Researchers in the microservices community often resort to demonstrating the impact of their proposed advancements on custom-made microservices projects. This is a possible source of bias that can reduce the trustworthiness of the results. Moreover, it is hard to compare advances in small projects, often developed due to lack of time. It is common across disciplines to recognize benchmarks that mitigate bias and unify the advancements' impact. To facilitate the identification of available open-source microservice projects (OSS-MS), we performed a comprehensive study to identify, curate, and catalog OSS-MS. We started with 389559 projects and filtered them down to 3804 projects that we manually labeled. After manual labeling, our dataset contains 378 projects with three or more microservices and with over 100 commits. We document the projects from many perspectives, including project size, platform, number of contributors, project purpose, and foundation support. This dataset can serve researchers as a roadmap to identify benchmarks, as our dataset can be used to answer questions such as whether the number of services impacts the issue count.

Satellite microwave remote sensing holds the capability to collect large-scale spatially distributed surface soil moisture information. This study presents an assessment of the soil moisture retrievals derived from the NASA Soil Moisture Active Passive (SMAP) (SPL3SMP_E V003 and V002) and the ESA Climate Change Initiative (CCI) Combined and Passive soil moisture products over the southern portion of the Great Lakes Basin using in situ soil moisture measurements. Overall, the CCI Combined product performed best, while the CCI Passive soil moisture showed the lowest performance. On average, the unbiased Root Mean Squared Error (ubRMSE) values for SPL3SMP_E V003, SPL3SMP_E V002, CCI Combined, and CCI Passive products are 0.064 m3m-3, 0.071 m3m-3, 0.042 m3m-3, 0.117 m3m-3, respectively. The results can provide guidance for the application of these satellite products in hydrological and meteorological studies.

We introduce and study the problem of planning a trajectory for an agent to carry out a reconnaissance mission while avoiding being detected by an adversarial guard. This introduces a multi-objective version of classical visibility-based target search and pursuit-evasion problem. In our formulation, the agent receives a positive reward for increasing its visibility (by exploring new regions) and a negative penalty every time it is detected by the guard. The objective is to find a finite-horizon path for the agent that balances the trade off between maximizing visibility and minimizing detectability.We model this problem as a discrete, sequential, two-player, zero-sum game. We use two types of game tree search algorithms to solve this problem: minimax search tree and Monte-Carlo search tree. Both search trees can yield the optimal policy but may require possibly exponential computational time and space. We propose several pruning techniques to reduce the computational cost while still preserving optimality guarantees. Simulation results show that the proposed strategy prunes approximately three orders of magnitude nodes as compared to the brute-force strategy. We also find that the Monte-Carlo search tree saves approximately one order of computational time as compared to the minimax search tree.

We propose a visual programming system that allows users to specify navigation tasks for mobile robots using high-level landmarks in a virtual reality (VR) environment constructed from the output of visual simultaneous localization and mapping (vSLAM). The VR environment provides a Google Street View-like interface for users to familiarize themselves with the robot's working environment, specify high-level landmarks, and determine task-level motion commands related to each landmark. Our system builds a roadmap by using the pose graph from the vSLAM outputs. Based on the roadmap, the high-level landmarks, and task-level motion commands, our system generates an output path for the robot to accomplish the navigation task. We present data structures, architecture, interface, and algorithms for our system and show that, given nssearch-type motion commands, our system generates a path in O(ns(nrlognr+mr)) time, where nrand mrare the number of roadmap nodes and edges, respectively. We have implemented our system and tested it on real world data.

Foundational models of object-oriented constructs typically model objects as records with a structural type. However, many object-oriented languages are class-based; statically-typed formal models of these languages tend to sacrifice the foundational nature of the record-based models, and in addition cannot express dynamic class loading or creation. In this paper, we explore how to model statically-typed object-oriented languages that support dynamic class creation using foundational constructs of type theory. We start with an extensible tag construct motivated by type theory, and adapt it to support static reasoning about class hierarchy and the tags supported by each object. The result is a model that better explains the relationship between object-oriented and functional programming paradigms, suggests a useful enhancement to functional programming languages, and paves the way for more expressive statically typed object-oriented languages. In that vein, we describe the design and implementation of the Wyvern language, which leverages our theory.

