive and negative examples are typically required to learn an accurate classifier. However, in practice, it is costly to obtain the class labels for large sets of training examples, and oftentimes the negative examples are lacking. Such practical considerations motivate the development of a new set of classification algorithms that can learn from a set of labeled positive examples P augmented with a set of unlabeled examples U (which contains both hidden positive and hidden negative examples). That is, we want to build a classifier using P and U in the absence of negative examples to classify the data in U as well as future test data. This problem is called the Positive Unlabelled learning problem or PU learning problem. For instance, a computer scientist may want to build an up-to-date repository of machine learning (ML) papers. Ideally, one can start with an initial set of ML papers (e.g., a personal collection) and then use it to find other ML papers from related online journals or conference series, e.g., Artificial Intelligence journal, AAAI (National Conference on Artificial Intelligence), IJCAI (International Joint Conferences on Artificial Intelligence), SIGIR (ACM Conference on Research & Development on Information Retrieval), and KDD (ACM International Conference on Knowledge Discovery and Data Mining) etc. With the enormous volume of text documents on the Web, Internet news feeds, and digital libraries, finding those documents that are related to one’s interest can be a real challenge. In the application above, the class of documents that one is interested in is called the positive documents (i.e. the ML papers in the online sources). The set of known positive documents are represented as P (namely, the initial personal collection of ML papers). The unlabelled set U (papers from AAAI Proceedings etc) contains two groups of documents. One group contains documents of class P, which are the hidden positive documents in U (e.g., the ML papers in an AAAI Proceedings). The other group, which comprises the rest of the documents in U, are the negative documents (e.g., the non-ML papers in an AAAI Proceedings) since they do not belong to positive class. Given a positive set P, PU learning aims to identify a particular class P of documents from U or classify the future test set into positive and negative classes. Note that collecting unlabeled documents is normally easy and inexpensive, especially those involving online sources.
Quantization of Continuous Data for Pattern Based Rule Extraction
Authors: Hamilton-Wright Andrew , Stashuk Daniel W.
Published: May 2011

A great deal of interesting real-world data is encountered through the analysis of continuous variables, however many of the robust tools for rule discovery and data characterization depend upon the underlying data existing in an ordinal, enumerable or discrete data domain. Tools that fall into this category include much of the current work in fuzzy logic and rough sets, as well as all forms of event-based pattern discovery tools based on probabilistic inference. Through the application of discretization techniques, continuous data is made accessible to the analysis provided by the strong tools of discrete-valued data mining. The most common approach for discretization is quantization, in which the range of observed continuous valued data are assigned to a fixed number of quanta, each of which covers a particular portion of the range within the bounds provided by the most extreme points observed within the continuous domain. This chapter explores the effects such quantization may have, and the techniques that are available to ameliorate the negative effects of these efforts, notably fuzzy systems and rough sets.
Reflecting Reporting Problems and Data Warehousing
Authors: Kontio Juha
Published: May 2011

Reporting is one of the basic processes in all organizations. Reports should offer relevant information for guiding the decision-making. Reporting provides information for planning and on the other hand it provides information for analyzing the correctness of the decisions made at the beginning of the processes. Reporting is based on the data the operational information systems contain. Reports can be produced directly from these operational databases, but an operational database is not organized in a way that naturally supports analysis. An alternative way is to organize the data in such a way that supports analysis easily. Typically this leads to the introduction of a data warehouse. In summer 2002 a multiple case study research was launched in six Finnish organizations. The research studied the databases of these organizations and identified the trends in database exploitation. One of the main ideas was to study the diffusion of database innovations. In practice this meant that the present database architecture was described and the future plans and present problems were identified. The data was mainly collected with semi-structured interviews and altogether 54 interviews were arranged. The research processed data of 44 different information systems. Most (40 %) of the analyzed information systems were online transaction processing systems like order-entry systems. Second biggest category (30 %) was information systems relating to decision support and reporting. Only one pilot data warehouse was among these, but on the other hand customized reporting systems was used for example in SOK, SSP and OPTI. Reporting was anyway commonly recognized as an area where interviewees were not satisfied and improvements were hoped. Turku University of Applied Sciences is one of the largest of its kind in Finland with almost 9000 students and 33 Degree Programs. Our University is organized in six units of education that promote multidisciplinary learning. In autumn 2005 an enterprise resource planning system was introduced at Turku University of Applied Sciences. At the heart of this information system is a data warehouse collecting necessary information from the operational databases. This paper concentrates on briefly describing the identified problems in reporting in the earlier research and how a data warehouse might help overcoming these problems (a more thorough description is provided at (Kontio 2005)). These ideas are benchmarked with usage experiences of the data warehouse based ERP at Turku University of Applied Sciences resulting to some generalizations and confirmation.
Robust Face Recognition for Data Mining
Authors: Lovell Brian C. , Chen Shaokang , Shan Ting
Published: May 2011

