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AgentSwitch: Towards smart energy tariff selection

AgentSwitch: Towards smart energy tariff selection

引用

12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013

作者： Ramchurn, Sarvapali D. Osborne, Michael A. Parson, Oliver Rahwan, Talal Maleki, Sasan Reece, Steve Huynh, Trung D. Alam, Mudasser Fischer, Joel E. Rodden, Tom Moreau, Luc Roberts, Stephen Electronics and Computer Science University of Southampton United Kingdom Pattern Recognition Lab. University of Oxford United Kingdom Mixed Reality Lab. University of Nottingham United Kingdom

ISBN: (纸本)9781627487665

In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. Agent-Switch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalab.e collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the flow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Binary alloys

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AgentSwitch: Towards smart energy teriff selection

AgentSwitch: Towards smart energy teriff selection

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12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013

作者： Ramchum, Sarvapali D. Osborne, Michael A. Parson, Oliver Rahwan, Talal Maleki, Sasan Reece, Steve Huynh, Trung D. Alam, Mudasser Fischer, Joel E. Rodden, Tom Moreau, Luc Roberts, Stephen Electronics and Computer Science University of Southampton United Kingdom Pattern Recognition Lab. University of Oxford United Kingdom Mixed Reality Lab. University of Nottingham United Kingdom

ISBN: (纸本)9781627487665

We present AgentSwitch, a prototype agent-based platform to solve the tariff selection problem for homeowners. AgentSwitch incorporates novel algorithms that work on the coarse data provided by smart meters to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. Our demo will allow users to interact with AgentSwitch and explore test user accounts in order to understand the impact of different usage profiles and appliance loads. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Smart power grids

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Depth Estimation From Single Image And Semantic Prior

Depth Estimation From Single Image And Semantic Prior

引用

IEEE International Conference on Image Processing

作者： Praful Hambarde Akshay Dudhane Prashant W. Patil Subrahmanyam Murala Abhinav Dhall Computer Vision and Pattern Recognition Lab IIT Ropar

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

The multi-modality sensor fusion technique is an active research area in scene understating. In this work, we explore the RGB image and semantic-map fusion methods for depth estimation. The LiDARs, Kinect, and TOF depth sensors are unable to predict the depth-map at illuminate and monotonous pattern surface. In this paper, we propose a semantic-to-depth generative adversarial network (S2D-GAN) for depth estimation from RGB image and its semantic-map. In the first stage, the proposed S2D-GAN estimates the coarse level depthmap using a semantic-to-coarse-depth generative adversarial network (S2CD-GAN) while the second stage estimates the fine-level depth-map using a cascaded multi-scale spatial pooling network. The experimental analysis of the proposed S2D-GAN performed on NYU-Depth-V2 dataset shows that the proposed S2D-GAN gives outstanding result over existing single image depth estimation and RGB with sparse samples methods. The proposed S2D-GAN also gives efficient results on the real-world indoor and outdoor image depth estimation.

关键词： Estimation Semantics Generators Robot sensing systems Laser radar Generative adversarial networks Training

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Architectural style classification of building facade windows

Architectural style classification of building facade window...

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7th International Symposium on Visual Computing, ISVC 2011

作者： Shalunts, Gayane Haxhimusa, Yll Sablatnig, Robert Institute of Computer Aided Automation Computer Vision Lab. Vienna University of Technology Austria Institute of Computer Graphics and Algorithms Pattern Recongition and Image Processing Lab. Vienna University of Technology Austria

Building facade classification by architectural styles allows categorization of large databases of building images into semantic categories belonging to certain historic periods, regions and cultural influences. Image databases sorted by architectural styles permit effective and fast image search for the purposes of content-based image retrieval, 3D reconstruction, 3D city-modeling, virtual tourism and indexing of cultural heritage buildings. Building facade classification is viewed as a task of classifying separate architectural structural elements, like windows, domes, towers, columns, etc, as every architectural style applies certain rules and characteristic forms for the design and construction of the structural parts mentioned. In the context of building facade architectural style classification the current paper objective is to classify the architectural style of facade windows. Typical windows belonging to Romanesque, Gothic and Renaissance/Baroque European main architectural periods are classified. The approach is based on clustering and learning of local features, applying intelligence that architects use to classify windows of the mentioned architectural styles in the training stage. © 2011 Springer-Verlag.

