Informatica

Software size is an important attribute in software project planning. Several methods for software size estimation are available; most of them are based on function points. Albrecht introduced function points as a technologically independent method with its own software abstraction layer. However, it is difficult to apply original abstraction elements to current technologies. Therefore researchers introduced additional rules and mappings for object-based solutions. In this paper several mapping strategies are discussed and compared. Based on the similarities in compared mappings, a common mapping strategy is then defined. This mapping is then tested on the reference application portfolio containing five applications. The aim of the test scenario is to evaluate the impact of the diverse detail levels in the class diagrams on software size measurement. Although the question of how to perform quality size measurements in object-oriented projects remains unanswered, the paper gives valuable information on the topic, supported by mathematics.

The paper presents a secure and usable variant of the Game Changer Password System, first proposed by McLennan, Manning, and Tuft. Unlike the initial proposal based on inadequately secure Monopoly and Chess, we propose an improved version based on a layered “Battleship” game resilient against brute force and dictionary attacks. Since the initially proposed scheme did not check for the memorability and usability of a layered version, we conducted an experiment on the usability and memorability aspects. Surprisingly, layered passwords are just as memorable as single ones and, with an 80% recall rate, comparable to other graphical password systems. The claim that memorability is the most vital aspect of game-based password systems cannot be disproved. However, the experiment revealed that the usability decreased to such a low level that users felt less inclined to use such a system daily or recommend it to others.

After Morris and Thompson wrote the first paper on password security in 1979, strict password policies have been enforced to make sure users follow the rules on passwords. Many such policies require users to select and use a system-generated password. The objective of this paper is to analyse the effectiveness of strict password management policies with respect to how users remember system-generated passwords of different textual types – plaintext strings, passphrases, and hybrid graphical-textual PsychoPass passwords. In an experiment, participants were assigned a random string, passphrase, and PsychoPass passwords and had to memorize them. Surprisingly, no one has remembered either the random string or the passphrase, whereas only 10% of the participants remembered their PsychoPass password. The policies where administrators let systems assign passwords to users are not appropriate. Although PsychoPass passwords are easier to remember, the recall rate of any system-assigned password is below the acceptable level. The findings of this study explain that system-assigned strong passwords are inappropriate and put unacceptable memory burden on users.

Background: In the area of artificial learners, not much research on the question of an appropriate description of artificial learner's (empirical) performance has been conducted. The optimal solution of describing a learning problem would be a functional dependency between the data, the learning algorithm's internal specifics and its performance. Unfortunately, a general, restrictions-free theory on performance of arbitrary artificial learners has not been developed yet.

In a supervised learning, the relationship between the available data and the performance (what is learnt) is not well understood. How much data to use, or when to stop the learning process, are the key questions.

One of the tasks of data mining is classification, which provides a mapping from attributes (observations) to pre-specified classes. Classification models are built by using underlying data. In principle, the models built with more data yield better results. However, the relationship between the available data and the performance is not well understood, except that the accuracy of a classification model has diminishing improvements as a function of data size. In this paper, we present an approach for an early assessment of the extracted knowledge (classification models) in the terms of performance (accuracy), based on the amount of data used. The assessment is based on the observation of the performance on smaller sample sizes. The solution is formally defined and used in an experiment. In experiments we show the correctness and utility of the approach.

In the paper, we present an algorithm that can be applied to protect data before a data mining process takes place. The data mining, a part of the knowledge discovery process, is mainly about building models from data. We address the following question: can we protect the data and still allow the data modelling process to take place? We consider the case where the distributions of original data values are preserved while the values themselves change, so that the resulting model is equivalent to the one built with original data. The presented formal approach is especially useful when the knowledge discovery process is outsourced. The application of the algorithm is demonstrated through an example.