26 research outputs found
High performance annotation-aware JVM for Java cards
Early applications of smart cards have focused in the area of per-sonal security. Recently, there has been an increasing demand for networked, multi-application cards. In this new scenario, enhanced application-specific on-card Java applets and complex cryptographic services are executed through the smart card Java Virtual Machine (JVM). In order to support such computation-intensive applica-tions, contemporary smart cards are designed with built-in micro-processors and memory. As smart cards are highly area-constrained environments with memory, CPU and peripherals competing for a very small die space, the VM execution engine of choice is often a small, slow interpreter. In addition, support for multiple applica-tions and cryptographic services demands high performance VM execution engine. The above necessitates the optimization of the JVM for Java Cards
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
One of the key challenges of learning an online recommendation model is the
temporal domain shift, which causes the mismatch between the training and
testing data distribution and hence domain generalization error. To overcome,
we propose to learn a meta future gradient generator that forecasts the
gradient information of the future data distribution for training so that the
recommendation model can be trained as if we were able to look ahead at the
future of its deployment. Compared with Batch Update, a widely used paradigm,
our theory suggests that the proposed algorithm achieves smaller temporal
domain generalization error measured by a gradient variation term in a local
regret. We demonstrate the empirical advantage by comparing with various
representative baselines
Real Time Analytics: Algorithms and Systems
ABSTRACT Velocity is one of the 4 Vs commonly used to characterize Big Data In this tutorial, we shall present an in-depth overview of streaming analytics -applications, algorithms and platforms -landscape. We shall walk through how the field has evolved over the last decade and then discuss the current challenges -the impact of the other three Vs, viz., Volume, Variety and Veracity, on Big Data streaming analytics. The tutorial is intended for both researchers and practitioners in the industry. We shall also present state-of-the-affairs of streaming analytics at Twitter