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Multimedia data mining attracts considerable attention from researchers, but multimedia data mining is still at the experimental stage (Hsu, Lee & Zhang, 2002). Nowadays, the most effective way to search multimedia archives is to search the metadata of the archive, which are normally labeled manually by humans. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them — and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, postproduction of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the internet backbone.
Semantic Data Mining
Authors: Banerjee Protima
Published: May 2011

Over the past few decades, data mining has emerged as a field of research critical to understanding and assimilating the large stores of data accumulated by corporations, government agencies, and laboratories. Early on, mining algorithms and techniques were limited to relational data sets coming directly from On-Line Transaction Processing (OLTP) systems, or from a consolidated enterprise data warehouse. However, recent work has begun to extend the limits of data mining strategies to include “semi-structured data such as HTML and XML texts, symbolic sequences, ordered trees and relations represented by advanced logics.” (Washio and Motoda, 2003) The goal of any data mining endeavor is to detect and extract patterns in the data sets being examined. Semantic data mining is a novel approach that makes use of graph topology, one of the most fundamental and generic mathematical constructs, and semantic meaning, to scan semi-structured data for patterns. This technique has the potential to be especially powerful as graph data representation can capture so many types of semantic relationships. Current research efforts in this field are focused on utilizing graph-structured semantic information to derive complex and meaningful relationships in a wide variety of application areas- - national security and web mining being foremost among these. In this article, we review significant segments of recent data mining research that feed into semantic data mining and describe some promising application areas.
Statistical Data Editing
Authors: Conversano Claudio , Siciliano Roberta
Published: May 2011

Statistical Data Editing (SDE) is the process of checking and correcting data for errors. Winkler (1999) defines it the set of methods used to edit (clean-up) and impute (fill-in) missing or contradictory data. The result of SDE is data that can be used for analytic purposes. Editing literature goes back to 60’s with the contributions of Nordbotten (1965), Pritzker et al. (1965) and Freund and Hartley (1967). A first mathematical formalization of the editing process is in Naus et al. (1972), who introduce a probabilistic criterion for the identification of records (or the part of them) that failed the editing process. A solid methodology for generalized editing and imputation systems is developed in Fellegi and Holt (1976). The great break in rationalizing the process came as a direct consequence of the PC evolution in the 80’s: Editing started to be performed on-line on PCs even during the interview and by the respondent in computer assisted self-interviewing (CASI) models of data collection (Bethlehem et al., 1989). Nowadays, SDE is a research topic in academia and statistical agencies. The European Economic Commission periodically organizes a workshop on the subject concerning both scientific and managerial aspects of SDE (www.unece.org/stats).
Statistical Models for Operational Risk
Authors: Bonafede Concetto Elvio
Published: May 2011

A statistical model is a possible representation (not necessarily complex) of a situation of the real world. Models are useful to give a good knowledge of the principal elements of the examined situation and so to make previsions or to control such a situation. In the banking sector, models, techniques and regulations have been developed for evaluating Market and Credit risks, for linking together risks, capital and profit opportunity. The regulations and vigilance standards on the capital have been developed from the Basel Committee founded at the end of 1974 by the G10. The standards for the capital’s measurement system were defined in 1988 with the “Capital Accord” (BIS, 1988); nowadays, it is supported from over 150 countries around the world. In January 2001 the Basel Committee published the document “The New Basel Capital Accord” (BIS, 2001), which is a consultative document to define the new regulation for the bank capital requirement. Such a document has been revisited many times (see BIS, 2005). With the new accord there is the necessity of appraising and managing, beyond the financial risks, also the category of the operational risks (OR) already responsible of losses and bankruptcies (Cruz (Ed.), 2004; Alexander (Ed.), 2003; Cruz, 2002).
Temporal Extension for a Conceptual Multidimensional Model
Authors: Malinowski Elzbieta , Zimányi Esteban
Published: May 2011

