A review of contractor selection practice in the U.K. construction industry

Background, prime influences upon present day methods and current trends in United Kingdom tendering practices are reviewed. Significant weaknesses are identified, confirming the need for some revision of existing contractor selection methods. The findings compliment recommendations of the recent government/industry review of the construction sector headed by Sir Michael Latham. Specifically, that tendering procedure should embrace investigation of contractors' potential to deliver a service of acceptable standard, on time, within budget. Present selection methods often fail in this objective, being inclined to discriminate predominantly on cost.     

Robust pole assignment for incomplete nonlinear decoupling applied to robots

A robustness analysis and synthesis for incomplete nonlinear decoupling for a class of nonlinear systems is discussed. Rigid and elastic-joint robot models belong to this class. For the elastic case, a transformation facilitates the robustness analysis under a weak assumption. Charts with H ₁- and H - norms of closed-loop disturbance transfer functions of the nonlinear-decoupled system are presented for a robust pole assignment.     

HihO: accelerating artificial intelligence interpretability for medical imaging in IoT applications using hierarchical occlusion

Neural Computing and Applications - In the medical imaging domain, nonlinear warping has enabled pixel-by-pixel mapping of one image dataset to a reference dataset. This co-registration of data...     

Investigation of long term drift of an intraocular pressure sensor

Microsystem Technologies - “Zero drift” behavior of an optical intraocular pressure sensor is studied using an analytical model based on the deflection of a circular membrane. Results...     

Context relevance assessment and exploitation in mobile recommender systems

In order to generate relevant recommendations, a context-aware recommender system (CARS) not only makes use of user preferences, but also exploits information about the specific contextual situation in which the recommended item will be consumed. For instance, when recommending a holiday destination, a CARS could take into account whether the trip will happen in summer or winter. It is unclear, however, which contextual factors are important and to which degree they influence user ratings. A large amount of data and complex context-aware predictive models must be exploited to understand these relationships. In this paper, we take a new approach for assessing and modeling the relationship between contextual factors and item ratings. Rather than using the traditional approach to data collection, where recommendations are rated with respect to real situations as participants go about their lives as normal, we simulate contextual situations to more easily capture data regarding how the context influences user ratings. To this end, we have designed a methodology whereby users are asked to judge whether a contextual factor (e.g., season) influences the rating given a certain contextual condition (e.g., season is summer). Based on the analyses of these data, we built a context-aware mobile recommender system that utilizes the contextual factors shown to be important. In a subsequent user evaluation, this system was preferred to a similar variant that did not exploit contextual information.     

A digital library framework for heterogeneous music collections: from document acquisition to cross-modal interaction

In this paper, we present a digital library system for managing heterogeneous music collections. The heterogeneity refers to various document types and formats as well as to different modalities, e. g., CD-audio recordings, scanned sheet music, and lyrics. The system offers a full-fledged, widely automated document processing chain: digitization, indexing, annotation, access, and presentation. Our system is implemented as a generic and modular music repository based on a service-oriented software architecture. As a particular strength of our approach, the various documents representing aspects of a piece of music are jointly considered in all stages of the document processing chain. Our user interfaces allow for a multimodal and synchronized presentation of documents (WYSIWYH: what you see is what you hear), a score- or lyrics-based navigation in audio, as well as a cross- and multimodal retrieval. Hence, our music repository may be called a truly cross-modal library system. In our paper, we describe the system components, outline the techniques of the document processing chain, and illustrate the implemented functionalities for user interaction. We describe how the system is put into practice at the Bavarian State Library (BSB) Munich as a part of the German PROBADO Digital Library Initiative (PDLI).     

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of knowledge they contain (i.e., the level of support they provide) and in their ability to learn (i.e., their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings.Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics (e.g., Prolog) are not, however, well suited to higher-order applications. Instead, we employ Prolog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and training instance upon which EBG relies, we extend Prolog with the necessity operator of modal logic. We develop a meta-interpreter realizing EBG for the extended language, Prolog, and provide examples of higher-order EBG.     

Bit transposition for very large scientific and statistical databases

Conventional access methods cannot be effectively used in large Scientific/Statistical Database (SSDB) applications. A file structure (called bit transposed file (BTF)) is proposed which offers several attractive features that are better suited for the special characteristics that SSDBs exhibit. This file structure is an extreme version of the (attribute) transposed file. The data are stored by vertical bit partitions. The bit patterns of attributes are assigned using one of several data encoding methods. Each of these encoding methods is appropriate for different query types. The bit partitions can also be compressed using a version of the run length encoding scheme. Efficient operators on compressed bit vectors have been developed and form the basis of a query language. Because of the simplicity of the file structure and query language, optimization problems for database design, query evaluation, and common subexpression removal can be formalized and efficient exact solution or near optimal solution can be achieved. In addition to selective power with low overheads for SSDBs, the BTF is also amenable to special parallel hardware. Results from experiments with the file structure suggest that this approach may be a reasonable alternative file structure for large SSDBs.