关键词： Semantics

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Dual training and dual prediction for polarity classification

Dual training and dual prediction for polarity classificatio...

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Xia, Rui Wang, Tao Hu, Xuelei Li, Shoushan Zong, Chengqing Department of Computer Science Nanjing University of Science and Technology China NLP Lab. Department of Computer Science Soochow University China National Lab. of Pattern Recognition Institute of Automation CAS China

ISBN: (纸本)9781937284510

Bag-of-words (BOW) is now the most popular way to model text in machine learning based sentiment classification. However, the performance of such approach sometimes remains rather limited due to some fundamental deficiencies of the BOW model. In this paper, we focus on the polarity shift problem, and propose a novel approach, called dual training and dual prediction (DTDP), to address it. The basic idea of DTDP is to first generate artificial samples that are polarity-opposite to the original samples by polarity reversion, and then leverage both the original and opposite samples for (dual) training and (dual) prediction. Experimental results on four datasets demonstrate the effectiveness of the proposed approach for polarity classification. © 2013 Association for Computational Linguistics.

关键词： Forecasting

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Dynamic Feature Queue for Surveillance Face Anti-spoofing via Progressive Training

Dynamic Feature Queue for Surveillance Face Anti-spoofing vi...

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2023 IEEE/CVF Conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Wang, Keyao Huang, Mouxiao Zhang, Guosheng Yue, Haixiao Zhang, Gang Qiao, Yu China Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350302493

In recent years, face recognition systems have faced increasingly security threats, making it essential to employ Face Anti-spoofing (FAS) to protect against various types of attacks in traditional scenarios like phone unlocking, face payment and self-service security inspection. However, further exploration is required to fully secure FAS in long-distance settings. In this paper, we propose two contributions to enhance the security of face recognition systems: Dynamic Feature Queue (DFQ) and Progressive Training Strategy (PTS). DFQ converts the conventional binary classification task into a multi-classification task. It treats live samples as a closed set and attack samples as an open set by using a dynamic queue that stores the features of spoofing samples and updates them. On the other hand, PTS targets difficult samples and iteratively adds them in batches for training. The proposed PTS divides the entire training set into blocks, trains only a small portion of the data, and gradually increases the training data with each stage while also incorporating low-scoring positive samples and high-scoring spoof samples from the test set. These two contributions complement each other by enhancing the model's ability to generalize and defend against various types of attacks, making the face recognition system more secure and reliable. Our proposed methods have achieved top performance on ACER metric with 4.73% on the SuHiFiMask dataset [11] and won the first prize in Surveillance Face Anti-spoofing track of the Challenge@CVPR 2023. © 2023 IEEE.

关键词： Iterative methods

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Speech emotion recognition using an enhanced co-training algorithm

Speech emotion recognition using an enhanced co-training alg...

引用

IEEE International Conference onMultimedia and Expo, ICME 2007

作者： Jia, Liu Chun, Chen Jiajun, Bu Mingyu, You Jianhua, Tao College of Computer Science Zhejiang University Hangzhou 310027 China National Lab. of Pattern Recognition Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)1424410177

In previous systems of speech emotion recognition, supervised learning are frequently employed to train classifiers on lots of lab.led examples. However, the lab.ling of abundant data requires much time and many human efforts. This paper presents an enhanced co-training algorithm to utilize a large amount of unlab.led speech utterances for building a semisupervised learning system. It uses two conditionally independent attribute views(i.e. temporal features and statistic features) of unlab.led examples to augment a much smaller set of lab.led examples. Our experimental results demonstrate that compared with the method based on the supervised training, the proposed system makes 9.0% absolute improvement on female model and 7.4% on male model in terms of average accuracy. Moreover, the enhanced co-training algorithm achieves comparable performance to the co-training prototype, while it can reduce the classification noise which is produced by error lab.ling in the process of semi-supervised learning. © 2007 IEEE.

