Abstract. This paper presents an overview of the development of the field of temporal and modal logic programming. We review temporal and modal logic programming languages under three headings: (1) languages based on interval logic, (2) languages based on temporal logic, and (3) languages based on (multi)modal logics. The overview includes most of the major results developed, and points out some of the similarities, and the differences, between languages and systems based on diverse temporal and modal logics. The paper concludes with a brief summary and discussion.

This paper addresses an open challenge in educational data mining, i.e., the problem of using observed prerequisite relations among courses to learn a directed universal concept graph, and using the induced graph to predict unobserved prerequisite relations among a broader range of courses. This is particularly useful to induce prerequisite relations among courses from different providers (universities, MOOCs, etc.). We propose a new framework for inference within and across two graphs-at the course level and at the induced concept level-which we call Concept Graph Learning (CGL). In the training phase, our system projects the course-level links onto the concept space to induce directed concept links; in the testing phase, the concept links are used to predict (unobserved) prerequisite links for test-set courses within the same institution or across institutions. The dual mappings enable our system to perform an interlingua-style transfer learning, e.g. treating the concept graph as the interlingua, and inducing prerequisite links in a transferable manner across different universities. Experiments on our newly collected data sets of courses from MIT, Caltech, Princeton and CMU show promising results, including the viability of CGL for transfer learning.

This paper addresses an open challenge in educational data mining, i.e., the problem of automatically mapping online courses from different providers (universities, MOOCs, etc.) onto a universal space of concepts, and predicting latent prerequisite dependencies (directed links) among both concepts and courses. We propose a novel approach for inference within and across course-level and concept-level directed graphs. In the training phase, our system projects partially observed course-level prerequisite links onto directed concept-level links; in the testing phase, the induced concept-level links are used to infer the unknown courselevel prerequisite links. Whereas courses may be specific to one institution, concepts are shared across different providers. The bi-directional mappings enable our system to perform interlingua-style transfer learning, e.g. treating the concept graph as the interlingua and transferring the prerequisite relations across universities via the interlingua. Experiments on our newly collected datasets of courses from MIT, Caltech, Princeton and CMU show promising results.

No abstract

No abstract

No abstract

Passwords are the first line of defense for many computerized systems. The quality of these passwords decides the security strength of these systems. Many studies advocate using password entropy as an indicator for password quality where lower entropy suggests a weaker or less secure password. However, a closer examination of this literature shows that password entropy is very loosely defined. In this paper, we first discuss the calculation of password entropy and explain why it is an inadequate indicator of password quality. We then establish a password quality assessment scheme: password quality indicator (PQI). The PQI of a password is a pair ) , ( L D = λ , where D is the Levenshtein's editing distance of the password in relation to a dictionary of words and common mnemonics, and L is the effective password length. Finally, we propose to use PQI to prescribe the characteristics of good quality passwords.

No abstract