Data warehouses integrate data from different source systems to support the decision process of users at different management levels. Data warehouses rely on a multidimensional view of data usually represented as relational tables with structures called star or snowflake schemas. These consist of fact tables, which link to other relations called dimension tables. A fact table represents the focus of analysis (e.g., analysis of sales) and typically includes attributes called measures. Measures are usually numeric values (e.g., quantity) used for performing quantitative evaluation of different aspects in an organization. Measures can be analyzed according to different analysis criteria or dimensions (e.g., store dimension). Dimensions may include hierarchies (e.g., month-year in the time dimension) for analyzing measures at different levels of detail. This analysis can be done using on-line analytical processing (OLAP) systems, which allow dynamic data manipulations and aggregations. For example, the roll-up operation transforms detailed measures into aggregated data (e.g., daily into monthly or yearly sales) while the drill-down operations does the contrary. Multidimensional models include a time dimension indicating the timeframe for measures, e.g., 100 units of a product were sold in March 2007. However, the time dimension cannot be used to keep track of changes in other dimensions, e.g., when a product changes its ingredients. In many cases the changes of dimension data and the time when they have occurred are important for analysis purposes. Kimball and Ross (2002) proposed several implementation solutions for this problem in the context of relational databases, the so-called slowly-changing dimensions. Nevertheless, these solutions are not satisfactory since either they do not preserve the entire history of data or are difficult to implement. Further, they do not consider the research realized in the field of temporal databases. Temporal databases are databases that support some aspects of time (Jensen & Snodgrass, 2000). This support is provided by means of different temporality types1, to which we refer in the next section. However, even though temporal databases allow to represent and to manage time-varying information, they do not provide facilities for supporting decision-making process when aggregations of high volumes of historical data are required. Therefore, a new field called temporal data warehouses joins the research achievements of temporal databases and data warehouses in order to manage time-varying multidimensional data.
Text Categorization
Authors: Chenoweth Megan , Song Min
Published: May 2011

Text categorization (TC) is a data mining technique for automatically classifying documents to one or more predefined categories. This paper will introduce the principles of TC, discuss common TC methods and steps, give an overview of the various types of TC systems, and discuss future trends. TC systems begin with a group of known categories and a set of training documents already assigned to a category, usually by a human expert. Depending on the system, the documents may undergo a process called dimensionality reduction, which reduces the number of words or features that the classifier evaluates during the learning process. The system then analyzes the documents and “learns” which words or features of each document caused it to be classified into a particular category. This is known as supervised learning, because it is based on human knowledge of the categories and their criteria. The learning process results in a classifier which can apply the rules it learned during the training phase to additional documents.
Time-Constrained Sequential Pattern Mining
Authors: Lin Ming-Yen
Published: May 2011

Sequential pattern mining is one of the important issues in the research of data mining (Agrawal & Srikant, 1995; Ayres, Gehrke, & Yiu, 2002; Han, Pei, & Yan, 2004; Lin & Lee, 2004; Lin & Lee, 2005b; Roddick & Spiliopoulou, 2002). A typical example is a retail database where each record corresponds to a customer’s purchasing sequence, called data sequence. A data sequence is composed of all the customer’s transactions ordered by transaction time. Each transaction is represented by a set of literals indicating the set of items (called itemset) purchased in the transaction. The objective is to find all the frequent sub-sequences (called sequential patterns) in the sequence database. Whether a sub-sequence is frequent or not is determined by its frequency, named support, in the sequence database. An example sequential pattern might be that 40% customers bought PC and printer, followed by the purchase of scanner and graphics-software, and then digital camera. Such a pattern, denoted by , has three elements where each element is an itemset. Although the issue is motivated by the retail industry, the mining technique is applicable to domains bearing sequence characteristics, including the analysis of Web traversal patterns, medical treatments, natural disasters, DNA sequences, and so forth. In order to have more accurate results, constraints in addition to the support threshold need to be specified in the mining (Pei, Han, & Wang, 2007; Chen & Yu, 2006; Garofalakis, Rastogi, & Shim, 2002; Lin & Lee, 2005a; Masseglia, Poncelet, & Teisseire, 2004). Most time-independent constraints can be handled, without modifying the fundamental mining algorithm, by a postprocessing on the result of sequential pattern mining without constraints. Time-constraints, however, cannot be managed by retrieving patterns because the support computation of patterns must validate the time attributes for every data sequence in the mining process. Therefore, time-constrained sequential pattern mining (Lin & Lee, 2005a; Lin, Hsueh, & Chang, 2006; Masseglia, Poncelet, & Teisseire, 2004;) is more challenging, and more important in the aspect of temporal relationship discovery, than conventional pattern mining.
A User-Aware Multi-Agent System for Team Building
Authors: De Meo Pasquale , Plutino Diego , Quattrone Giovanni , Ursino Domenico
Published: May 2011

