Delegation by Object Composition

AbstractClass inheritance and method overriding, as provided by standard class-based languages, are often not flexible enough to represent objects with some dynamic behavior. In this respect, object composition equipped with different forms of method body lookup is often advocated as a more flexible alternative to class inheritance since it takes place at run time, thus permitting the behavior of objects to be specialized dynamically. In this paper, we illustrate Incomplete Featherweight Java (IFJ), an extension of Featherweight Java with a novel linguistic construct, the incomplete object. Incomplete objects require some missing methods which can be provided at run time by composition with another (complete) object. Furthermore, we present two mechanisms for the method body lookup on (composed) objects, one based on delegation and the other based on consultation. Thanks to the design of the language, the consultation-based lookup is a simple extension of the delegation-based one. Both mechanisms are disciplined by static typing, therefore the language enjoys type safety (which implies no “message-not-understood” run-time errors) and avoids possible accidental overrides due to method name clashes

Towards Object-Oriented Klaim

Abstract By its own nature, mobile code requires flexibility in order to be adaptive to any execution context it may be run in. In this paper we investigate this flexibility requirement from the design point of view, and propose a solution based on the mixin technique to fulfill it. We also propose an extension of the language K laim with object-oriented features, as an application of this approach

Java & Lambda: a Featherweight Story

We present FJ&$\lambda$, a new core calculus that extends Featherweight Java (FJ) with interfaces, supporting multiple inheritance in a restricted form, $\lambda$-expressions, and intersection types. Our main goal is to formalise how lambdas and intersection types are grafted on Java 8, by studying their properties in a formal setting. We show how intersection types play a significant role in several cases, in particular in the typecast of a $\lambda$-expression and in the typing of conditional expressions. We also embody interface \emph{default methods} in FJ&$\lambda$, since they increase the dynamism of $\lambda$-expressions, by allowing these methods to be called on $\lambda$-expressions. The crucial point in Java 8 and in our calculus is that $\lambda$-expressions can have various types according to the context requirements (target types): indeed, Java code does not compile when $\lambda$-expressions come without target types. In particular, in the operational semantics we must record target types by decorating $\lambda$-expressions, otherwise they would be lost in the runtime expressions. We prove the subject reduction property and progress for the resulting calculus, and we give a type inference algorithm that returns the type of a given program if it is well typed. The design of FJ&$\lambda$ has been driven by the aim of making it a subset of Java 8, while preserving the elegance and compactness of FJ. Indeed, FJ&$\lambda$ programs are typed and behave the same as Java programs

The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence

Real-world experience is typically multimodal. Evidence indicates that the facilitation in the detection of multisensory stimuli is modulated by the perceptual load, the amount of information involved in the processing of the stimuli. Here, we used a realistic virtual reality environment while concomitantly acquiring Electroencephalography (EEG) and Galvanic Skin Response (GSR) to investigate how multisensory signals impact target detection in two conditions, high and low perceptual load. Different multimodal stimuli (auditory and vibrotactile) were presented, alone or in combination with the visual target. Results showed that only in the high load condition, multisensory stimuli significantly improve performance, compared to visual stimulation alone. Multisensory stimulation also decreases the EEG-based workload. Instead, the perceived workload, according to the "NASA Task Load Index" questionnaire, was reduced only by the trimodal condition (i.e., visual, auditory, tactile). This trimodal stimulation was more effective in enhancing the sense of presence, that is the feeling of being in the virtual environment, compared to the bimodal or unimodal stimulation. Also, we show that in the high load task, the GSR components are higher compared to the low load condition. Finally, the multimodal stimulation (Visual-Audio-Tactile-VAT and Visual-Audio-VA) induced a significant decrease in latency, and a significant increase in the amplitude of the P300 potentials with respect to the unimodal (visual) and visual and tactile bimodal stimulation, suggesting a faster and more effective processing and detection of stimuli if auditory stimulation is included. Overall, these findings provide insights into the relationship between multisensory integration and human behavior and cognition

How neurophysiological measures can be used to enhance the evaluation of remote tower solutions

International audienceNew solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgement from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload. In this study we aimed to demonstrate i) the higher resolution of neurophysiological measures in comparison to subjective ones, and ii) how the simultaneous employment of neurophysiological measures and behavioural ones could allow a holistic assessment of operational tools. In this regard, we tested the effectiveness of an EEG-based neurophysiological index (WEEG index) in comparing two different solutions (i.e. Normal and Augmented) in terms of experienced workload. In this regard, 16 professional Air Traffic Controllers (ATCOs) have been asked to perform two operational scenarios. Galvanic Skin Response (GSR) has also been recorded to evaluate the level of arousal (i.e. operator involvement) during the two scenarios execution. NASA-TLX questionnaire has been used to evaluate the perceived workload, and an expert was asked to assess performance achieved by the ATCOs. Finally, reaction times on specific operational events relevant for the assessment of the two solutions, have also been collected. Results highlighted that the Augmented solution induced a local increase in subjects performance (Reaction times). At the same time, this solution induced an increase in the workload experienced by the participants (WEEG). Anyhow, this increase is still acceptable, since it did not negatively impact the performance and has to be intended only as a consequence of the higher engagement of the ATCOs. This behavioural effect is totally in line with physiological results obtained in terms of arousal (GSR), that increased during the scenario with augmentation. Subjective measures (NASA-TLX) did not highlight any significant variation in perceived workload. These results suggest that neurophysiological measure provide additional information than behavioural and subjective ones, even at a level of few seconds, and its employment during the pre-operational activities (e.g. design process) could allow a more holistic and accurate evaluation of new solutions

Early EEG responses to pre-electoral survey items reflect political attitudes and predict voting behavior

Self-reports are conventionally used to measure political preferences, yet individuals may be unable or unwilling to report their political attitudes. Here, in 69 participants we compared implicit and explicit methods of political attitude assessment and focused our investigation on populist attitudes. Ahead of the 2019 European Parliament election, we recorded electroencephalography (EEG) from future voters while they completed a survey that measured levels of agreement on different political issues. An Implicit Association Test (IAT) was administered at the end of the recording session. Neural signals differed as a function of future vote for a populist or mainstream party and of whether survey items expressed populist or non-populist views. The combination of EEG responses and self-reported preferences predicted electoral choice better than traditional socio-demographic and ideological variables, while IAT scores were not a significant predictor. These findings suggest that measurements of brain activity can refine the assessment of socio-political attitudes, even when those attitudes are not based on traditional ideological divides