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

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

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely. Given the limited hardware resources, existing 3D perception models are not able to recognize small instances (e.g., pedestrians, cyclists) very well due to the low-resolution voxelization and aggressive downsampling. To this end, we propose Sparse Point-Voxel Convolution (SPVConv), a lightweight 3D module that equips the vanilla Sparse Convolution with the high-resolution point-based branch. With negligible overhead, this point-based branch is able to preserve the fine details even from large outdoor scenes. To explore the spectrum of efficient 3D models, we first define a flexible architecture design space based on SPVConv, and we then present 3D Neural Architecture Search (3D-NAS) to search the optimal network architecture over this diverse design space efficiently and effectively. Experimental results validate that the resulting SPVNAS model is fast and accurate: it outperforms the state-of-the-art MinkowskiNet by 3.3%, ranking 1st on the competitive SemanticKITTI leaderboard. It also achieves 8x computation reduction and 3x measured speedup over MinkowskiNet with higher accuracy. Finally, we transfer our method to 3D object detection, and it achieves consistent improvements over the one-stage detection baseline on KITTI.Comment: ECCV 2020. The first two authors contributed equally to this work. Project page: http://spvnas.mit.edu

GridEcon - A Market Place for Computing Resources

This paper discusses the rationales for a Grid market and, in particular, the introduction of a market place for trading commoditized computing resources. The market place proposed makes computing resources from different providers substitutable through virtualization. This includes the definition of a spot and future market as well as the parameters that a market mechanism for computing resources should consider. The above market place is complemented by a set of value-added services (e.g. insurance against resource failures, capacity planning, resource quality assurance, stable price offering) that ensure quality for Grid users over time. The market place technology for all of the above services has been designed by the GridEcon project, contributing to a broader adoption of Grid technology and enabling a service-oriented knowledge utility environment.This work has been funded by the European Commission within the context of the FP6 Project GridEcon, Grid Economics and Business Models, (FP6-2005-IST5-033634)

Active to Sterile Neutrino Mixing Limits from Neutral-Current Interactions in MINOS

Results are reported from a search for active to sterile neutrino oscillations in the MINOS long-baseline experiment, based on the observation of neutral-current neutrino interactions, from an exposure to the NuMI neutrino beam of 7.07×10^(20) protons on target. A total of 802 neutral-current event candidates is observed in the Far Detector, compared to an expected number of 754±28(stat)±37(syst) for oscillations among three active flavors. The fraction f_s of disappearing ν_μ that may transition to ν_s is found to be less than 22% at the 90% C.L

Neutrino and Antineutrino Inclusive Charged-current Cross Section Measurements with the MINOS Near Detector

The energy dependence of the neutrino-iron and antineutrino-iron inclusive charged-current cross sections and their ratio have been measured using a high-statistics sample with the MINOS Near Detector exposed to the NuMI beam from the Main Injector at Fermilab. Neutrino and antineutrino fluxes were determined using a low hadronic energy subsample of charged-current events. We report measurements of neutrino-Fe (antineutrinoFe) cross section in the energy range 3-50 GeV (5-50 GeV) with precision of 2-8% (3-9%) and their ratio which is measured with precision 2-8%. The data set spans the region from low energy, where accurate measurements are sparse, up to the high-energy scaling region where the cross section is well understood.Comment: accepted by PR

Measurement of the Neutrino Mass Splitting and Flavor Mixing by MINOS

Measurements of neutrino oscillations using the disappearance of muon neutrinos from the Fermilab NuMI neutrino beam as observed by the two MINOS detectors are reported. New analysis methods have been applied to an enlarged data sample from an exposure of 7.25×10^(20) protons on target. A fit to neutrino oscillations yields values of |Δm^2|=(2.32_(-0.08)^(+0.12))×10^(-3)  eV^2 for the atmospheric mass splitting and sin^2(2θ)>0.90 (90% C.L.) for the mixing angle. Pure neutrino decay and quantum decoherence hypotheses are excluded at 7 and 9 standard deviations, respectively