In this chapter we present a system for the management of team building and team update activities in the current human resource management scenario. The proposed system presents three important characteristics that appear particularly relevant in this scenario. Firstly, it exploits a suitable standard to uniformly represent and handle expert skills. Secondly, it is highly distributed and, therefore, is well suited for the typical organization of the current job market where consulting firms are intermediating most job positions. Finally, it considers not only experts’ technical skills but also their social and organizational capabilities, as well as the affinity degree possibly shown by them when they worked together in the past.
Video Data Mining
Authors: Oh JungHwan
Published: May 2011

Data mining, which is defined as the process of extracting previously unknown knowledge and detecting interesting patterns from a massive set of data, has been an active research area. As a result, several commercial products and research prototypes are available nowadays. However, most of these studies have focused on corporate data — typically in an alpha-numeric database, and relatively less work has been pursued for the mining of multimedia data (Zaïane, Han, & Zhu, 2000). Digital multimedia differs from previous forms of combined media in that the bits representing texts, images, audios, and videos can be treated as data by computer programs (Simoff, Djeraba, & Zaïane, 2002). One facet of these diverse data in terms of underlying models and formats is that they are synchronized and integrated hence, can be treated as integrated data records. The collection of such integral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has lead to research and development in the area of multimedia data mining. This is a challenging field due to the non-structured nature of multimedia data. Such ubiquitous data is required in many applications such as financial, medical, advertising and Command, Control, Communications and Intelligence (C3I) (Thuraisingham, Clifton, Maurer, & Ceruti, 2001). Multimedia databases are widespread and multimedia data sets are extremely large. There are tools for managing and searching within such collections, but the need for tools to extract hidden and useful knowledge embedded within multimedia data is becoming critical for many decision-making applications.
Web Design Based on User Browsing Patterns
Authors: Yang Yinghui
Published: May 2011

It is hard to organize a website such that pages are located where users expect to find them. Consider a visitor to an e-Commerce website in searching for a scanner. There are two ways he could find information he is looking for. One is to use the search function provided by the website. The other one is to follow the links on the website. This chapter focuses on the second case. Will he click on the link “Electronics” or “Computers” to find the scanner? For the website designer, should the scanner page be put under Electronics, Computers or both? This problem occurs across all kinds of websites, including B2C shops, B2B marketplaces, corporate web-sites and content websites. Through web usages mining, we can automatically discover pages in a website whose location is different from where users expect to find them. This problem of matching website organization with user expectations is pervasive across most websites. Since web users are heterogeneous, the question is essentially how to design a website so that majority of the users find it easy to navigate. Here, we focus on the problem of browsing within a single domain/web site (search engines are not involved since it’s a totally different way of finding information on a web site.) There are numerous reasons why users fail to find the information they are looking for when browse on a web site. Here in this chapter, we focus on the following reason. Users follow links when browsing online. Information scent guides them to select certain links to follow in search for information. If the content is not located where the users expect it to be, the users will fail to find it. How we analyze web navigation data to identify such user browsing patterns and use them to improve web design is an important task.
XML-Enabled Association Analysis
Authors: Feng Ling
Published: May 2011

The discovery of association rules from large amounts of structured or semi-structured data is an important data mining problem [Agrawal et al. 1993, Agrawal and Srikant 1994, Miyahara et al. 2001, Termier et al. 2002, Braga et al. 2002, Cong et al. 2002, Braga et al. 2003, Xiao et al. 2003, Maruyama and Uehara 2000, Wang and Liu 2000]. It has crucial applications in decision support and marketing strategy. The most prototypical application of association rules is market basket analysis using transaction databases from supermarkets. These databases contain sales transaction records, each of which details items bought by a customer in the transaction. Mining association rules is the process of discovering knowledge such as “80% of customers who bought diapers also bought beer, and 35% of customers bought both diapers and beer”, which can be expressed as “diaper ? beer” (35%, 80%), where 80% is the confidence level of the rule, and 35% is the support level of the rule indicating how frequently the customers bought both diapers and beer. In general, an association rule takes the form X ? Y (s, c), where X and Y are sets of items, and s and c are support and confidence, respectively. In the XML Era, mining association rules is confronted with more challenges than in the traditional well-structured world due to the inherent flexibilities of XML in both structure and semantics [Feng and Dillon 2005]. First, XML data has a more complex hierarchical structure than a database record. Second, elements in XML data have contextual positions, which thus carry the order notion. Third, XML data appears to be much bigger than traditional data. To address these challenges, the classic association rule mining framework originating with transactional databases needs to be re-examined.
Anthropologic Concepts in C2C in Virtual Communities
Authors: Leonard Lori N.K. , Jones Kiku
Published: May 2011

