Educators and parents alike are seeking innovative ways to introduce young students to computer programming. The hope is to capture children's attention and foster learning at the same time. The goal of this work was to not only introduce elementary students to the fundamentals of computer programming, but also help them explore more complex concepts in an engaging way. Studies have shown that factors that inspire children's continued interest can sometimes vary by gender at this age; this work specifically addresses how to incorporate elements that will appeal to these potential differences in motivation. This study describes the design and implementation of a computer microworld game designed to introduce the core constructs and techniques of computer programming. By instructing a virtual robot to complete obstacle courses, students become familiar with core programming concepts such as: algorithms, repetition, conditional logic, debugging, functions, and optimization. We conducted several interactive sessions with a group of elementary school students in order to evaluate the effectiveness of the game. Our results showed the game effectively familiarized students with both computer language constructs and the essentials of algorithmic thinking. Students were quickly able to learn core-programming concepts and apply these concepts to free form solutions.

Publicly accessible adaptive systems such as collaborative recommender systems present a security problem. Attackers, who cannot be readily distinguished from ordinary users, may inject biased profiles in an attempt to force a system to “adapt” in a manner advantageous to them. Such attacks may lead to a degradation of user trust in the objectivity and accuracy of the system. Recent research has begun to examine the vulnerabilities and robustness of different collaborative recommendation techniques in the face of “profile injection” attacks. In this article, we outline some of the major issues in building secure recommender systems, concentrating in particular on the modeling of attacks and their impact on various recommendation algorithms. We introduce several new attack models and perform extensive simulation-based evaluations to show which attacks are most successful and practical against common recommendation techniques. Our study shows that both user-based and item-based algorithms are highly vulnerable to specific attack models, but that hybrid algorithms may provide a higher degree of robustness. Using our formal characterization of attack models, we also introduce a novel classification-based approach for detecting attack profiles and evaluate its effectiveness in neutralizing attacks.

Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in recommendations that favor or disfavor given items. Since collaborative recommender systems must be open to user input, it is difficult to design a system that cannot be so attacked. Researchers studying robust recommendation have therefore begun to identify types of attacks and study mechanisms for recognizing and defeating them. In this paper, we propose and study different attributes derived from user profiles for their utility in attack detection. We show that a machine learning classification approach that includes attributes derived from attack models is more successful than more generalized detection algorithms previously studied.

Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. Researchers have shown that attackers can manipulate a system's recommendations by injecting biased profiles into it. In this paper, we examine attacks that concentrate on a targeted set of users with similar tastes, biasing the system's responses to these users. We show that such attacks are both pragmatically reasonable and also highly effective against both user-based and item-based algorithms. As a result, an attacker can mount such a "segmented" attack with little knowledge of the specific system being targeted and with strong likelihood of success.