Concept inventories are standardized assessments that evaluate student understanding of key concepts within academic disciplines. While prevalent across STEM fields, their development lags for advanced computer science topics like dynamic programming (DP)---an algorithmic technique that poses significant conceptual challenges for undergraduates. To fill this gap, we developed and validated a Dynamic Programming Concept Inventory (DPCI). We detail the iterative process used to formulate multiple-choice questions targeting known student misconceptions about DP concepts identified through prior research studies. We discuss key decisions, tradeoffs, and challenges faced in crafting probing questions to subtly reveal these conceptual misunderstandings. We conducted a preliminary psychometric validation by administering the DPCI to 172 undergraduate CS students finding our questions to be of appropriate difficulty and effectively discriminating between differing levels of student understanding. Taken together, our validated DPCI will enable instructors to accurately assess student mastery of DP. Moreover, our approach for devising a concept inventory for an advanced theoretical computer science concept can guide future efforts to create assessments for other under-evaluated areas currently lacking coverage.

Tor is one of the most widely used anonymous communication networks today. A popular feature of Tor is its onion services, anonymous network services that can only be accessed via the Tor network. This enables users to both host and access such services anonymously, protecting onion services from censorship and take-down. According to Tor Metrics, over 150,000 onion services collectively serve traffic at a rate of nearly 4 Gbps, with applications ranging from news services to chat to whistleblowing. Unfortunately, onion services also suffer from a variety of performance and security concerns. Latency can be extremely high, and many services face denial of service and deanonymization attacks due to the content and types of services that they host. In this work we seek to help address these concerns without making any changes to Tor, thus making our improvements immediately useful and deployable. To do this, we leverage a recent advance in programmable anonymity networks, which allows one to deploy user-written functions on willing Tor relays. We use this architecture to design the first Content Delivery Network (CDN) for onion services, which we call CenTor. CenTor allows onion services to take advantage of many traditional CDN benefits, such as replication and load balancing and bringing content (geographically) closer to the client. These techniques and applications raise an interesting trade-off between performance and anonymity for users, which we rigorously explore and quantify. We implement, deploy, and evaluate our architecture on the Tor network, demonstrating how these techniques are immediately able to extend and improve the capabilities, performance, and defenses of onion services, without any changes to the Tor protocol.

Algorithms courses are a core part of many CS programs, but have received little focus in computing education, lacking statistical data about how they are generally taught. To remedy this, we present the results of the first large-scale comprehensive survey of undergraduate introductory algorithms courses at four-year institutions in the United States. Questions in the survey targeted instructor information, course concepts, the ways students are evaluated, challenges instructors encountered, and instructor envisioned improvements. We received 87 responses from 34 different states, across a wide variety of 4-year institutions. The results indicate that algorithms courses vary dramatically in most surveyed areas.

Tor is a powerful and important tool for providing anonymity and censorship resistance to users around the world. Yet it is surprisingly difficult to deploy new services in Tor—it is largely relegated to proxies and hidden services—or to nimbly react to new forms of attack. Conversely, “non-anonymous” Internet services are thriving like never before because of recent advances in programmable networks, such as Network Function Virtualization (NFV) which provides programmable in-network middleboxes.

Tor is a powerful and important tool for providing anonymity and censorship resistance to users around the world. Yet it is surprisingly difficult to deploy new services in Tor---it is largely relegated to proxies and hidden services---or to nimbly react to new forms of attack. Conversely, "non-anonymous" Internet services are thriving like never before because of recent advances in programmable networks, such as Network Function Virtualization (NFV) which provides programmable in-network middleboxes.

Annually the gaming industry spends approximately $15 billion in marketing reinvestment. However, this amount is spent without any consideration for the skill and luck of the player. For a casino, an unskilled player could fetch ~4× more revenue than a skilled player. This paper describes a video recognition system that is based on an extension of the Mask R-CNN model. Our system digitizes the game of blackjack by detecting cards and player bets in real time and processes decisions they took in order to create accurate player personas. Our proposed supervised learning approach consists of a specialized three-stage pipeline that takes images from two viewpoints of the casino table and does instance segmentation to generate masks on proposed regions of interests. These predicted masks along with derivative features are used to classify image attributes that are passed onto the next stage to assimilate the gameplay understanding. Our end-to-end model yields an accuracy of ~95% for main bet detection and ~98% for card detection in a controlled environment trained using transfer learning approach with 900 training examples. Our approach is generalizable and scalable and shows promising results in varied gaming scenarios and test data. Such granular level gathered data, helped in understanding player's deviation from optimal strategy and thereby separate the skill of the player from the luck of the game. Our system also assesses the likelihood of card counting by correlating player's betting pattern to the deck's scaled count. Such a system lets casinos flag fraudulent activity and calculate expected personalized profitability for each player and tailor their marketing reinvestment decisions.