Consumer-to-consumer (C2C) electronic commerce (ecommerce) is increasing as a means for individuals to buy and sell products (eMarketer, 2007). The majority of research surrounding C2C e-commerce deals with online auctions (Lin, Li, Janamanchi, & Huang, 2006; Melnik & Alm, 2002) or aspects of online auctions such as the reputation systems (Standifird, 2001). However, C2C e-commerce is being conducted in many different venues in addition to online auctions, such as third party listing services and virtual communities (Jones & Leonard, 2006). Consumers can be quite resourceful when identifying one another to buy/sell their products even when a formal structure to conduct such transactions is not provided. However, when C2C e-commerce is conducted outside a formalized venue such as online auctions and third party listing services, the lines of accountability can be blurred. It makes one wonder why a consumer would choose to participate in C2C e-commerce in venues not designed to facilitate this kind of exchange. One such unstructured venue is a virtual community. This article will discuss the possible reasons why consumers are feeling more comfortable transacting with one another in this particular venue.
Application of the P2P Model for Adaptive Host Protection
Authors: Czirkos Zoltán
Published: May 2011

The importance of the network security problems comes into prominence by the growth of the Internet. This article introduces the basics of the host security problem, reviews the most important intrusion detection methods, and finally proposes a novel solution. Different kinds of security software utilizing the network have been described (Snort, 2006). The novelty of the proposed method is that its clients running in each host create a peer-to-peer (P2P) overlay network. Organization is automatic; it requires no user interaction. This network model ensures stability, which is important for quick and reliable communication between nodes. Its main idea is that the network that is the easiest way to attack the networked computers is utilized in the novel approach in order to improve the efficiency of the protection. By this build-up the system remains useful over the unstable network. The first implementation of the proposed method has proved its ability to protect operating systems of networked hosts.
Calm Technologies as the Future Goal of Information Technologies
Authors: Tugui Alexandru
Published: May 2011

The evolution of the human society over the last 50,000 years has been greatly influenced by technology. The last 200 years have brought about technological achievements at a breathtaking speed. For about six decades, we have been the beneficiaries of the information technologies, which have acquired, over the last 20-25 years, due to the communication technology, an exponential proliferation. In an ideal world, computers will blend into the landscape, will inform but not overburden you with information, and make you aware of them only when you need them. Therefore, the human being is a mere subject of technology, and his everyday life has become increasingly stressing. In order to diminish this stress, solutions have been considered, designed to “tame” the technologies that the human being uses, so as to become calm technologies, that is, technologies that affect neither the human life nor the environment. We can say that everything that surrounds us is technology! Be it or not natural, It influences the environment we live in. Here is a serious reason for every technology to be calm! This article basically deals with the concept of calm technologies and with the characteristics that should be emphasized in the field of the information and communication technologies aimed at turning them into calm technologies.
Current Impact and Future Trends of Mobile Devices and Mobile Applications
Authors: Raisinghani Mahesh S.
Published: May 2011

People have become accustomed to changes in their environment with every new generation of technology. It is through the shift in technology that people are seeing the world through new views and paradigms. We see these paradigm shifts in phases, such as when our parents went from listening to radio to watching television. We have seen the shift in the paradigm when our generation went from stand-alone personal computers to retrieving information off the Internet (Singh, 2003). But the latest shift in paradigm is the explosive developments of the mobile devices and the applications that are constantly being expanded upon to further the potential use of these devices from everyday personal needs to strategic business processes. People today are reeling from the benefits of mobile devices through increased productivity. The people that are benefiting the most are the mobile workers, especially the executives, middle management managers, and salespeople who are not bound by a desk or specific work locations (Cozza, 2005). Mobile devices have given added levels of service to people by allowing them to stay on top of customer support through improved customer care that has increased the company return on investment (Cozza, 2005). Employees can now access their email, contacts, corporate data and up to date meeting schedules by proving invaluable asset information to the corporate employee of today.
Customizing Multimedia with Multi-Trees
Authors: Wagner Ralf
Published: May 2011

