An Exploration Of Parameters Affecting Employee Energy Conversation Behaviour At The Workplace, Towards IOT-Enabled Behavioural Interventions

Energy conservation is one of the widely recognised important means towards addressing CO2 emissions and the resulting global issue of climate change. Furthermore, public buildings have been recognised as contributing significantly to the consumption of energy worldwide. More importantly, occupant behaviour, a factor that needs to be studied further, can have a high impact on the energy consumed within public buildings. Through our study, we have conducted an exploratory study on the parameters affecting employee energy conservation behaviour in public buildings, towards constructing a behavioural model that can be employed in IoT-enabled personalised energy disaggregation initiatives. We propose an extension to an existing model of employee energy behaviour based on Values Beliefs Norms (VBN) theory, with the addition of five parameters – comfort levels, burnout, locus of control, personal disadvantages and energy awareness. In addition, we discriminate between two groups of inter-related energy conservation behaviours at work – popular and unpopular energy conservation behaviours – and explain our resulting behavioural models’ utility towards IoT-enabled energy conservation, within workplaces. We find that promoting employees’ energy awareness levels, as well as positively affecting their environmental worldviews and personal norms are important factors that should be considered in behavioural interventions toward energy conservation at the workplace

Aff-Wild: Valence and Arousal ‘in-the-wild’ Challenge

The Affect-in-the-Wild (Aff-Wild) Challenge proposes a new comprehensive benchmark for assessing the performance of facial affect/behaviour analysis/understanding 'in-the-wild'. The Aff-wild benchmark contains about 300 videos (over 2,000 minutes of data) annotated with regards to valence and arousal, all captured 'in-the-wild' (the main source being Youtube videos). The paper presents the database description, the experimental set up, the baseline method used for the Challenge and finally the summary of the performance of the different methods submitted to the Affect-in-the-Wild Challenge for Valence and Arousal estimation. The challenge demonstrates that meticulously designed deep neural networks can achieve very good performance when trained with in-the-wild data

A Mechanism that Provides Incentives for Truthful Feedback in Peer-to-Peer Systems

We propose a mechanism for providing the incentives for reporting truthful feedback in a peer-to-peer system for exchanging services (or content). This mechanism is to complement reputation mechanisms that employ ratings' feedback on the various transactions in order to provide incentives to peers for offering better services to others. Under our approach, each of the transacting peers (rather than just the client) submits a rating on the performance of their mutual transaction. If these are in disagreement, then both transacting peers are punished, since such an occasion is a sign that one of them is lying. The severity of each peer's punishment is determined by his corresponding non- credibility metric; this is maintained by the mechanism and evolves according to the peer's record. When under punishment, a peer does not transact with others. We model the punishment effect of the mechanism in a peer-to-peer system as a Markov chain that is experimentally proved to be very accurate. According to this model, the credibility mechanism leads the peer-to-peer system to a desirable steady state isolating liars. Then, we define a procedure for the optimization of the punishment parameters of the mechanism for peer-to-peer systems of various characteristics. We experimentally prove that this optimization procedure is effective and necessary for the successful employment of the mechanism in real peer-to-peer systems. Then, the optimized credibility mechanism is combined with reputation-based policies to provide a complete solution for high performance and truthful rating in peer-to-peer systems. The combined mechanism was experimentally proved to deal very effectively with large fractions of collaborated liar peers that follow static or dynamic rational lying strategies in peer-to-peer systems with dynamically renewed population, while the efficiency loss induced to sincere peers by the presence of liars is diminished. Finally, we describe the potential implementation of the mechanism in real peer-to-peer systems

Deep affect prediction in-the-wild: Aff-wild database and challenge, deep architectures, and beyond

Automatic understanding of human affect using visual signals is of great importance in everyday human–machine interac- tions. Appraising human emotional states, behaviors and reactions displayed in real-world settings, can be accomplished using latent continuous dimensions (e.g., the circumplex model of affect). Valence (i.e., how positive or negative is an emo- tion) and arousal (i.e., power of the activation of the emotion) constitute popular and effective representations for affect. Nevertheless, the majority of collected datasets this far, although containing naturalistic emotional states, have been captured in highly controlled recording conditions. In this paper, we introduce the Aff-Wild benchmark for training and evaluating affect recognition algorithms. We also report on the results of the First Affect-in-the-wild Challenge (Aff-Wild Challenge) that was recently organized in conjunction with CVPR 2017 on the Aff-Wild database, and was the first ever challenge on the estimation of valence and arousal in-the-wild. Furthermore, we design and extensively train an end-to-end deep neural architecture which performs prediction of continuous emotion dimensions based on visual cues. The proposed deep learning architecture, AffWildNet, includes convolutional and recurrent neural network layers, exploiting the invariant properties of convolutional features, while also modeling temporal dynamics that arise in human behavior via the recurrent layers. The AffWildNet produced state-of-the-art results on the Aff-Wild Challenge. We then exploit the AffWild database for learning features, which can be used as priors for achieving best performances both for dimensional, as well as categorical emo- tion recognition, using the RECOLA, AFEW-VA and EmotiW 2017 datasets, compared to all other methods designed for the same goal. The database and emotion recognition models are available at http://ibug.doc.ic.ac.uk/resources/first-affect-wild-challenge

Low temperature methane conversion with perovskite-supported: exo / endo-particles

Lowering the temperature at which CH(4)is converted to useful products has been long-sought in energy conversion applications. Selective conversion to syngas is additionally desirable. Generally, most of the current CH(4)activation processes operate at temperatures between 600 and 900 degrees C when non-noble metal systems are used. These temperatures can be even higher for redox processes where a gas phase-solid reaction must occur. Here we employ the endogenous-exsolution concept to create a perovskite oxide with surface and embedded metal nanoparticles able to activate methane at temperatures as low as 450 degrees C in a cyclic redox process. We achieve this by using a non-noble, Co-Ni-based system with tailored nano- and micro-structure. The materials designed and prepared in this study demonstrate long-term stability and resistance to deactivation mechanisms while still being selective when applied for chemical looping partial oxidation of methane

THE INFLUENCE OF A CONVERGENT NOZZLE ON THE FLOW FIELD OF DOWNSTREAM LOCATED MILD STENOSES

ABSTRACT The scope of this study is the investigation of the influence of a convergent nozzle type stent on the flow field of one or two downstream located 50% stenoses. Both steady and unsteady inlet flow conditions are examined. The flow is predicted using the commercial code FLUENT, and the recirculation zones downstream of each stenosis are examined through flow visualization in a suitable experimental setup