In the past years, the authors held computer programming and machine learning summer camp for high-school students with disabilities. Due to the pandemic, the summer camp was offered virtually in 2020 and 2021. This paper reports our experience of teaching this summer camp. The main goal of the summer camp was to let students with disabilities get first-hand experience of working in STEM fields to encourage them to pursue STEM careers. The curriculum was primarily composed of hands-on activities for Python programming and Computer Vision. Besides lectures and programming tasks, there were also guest speakers and an external panel to offer their personal experiences of working in STEM fields.

In this paper, we consider the Visibility Graph Recognition and Reconstruction problems in the context of terrains. Here, we are given a graph G with labeled vertices v₀, v₁, …, v_{n-1} such that the labeling corresponds with a Hamiltonian path H. G also may contain other edges. We are interested in determining if there is a terrain T with vertices p₀, p₁, …, p_{n-1} such that G is the visibility graph of T and the boundary of T corresponds with H. G is said to be persistent if and only if it satisfies the so-called X-property and Bar-property. It is known that every "pseudo-terrain" has a persistent visibility graph and that every persistent graph is the visibility graph for some pseudo-terrain. The connection is not as clear for (geometric) terrains. It is known that the visibility graph of any terrain T is persistent, but it has been unclear whether every persistent graph G has a terrain T such that G is the visibility graph of T. There actually have been several papers that claim this to be the case (although no formal proof has ever been published), and recent works made steps towards building a terrain reconstruction algorithm for any persistent graph. In this paper, we show that there exists a persistent graph G that is not the visibility graph for any terrain T. This means persistence is not enough by itself to characterize the visibility graphs of terrains, and implies that pseudo-terrains are not stretchable.

It is difficult to access data generated by different data sources due to the representation and format differences. ETL, KETL, Jedox, Apatar are some examples of data translation and fusion packages that can be used to resolve representation differences of data coming from different data sources have been favored. However, most tools require significant manual effort to map the data sources. Structural mismatch of data between objects with the same semantics reduces the accessibility of data. Here we discuss our initial efforts toward a scalable unsu-pervised system and algorithms to generate Unified Famous Objects (UFO) - the self-learning “intelligent” data structures that help automate data fusion at scale [Gubanov et al., 2009], [Gubanov et al., 2011]. UFO is a data structure encapsulating different representations of the same data object (e.g. Songs), hence capable of automatically recognizing and mapping such object in different data sources, and significantly reducing manual effort during data integration process. We evaluate our algorithms on a large-scale Web tables corpus having ≈ 64 million of tuples.

Internet mail spam is a problem for most organizations and individuals. Spam can be classified into two categories: fraud and commercial. The fraud category includes phishing, scams, malware, counterfeit products and any other criminal activities. The commercial category includes promotional messages and newsletters that we do not want to receive, being sent illegally from legitimate organizations. Fraud can be seen as being a high threat with high volume while commercial spam is the opposite. Similar to mail, there are spam Web tables that do not have any useful content. Here we describe our machine-learning classifier for efficient and effective Web tables spam filtering that was tested on a large-scale Web tables corpus of ≈ 36 million tables.