The majority of multimedia applications rely on hypermedia technologies, such as HTML, XML, or PHP (cf. Lang, 2005, for a review on design issues of hypermedia systems). These technologies enable the presentation of any content such as entries in a digital encyclopedia or products on a company’s homepage. In contrast to database queries, the hypermedia has to be navigated interactively. The navigation process frequently fails, and the user gets lost in hyperspace. This widespread phenomenon (Shneiderman & Plaisant, 2005) is caused mainly by an inadequate navigational design of the hypermedia. Making up an adequate navigational design becomes even more challenging if groups of users differ with respect to their knowledge of a topic’s structure and if they have overlapping interests. The navigational design comprises two components: the structure of the hypermedia and the layout of user interfaces. The latter aspect is the focus of usability studies (e.g., Falk & Sockel, 2005); whereas, the former is less frequently discussed in the literature and is given scent mention in lectures at universities or business schools. This article is mainly devoted to the former aspect, and: • outlines the graph theoretic foundations for structuring hypermedia, • introduces multi-trees for customizing hypermedia with respect to different user groups, and • provides an overview of metrics to assess the navigational efforts of the user. The approach presented herein differs from wellestablished human-computer interaction studies (e.g., Arroyo, Selker, & Wei, 2006), because it aims at quantifying the users’ navigational efforts with respect to the structure of hypermedia systems rather than the interface design. This article presents a modeling approach, and all results are derived by a deductive analysis. The remainder of this conceptual article is structured as follows: subsequently, the opportunities of structuring hypermedia are outlined. Then components of users’ navigation efforts are discussed, and metrics for the assessment of navigational burdens are presented. Afterward, advantages of multi-trees are highlighted using a numerical example. Starting from a discussion of the limitations of this approach, avenues of future research are pinpointed. The final section provides the conclusions of this study.
Developments and Defenses of Malicious Code
Authors: Luo Xin , Warkentin Merrill
Published: May 2011

The continuous evolution of information security threats, coupled with increasing sophistication of malicious codes and the greater flexibility in working practices demanded by organizations and individual users, have imposed further burdens on the development of effective anti-malware defenses. Despite the fact that the IT community is endeavoring to prevent and thwart security threats, the Internet is perceived as the medium that transmits not only legitimate information but also malicious codes. In this cat-and-mouse predicament, it is widely acknowledged that, as new security countermeasures arise, malware authors are always able to learn how to manipulate the loopholes or vulnerabilities of these technologies, and can thereby weaponize new streams of malicious attacks. From e-mail attachments embedded with Trojan horses to recent advanced malware attacks such as Gozi programs, which compromise and transmit users’ highly sensitive information in a clandestine way, malware continues to evolve to be increasingly surreptitious and deadly. This trend of malware development seems foreseeable, yet making it increasingly arduous for organizations and/or individuals to detect and remove malicious codes and to defend against profit-driven perpetrators in the cyber world. This article introduces new malware threats such as ransomware, spyware, and rootkits, discusses the trends of malware development, and provides analysis for malware defenses. Keywords: Ransomware, Spyware, Anti-Virus, Malware, Malicious Code, Background Various forms of malware have been a part of the computing environment since before the implementation of the public Internet. However, the Internet’s ubiquity has ushered in an explosion in the severity and complexity of various forms of malicious applications delivered via increasingly ingenious methods. The original malware attacks were perpetrated via e-mail attachments, but new vulnerabilities have been identified and exploited by a variety of perpetrators who range from merely curious hackers to sophisticated organized criminals and identify thieves. In an earlier manuscript (Luo & Warkentin, 2005), the authors established the basic taxonomy of malware that included various types of computer viruses (boot sector viruses, macro viruses, etc.), worms, and Trojan horses. Since that time, numerous new forms of malicious code have been found “in the wild.”
Educational Technology Standards in Focus
Authors: O’Dea Michael
Published: May 2011

