By using different interface adapters for different methods, it is possible to construct a maximally covering web of interface adapters which incurs minimum loss during interface adaptation. We introduce a polynomial-time algorithm that can achieve this. However, we also show that minimizing the number of adapters included in a maximally covering web of interface adapters is an NP-complete problem.
@cite_2 proposes an interface adaptation framework which attempts to minimize the loss incurred by an interface adapter chain, and @cite_3 rigorously defines the mathematical background required to implement such a framework. These only consider the use of single chains of interface adapters.
@cite_3 @cite_2
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Despite providing similar functionality, multiple network services may require the use of different interfaces to access the functionality, and this problem will only become worse with the widespread deployment of ubiquitous computing environments. One way around this problem is to use interface adapters that adapt one interface into another. Chaining these adapters allows flexible interface adaptation with fewer adapters, but the loss incurred because of imperfect interface adaptation must be considered. This study outlines a matrix-based mathematical basis for analysing the chaining of lossy interface adapters. The authors also show that the problem of finding an optimal interface adapter chain is NP-complete with a reduction from 3SAT. A key feature of ubiquitous computing is service continuity which allows a user to transparently continue his task regardless of his movement. For service continuity, the underlying system needs to not only discover a service satisfying a user's request, but also provide an interface differences resolution scheme if the interface of the service found is not the same as that of the service requested. For resolving interface mismatches, one of solutions is to use an interface adapter. The most serious problem in the interface adapter-based approach is the overhead of adapter generation. There are many research efforts about adapter generation load reduction and this paper focuses on an adapter chaining scheme to reduce the number of necessary adapters among different service interfaces. We propose a construction-time adaptation loss evaluation scheme and an adapter chain construction algorithm, which finds an adapter chain with minimal adaptation loss. Wireless interfaces are major power consumers on mobile systems. Considerable research has improved the energy efficiency of elongated idle periods or created more elongated idle periods in wireless interfaces, often requiring cooperation from applications or the network infrastructure. With increasing wireless mobile data, it has become critical to improve the energy efficiency of active wireless interfaces. In this work, we present micro power management (μPM), a solution inspired by the mismatch between the high performance of state-of-the-art 802.11 interfaces and the modest data rate requirements by many popular network applications. μPM enables an 802.11 interface to enter unreachable power-saving modes even between MAC frames, without noticeable impact on the traffic flow. To control data loss, μPM leverages the retransmission mechanism in 802.11 and controls frame delay to adapt to demanded network throughput with minimal cooperation from the access point. Based on a theoretical framework, we employ simulation to systematically investigate an effective and efficient implementation of μPM. We have built a prototype μPM on an open-access wireless hardware platform. Measurements show that more than 30 power reduction for the wireless transceiver can be achieved with μPM for various applications without perceptible quality degradation. In today's Web, many functionality-wise similar Web services are offered through heterogeneous interfaces (operation definitions) and business protocols (ordering constraints defined on legal operation invocation sequences). The typical approach to enable interoperation in such a heterogeneous setting is through developing adapters. There have been approaches for classifying possible mismatches between service interfaces and business protocols to facilitate adapter development. However, the hard job is that of identifying, given two service specifications, the actual mismatches between their interfaces and business protocols. In this paper we present novel techniques and a tool that provides semi-automated support for identifying and resolution of mismatches between service interfaces and protocols, and for generating adapter specification. We make the following main contributions: (i) we identify mismatches between service interfaces, which leads to finding mismatches of type of signature, merge split, and extra missing messages; (ii) we identify all ordering mismatches between service protocols and generate a tree, called mismatch tree, for mismatches that require developers' input for their resolution. In addition, we provide semi-automated support in analyzing the mismatch tree to help in resolving such mismatches. We have implemented the approach in a tool inside IBM WID (WebSphere Integration Developer). Our experiments with some real-world case studies show the viability of the proposed approach. The methods and tool are significant in that they considerably simplify the problem of adapting services so that interoperation is possible. Approximate Bayesian computation (ABC) is a popular approach to address inference problems where the likelihood function is intractable, or expensive to calculate. To improve over Markov chain Monte Carlo (MCMC) implementations of ABC, the use of sequential Monte Carlo (SMC) methods has recently been suggested. Most effective SMC algorithms that are currently available for ABC have a computational complexity that is quadratic in the number of Monte Carlo samples (, Biometrika 86:983---990, 2009; , Technical report, 2008; , J. Roy. Soc. Interface 6:187---202, 2009) and require the careful choice of simulation parameters. In this article an adaptive SMC algorithm is proposed which admits a computational complexity that is linear in the number of samples and adaptively determines the simulation parameters. We demonstrate our algorithm on a toy example and on a birth-death-mutation model arising in epidemiology. Compositional reuse of software components requires standardized specification techniques if applications are created by combining third party components Adequate techniques need to be used in order to specify not only technical but also business related aspects of software components The different specification aspects of software components are summarized in a multi-layer specification framework with formal specification techniques defined for each level of abstraction The use of formal specification techniques is a prerequisite for compatibility tests on component specifications Compatibility tests are necessary for the identification