Adaptive Language-based Mental Health Assessment with Item-Response Theory

Mental health issues widely vary across individuals - the manifestations of signs and symptoms can be fairly heterogeneous. Recently, language-based depression and anxiety assessments have shown promise for capturing this heterogeneous nature by evaluating a patient's own language, but such approaches require a large sample of words per person to be accurate. In this work, we introduce adaptive language-based assessment - the task of iteratively estimating an individual's psychological score based on limited language responses to questions that the model also decides to ask. To this end, we explore two statistical learning-based approaches for measurement/scoring: classical test theory (CTT) and item response theory (IRT). We find that using adaptive testing in general can significantly reduce the number of questions required to achieve high validity (r ~ 0.7) with standardized tests, bringing down from 11 total questions down to 3 for depression and 5 for anxiety. Given the combinatorial nature of the problem, we empirically evaluate multiple strategies for both the ordering and scoring objectives, introducing two new methods: a semi-supervised item response theory based method (ALIRT), and a supervised actor-critic based model. While both of the models achieve significant improvements over random and fixed orderings, we find ALIRT to be a scalable model that achieves the highest accuracy with lower numbers of questions (e.g. achieves Pearson r ~ 0.93 after only 3 questions versus asking all 11 questions). Overall, ALIRT allows prompting a reduced number of questions without compromising accuracy or overhead computational costs

Improving the estimation of project overheads in construction companies in Hong Kong

Project overheads cover the site cost of administrating a project as a whole, rather than a particular work section. Estimation of these items is one of the routine tasks of all parties including the contractors and project owners. Nevertheless, our understanding of this subject mainly lies on the theoretical level due to the limited empirical study in the past. More importantly, estimation of project overheads demands a lot of expertise but still exhibits a high risk of inaccuracy. Therefore, the aims of this research study are to explore the estimation and expenses of project overheads in practice and to devise an efficient model for project overheads estimation. [Continues.

Advances in Methodology and Applications of Decision Support Systems

These Proceedings are composed of a selection of papers of the Workshop on Advances in Methodology and Applications of Decision Support Systems, organized by the System and Decision Sciences (SDS) Program of IIASA and the Japan Institute of Systems Research (JISR). The workshop was held at IIASA on August 20-22, 1990. The Methodology of Decision Analysis (MDA) Project of the SDS Program focuses on a system-analytical approach to decision support and is devoted to developing methodology, software and applications of decision support systems concentrated primarily around interactive systems for data analysis, interpretation and multiobjective decisionmaking, including uncertainty analysis and group decision making situations in both their cooperative and noncooperative aspects. The objectives of the research on decision support systems (DSS) performed in cooperation with the MDA Project are to: compare various approaches to decision support systems; advance theory and methodology of decision support; convert existing theories and methodologies into usable (simple to use, user-friendly and robust) tools that could easily be used in solving real-life problems. A principal characteristic of decision support systems is that they must be tuned to specific decision situations, to complex real-life characteristics of every application. Even if the theory and methodology of decision support is quite advanced, every application might provide impulses for further theoretical and methodological advances. Therefore the principle underlying this project is that theoretical and methodological research should be strongly connected to the implementation and applications of its results to sufficiently complicated, real-life examples. This approach results in obtaining really applicable working tools for decision support. The papers for this Proceedings have been selected according to the above summarized framework of the research activities. Therefore, the papers deal both with theoretical and methodological problems and with real-life applications

Using data mining to dynamically build up just in time learner models

Using rich data collected from e-learning systems, it may be possible to build up just in time dynamic learner models to analyze learners' behaviours and to evaluate learners' performance in online education systems. The goal is to create metrics to measure learners' characteristics from usage data. To achieve this goal we need to use data mining methods, especially clustering algorithms, to find patterns from which metrics can be derived from usage data. In this thesis, we propose a six layer model (raw data layer, fact data layer, data mining layer, measurement layer, metric layer and pedagogical application layer) to create a just in time learner model which draws inferences from usage data. In this approach, we collect raw data from online systems, filter fact data from raw data, and then use clustering mining methods to create measurements and metrics. In a pilot study, we used usage data collected from the iHelp system to create measurements and metrics to observe learners' behaviours in a real online system. The measurements and metrics relate to a learner's sociability, activity levels, learning styles, and knowledge levels. To validate the approach we designed two experiments to compare the metrics and measurements extracted from the iHelp system: expert evaluations and learner self evaluations. Even though the experiments did not produce statistically significant results, this approach shows promise to describe learners' behaviours through dynamically generated measurements and metric. Continued research on these kinds of methodologies is promising