关键词： Learning algorithms

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Image co-clustering with multi-modality features and user feedbacks 09

Image co-clustering with multi-modality features and user fe...

引用

17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums

作者： Chen, Yanhua Dong, Ming Wan, Wanggen Machine Vision and Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States School of Communication and Info. Eng. Shanghai University Shanghai 200072 China

ISBN: (纸本)9781605586083

In Content-based Image Retrieval (CBIR) research, advanced technology that fuses the heterogeneous information into image clustering has drawn extensive attention recently. However, using multiple features for co-clustering images without any user feedbacks is a challenging problem. In this paper, we propose a Semi-Supervised Non-negative Matrix Factorization (SS-NMF) framework for image co-clustering. Our method computes new relational matrices by incorporating user provided feedbacks into images through simultaneous distance metric learning and feature selection for different low-level visual features. Using an iterative algorithm, we perform tri-factorizations of the new matrices to infer image clusters. Theoretically, we show the convergence and correctness of SS-NMF co-clustering and the advantages of SS-NMF co-clustering over existing approaches. Through extensive experiments conducted on image data sets, we demonstrate that SS-NMF provides an effective and efficient solution for image co-clustering. Copyright 2009 ACM.

关键词： Matrix algebra

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Assessment of time-of-flight cameras for short camera-object distances

Assessment of time-of-flight cameras for short camera-object...

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16th International Workshop on vision, Modeling and Visualization, VMV 2011

作者： Stunner, Michael Becker, Guido Hornegger, Joachim Pattern Recognition Lab. Department of Computer Science Friedrich-Alexander-University Erlangen-Niirnberg Germany Germany Lion Systems S A. Luxembourg

ISBN: (纸本)9783905673852

In this paper we have compared Time-of-Flight cameras of different vendors at object-camera distances of 500 mm, 1500 mm and 2500 mm. The aim was to find the highest possible precision at the distance of 500 mm, to estimate the change of the accuracy depending on scene-reflectivity and working distance and to investigate the possibility to use the cameras as per-pixel sub-centimeter accurate measuring devices. To this end, we have evaluated the variation of the distance measurement noise over several distances as well as the minimum noise we could achieve with each camera. As the amplitude-dependent distance error may become significantly large, we also tried to quantify it in order to estimate if it can be reduced to fulfill given accuracy requirements. We compared a Camcube3 from PMD Technologies, a Swissranger 4000 from MESA Imaging and a C70E from Fotonic. All cameras showed different behaviors in terms of temporal noise, variation of noise and amplitude dependency. The Camcube showed the strongest amplitude dependent effects. The minimum standard deviations at 500 mm distance were at 4.8 mm for the Camcube, 1.6 mm for the Swissranger and 0.9 mm for the C70E. © The Eurographics Association 2011.

关键词： Cameras

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Deriving semantics for image clustering from accumulated user feedbacks

Deriving semantics for image clustering from accumulated use...

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15th ACM International Conference on Multimedia, MM'07

作者： Chen, Yanhua Rege, Manjeet Dong, Ming Fotouhi, Farshad Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Database and Multimedia Systems Group Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Image clustering solely based on visual features without any knowledge or background information suffers from the problem of semantic gap. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for image clustering. Accumulated relevance feedback in a CBIR system is treated as user provided supervision for guiding the image clustering. We consider the set of positive images in the feedback as constraints on the clustering specifying that the images "must" be clustered together. Similarly, negative images provide constraints specifying that they "cannot" be clustered along with the positive images. Through an iterative algorithm, we perform symmetric tri-factorization of the image-image similarity matrix to infer the clustering. Theoretically, we prove the correctness of SS-NMF by showing that the algorithm is guaranteed to converge. Through experiments conducted on general purpose image datasets, we demonstrate the superior performance of SS-NMF for clustering images effectively. Copyright 2007 ACM.

关键词： Image analysis

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