Visualizing “typical” and “exotic” Internet traffic data

The threat of cyber attacks motivates the need to monitor Internet traffic data for potentially abnormal behavior. Due to the enormous volumes of such data, statistical process monitoring tools, such as those traditionally used on data in the product manufacturing arena, are inadequate. “Exotic” data may indicate a potential attack; detecting such data requires a characterization of “typical” data. We devise some new graphical displays, including a “skyline plot,” that permit ready visual identification of unusual Internet traffic patterns in “streaming” data, and use appropriate statistical measures to help identify potential cyberattacks. These methods are illustrated on a moderate-sized data set (135,605 records) collected at George Mason University.     

A correction to “Nonmonotonic inconsistency” [Artificial Intelligence 149 (2003) 161–178]

This note corrects an error in the statement and proof of Propositions 9 and 10 of [C. Cross, Nonmonotonic inconsistency, Artificial Intelligence 149 (2) (2003) 161–178]. Both results turn out to depend on the postulate of Consistency Preservation.     

Integration of “Components” to Test Software Components

We present an ongoing research project aimed at developing a framework for component-based testing, in which we re-use and suitably combine some existing tools: the system architecture and the components are specified by the UML, and specifically the recently proposed UML Components methodology; the test cases are derived by applying the Cow_Suite, an environment for UML-based testing, previously conceived for the integration testing of OO systems; and the tests are codified and executed within the CDT, a framework under development, allowing for the decoupling between the abstract specification of tests, which is made against an architectural model, and their concrete execution, which needs to take into account the component implementations.     

“Hits” (not “Discussion Posts”) predict student success in online courses: A double cross-validation study

The efficacy of individual components of an online course on positive course outcome was examined via stepwise multiple regression analysis. Outcome was measured as the student’s total score on all exams given during the course. The predictors were page hits, discussion posts, and discussion reads. The vast majority of the variance of outcome was accounted for by total page hits. Participation in discussion had little to no effect on performance as measured by outcome on exams. The results were double cross-validated with a sample chosen from another class. There was no shrinkage, indicating that the equations derived from the two samples were very reliable.     

Skill based manufacturing vs. “unmanned factory”—which is superior?

Past development of small batch production has led to advanced horizontal and vertical division of labor, job shop manufacturing, and the use of NC machines. This has caused serious difficulties such as long and variant throughput times, an unfavorable ratio of unproductive to productive workers, and low quality of work. Additionally confronted with deeply changed market conditions, the management is split over the right answer to this challenge. Most managers and production planners try to replace human work still further by enforced use of computers on the shop floor and in the technical office in an integrated manner (CIM). Since this strategy is very likely to create new severe problems, the minority seeks to avoid them by reorganizing production and rearranging the division of functions between humans and machines profoundly (skill based manufacturing). This position is outlined in this paper in some detail and examples of realized subsystems, as first steps on this development path, are included. Finally, reasons for its superiority and resistance to implement it are discussed.     

Analysis of “Salary man” motivation and the will to work (YARUKI)

The Inner Graphic Formula Method (IGF) which was originally conceived by Professor Ishiketa and further developed by him and his associates was used to investigate the motivation of new company employees.

Japanese companies traditionally recruit new employees from senior classes and notify successful candidates of their intention to employ them around the first of January. Since graduation is in March, April first is, then, the first day of work for almost all of these graduates in their new companies.

The investigation period for this study covers the eleven months from January until the middle of November, and therefore includes the three month period after notification but prior to actual work, from January first until March thirty-first, and the first eight month of actual work, from April first to the middle of November. The subjects fell, naturally, into two groups; a “Blue Collar” group and a “White Collar” group.

This paper deals with the motivation of these newly employed workers in general and, specifically, with the difference in motivational tendencies between “Blue Collar” and “White Collar” workers. As expected analysis showed that clear motivational differences appeared.

Motivation in the white collar workers tended to raise after an initial downturn, while a general downward trend was detected for the blue collar workers. White collar worker's attitudes toward themselves and toward their work seemed to change for the better as a result of having the chance to become introspective while plotting the graph and writing the anecdotal responses needed to complete the investigative sheet for this study.