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Added by
Hubie Chen
Publication date
2012
fields
Informatics Engineering
and research's language is
English
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We present a domain-independent algorithm that computes macros in a novel way. Our algorithm computes macros on-the-fly for a given set of states and does not require previously learned or inferred information, nor prior domain knowledge. The algorithm is used to define new domain-independent tractable classes of classical planning that are proved to include emph{Blocksworld-arm} and emph{Towers of Hanoi}.
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We present Turnstile+, a high-level, macros-based metaDSL for building dependently typed languages. With it, programmers may rapidly prototype and iterate on the design of new dependently typed features and extensions. Or they may create entirely new DSLs whose dependent type power is tailored to a specific domain. Our frameworks support of language-oriented programming also makes it suitable for experimenting with systems of interacting components, e.g., a proof assistant and its companion DSLs. This paper explains the implementation details of Turnstile+, as well as how it may be used to create a wide-variety of dependently typed languages, from a lightweight one with indexed types, to a full spectrum proof assistant, complete with a tactic system and extensions for features like sized types and SMT interaction.
Name disambiguation is a key and also a very tough problem in many online systems such as social search and academic search. Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly -- to complete the disambiguation in the real-time. This is very challenging, as the disambiguation algorithm must be accurate, efficient, and error tolerance. In this paper, we propose a novel framework -- CONNA -- to train a matching component and a decision component jointly via reinforcement learning. The matching component is responsible for finding the top matched candidate for the given paper, and the decision component is responsible for deciding on assigning the top matched person or creating a new person. The two components are intertwined and can be bootstrapped via jointly training. Empirically, we evaluate CONNA on two name disambiguation datasets. Experimental results show that the proposed framework can achieve a 1.21%-19.84% improvement on F1-score using joint training of the matching and the decision components. The proposed CONNA has been successfully deployed on AMiner -- a large online academic search system.
The Internet of Things (IoT) is a fast growing field of devices being added to an interconnected environment in an abstract heterogeneous array of servers and other devices, called smart environments, ranging from private local (home) environments to nation-wide infrastructures, often accessible via unsecured wireless communications and information technologies, hence, massively open to attacks. In this paper we address some of issues that arise when connecting smart devices endowed with low computational capabilities to a home gateway via unsecured wireless communication channels, by using a One Time Pad (OTP) protocol based upon an On-the-fly Diffie-Hellman Key Exchange. Our assumptions are that only a user and the gateway have enough processing power to perform - say - secured RSA encrypted communication, hence relaxing the need for a trusted secure server outside the domain and that the protocol should at least be secure for a range of known attacks, as replay or DoS attacks.
We propose a distance between continuous-time Markov chains (CTMCs) and study the problem of computing it by comparing three different algorithmic methodologies: iterative, linear program, and on-the-fly. In a work presented at FoSSaCS12, Chen et al. characterized the bisimilarity distance of Desharnais et al. between discrete-time Markov chains as an optimal solution of a linear program that can be solved by using the ellipsoid method. Inspired by their result, we propose a novel linear program characterization to compute the distance in the continuous-time setting. Differently from previous proposals, ours has a number of constraints that is bounded by a polynomial in the size of the CTMC. This, in particular, proves that the distance we propose can be computed in polynomial time. Despite its theoretical importance, the proposed linear program characterization turns out to be inefficient in practice. Nevertheless, driven by the encouraging results of our previous work presented at TACAS13, we propose an efficient on-the-fly algorithm, which, unlike the other mentioned solutions, computes the distances between two given states avoiding an exhaustive exploration of the state space. This technique works by successively refining over-approximations of the target distances using a greedy strategy, which ensures that the state space is further explored only when the current approximations are improved. Tests performed on a consistent set of (pseudo)randomly generated CTMCs show that our algorithm improves, on average, the efficiency of the corresponding iterative and linear program methods with orders of magnitude.
Knowledge distillation is effective to train small and generalisable network models for meeting the low-memory and fast running requirements. Existing offline distillation methods rely on a strong pre-trained teacher, which enables favourable knowledge discovery and transfer but requires a complex two-phase training procedure. Online counterparts address this limitation at the price of lacking a highcapacity teacher. In this work, we present an On-the-fly Native Ensemble (ONE) strategy for one-stage online distillation. Specifically, ONE trains only a single multi-branch network while simultaneously establishing a strong teacher on-the- fly to enhance the learning of target network. Extensive evaluations show that ONE improves the generalisation performance a variety of deep neural networks more significantly than alternative methods on four image classification dataset: CIFAR10, CIFAR100, SVHN, and ImageNet, whilst having the computational efficiency advantages.
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