In order to improve the user oriented service delivery, we present a soft service robot, e-SBOT. It plays the "bridge" role between a user and massive external services. By sensing explicit and implicit intentions and demands of a user, it helps find appropriate services and construct coarse-grained service solutions. Both global and personal knowledge graph are utilized to for intention reasoning and service solution planning. Sensors are designed for the dynamic changes of external services and resources. By e-SBOT, the cognition of a user could be amplified to a large extent. This paper gives a brief introduction to e-SBOT's technical challenges, architecture design, and key theoretical problems.

In this paper, we introduce the task of resource citation classification for scientific literature using a context-based framework. This task is to analyze the purpose of citing an on-line resource in scientific text by modeling the role and function of each resource citation. It can be incorporated into resource indexing and recommendation systems to help better understand and classify on-line resources in scientific literature. We propose a new annotation scheme for this task and develop a dataset of 3,088 manually annotated resource citations. We adopt a neural-based model to build the classifiers and apply them on the large ARC dataset to examine the revolution of scientific resources from trends in their function over time.

With the help of simulation tools, users can evaluate new proposals in cluster environment efficiently. However, current cloud simulators cannot meet the needs of application-driven simulation scenarios. In this paper, we propose Pallas, a task and network simulation framework that supports various cloud applications. Task-aware network scheduling and network-perceived task placement algorithms can be easily implemented in Pallas. We present the architecture and main components of Pallas and evaluate its effectiveness by comparing algorithm improvements to the actual results.

Currently how to make the hearing aids more and more intelligent has attracted our interests. In order to realize adaptive amplification strategy to improve the audibility, context-aware hearing is crucial. In context-aware hearing, a big difficulty to be solved is context sensing since it is not applicable to implant multiple sensors in the limited space of hearing devices. Therefore, we propose a new context recognition scheme which adopt smart phone to collect sensing data and infer a scene to adapt level of hearing aid. A context recognition framework with a context reasoning model for scene recognition and activity recognition are given. Once scene and activity are confirmed, the smart phone would send a command to hearing aid to actuate the amplification process by Bluetooth. Since smart phones are carried by people in normal life, employing smart phone to sense context data and inference scene is quite a reasonable way to improve the adaptiveness of hearing aids for most people without any other extra devices or sensors. Our contribution in this paper is that let smart phones stay in pockets or bags as exact normal life, the scene can still be inferred to conduct hearing aids. Besides, we conduct some experiments, the results are encouraging and time cost is acceptable.