In introductory programming courses, proficiency is typically achieved through substantial practice in the form of relatively small assignments and quizzes. Unfortunately, creating programming assignments and quizzes is both, time consuming and error prone. Furthermore, grading the assignments and providing timely and detailed feedback is paramount to student improvement. We use Automatic Item Generation (AIG) in order to address the problem of creating numerous programming exercises that can be used for assignments or quizzes in introductory programming courses. AIG is based on the use of test-item templates with embedded variables and formulas. The variables and formulas in the template are resolved by a computer program with actual values to generate test-items. Thus, hundreds or even thousands of test-items can be generated with a single test-item template. We discuss a semantic-based AIG approach for automatically generating programming exercises. The approach was incorporated into an existing selfassessment and practice tool for students learning computer programming. The tool has been used in different introductory programming courses to generate a set of practice exercises different for each student, but with the same difficulty and quality.

Combinatorial auctions (CAs) are a great way to solve complex resource allocation and coordination problems. However, CAs require a central auctioneer who receives the bids and solves the winner determination problem, an NP-hard problem. Unfortunately, a centralized auction is not a good fit for real world situations where the participants have proprietary interests that they wish to remain private or when it is difficult to establish a trusted auctioneer. The work presented here is motivated by the vision of distributed CAs; incentive compatible peer-to-peer mechanisms to solve the allocation problem, where bidders carry out the needed computation. For such a system to exist, both a protocol that distributes the computational task amongst the bidders and strategies for bidding behavior are needed. PAUSE is combinatorial auction mechanism that naturally distributes the computational load amongst the bidders, establishing the protocol or rules the participants must follow. However, it does not provide bidders with bidding strategies. This article revisits and reevaluates a set of bidding algorithms that represent different bidding strategies that bidders can use to engage in a PAUSE auction, presenting a study that analyzes them with respect to the number of goods, bids, and bidders. Results show that PAUSE, along with the aforementioned heuristic bidding algorithms, is a viable method for solving combinatorial allocation problems without a centralized auctioneer.

No abstract

No abstract

As IoT devices become more ubiquitous, the security of IoT-based networks becomes paramount. Machine Learning-based cybersecurity enables autonomous threat detection and prevention. However, one of the challenges of applying Machine Learning-based cybersecurity in IoT devices is feature selection as most IoT devices are resource-constrained. This paper studies two feature selection algorithms: Information Gain and PSO-based, to select a minimum number of attack features, and Decision Tree and SVM are utilized for performance comparison. The consistent use of the same metrics in feature selection and detection algorithms substantially enhances the classification accuracy compared to the non-consistent use in feature selection by Information Gain (entropy) and Tree detection algorithm by classification. Furthermore, the Tree with consistent feature selection is comparable to the ensemble that provides excellent performance at the cost of computation complexity.