Intellectual Property (IP) includes ideas, innovations, methodologies, works of authorship (viz., literary and artistic works), emblems, brands, images, etc. This property is intangible since it is pertinent to the human intellect. Therefore, IP entities are indisputably vulnerable to infringements and modifications without the owner’s consent. IP protection regulations have been deployed and are still in practice, including patents, copyrights, contracts, trademarks, trade secrets, etc., to address these challenges. Unfortunately, these protections are insufficient to keep IP entities from being changed or stolen without permission. As for this, some IPs require hardware IP protection mechanisms, and others require software IP protection techniques. To secure these IPs, researchers have explored the domain of Intellectual Property Protection (IPP) using different approaches. In this paper, we discuss the existing IP rights and concurrent breakthroughs in the field of IPP research; provide discussions on hardware IP and software IP attacks and defense techniques; summarize different applications of IP protection; and lastly, identify the challenges and future research prospects in hardware and software IP security.

Since its launch in 2014, Amazon Echo family of devices has seen a considerable increase in adaptation in consumer homes and offices. With a market worth millions of dollars, Echo is used for diverse tasks such as accessing online information, making phone calls, purchasing items, and controlling the smart home. Echo offers user-friendly voice interaction to automate everyday tasks making it a massive success. Though many people view Amazon Echo as a helpful assistant at home or office, few know its underlying security and privacy implications. In this paper, we present the findings of our research on Amazon Echo’s security and privacy concerns. The findings are divided into different categories by vulnerability or attacks. The proposed mitigation(s) to the vulnerabilities are also presented in the paper. We conclude that though numerous privacy concerns and security vulnerabilities associated with the device are mitigated, many vulnerabilities still need to be addressed.

Our university, as with many others throughout the world, has a relatively low pass rate in the introductory computer science courses. Over the course of more than fifteen years, various changes have been made to the introductory course sequence with the hope of improving student success. We describe these changes and perform an initial analysis of student course performance that finds little change in pass rates. We propose new changes to the course sequence and to individual courses within this sequence. These new changes are focused on increasing student engagement and developing the problem solving skills that are necessary for being a successful computer science major. We propose pilot projects that implement these changes and outline evaluation strategies for these pilot projects.

In this work, we seek to better understand how game design principles impact the effectiveness of a video game learning environment for computational problem solving. Video games naturally encourage problem solving and incremental learning of their game play systems, but it remains unclear how well that transfers to learning the structures and processes of computing. We are building a gateway game that establishes familiar problem solving game play, then expands player affordances to include the use of automation and abstraction. Programming in-game entities is presented to players as a constrained selection and refinement process, which they explore and master in order to achieve in-game goals. We are exploring whether computational problem solving can be cast as accessible, intrinsically engaging game play, by following principles of good game design: exploratory player-controlled pace and direction, encouraging trial and low-cost failure, immediate formative feedback, level-appropriate challenge, non-intrusive assessment and expanding affordances. The game we are building to evaluate these factors will be positioned as a bridge for middle- and early high-school students to engage with computational problem solving.

The pipeline of three introductory computer science courses at our university have low pass rates. As part of our efforts to improve the pass rates in these courses, we examine the students' mathematics preparation and their progress from one course to the next. We find that the minority of students who enter the university with good mathematics preparation are likely to succeed in our introductory computer science courses. For the majority of students, we suggest a series of interventions throughout the pipeline. Prior to taking Computer Science 1, students could gain more experience with problem solving during the Introduction to Computer Science and Introduction to Computer Engineering courses. We suggest adding precalculus as a prerequisite for Computer Science 2. For students who do not earn an A in Computer Science 2, we plan to examine whether taking a class in a second programming language before taking Computer Science 3 will improve performance in Computer Science 3. Our goal is to improve student learning and retention through these efforts, combined with continual innovation in the classroom.

At our university, like at many other institutions, the pass rate in the introductory programming (CS1) course is relatively low, in the approximate range of 50% to 75%. We would like to discover factors that have an impact on student success in introductory programming in order to better advise and place our students into the course at the appropriate point in their academic careers. Our main motivation for increasing the CS1 pass rate is to help our students graduate on time. It has long been thought that mathematics background is a factor in determining a student's success in introductory computer science courses. Currently, our students must be concurrently enrolled in college algebra or placed into a higher mathematics course. However, given our current low pass rate, we wonder whether this is the proper prerequisite. In order to gather information about mathematics background, we administered a questionnaire to the students enrolled in the class during Spring 2013. We then analyzed the pass rate of our introductory programming students in relation to their mathematics background. Our findings indicate that students who are prepared to take calculus I in the same semester as they take introductory programming were more likely to pass introductory programming. These findings suggest that changing our math prerequisite to pre-calculus may increase the pass rate in our introductory programming course.

The dominant MMORPG quest model suffers from a significant incompatibility between dynamic world changes, stemming from player actions, and the authored narratives that guide and motivate gameplay. Both features are highly valued, and developers are trying different approaches to have them co-exist. In this work, we examine one aspect of that problem: player impact vs. competition in so-called kill quests. We introduce an incremental model of adaptive quests and a simulation system with initial results showing progress towards mitigating the incompatibility.

One of the central problems of artificial intelligence is capturing the breadth and flexibility of human common sense reasoning. One way to evaluate common sense is to use versions of human tests that rely on everyday reasoning. The Bennett Mechanical Comprehension Test consists of everyday reasoning problems posed via pictures and is used to evaluate technicians. This test is challenging because it requires conceptual knowledge spanning a broad range of domains, experience with a wide variety of everyday situations, and spatial reasoning. This article describes how we have extended our Companion Cognitive Architecture, which treats analogical processing as central, to perform well over a subset of the Bennett test. We introduce analogical model formulation as a robust method for reasoning about everyday scenarios, by analogy with cases that represent prior experiences. This enables a companion to perform qualitative reasoning (QR) without a complete domain theory, as typically required for QR. We introduce sketch annotations to communicate linkages between visual and conceptual properties in sketches. We introduce analogical reference frames to enable comparative analysis to operate over a broader range of problems than prior techniques. We show that these techniques enable a companion to score reasonably well on a difficult subset of the Bennett test.