A Balance between Ideals and Reality — Establishing and Evaluating a Resilient City Indicator System for Central Chinese Cities

Recent years have seen a gradual shift in focus of international policies from a national and regional perspective to that of cities, a shift which is closely related to the rapid urbanization of developing countries. As revealed in the 2011 Revision of the World Urbanization Prospects published by the United Nations, 51% of the global population (approximately 3.6 billion people) lives in cities. The report predicts that by 2050, the world’s urban population will increase by 2.3 billion, making up 68% of the population. The growth of urbanization in the next few decades is expected to primarily come from developing countries, one third of which will be in China and India. With rapid urbanization and the ongoing growth of mega cities, cities must become increasingly resilient and intelligent to cope with numerous challenges and crises like droughts and floods arising from extreme climate, destruction brought by severe natural disasters, and aggregated social contradictions resulting from economic crises. All cities face the urban development dynamics and uncertainties arising from these problems. Under such circumstances, cities are considered the critical path from crisis to prosperity, so scholars and organizations have proposed the construction of “resilient cities.” On the one hand, this theory emphasizes cities’ defenses and buffering capacity against disasters, crises and uncertainties, as well as recovery after destruction; on the other hand, it highlights the learning capacity of urban systems, identification of opportunities amid challenges, and maintenance of development vitality. Some scholars even believe that urban resilience is a powerful supplement to sustainable development. Hence, resilience assessment has become the latest and most important perspective for evaluating the development and crisis defense capacity of cities. Rather than a general abstract concept, urban resilience is a comprehensive measurement of a city’s level of development. The dynamic development of problems is reflected through quantitative indicators and appraisal systems not only from the perspective of academic research, but also governmental policy, so as to scientifically guide development, and measure and compare cities’ development levels. Although international scholars have proposed quantitative methods for urban resilience assessment, they are however insufficiently systematic and regionally adaptive for China’s current urban development needs. On the basis of comparative study on European and North American resilient city theories, therefore, this paper puts forwards a theoretical framework for resilient city systems consistent with China’s national conditions in light of economic development pressure, natural resource depletion, pollution, and other salient development crises in China. The key factors influencing urban resilience are taken into full consideration; expert appraisal is conducted based on the Delphi Method and the analytic hierarchy process (AHP) to design an extensible and updatable resilient city evaluation system which is sufficiently systematic, geographically adaptable, and sustainable for China’s current urban development needs. Finally, Changsha is taken as the main case for empirical study on comprehensive evaluation of similar cities in Central China to improve the indicator system

Mathematical Models for Planning and Controlling Air Quality; Proceedings of an IIASA Workshop, October 1979

Air-quality management problems fall into three main classes: it is difficult to obtain a reliable picture of all the physicochemical processes involved, comprehensive assessments of the costs and benefits of alternative control strategies are not easily made, and the technology for pollution abatement is not yet well established. Various mathematical or formal management models do exist but the overall impact of modeling on decision making has so far been relatively small. The first aim of the IIASA Workshop on which this volume is based was to bridge the gap between air-quality modeling and management. As described in the ten papers in Part One, Workshop participants examined the goals actually pursued by decision makers, the potential role of mathematical models in air-quality management, and the extent to which modeling has been used in real situations in a number of countries. The Workshop's second aim, reported in the eight papers in Part Two, was to consider the unusual strategy of real-time emission control. An extended description of the IIASA case study of the Venetian Lagoon area was presented, together with contributions on real-time forecast and control schemes in operation in Japan and Italy

Єдина Європа: Погляд у майбутнє

Викладено результати наукових досліджень щодо сучасних проблем глобалізації та інтеграційних процесів, участі в них українських та польських підприємств, вирішення екологічних та соціально-економічних питань, пов’язаних з інтеграційними процесами, підвищенням конкуренції та інтенсифікацією виробництва. Видання буде корисним для наукових співробітників, фахівців-практиків, які займаються проблемами європейського розвитку, викладачів, аспірантів, студентів вищих навчальних закладів, урядових і неурядових аналітичних організацій, інституцій ЄС

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

Data mining industry : emerging trends and new opportunities

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2000."May 2000."Includes bibliographical references (leaves 170-179).by Walter Alberto Aldana.M.Eng

Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed