Can computer models be used for social learning? A serious game in water management

Computer simulation models are increasingly used to support solving complex problems in natural resource management, with social learning as subsidiary goal of the solution process. In this research, a serious game on water management is used where participants receive feedback on consequences of their choices from an Integrated Assessment Meta Model. This study aims to determine if and how social learning takes place and explores the role of the model in social learning. Group discussions were qualitatively analysed to uncover and understand the mechanisms in this process. Results show that social learning took place in 10 of the 12 game sessions. Though model feedback was an important driver for social learning, social learning was driven most by the team's reflection on their perspective. We conclude that using a model can facilitate social learning in a serious-game setting, in particular in combination with reflection on teams' perspectives.     

Can predicate-argument structures be used for contextual opinion retrieval from blogs?

We present the results of our investigation on the use of predicate-argument structures for contextual opinion retrieval. The use of predicate-argument structure for opinion retrieval is a novel approach that exploits the grammatical derivation of sentences to show contextual and subjective relevance. We do not use frequency of certain keywords as it is usually done in keyword-based opinion retrieval approaches. Rather, our novel solution is based on frequency of contextually relevant and subjective sentences. We use a linear relevance model that leverages semantic similarities among predicate-argument structures of sentences. Thus, this paper presents the evaluation results of the linear relevance model. The model does a linear combination of a popular relevance model, our proposed transformed terms similarity model, and the absolute value of a sentence subjectivity scoring scheme. The predicate-argument structures are derived from the grammatical derivations of natural language query topics and the well formed sentences from blog documents. The derived predicate-argument structures are then semantically compared to compute an opinion relevance score. Our scoring technique uses the highest frequency of semantically related predicate-argument structures enriched with the total subjectivity score from sentences. Evaluation and experimental results show that predicate-argument structures can indeed be used for contextual opinion retrieval as it improves performance of opinion retrieval task by 15% over the popular TREC baselines.     

Can EVI-derived land-surface phenology be used as a surrogate for phenology of canopy photosynthesis?

Canopy phenology plays a prominent role in determining the timing and magnitude of carbon uptake by many ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) Global Land Cover Dynamics product developed from the enhanced vegetation index (EVI) provides broad spatial and temporal coverage of land-surface phenology (LSP), and may serve as a useful proxy for the phenology of canopy photosynthesis. Here, we compare the MODIS growing season start and end dates (SOS and EOS) with the seasonal phenology of canopy photosynthesis estimated using the eddy covariance approach. Using 153 site-years obtained from the Ameriflux database, we calculated the SOS and EOS of gross primary production (GPP) and canopy photosynthesis capacity (CPC) for seven different boreal and temperate vegetation types. CPC is GPP at maximum radiation, estimated by fitting half-hourly GPP and radiation to a rectangular hyperbolic function. We found large mean absolute differences of up to 53 days, depending on vegetation type, between the phenology of canopy development and photosynthesis, indicating that remotely sensed LSP is not a robust surrogate of seasonal changes in GPP, particularly for evergreen needleleaf forests. This limited correspondence of ecosystem carbon uptake with the MODIS LSP product points to the need for improved remotely sensed proxies of GPP phenology.     

Can virtual reality be used to measure and train surgical skills?

The quantitative literature on the use of virtual environments to measure and train a variety of surgical skills is critically reviewed. We selected works from the years 1995-2000. Theoretical perspectives, such as those of Saltzman (1979), Bernstein (1967) and Schmidt (1975) and techniques, such as hierarchical task analysis, are presented and contrasted with the largely atheoretical approach of the practitioners of virtual surgery. It is concluded that the quantitative work discussed provides few findings of value to practising surgeons. This may be due in part to the lack of consideration paid to fundamental issues in the learning of motor skills, such as whether motor skills learning is most effective with varying training conditions and the distinction between purely motoric aspects and knowledge of procedures. Possible ways forward for surgical training are outlined. It is suggested that the theoretical perspectives and techniques available in the area of motor behaviour should be incorporated into future experimental studies of surgery in virtual environments.     

Can computers be teammates?

This study investigated the claim that humans will readily form team relationships with computers. Drawing from the group dynamic literature in human-human interactions, a laboratory experiment (n=56) manipulated identity and interdependence to create team affiliation in a human-computer interaction. The data show that subjects who are told they are interdependent with the computer affiliate with the computer as a team. The data also show that the effects of being in a team with a computer are the same as the effects of being in a team with another human: subjects in the interdependence conditions perceived the computer to be more similar to themselves, saw themselves as more cooperative, were more open to influence from the computer, thought the information from the computer was of higher quality, found the information from the computer friendlier, and conformed more to the computer's information. Subjects in the identity conditions showed neither team affiliation nor the effects of team affiliation.     

Can privacy concerns for insurance of connected cars be compensated?

Internet-of-things technologies enable service providers such as insurance companies to collect vast amounts of privacy-sensitive data on car drivers. This paper studies whether and how privacy concerns of car owners can be compensated by offering monetary benefits. We study the case of usage based car insurance services for which the insurance fee is adapted to measured mileage and driving behaviour. A conjoint experiment shows that consumers prefer their current insurance products to usage based car insurance. However, when offered a minor financial compensation, they are willing to give up their privacy to car insurers. Consumers find privacy of behaviour and action more valuable than privacy of location and space. The study is a first to compare different forms of privacy in the acceptance of connected car services. Hereby, we contribute to more fine-grained understanding of privacy concerns in the acceptance of digital services, which will become more important in the upcoming Internet-of-things era.     

Vibration and passenger comfort: Can data from subjects be used to predict passenger comfort?

This paper presents a review of both field and laboratory studies of human reaction to vibration, to try to answer the question whether laboratory based studies may be used to predict comfort levels for passenger vehicles. The conclusion is reached that such studies may be used, provided their restrictions are understood. Finally, tentative suggestions are made for acceptable levels of vibration in passenger transport vehicles.     

Ancilla models for quantum operations: for what unitaries does the ancilla state have to be physical?

Any evolution described by a completely positive trace-preserving linear map can be imagined as arising from the interaction of the evolving system with an initially uncorrelated ancilla. The interaction is given by a joint unitary operator, acting on the system and the ancilla. Here we study the properties such a unitary operator must have in order to force the choice of a physical—that is, positive—state for the ancilla if the end result is to be a physical—that is, completely positive—evolution of the system.     

Office automation — Can it be justified?

The use of office automation has increased in recent years. However, its implementation has not taken place as quickly as was originally anticipated. Suppliers have faced cutbacks in demand and users have remained unconvinced by the advantages. This paper outlines the results of an NCC subscriber survey into the justification, monitoring and use of office automation. Areas such as text processing word processing, personal communications, information access and personal computing are discussed.     

Wellformedness properties in Euler diagrams: which should be used?

Euler diagrams are often used to visualize intersecting data sets in applications such as criminology; genetics, medicine, and computer file systems. One interesting aspect of these diagrams is that some data sets cannot be drawn without breaking one or more "wellformedness properties," which are considered to reduce the user comprehension of the diagrams. However, it is possible to draw the same data with different diagrams, each of which breaks different wellformedness properties. Hence, some properties are "swappable," so motivating the study of which of the alternatives would be best to use. This paper reports on the two empirical studies to determine how wellformedness properties affect comprehension. One study was with abstract data, the other was with concrete data that visualized students' enrollment on university modules. We have results from both studies that imply that diagrams with concurrency or disconnected zones perform less well than other some other properties. Further, we have no results that imply that diagrams with brushing points adversely affect performance. Our data also indicate that nonsimple curves are preferred less than diagrams with other properties. These results will inform both human diagram designers and the developers of automated drawing systems on the best way to visualize data using Euler diagrams.     

Computer games created by middle school girls: Can they be used to measure understanding of computer science concepts?

Computer game programming has been touted as a promising strategy for engaging children in the kinds of thinking that will prepare them to be producers, not just users of technology. But little is known about what they learn when programming a game. In this article, we present a strategy for coding student games, and summarize the results of an analysis of 108 games created by middle school girls using Stagecast Creator in an after school class. The findings show that students engaged in moderate levels of complex programming activity, created games with moderate levels of usability, and that the games were characterized by low levels of code organization and documentation. These results provide evidence that game construction involving both design and programming activities can support the learning of computer science concepts.     

Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging

Early and accurate diagnosis of Parkinson’s disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with ¹²³I-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the ¹²³I-Ioflupane SPECT scans (as obtained from the Parkinson’s progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.     

Can bi‐cubic surfaces be class A?

'Class A surface’ is a term in the automotive design industry, describing spline surfaces with aesthetic, non‐oscillating highlight lines. Tensor‐product B‐splines of degree bi‐3 (bicubic) are routinely used to generate smooth design surfaces and are often the de facto standard for downstream processing. To bridge the gap, this paper explores and gives a concrete suggestion, how to achieve good highlight line distributions for irregular bi‐3 tensor‐product patch layout by allowing, along some seams, a slight mismatch of normals below the industry‐accepted tolerance of one tenth of a degree. Near the irregularities, the solution can be viewed as transforming a higher‐degree, high‐quality formally smooth surface into a bi‐3 spline surface with few pieces, sacrificing formal smoothness but qualitatively retaining the shape.     

Is non-parametric hypothesis testing model robust for statistical fault localization?

Fault localization is one of the most difficult activities in software debugging. Many existing statistical fault-localization techniques estimate the fault positions of programs by comparing the program feature spectra between passed runs and failed runs. Some existing approaches develop estimation formulas based on mean values of the underlying program feature spectra and their distributions alike. Our previous work advocates the use of a non-parametric approach in estimation formulas to pinpoint fault-relevant positions. It is worthy of further study to resolve the two schools of thought by examining the fundamental, underlying properties of distributions related to fault localization. In particular, we ask: Can the feature spectra of program elements be safely considered as normal distributions so that parametric techniques can be soundly and powerfully applied? In this paper, we empirically investigate this question from the program predicate perspective. We conduct an experimental study based on the Siemens suite of programs. We examine the degree of normality on the distributions of evaluation biases of the predicates, and obtain three major results from the study. First, almost all examined distributions of evaluation biases are either normal or far from normal, but not in between. Second, the most fault-relevant predicates are less likely to exhibit normal distributions in terms of evaluation biases than other predicates. Our results show that normality is not common as far as evaluation bias can represent. Furthermore, the effectiveness of our non-parametric predicate-based fault-localization technique weakly correlates with the distributions of evaluation biases, making the technique robust to this type of uncertainty in the underlying program spectra.