AceKG 
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine pro- cessing. In this paper, we present AceKG, a new large-scale KG in academic domain. AceKG not only provides clean academic information, but also offers a large-scale benchmark dataset for researchers to conduct challenging data mining projects including link prediction, community detection and scholar classification. Specifically, AceKG describes 3.13 billion triples of academic facts based on a consistent ontology, including necessary properties of papers, authors, fields of study, venues and institutes, as well as the relations among them. To enrich the proposed knowledge graph, we also perform entity alignment with existing databases and rule-based inference. Based on AceKG, we conduct experiments of three typical academic data mining tasks and evaluate several state-of- the-art knowledge embedding and network representation learning approaches on the benchmark datasets built from AceKG. Finally, we discuss several promising research directions that benefit from AceKG. …
ShelfNet
In this project, we present ShelfNet, a lightweight convolutional neural network for accurate real-time semantic segmentation. Different from the standard encoder-decoder structure, ShelfNet has multiple encoder-decoder branch pairs with skip connections at each spatial level, which looks like a shelf with multiple columns. The shelf-shaped structure provides multiple paths for information flow and improves segmentation accuracy. Inspired by the success of recurrent convolutional neural networks, we use modified residual blocks where two convolutional layers share weights. The shared-weight block enables efficient feature extraction and model size reduction. We tested ShelfNet with ResNet50 and ResNet101 as the backbone respectively: they achieved 59 FPS and 42 FPS respectively on a GTX 1080Ti GPU with a 512×512 input image. ShelfNet achieved high accuracy: on PASCAL VOC 2012 test set, it achieved 84.2% mIoU with ResNet101 backbone and 82.8% mIoU with ResNet50 backbone; it achieved 75.8% mIoU with ResNet50 backbone on Cityscapes dataset. ShelfNet achieved both higher mIoU and faster inference speed compared with state-of-the-art real-time semantic segmentation models. We provide the implementation https://…/ShelfNet. …
Event Stream Processing (ESP)
Event stream processing, or ESP, is a set of technologies designed to assist the construction of event-driven information systems. ESP technologies include event visualization, event databases, event-driven middleware, and event processing languages, or complex event processing (CEP). In practice, the terms ESP and CEP are often used interchangeably. ESP deals with the task of processing streams of event data with the goal of identifying the meaningful pattern within those streams, employing techniques such as detection of relationships between multiple events, event correlation, event hierarchies, and other aspects such as causality, membership and timing. ESP enables many different applications such as algorithmic trading in financial services, RFID event processing applications, fraud detection, process monitoring, and location-based services in telecommunications. …
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