Data Fabric
The Data Fabric is the platform that supports all the data in the company. How it’s managed, described, combined and universally accessed. This platform is formed from an Enterprise Knowledge Graph to create an uniform and unified data environment. …

Semantic Edge-Aware Strategy (SEMEDA)
While nowadays deep neural networks achieve impressive performances on semantic segmentation tasks, they are usually trained by optimizing pixel-wise losses such as cross-entropy. As a result, the predictions outputted by such networks usually struggle to accurately capture the object boundaries and exhibit holes inside the objects. In this paper, we propose a novel approach to improve the structure of the predicted segmentation masks. We introduce a novel semantic edge detection network, which allows to match the predicted and ground truth segmentation masks. This Semantic Edge-Aware strategy (SEMEDA) can be combined with any backbone deep network in an end-to-end training framework. Through thorough experimental validation on Pascal VOC 2012 and Cityscapes datasets, we show that the proposed SEMEDA approach enhances the structure of the predicted segmentation masks by enforcing sharp boundaries and avoiding discontinuities inside objects, improving the segmentation performance. In addition, our semantic edge-aware loss can be integrated into any popular segmentation network without requiring any additional annotation and with negligible computational load, as compared to standard pixel-wise cross-entropy loss. …

Fuzzing
Fuzzing or fuzz testing is an automated software testing technique that involves providing invalid, unexpected, or random data as inputs to a computer program. The program is then monitored for exceptions such as crashes, failing built-in code assertions, or potential memory leaks. Typically, fuzzers are used to test programs that take structured inputs. This structure is specified, e.g., in a file format or protocol and distinguishes valid from invalid input. An effective fuzzer generates semi-valid inputs that are ‘valid enough’ in that they are not directly rejected by the parser, but do create unexpected behaviors deeper in the program and are ‘invalid enough’ to expose corner cases that have not been properly dealt with. For the purpose of security, input that crosses a trust boundary is often the most interesting.[1] For example, it is more important to fuzz code that handles the upload of a file by any user than it is to fuzz the code that parses a configuration file that is accessible only to a privileged user.
Fuzzing: Art, Science, and Engineering …

A-Tree
Index structures are one of the most important tools that DBAs leverage in order to improve the performance of analytics and transactional workloads. However, with the explosion of data that is constantly being generated in a wide variety of domains including autonomous vehicles, Internet of Things (IoT) devices, and E-commerce sites, building several indexes can often become prohibitive and consume valuable system resources. In fact, a recent study has shown that indexes created as part of the TPC-C benchmark can account for 55% of the total memory available in a state-of-the-art in-memory DBMS. This overhead consumes valuable and expensive main memory, and limits the amount of space that a database has available to store new data or process existing data. In this paper, we present a novel approximate index structure called A-Tree. At the core of our index is a tunable error parameter that allows a DBA to balance lookup performance and space consumption. To navigate this tradeoff, we provide a cost model that helps the DBA choose an appropriate error parameter given either (1) a lookup latency requirement (e.g., 500ns) or (2) a storage budget (e.g., 100MB). Using a variety of real-world datasets, we show that our index structure is able to provide performance that is comparable to full index structures while reducing the storage footprint by orders of magnitude. …