Educational technology standards, or learning technology standards, as they are also known, have become an increasingly important area of multimedia technology and e-learning over the past decade. These standards have been developed and refined, and have grown to encompass wider aspects of e-learning as the discipline has matured. The scope and reach of e-learning and technology-enhanced systems has increased as a result of this maturing of the discipline. The “holy grail” of e-learning is to enable individualized, flexible, adaptive learning environments that support different learning models or pedagogical approaches to any Internet-enabled user, that these environments should also integrate into the wider MIS/student records system of the teaching institution, and that they should be cost-effective to develop, maintain, and update. The level of functionality of the current systems certainly has not gotten to this level yet, but there have been a number of big improvements made recently in certain of these areas, in particular, in how to make it less time-consuming to develop, more cost-effective, and interoperable. Educational Technology Standards have been in the forefront of these developments. The learning technology standardization process is leading the research effort in Web-based education. Standardization is needed for two main reasons: (1) educational resources are defined, structured, and presented using various formats; and (2) functional modules embedded in a particular learning system cannot be reused by another system in a straightforward way (Anido-Rifon, Fernandez-Iglesias, Llamas-Nistal, Caeiro-Rodriguez, & Santos-Gago, 2001). In this article, the main Educational Technology Standards will be presented, notably, LOM, SCORM, and OKI; their uses and coverage will be outlined; their shortcomings will be discussed; and the current areas of research will be reviewed.
Efficiency and Performance of Work Teams in Collaborative Online Learning
Authors: Moreau Éliane M.F.
Published: May 2011

Online learning, or e-learning, can be an interesting way of encouraging employees to collaborate in performing their work (Fichter, 2002). For example, it can help employees to learn quickly and efficiently, without the inconvenience of absence from the workplace. It can take place at the location desired by the employee, for example, at the office or at home, when the employee wants and needs it, and at a suitable pace (Mingasson, 2002). Employees can, therefore, control their learning progress without having to travel to a classroom. Some find online learning less intimidating and less risky than classroom-based courses given by trainers (Fichter, 2002). If online learning is to be effective, however, employees need a high local network capacity, an Internet connection, and a computer support system to ensure that both hardware and software function properly (Muianga, 2005). The purpose of the research described in this article is to examine the impact of interaction efficiency on the ability of teams to work together and on their learning performance. The article begins by examining the main variables of e-learning use, and goes on to propose a model of work team efficiency and performance in collaborative online learning. It also presents the study’s methodological considerations. Pilot projects were carried out in two universities in Québec, Canada. Virtual teams of five students were formed, and an academic task was handed in to the professors in charge of the projects. The students then completed a questionnaire. The article analyses the benefits of using new technology in university-level courses, and proposes avenues for future research.
Experience Factors and Tool Use in End-User Web Development
Authors: Kase Sue E.
Published: May 2011

In 1995, based on an earlier survey by the U.S. Bureau of Labor Statistics (USBLS), Boehm predicted that the number of end-users performing programminglike tasks would reach 55 million by 2005 (Boehm, Clark, Horowitz, Madachy, Selby & Westland, 1995). Adjusting this information for the accelerated rate of computer usage and other factors, Schaffidi, Shaw, and Myers (2005b) now predict the end-user population at American workplaces will increase to 90 million by 2012, and that these workers will probably execute some type of programming-like task. In a 2004 report, USBLS published projections of occupational growth patterns to 2012 and reported slightly over 3 million professionals in computer-programming occupations in 2002. To summarize, the probability is that 90 million end-users are engaged in programming-like tasks at work compared to only 3 million professionally trained programmers. Thus, the pool of end-user programmers will substantially exceed the small population who view themselves as programmers for the foreseeable future. Programming systems employed by end-users include spreadsheets, Web authoring tools, business authoring tools, graphical languages, and scripting and programming languages (Myers, Ko & Burnett, 2006). Myers et al. (2006) estimates that 50 million people in American workplaces currently use spreadsheets or databases (and therefore may do programming). More specifically, Myers et al. (2006) estimates that over 12 million people in the workplace would say that they actually do programming at work. This diverse and growing population of end-user developers performing programming-like tasks is researched with respect to the emerging subpopulations forming around application specific activities (e.g., spreadsheets, database, Web development). Each of these subpopulations or communities of end-users has characteristic needs and abilities requiring specialized attention. There are even more end-users participating in Internet- based tasks related to programming. During 2003, the Pew Internet and American Life Project found that more than 53 million American adults used the Internet to publish their thoughts, repond to others, post pictures, share files and otherwise contribute to the explosion of content available online. At least 13% (nearly 7 million) of those Internet users claimed they maintained their own Web sites (Pew Internet and American Life Project, 2003). We characterize this nonprofessional population as end-user web developers, in that they have not been trained to develop software as part of their work responsibilities, but nevertheless have found themselves developing and maintaining Web content more and more as part of their daily activities. This review targets this large and growing population, one that presents both opportunities and challenges for information systems researchers studying Web development tools, resources, and education.
Implementing DWDM Lambda-Grids
Authors: Kemper Littman Marlyn
Published: May 2011

