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In the biometric system, fingerprints are widely used to recognise an individual’s identity and are vulnerable to presentation attacks such as spoofing. Spoof fingerprints are fake fingerprints created using artificial materials like play-do, silicone, etc., which fool the biometric identification system. Spoof handling is an urgent need nowadays, as these attacks’ success rate is more than 70%. Much work has been done on fingerprint spoof detection, but mostly involved closed-set problems, i.e. spoof materials used are the same for training and testing set, but they face poor generalisation problem. Hence to overcome closed-set problems, Open-set solution came into existence in which some novel spoof materials were introduced in the testing set, which was not seen during training. This paper will give a brief review of various closed-set and open-set solution techniques along with the tabular view of their Average Classification Error (ACE) scores.

The massive open online course (MOOC) is emerging as the new paradigm for modern education. The success of MOOCs depends on learners' continued usage. Drawing upon the information systems success model (IS success model) and technology acceptance model, a theoretical model for studying learners' continuance intentions toward participation in MOOCs was developed. Based on survey data from 294 respondents, structural equation modeling was employed to assess the model. The results of this analysis indicate that system quality, course quality, and service quality were significant antecedents of the continuance intention of individuals, and the effect of course quality and service quality were mediated by perceived usefulness. The results contribute to the extant literatures in the context of MOOCs learning by identifying the critical quality factors, and provide managerial guidelines for MOOCs utilization and generalization. The implications of the present findings for research and managerial practice are discussed.

Phasor Measurement Units (PMUs) are starting to see increased deployment, enabling accurate measurement of power grid electrical properties to determine system health. Due to the costs associated with PMU acquisition and maintenance, it is practically important to place the minimum number of PMUs in order to achieve system complete observability. In this paper, we consider a variety of optimization models for the PMU placement problem that addresses more realistic assumptions than simple infinite-capacity placement models. Specifically, instead of assuming that a PMU can sense all lines incident to the bus at which it is placed, we impose the more realistic assumption that PMUs have restricted channel capacity, with per-unit cost given as a function of channel capacity. The optimization objective is then to minimize the total cost of placed PMUs, in contrast to their number. Further, we leverage the zero-injection bus properties to reduce the quantity and cost of placed PMUs. In formulating our optimization models, we identify a close relationship between the PMU placement problem (PPP) and a classic combinatorial problem, the set cover problem (SCP). If channel capacity limits are ignored, there is a close relationship between the PPP and the dominating set problem (DSP), a special case of the SCP. Similarly, when measurement redundancy is imposed as a design requirement, there is a close relationship between the PPP and the set multi-cover problem (SMCP), a generalized version of the SCP. These connections to well-studied combinational problems are not well-known in the power systems literature, and can be leveraged to improve solution algorithms. We demonstrate that more realistic, high-fidelity PPP optimization models can be solved to optimality using commercial integer programing solvers such as CPLEX. Specifically, run-times for all test cases, ranging from IEEE 14-bus to 300-bus test systems, are less than a second. This result indicates that the size of system that can be analyzed using state-of-the-art solvers is considerable. Further, our results call into question the need for problem-specific heuristic solution algorithms for the PPP, many of which have been proposed over the past decade. Finally, we analyze cost versus performance tradeoffs using our PPP optimization models on various IEEE test systems.

Price experimentation is an important tool for firms to find the optimal selling price of their products. It should be conducted properly, since experimenting with selling prices can be costly. A firm, therefore, needs to find a pricing policy that optimally balances between learning the optimal price and gaining revenue. In this paper, we propose such a pricing policy, called controlled variance pricing (CVP). The key idea of the policy is to enhance the certainty equivalent pricing policy with a taboo interval around the average of previously chosen prices. The width of the taboo interval shrinks at an appropriate rate as the amount of data gathered gets large; this guarantees sufficient price dispersion. For a large class of demand models, we show that this procedure is strongly consistent, which means that eventually the value of the optimal price will be learned, and derive upper bounds on the regret, which is the expected amount of money lost due to not using the optimal price. Numerical tests indicate that CVP performs well on different demand models and time scales. This paper was accepted by Assaf Zeevi, stochastic models and simulation.

Teachers spend much time considering the mathematical idea(s) they are working with in the classroom. They examine the foundational aspects of the concept(s) so that they have an understanding of how to approach them in the classroom and what to look for in terms of student understanding. Teachers also take time to consider common stumbling blocks that students may encounter when working with the idea(s). To know the concept well is to be prepared in supporting student understanding. Here, Costello proposes the notion that not only do they, as teachers, have to consider the foundational aspects and common stumbling blocks associated with concept(s) being explored, but they must consider the types of problems they are assigning as a means to support student conceptual understanding.

Searching is basic to all work with books. Often routine and obvious, it can also be laborious, problematical, time-consuming, and dependent on special skills, expertise, and insights. It requires a knowledge of bibliographical sources, often uncommon, idiosyncratic, and unusual in their citation practices. Above all, especially when it is difficult, it can be a learning experience.

In this note we give an overview of the currently known best lower and upper bounds on the size of a subset of ℤ avoiding -term arithmetic progression. We will focus on the case when the length of the forbidden progression is 3. We also formulate some open questions.

The maximum independent set problem is NP-hard and particularly difficult to solve in sparse graphs, which typically take exponential time to solve exactly using the best-known exact algorithms. In this paper, we present two new novel heuristic algorithms for computing large independent sets on huge sparse graphs, which are intractable in practice. First, we develop an advanced evolutionary algorithm that uses fast graph partitioning with local search algorithms to implement efficient combine operations that exchange whole blocks of given independent sets. Though the evolutionary algorithm itself is highly competitive with existing heuristic algorithms on large social networks, we further show that it can be effectively used as an oracle to guess vertices that are likely to be in large independent sets. We then show how to combine these guesses with kernelization techniques in a branch-and-reduce-like algorithm to compute high-quality independent sets quickly in huge complex networks. Our experiments against a recent (and fast) exact algorithm for large sparse graphs show that our technique always computes an optimal solution when the exact solution is known, and it further computes consistent results on even larger instances where the solution is unknown. Ultimately, we show that identifying and removing vertices likely to be in large independent sets opens up the reduction space—which not only speeds up the computation of large independent sets drastically, but also enables us to compute high-quality independent sets on much larger instances than previously reported in the literature.

Do gestures merely reflect problem-solving processes, or do they play a functional role in problem solving? We hypothesized that gestures highlight and structure perceptual-motor information, and thereby make such information more likely to be used in problem solving. Participants in two experiments solved problems requiring the prediction of gear movement, either with gesture allowed or with gesture prohibited. Such problems can be correctly solved using either a perceptual-motor strategy (simulation of gear movements) or an abstract strategy (the parity strategy). Participants in the gesture-allowed condition were more likely to use perceptual-motor strategies than were participants in the gesture-prohibited condition. Gesture promoted use of perceptual-motor strategies both for participants who talked aloud while solving the problems (Experiment 1) and for participants who solved the problems silently (Experiment 2). Thus, spontaneous gestures influence strategy choices in problem solving.

Previous research has highlighted the importance of social relationships in mathematical group work while working on modelling activities. This study analyses the interaction of sixth-grade students in Primary Education (11 to 12 years old) carrying out a modelling task in groups with a Fermi problem used as the modelling activity. The focus of the study was to explore how students develop a mathematical model to solve a Fermi problem in groups. The data collected mainly came from the group discussions, although the students’ productions were also considered. The results show that a variety of factors can influence group work and that model development is based on one student introducing an initial model and then, through social interaction with the other group members, the model is improved to develop a solid strategy that may be useful for solving the problem at hand.