of required components, which are traded on component markets The focus of this paper is to present an algorithm for compatibility test on interface level, where Interface Definition Language (IDL) has been used as formal specification language In order to test characteristics where e.g the order of parameter values or the order of consisting data types within a complex data type are not identical with the specification, adapters are generated for mapping the component interfaces. This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy. This paper describes technology and tools for intelligent human-computer interaction (IHCI) in which human cognitive, perceptual, motor and affective factors are modeled and used to adapt the H-C interface. IHCI emphasizes that human behavior encompasses both apparent human behavior and the hidden mental state behind behavioral performance. IHCI expands on the interpretation of human activities, known as W4 (what, where, when, who). While W4 only addresses the apparent perceptual aspect of human behavior the W5+ technology for IHCI described in this paper addresses also the why and how questions, whose solution requires recognizing specific cognitive states. IHCI integrates parsing and interpretation of nonverbal information with a computational cognitive model of the user which, in turn, feeds into processes that adapt the interface to enhance operator performance and provide for rational decision-making. The technology proposed is based on a general four-stage interactive framework, which moves from parsing the raw sensory-motor input, to interpreting the user's motions and emotions, to building an understanding of the user's current cognitive state. It then diagnoses various problems in the situation and adapts the interface appropriately. The interactive component of the system improves processing at each stage. Examples of perceptual, behavioral, and cognitive tools are described throughout the paper Adaptive and intelligent HCI are important for novel applications of computing, including ubiquitous and human-centered computing. In this paper, we propose the Web Service Discovery Architecture (WSDA). At runtime, Grid applications can use this architecture to discover and adapt to remote services. WSDA promotes an interoperable web service discovery layer by defining appropriate services, interfaces, operations and protocol bindings, based on industry standards. It is unified because it subsumes an array of disparate concepts, interfaces and protocols under a single semi-transparent umbrella. It is modular because it defines a small set of orthogonal multi-purpose communication primitives (building blocks) for discovery. These primitives cover service identification, service description retrieval, data publication as well as minimal and powerful query support. The architecture is open and flexible because each primitive can be used, implemented, customized and extended in many ways. It is powerful because the individual primitives can be combined and plugged together by specific clients and services to yield a wide range of behaviors and emerging synergies. Dynamic optimization is emerging as a promising approach to overcome many of the obstacles of traditional static compilation. But while there are a number of compiler infrastructures for developing static optimizations, there are very few for developing dynamic optimizations. We present a framework for implementing dynamic analyses and optimizations. We provide an interface for building external modules, or clients, for the DynamoRIO dynamic code modification system. This interface abstracts away many low-level details of the DynamoRIO runtime system while exposing a simple and powerful, yet efficient and lightweight API. This is achieved by restricting optimization units to linear streams of code and using adaptive levels of detail for representing instructions. The interface is not restricted to optimization and can be used for instrumentation, profiling, dynamic translation, etc. To demonstrate the usefulness and effectiveness of our framework, we implemented several optimizations. These improve the performance of some applications by as much as 40 relative to native execution. The average speedup relative to base DynamoRIO performance is 12 . The push toward business process automation has generated the need for integrating different enterprise applications involved in such processes. The typical approach to integration and to process automation is based on the use of adapters and message brokers. The need for adapters in Web services mainly comes from two sources: one is the heterogeneity at the higher levels of the interoperability stack, and the other is the high number of clients, each of which can support different interfaces and protocols, thereby generating the need for providing multiple interfaces to the same service. In this paper, we characterize the problem of adaptation of web services by identifying and classifying different kinds of adaptation requirements. Then, we focus on business protocol adapters, and we classify the different ways in which two protocols may differ. Next, we propose a methodology for developing adapters in Web services, based on the use of mismatch patterns and service composition technologies. In this report we present our tools for prototyping adaptive user interfaces in the context of real-time musical instrument control. Characteristic of most human communication is the simultaneous use of classified events and estimated parameters. We have integrated a neural network object into the MAX language to explore adaptive user interfaces that considers these facets of human communication. By placing the neural processing in the context of a flexible real-time musical programming environment, we can rapidly prototype experiments on applications of adaptive interfaces and learning systems to musical problems. We have trained networks to recognize gestures from a Mathews radio baton, Nintendo Power GloveTM, and MIDI keyboard gestural input devices. In one experiment, a network successfully extracted classification and attribute data from gestural contours transduced by a continuous space controller, suggesting their application in the interpretation of conducting gestures and musical instrument control. We discuss network architectures, low-level features extracted for the networks to operate on, training methods, and musical applications of adaptive techniques.