Unprecedented demand for ultrafast and dependable access to computing Grids contributes to the accelerating use of dense wavelength division multiplexing (DWDM) technology as a Lambda-Grid enabler. In the Lambda-Grid space, the DWDM infrastructure provisions dynamic lambdas or wavelengths of light ondemand to support terabyte and petabyte transmission rates; seamless access to large-scale aggregations of feature-rich resources; and extendible Grid and inter- Grid services with predictable performance guarantees (Boutaba, Golab, Iraqi, Li, & St. Arnaud, 2003). DWDM Lambda-Grids consist of shared network components that include interconnected federations of other Grids, dense collections of computational simulations, massive datasets, specialized scientific instruments, metadata repositories, large-scale storage systems, digital libraries, and clusters of supercomputers (Naiksatam, Figueira, Chiappari, & Bhatnagar, 2005). As a consequence of the convergence of remarkable advances in DWDM technology and high-performance computing, Lambda-Grids support complex problem resolution in fields that include seismology, neuroscience, bioinformatics, chemistry, and nuclear physics. This chapter begins with a discussion of Grid development and DWDM technical fundamentals. In the sections that follow, the role of the virtual organization (VO) in establishing and supporting DWDM Lambda-Grid initiatives; capabilities of the Globus Toolkit (GT) in facilitating Lambda-Grid construction; distinguishing characteristics of Lambda-Grid operations, architectures, and protocols; and major Web services (WS) specifications in the Lambda-Grid space are examined. Descriptions of DWDM Lambda- Grid initiatives and security challenges associated with DWDM Lambda-Grid implementations are presented. Finally, trends in DWDM Lambda-Grid research are introduced.
Information Security Threats to Network Based Information Systems
Authors: Gupta Sumeet
Published: May 2011

While Internet has opened a whole new world of opportunity for interaction and business by removing many trade barriers, it has also opened up new possibilities and means of criminal acts altogether unheard of in the off-line world. Why do people commit crimes online? Perhaps, some of them attempt to gain unauthorised access to other’s money. Some people have fun doing so and there are others who do it to take revenge or to harm others. While the motivation of conducting criminal acts may be the same as in the off-line world, the manner of such criminal acts is unique to the Internet. The vulnerability of the information transmitted over Internet is the root cause of the sprawling of criminal acts over Internet. Both users and vendors become vulnerable to criminal acts that undermine security due to easy accessibility of Internet and easy exploitation of security loopholes in the Internet. These criminal acts can adversely affect Internet users, particularly online vendors and customers. Therefore, it is important that Internet users not only become conversant of such criminal acts but also take suitable measures to counter and avoid becoming victims of these criminal acts. In this article we examine some of the major information security threats to Internet users with particular emphasis on electronic commerce and propose plausible solutions for a safer online experience. The information security threats can be categorised into threats to the users, threats to the vendors, and threats to both users and vendors. Electronic embezzlement, sniffing and spoofing, and denial-of-service attacks are examples of threat to the vendor. Credit card frauds and malicious codes are examples of threats to the users. Cybervandalism and phishing are examples of threats to both users and vendors.
Issue and Practices of Electronic Learning
Authors: Danenberg James O.
Published: May 2011

E-learning (a major subcomponent of the broader term “distance learning”) is one of the tools with which education can be delivered at a distance, electronically. However, today e-learning is not just reserved for geographically-dispersed learners, but instead is now widely used on campuses all over the world with students who do meet regularly. There are many definitions and terms used which are often substituted for e-learning, such as “distance education,” “distributed learning,” “remote education,” but those terms today have little in common. For instance, in the 1990’s the American Association of University Professors (AAUP) defined distance learning as education in which “the teacher and the student are separated geographically so that face-to-face communication is absent; communication is accomplished instead by one or more technological media, most often electronic” (AAUP, 1999). Although we often thought of e-learning and distance education to be synonymous, they are no more. A more accurate and contemporary definition of e-learning would allow for the occasional face-to-face encounter between teacher and student, both physically and electronically, along with the requirements of the teacher and student(s) separated at a distance, where technology is needed to bridge that gap. An elegant definition of e-learning might therefore be that posed by Holmes and Gardner (2006): “online access to learning resources, anywhere and anytime”. E-learning implies that the learning is delivered via Internet technology to overcome the barriers of place and time. Today, however, e-learning offers many other important opportunities for the enrichment of teaching and learning through virtual environments for the delivery, exploration, and application of new knowledge. E-learning allows for such things as cost saving, specialization not typically available on a traditional campus, and a platform where students can get training according to their particular learning styles and in a format and time frame suited to their needs.