Segmentation and mapping of planar building facades (PBFs) can increase a robot's ability of scene understanding and localization in urban environments which are often quasi-rectilinear and GPS-challenged. PBFs are basic components of the quasi-rectilinear environment. We propose a passive vision-based PBF segmentation and mapping algorithm by combining both appearance and geometric constraints. We propose a rectilinear index which allows us to segment out planar regions using appearance data. Then we combine geometric constraints such as reprojection errors, orientation constraints, and coplanarity constraints in an optimization process to improve the mapping of PBFs. We have implemented the algorithm and tested it in comparison with state-of-the-art. The results show that our method can reduce the angular error of scene structure by an average of 82.82%.

Registration between low-resolution images is a crucial step in super-resolution. Conventional methods tend to separate scale estimation from translation and rotation estimation. This is because the scale parameter is inherently related to the image resolution. In this paper, we present an area-based image registration technique that can simultaneously estimate translation, rotation, and scale parameters and also take into account differences in resolution between two images. We first develop a scale-space model that relates each reference pixel to a single observation pixel with a scale parameter. This model is then easily generalized to include x-y shift and rotation parameters. By integrating the scale-space model into a non-linear least squares method, the method can iteratively estimate the transformation (x-y shift, rotation, and scale) in an accurate and efficient manner. We compare our proposed scale-space integrated Lucas-Kanade's method (SILK) against Lucas-Kanade's optical flow and scale-invariant feature transform (SIFT) matching and show that our method is suitable for super-resolution from very low resolution image sequences.

As enterprise services increasingly interconnect as networked services in a service-oriented architecture (SOA), service level management (SLM) is becoming a complex problem and can no longer be handled by traditional monitoring tools like Microsoft SMS. SLM is a process managing the quality of services demanded by clients and offered by providers. This paper presents two contributions to the research of SLM. First, instead of considering monitoring as an isolated service, it incorporates monitoring as an integral part of a comprehensive QoS management framework. This framework consists of QoS management concepts and services including service level contract management, admission control, resource management, monitoring, diagnostics, and adaptation. Using this framework, clients are able to negotiate quality of service contracts with providers and providers are able to optimize system resources to meet contract requirements. The second contribution is the incorporation of diagnostic service in the QoS management framework. Based on data feed from monitoring service, diagnostic service is able to detect any condition changes and to reason about the causes of any degradation conditions in the networked enterprise system. With condition detection and situation understanding, QoS management can then proactively activate adaptation mechanisms to maximize the system's ability to meet QoS contract requirements of concurrent clients. Our monitoring service uses both reporting approach and probing approach to acquire the information of the health status of elements of a networked system. The monitored data is then fed to our diagnostic service to reason about root causes of anomalies, using graphical models. Depending on the system health status and root causes, appropriate adaptations are triggered proactively to improve the system performance under the constraints of concurrent QoS contracts. We validate our SLM approach using QoS management services integrated in a publish/subscribe style of SOA. We then demonstrate via experiments some benefits of QoS monitoring, diagnostics, and adaptation services for responsiveness SLM.

Traditional World Wide Web search engines, such as AltaVista.com, index and recommend individual Web pages to assist users in locating relevant documents. As the Web grows, however, the number of matching pages increases at a tremendous rate. Users are often overwhelmed by the large answer set recommended by the search engines. Also, if a matching document is a hypertext, the document structure is destroyed and the individual pages that compose the document are returned instead. The logical starting point of the hyperdocument is thus hidden among the large basket of matching pages. Users need to spend a lot of effort browsing through the pages to locate the starting point, a very time consuming process. This paper studies the anchor point indexing problem. The set of anchor points of a given user query is a small set of key pages from which the larger set of documents that are relevant to the query can be easily reached. The use of anchor points helps solve the problems of huge answer set and low precision suffered by most search engines by considering the hyperlink structures of the relevant documents, and by providing a summary view of the result set.