The TaskTracer system allows knowledge workers to define a set of activities that characterize their desktop work. It then associates with each user-defined activity the set of resources that the user accesses when performing that activity. In order to correctly associate resources with activities and provide useful activity-related services to the user, the system needs to know the current activity of the user at all times. It is often convenient for the user to explicitly declare which activity he/she is working on. But frequently the user forgets to do this. TaskTracer applies machine learning methods to detect undeclared activity switches and predict the correct activity of the user. This paper presents TaskPredictor2, a complete redesign of the activity predictor in TaskTracer and its notification user interface. TaskPredictor2 applies a novel online learning algorithm that is able to incorporate a richer set of features than our previous predictors. We prove an error bound for the algorithm and present experimental results that show improved accuracy and a 180-fold speedup on real user data. The user interface supports negotiated interruption and makes it easy for the user to correct both the predicted time of the task switch and the predicted activity.

In this paper, we apply stacking, an ensemble learning method, to the problem of building hybrid recommendation systems. We also introduce the novel idea of using runtime metrics which represent properties of the input users/items as additional metafeatures, allowing us to combine component recommendation engines at runtime based on user/item characteristics. In our system, component engines are level-1 predictors, and a level-2 predictor is learned to generate the final prediction of the hybrid system. The input features of the level-2 predictor are predictions from component engines and the runtime metrics. Experimental results show that our system outperforms each single component engine as well as a static hybrid system. Our method has the additional advantage of removing restrictions on component engines that can be employed; any engine applicable to the target recommendation task can be easily plugged into the system.

CoS 2 and FeS 2 nanoparticles with mesoporous structures embedded in carbon polyhedrons are prepared by reasonable design of materials (CoS 2 -FeS 2 -NC), which are used as a modified cathode material. This composite material can limit the shuttling of polysulfides and catalyze and adsorb polysulfides. Further, electrochemical properties of Li−S batteries are enhanced. Li−S batteries using S/CoS 2 -FeS 2 -NC electrodes have the best cycle and rate performance. The S/CoS 2 -FeS 2 -NC cathode's initial discharge capacity at 0.2C is 938.9 mAh g −1 , while its reversible capacity remains 394.8 mAh g −1 after 200 cycles, with a 0.28% decline on average. S/CoS 2 -NC and bare sulfur cathodes have maximal discharging capacities of 82.3 and 63.8% of S/CoS 2 -FeS 2 -NC cathodes at 0.2C. The initial discharge capacity of S/CoS 2 -FeS 2 -NC at 0.5C is 795.6 mAh g −1 , and the capacity of 354.4 mAh g −1 is maintained after 200 cycles. So, S/CoS 2 -FeS 2 -NC has excellent electrochemical properties.

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Among the numerous EMISs (Education Management Information System), the ubiquitous problems such as the difficulties for data sharing and the limited abilities for interoperating make the information systems of different colleges and different departments isolated. Therefore,the committee of Educational Information Technology Standardization(belong to Ministry of Education of China) issued specifications,Education Management Information System Interoperability Framework, which are good solutions for those problems. In this paper, the framework structures and data exchange models as well as the message formate of EMIF are discussed. The systematic analyses for functional demands of a Zone Integration Server (ZIS), the core component of the EMIF, are made, also its functional model is given. The implementation mechanism and algorithm based on message services, message transportation, and message management for the key functions such as message processing are given, and an instance for providing message processing is presented. Finally, the structure for the data interoperation among multi-EMIS on the campus net based on EMIF is discussed.

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Multi-touch attribution (MTA) estimates the relative contributions of the multiple ads a user may see prior to any observed conversions. Increasingly, advertisers also want to base budget and bidding decisions on these attributions, spending more on ads that drive more conversions. We describe two requirements for an MTA system to be suitable for this application: First, it must be able to handle continuously updated and incomplete data. Second, it must be sufficiently flexible to capture that an ad's effect will change over time. We describe an MTA system, consisting of a model for user conversion behavior and a credit assignment algorithm, that satisfies these requirements. Our model for user conversion behavior treats conversions as occurrences in an inhomogeneous Poisson process, while our attribution algorithm is based on iteratively removing the last ad in the path.