Adoption Behavior for Facilities Management Information Systems at Feature Level

Information technology adoption at the feature level is relative new and becoming a research area in the information system (IS). Features adoption is defined as a basket of information system features that can be used by a particular user to accomplish work task. Currently, information systems have multiple features so that multiple users can complete multiple tasks and accomplish specific work objectives. Their power can reveal only when their features support specific employees in completing their tasks efficiently and effectively. The integration of features, work processes and employees is critical. Moreover, bundles of new and old features with similar functions coexist in employees’ tool kits. Employees can cherry-pick their favorite work settings at different points in time. This situation leads to dynamic and complex nature of technology adoption behavior at feature level. Past research that has concentrated on adoption at the system level may be less relevant, overly simple or inappropriate to explain and predict adoption behavior at the feature level. This thesis builds upon two consecutive empirical projects and investigates forms of feature adoption behavior and their respective outcomes for individuals and organizations. It proposes feature substitution that employees substitute old features with new ones, having similar functions, is the desired form of adoption behavior because of positive outcomes attained. This thesis adopts the Expectancy Theory of Motivation, to explore the co-influence of personal experiential factors and cognitive factors on feature substitution, as goal-oriented and outcome-based behavior. Through investigating why and how specific behavior happens, the thesis has developed a theoretical framework to explain feature substitution at workplace context. Additionally, organizational factors are discovered that have a substantial indirect influence on the behavior, and therefore enrich our knowledge of the facilitating conditions. This finding becomes a guide to formulating effective organizational measures to strengthen the motivation for the behavior. Overall, this thesis reveals the key determinants of feature substitution, including experiential factors, benefit, personal intrinsic needs, work goal congruence and self-esteem, and organizational factor of self-learning environment. The service performance management approach may moderate those variables

Invisibility and interpretation

Invisibility is often thought to occur because of the low-level limitations of the visual system. For example, it is often assumed that backward masking renders a target invisible because the visual system is simply too slow to resolve the target and the mask separately. Here, we propose an alternative explanation in which invisibility is a goal rather than a limitation and occurs naturally when making sense out of the plethora of incoming information. For example, we present evidence that (in)visibility of an element can strongly depend on how it groups with other elements. Changing grouping changes visibility. In addition, we will show that features often just appear to be invisible but are in fact visible in a way the experimenter is not aware of

Application of factor decomposition techniques to vertical specialisation measurements

The increasing importance of vertical specialisation (VS) trade has been a notable feature of rapid economic globalisation and regional integration. In an attempt to understand countries’ depth of participation in global production chains, many Input-Output based VS indicators have been developed. However, most of them focus on showing the overall magnitude of a country’s VS trade, rather than explaining the roles that specific sectors or products play in VS trade and what factors make the VS change over time. Changes in vertical specialisation indicators are, in fact, determined by mixed and complex factors such as import substitution ratios, types of exported goods and domestic production networks. In this paper, decomposition techniques are applied to VS measurement based on the OECD Input-Output database. The decomposition results not only help us understand the structure of VS at detailed sector and product levels, but also show us the contributions of trade dependency, industrial structures of foreign trade and domestic production system to a country’s vertical specialisation trade.Developing countries, Developed countries, International trade, Input-output tables, Vertical specialisation, Factor decomposition, Input-output

Does Phenomenal Consciousness Overflow Attention? An Argument from Feature-Integration

In the past two decades a number of arguments have been given in favor of the possibility of phenomenal consciousness without attentional access, otherwise known as phenomenal overflow. This paper will show that the empirical data commonly cited in support of this thesis is, at best, ambiguous between two equally plausible interpretations, one of which does not posit phenomenology beyond attention. Next, after citing evidence for the feature-integration theory of attention, this paper will give an account of the relationship between consciousness and attention that accounts for both the empirical data and our phenomenological intuitions without positing phenomenal consciousness beyond attention. Having undercut the motivations for accepting phenomenal overflow along with having given reasons to think that phenomenal overflow does not occur, I end with the tentative conclusion that attention is a necessary condition for phenomenal consciousness

Efficient Pattern Matching in Python

Pattern matching is a powerful tool for symbolic computations. Applications include term rewriting systems, as well as the manipulation of symbolic expressions, abstract syntax trees, and XML and JSON data. It also allows for an intuitive description of algorithms in the form of rewrite rules. We present the open source Python module MatchPy, which offers functionality and expressiveness similar to the pattern matching in Mathematica. In particular, it includes syntactic pattern matching, as well as matching for commutative and/or associative functions, sequence variables, and matching with constraints. MatchPy uses new and improved algorithms to efficiently find matches for large pattern sets by exploiting similarities between patterns. The performance of MatchPy is investigated on several real-world problems

MatchPy: A Pattern Matching Library

Pattern matching is a powerful tool for symbolic computations, based on the well-defined theory of term rewriting systems. Application domains include algebraic expressions, abstract syntax trees, and XML and JSON data. Unfortunately, no lightweight implementation of pattern matching as general and flexible as Mathematica exists for Python Mathics,MacroPy,patterns,PyPatt. Therefore, we created the open source module MatchPy which offers similar pattern matching functionality in Python using a novel algorithm which finds matches for large pattern sets more efficiently by exploiting similarities between patterns.Comment: arXiv admin note: substantial text overlap with arXiv:1710.0007

Applying feature reduction analysis to a PPRLM-multiple Gaussian language identification system

This paper presents the application of a feature selection technique such as LDA to a language identification (LID) system. The baseline system consists of a PPRLM module followed by a multiple-Gaussian classifier. This classifier makes use of acoustic scores and duration features of each input utterance. We applied a dimension reduction of the feature space in order to achieve a faster and easier-trainable system. We imputed missing values of our vectors before projecting them on the new space. Our experiments show a very low performance reduction due to the dimension reduction approach. Using a single dimension projection the error rates we have obtained are about 8.73% taking into account the 22 most significant features