GRAMOFON: General model-selection framework based on networks

Ensembles constitute one of the most prominent class of hybrid prediction models. One basically assumes that different models compensate each other's errors if one combines them in an appropriate way. Often, a large number of various prediction models are available. However, many of them may share similar error characteristics, which highly depress the error compensation effect. Thus the selection of an appropriate subset of models is crucial. In this paper, we address this issue. As major contribution, for the case if large number of models is present, we propose a network-based framework for model selection while paying special attention to the interaction effect of models. In this framework, we introduce four ensemble techniques and compare them to the state-of-the-art in experiments on publicly available real-world data.     

Collaborative recommender systems: Combining effectiveness and efficiency

Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the “information overload” problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recommend by first developing a model of user ratings. In this paper, we bring to surface factors that affect CF process in order to identify existing false beliefs. In terms of accuracy, by being able to view the “big picture”, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% increase in precision in comparison to widely-used CF algorithms. In terms of efficiency, we propose a model-based approach based on latent semantic indexing (LSI), that reduces execution times at least 50% than the classic CF algorithms.     

Acute pulmonary embolism: assessment of helical CT for diagnosis

Male pigeons, Columba livia, employ intense mate guarding and frequent copulation apparently as strategies to ensure their paternity. The aim of this study was to investigate the benefits to females of mate guarding by males and frequent copulation. Field observations showed that females initiated the majority of copulations and females that solicited copulations more frequently were guarded more closely by their partner. Experimental removal of guarding male partners showed that: (1) unguarded, fertile females suffered increased harassment from extrapair males which reduced their foraging efficiency; and (2) unguarded, fertile females did not seek copulations with extrapair males. Various explanations for frequent pair copulation initiated by females are discussed and we conclude that female pigeons trade pair copulations for protection (mate guarding) against sexual harassment from other males. Copyright 1998 The Association for the Study of Animal Behaviour.     

Randomized selection in n+C+o(n) comparisons

In this paper we present a randomized selection algorithm that with high probability 1−1/n^ρ, for any constant ρ>1 requires n+C+o(n) comparisons to determine the Cth order statistic of n keys thus matching a corresponding lower bound on the average number of comparisons required. Low order terms in the number of comparisons performed can also be reduced by extending the algorithm of Floyd and Rivest and analyzing its resulting performance more carefully.     

Two-stage phone duration modelling with feature construction and feature vector extension for the needs of speech synthesis

We propose a two-stage phone duration modelling scheme, which can be applied for the improvement of prosody modelling in speech synthesis systems. This scheme builds on a number of independent feature constructors (FCs) employed in the first stage, and a phone duration model (PDM) which operates on an extended feature vector in the second stage. The feature vector, which acts as input to the first stage, consists of numerical and non-numerical linguistic features extracted from text. The extended feature vector is obtained by appending the phone duration predictions estimated by the FCs to the initial feature vector. Experiments on the American-English KED TIMIT and on the Modern Greek WCL-1 databases validated the advantage of the proposed two-stage scheme, improving prediction accuracy over the best individual predictor, and over a two-stage scheme which just fuses the first-stage outputs. Specifically, when compared to the best individual predictor, a relative reduction in the mean absolute error and the root mean square error of 3.9% and 3.9% on the KED TIMIT, and of 4.8% and 4.6% on the WCL-1 database, respectively, is observed.     

Automated context aggregation and file annotation for PAN-based computing

This paper presents a method for automatically annotating files created on portable devices with contextual metadata. We achieve this through the combination of two system components. One is a context dissemination mechanism which allows devices in a personal area network (PAN) to maintain a shared aggregate contextual perception. The other is a storage management system that uses such context information to automatically decorate files created on personal devices with annotations. As a result, the user is able to flexibly browse and lookup files that were generated on the move, based on the contextual situation at the time of their creation. What is equally important is that the user is relieved from the cumbersome task of having to manually provide annotations in an explicit fashion. This is especially valuable when generating files on the move, using U/I-restricted portable devices.

A platform for the automatic generation of attribute evaluation hardware systems

Attribute grammars (AG) allow the addition of context-sensitive properties into context free grammars, augmenting their expressional capabilities by using syntactic and semantic notations, making them in this way a really useful tool for a considerable number of applications. AGs have extensively been utilized in applications such as artificial intelligence, structural pattern recognition, compiler construction and even text editing. Obviously, the performance of an attribute evaluation system resides in the efficiency of the syntactic and semantic subsystems. In this paper, a hardware architecture for an attribute evaluation system is presented, which is based on an efficient combinatorial implementation of Earley's parallel parsing algorithm for the syntax part of the attribute grammar. The semantic part is managed by a special purpose module that traverses the parse tree and evaluates the attributes based on a proposed stack-based approach. The entire system is described in Verilog HDL (hardware design language), in a template form that given the specification of an arbitrary attribute grammar, the HDL synthesizable source code of the system is produced on the fly by a proposed automated tool. The generated code has been simulated for validation, synthesized and tested on an Xilinx FPGA (field programmable gate arrays) board for various AGs. Our method increases the performance up to three orders of magnitude compared to previous approaches, depending on the implementation, the size of the grammar and the input string length. This makes it particularly appealing for applications where attribute evaluation is a crucial aspect, like in real-time and embedded systems. Specifically, a natural language interface is presented, based on a question-answering application from the area of airline flights.     

Silence detection for multimedia communication systems

Silence detection and removal is an essential building block of any multimedia video conferencing system. It reduces the bandwidth requirements of the underlying network transport service and helps to maintain an acceptable end-to-end delay for audio. We analyze the requirements for a silence detection algorithm hosted on a multimedia communication system, and propose a novel low-complexity algorithm operating in the non-linear domain. After discussing the constraints which are imposed by the architecture of the system hardware (computer, packet-based network), we show that several recently proposed silence detection algorithms fail to meet all of these constraints. A new approach is then introduced, based on the small- and large-signal behavior of the speech waveform in the -law domain. The new algorithm is compared with a recent design that meets several of our requirements; experimental results indicate that it performs significantly better in the particular environment at hand.     

Review,analysis and synthesis of prognostic-based decision support methods for condition based maintenance

In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition based maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions based on the predicted condition of equipment. Although prognostic-based decision support for CBM is not an extensively explored area, there exist methods which have been developed in order to deal with specific challenges such as the need to cope with real-time information, to predict the health state of equipment and to continuously update maintenance-related recommendations. The current work aims at providing a literature review for prognostic-based decision support methods for CBM. We analyse the literature in order to identify combinations of methods for prognostic-based decision support for CBM, propose a practical technique for selecting suitable combinations of methods and set the guidelines for future research.     

Analysis of Autopilot Disengagements Occurring During Autonomous Vehicle Testing

In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle's safety and ride comfort. In the U.S state of California, the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing. On 1 January 2016, seven manufacturers submitted their first disengagement reports:Bosch, Delphi, Google, Nissan, Mercedes-Benz, Volkswagen, and Tesla Motors. This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers (SAE) Level 2 and Level 3 automation technologies. Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail. The mechanisms and time taken for take-over transition occurred in disengagements are studied. Finally, recommendations for OEMs, manufacturers, and government organizations are also discussed.