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. Domain adaptation aims to train a model on labeled data from a source domain while minimizing test error on a target domain. Most of existing domain adaptation methods only focus on reducing domain shift of single-modal data. In this paper, we consider a new problem of multimodal domain adaptation and propose a unified framework to solve it. The proposed multimodal domain adaptation neural networks(MDANN) consist of three important modules. (1) A covariant multimodal attention is designed to learn a common feature representation for multiple modalities. (2) A fusion module adaptively fuses attended features of different modalities. (3) Hybrid domain constraints are proposed to comprehensively learn domain-invariant features by constraining single modal features, fused features, and attention scores. Through jointly attending and fusing under an adversarial objective, the most discriminative and domain-adaptive parts of the features are adaptively fused together. Extensive experimental results on two real-world cross-domain applications (emotion recognition and cross-media retrieval) demonstrate the effectiveness of the proposed method. In this paper, we define the problem of simultaneously deploying multiple versions of a web service in the face of independently developed unsupervised clients. We then propose a solution in the form of a design technique called Chain of Adapters and argue that this approach strikes a good balance between the various requirements. We recount our experiences in automating the application of the technique and provide an initial analysis of the performance degradations it may occasion. The Chain of Adapters technique is particularly suitable for self-managed systems since it makes many version-related reconfiguration tasks safe, and thus subject to automation. MSRLM is the release of our internal language modeling tool chain used in Microsoft Research. It was used in our submission for NIST MT 2006. The main difference with other freely available tools is that it was designed to scale to large amounts of data. We successfully built a language model on high end hardware on 40 billion words of web data within 8 hours. It only supports a minimal set of features. Large gigaword language models may be consumed in a first pass machine translation decoding without further processing. This document describes the implementation and usage of the tools summarily. It is our stated goal and hope that this release will be useful to the scientific community. The tool may not be used in a commercial product, or to build models used in a commercial product, or in for any commercial purpose. In addition, we require that you kindly cite this technical report when publishing results derived with this language model tool chain. This describes the LM tool which is available as: http: research.microsoft.com research downloads details 78e26f9c-fc9a-44bb-80a769324c62df8c details.aspx We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controller, we explore a range of neural network-based models which vary in their ability to abstract the underlying algorithm from training instances and generalize to test examples with many thousands of digits. The controller is trained using @math -learning with several enhancements and we show that the bottleneck is in the capabilities of the controller rather than in the search incurred by @math -learning. ABSTRACT This paper considers the protein energy minimization problem for lattice and off-lattice protein folding models that explicitly represent side chains. Lattice models of proteins have proven useful tools for reasoning about protein folding in unrestricted continuous space through analogy. This paper provides the first illustration of how rigorous algorithmic analyses of lattice models can lead to rigorous algorithmic analyses of off-lattice models. We consider two side chain models: a lattice model that generalizes the HP model (Dill, 1985) to explicitly represent side chains on the cubic lattice and a new off-lattice model, the HP Tangent Spheres Side Chain model (HP-TSSC), that generalizes this model further by representing the backbone and side chains of proteins with tangent spheres. We describe algorithms with mathematically guaranteed error bounds for both of these models. In particular, we describe a linear time performance guaranteed approximation algorithm for the HP side chain model that... Programming by Demonstration (PbD) is a promising technique for programming mobile manipulators to perform complex tasks, such as stocking shelves in retail environments. However, programming such tasks purely by demonstration can be cumbersome and time-consuming as they involve many steps and they are different for each item being manipulated. We propose a system that allows programming new tasks with a combination of demonstration and adaptation. This approach eliminates the need to demonstrate repetitions within one task or variations of a task for different items, replacing those demonstrations with a much more time-efficient adaptation procedure. We develop a Graphical User Interface (GUI) that enables the adaptation procedure. This GUI allows grouping, duplicating, removing, reordering, and repositioning parts of a demonstration to adapt and extend it. We implement our approach on a single-armed mobile manipulator. We evaluate our system on several test scenarios with one expert user and four novice users. We demonstrate that the combination of demonstration and adaptation requires substantially less time to program than purely by demonstration. Many online social networks feature restrictive web interfaces which only allow the query of a user's local neighborhood through the interface. To enable analytics over such an online social network through its restrictive web interface, many recent efforts reuse the existing Markov Chain Monte Carlo methods such as random walks to sample the social network and support analytics based on the samples. The problem with such an approach, however, is the large amount of queries often required (i.e., a long “mixing time”) for a random walk to reach a desired (stationary) sampling distribution. In this paper, we consider a novel problem of enabling a faster random walk over online social networks by “rewiring” the social network on-the-fly. Specifically, we develop Modified TOpology (MTO)-Sampler which, by using only information exposed by the restrictive web interface, constructs a “virtual” overlay topology of the social network while performing a random walk, and ensures that the random walk follows the modified overlay topology rather than the original one. We show that MTO-Sampler not only provably enhances the efficiency of sampling, but also achieves significant savings on query cost over real-world online social networks such as Google Plus, Epinion etc. The logic-based machine-understandable framework of the Semantic Web typically challenges casual users when they try to query ontologies. An often proposed solution to help casual users is the use of natural language interfaces. Such tools, however, suffer from one of the biggest problems of natural language: ambiguities. Furthermore, the systems are hardly adaptable to new domains. This paper addresses these issues by presenting Querix, a domain-independent natural language interface for the Semantic Web. The approach allows queries in natural language, thereby asking the user for clarification in case of ambiguities. The preliminary evaluation showed good retrieval performance.