Abstract To check the satisfiability of (non-linear) real arithmetic formulas, modern satisfiability modulo theories (SMT) solving algorithms like NLSAT depend heavily on single cell construction , the task of generalizing a sample point to a connected subset (cell) of $$\mathbb {R}^n$$ R n , that contains the sample and over which a given set of polynomials is sign-invariant. In this paper, we propose to speed up the computation and simplify the representation of the resulting cell by dynamically extending the considered set of polynomials with further linear polynomials. While this increases the total number of (smaller) cells generated throughout the algorithm, our experiments show that it can pay off when using suitable heuristics due to the interaction with Boolean reasoning.

Nowadays, passwords are the prevalent authentication mechanism, even though it is proven to offer insufficient protection against cyber crime. Thus, FIDO2 was released with a more secure authentication mechanism. FIDO2 enables authentification with cryptographic hardware, such as USB sticks, NFC cards or in the smartphone integrated chips. A device with FIDO2 support is required to implement the whole FIDO2 stack and offer the required interfaces for the security hardware. However, many systems like for example Smart TVs can not make use of FIDO2 due to the lack of HW interfaces or the usage of outdated software. To overcome this, we present Remote WebAuthn, which enables secure authentification on such restricted devices through a remote authentication from a secondary, FIDO2 compatible device, such as a smartphone. We evaluate our approach to have better usability compared to FIDO2 while maintaining most security advantages.

Approximate dynamic programming approaches to the reinforcement learning problem are often categorized into greedy value function methods and value-based policy gradient methods. As our first main result, we show that an important subset of the latter methodology is, in fact, a limiting special case of a general formulation of the former methodology; optimistic policy iteration encompasses not only most of the greedy value function methods but also natural actor-critic methods, and permits one to directly interpolate between them. The resulting continuum adjusts the strength of the Markov assumption in policy improvement and, as such, can be seen as dual in spirit to the continuum in TD($\lambda$)-style algorithms in policy evaluation. As our second main result, we show for a substantial subset of soft-greedy value function approaches that, while having the potential to avoid policy oscillation and policy chattering, this subset can never converge toward any optimal policy, except in a certain pathological case. Consequently, in the context of approximations, the majority of greedy value function methods seem to be deemed to suffer either from the risk of oscillation/chattering or from the presence of systematic sub-optimality.

A majority of approximate dynamic programming approaches to the reinforcement learning problem can be categorized into greedy value function methods and value-based policy gradient methods. The former approach, although fast, is well known to be susceptible to the policy oscillation phenomenon. We take a fresh view to this phenomenon by casting a considerable subset of the former approach as a limiting special case of the latter. We explain the phenomenon in terms of this view and illustrate the underlying mechanism with artificial examples. We also use it to derive the constrained natural actor-critic algorithm that can interpolate between the aforementioned approaches. In addition, it has been suggested in the literature that the oscillation phenomenon might be subtly connected to the grossly suboptimal performance in the Tetris benchmark problem of all attempted approximate dynamic programming methods. We report empirical evidence against such a connection and in favor of an alternative explanation. Finally, we report scores in the Tetris problem that improve on existing dynamic programming based results.

The underlying processes that enable database query execution are fundamental to understanding database management systems. However, these processes are complex and can be difficult to explain and illustrate. To address this problem, we have developed a Java-based query simulation system that enables students to visualize the steps involved in processing DML queries. We performed a field experiment to evaluate the system, and the results suggest that the system improves student comprehension of the query execution process.

In this paper, we introduce a modeling approach called independent variable group analysis (IVGA) which can be used for finding an efficient structural representation for a given data set. The basic idea is to determine such a grouping for the variables of the data set that mutually dependent variables are grouped together whereas mutually independent or weakly dependent variables end up in separate groups. Computation of an IVGA model requires a combinatorial algorithm for grouping of the variables and a modeling algorithm for the groups. In order to be able to compare different groupings, a cost function which reflects the quality of a grouping is also required. Such a cost function can be derived, for example, using the variational Bayesian approach, which is employed in our study. This approach is also shown to be approximately equivalent to minimizing the mutual information between the groups. The modeling task is computationally demanding. We describe an efficient heuristic grouping algorithm for the variables and derive a computationally light nonlinear mixture model for modeling of the dependencies within the groups. Finally, we carry out a set of experiments which indicate that IVGA may turn out to be beneficial in many different applications.

While competition for scarce space in a Database Systems course curriculum increases, the amount of time spent in many such courses on data modeling decreases. We instead recommend increasing the amount of time spent in the study of data modeling and encourage data model study beyond formalism syntax. We do this in an attempt to help computer science students better understand complex data domains and to help develop higher-level skills that serve them well in a job market threatened by the increased outsourcing of lower level programming jobs. We further recommend the study of process skills as part of data modeling, and develop the idea of data patterns to assist students in the development of advanced data modeling skills.