the proceedings contain 186 papers. the topics discussed include: interaction network representations for human behavior prediction;demographic group prediction based on smart device user recognition gestures;cross-do...
ISBN:
(纸本)9781509061662
the proceedings contain 186 papers. the topics discussed include: interaction network representations for human behavior prediction;demographic group prediction based on smart device user recognition gestures;cross-document knowledge discovery using semantic concept topic model;domain ontology induction using word embeddings;machinelearning for plant disease incidence and severity measurements from leaf images;exposing in painting forgery in JPEG images under recompression attacks;recognition and analysis of the contours drawn during the Poppelreuter's test;automatic species recognition based on improved birdsong analysis;ECG biometric identification using wavelet analysis coupled with probabilistic random forest;toward an online anomaly intrusion detection system based on deep learning;investigating transfer learners for robustness to domain class imbalance;learning fairness under constraints: a decentralized resource allocation game;consensus clustering: a resampling-based method for building radiation hybrid maps;an led based indoor localization system using k-means clustering;phase identification in electric power distribution systems by clustering of smart meter data;identifying nontechnical power loss via spatial and temporal deep learning;a next-generation secure cloud-based deep learning license plate recognition for smart cities;using domain knowledge features for wind turbine diagnostics;and improving HSDPA traffic forecasting using ensemble of neural networks.
In this paper, the different machinelearning and data mining approaches used for Residential Energy Smart Management (RESM) will be discussed and classified according to some meaningful criteria. the proposed classif...
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ISBN:
(纸本)9781509061679
In this paper, the different machinelearning and data mining approaches used for Residential Energy Smart Management (RESM) will be discussed and classified according to some meaningful criteria. the proposed classification is an attempt to highlight the advantages and limitations of each category. Moreover, we emphasize the complementarity between approaches belonging to different categories and we point out the main challenges that still face RESM.
Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, Lidar, GNSS, vehicle odom...
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ISBN:
(纸本)9781509061679
Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, Lidar, GNSS, vehicle odometry, and computer vision. this sensory input provides a rich dataset that can be used in combination withmachinelearning models to tackle multiple problems in supervised settings. In this paper we focus on road detection through gray-scale images as the sole sensory input. Our contributions are twofold: first, we introduce an annotated dataset of urban roads for machinelearning tasks;second, we introduce a baseline road detection on this dataset through supervised classification and hand-crafted feature vectors.
Authorship identification is the task of identifying the author of a given text from a set of suspects. the main concern of this task is to define an appropriate characterization of texts that captures the writing sty...
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ISBN:
(纸本)9781509061679
Authorship identification is the task of identifying the author of a given text from a set of suspects. the main concern of this task is to define an appropriate characterization of texts that captures the writing style of authors. Although deep learning was recently used in different natural language processing tasks, it has not been used in author identification (to the best of our knowledge). In this paper, deep learning is used for feature extraction of documents represented using variable size character n-grams. We apply A Stacked Denoising AutoEncoder (SDAE) for extracting document features with different settings, and then a support vector machine classifier is used for classification. the results show that the proposed system outperforms its counterparts
We study multi-type resource allocation in multiagent system, where some constraints are enforced upon resource providers and users. these constraints are limitations of resource types and connection availabilities, w...
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ISBN:
(纸本)9781509061679
We study multi-type resource allocation in multiagent system, where some constraints are enforced upon resource providers and users. these constraints are limitations of resource types and connection availabilities, which may make the collaboration between agents infeasible. We discuss the notion of distributed resource fairness under these constraints. then we propose a game theory and reinforcement learning based solution for collaborative resource allocation, so that resources are assigned to users fairly and tasks are assigned to resource agents efficiently. We utilize data from Google data center as our input to simulations. Results show that our learning approach outperforms a greedy and random explorations in terms of resource utilization and fairness.
Sentiment analysis of customer reviews has a crucial impact on a business's development strategy. Despite the fact that a repository of reviews evolves over time, sentiment analysis often relies on offline solutio...
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ISBN:
(纸本)9781509061679
Sentiment analysis of customer reviews has a crucial impact on a business's development strategy. Despite the fact that a repository of reviews evolves over time, sentiment analysis often relies on offline solutions where training data is collected before the model is built. If we want to avoid retraining the entire model from time to time, incremental learning becomes the best alternative solution for this task. In this work, we present a variant of online random forests to perform sentiment analysis on customers' reviews. Our model is able to achieve accuracy similar to offline methods and comparable to other online models.
the emerging technology of Software-Defined Networking (SDN) affords a platform and architecture which is dynamic, manageable, cost-effective, and adaptable, making it ideal for many applicationsthat are high-bandwid...
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ISBN:
(纸本)9781509061679
the emerging technology of Software-Defined Networking (SDN) affords a platform and architecture which is dynamic, manageable, cost-effective, and adaptable, making it ideal for many applicationsthat are high-bandwidth and dynamic in nature. As this technology grows and matures, there is a need for cybersecurity applications to be designed, developed and evaluated. In this paper, we propose a development environment configuration to build security applications targeting the SDN-controller in an effort to explore the technology and the resources available to teach and research the platform from a security application development perspective.
To be applicable to real world problems, much reinforcement learning (RL) research has focused on continuous state spaces with function approximations. Some problems also require continuous actions, but searching for ...
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ISBN:
(纸本)9781509061679
To be applicable to real world problems, much reinforcement learning (RL) research has focused on continuous state spaces with function approximations. Some problems also require continuous actions, but searching for good actions in a continuous action space is problematic. this paper suggests a novel relevance vector sampling approach to action search in an RL framework with relevance vector machines (RVM-RL). We hypothesize that each relevance vector (RV) is placed on the modes of the value approximation surface as the learning converges. From the hypothesis, we select actions in RVs to maximize the estimated state-action values. We report the efficiency of the proposed approach by controlling a simulated octopus arm with RV-sampled actions.
the traffic has been transformed into the difficult structure in points of designing and managing by the reason of increasing number of vehicle. this situation has discovered road accidents problem, influenced public ...
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ISBN:
(纸本)9781509061679
the traffic has been transformed into the difficult structure in points of designing and managing by the reason of increasing number of vehicle. this situation has discovered road accidents problem, influenced public health and country economy and done the studies on solution of the problem. Large calibrated data agglomerations have increased by the reasons of the technological improvements and data storage with low cost. Arising the need of accession to information from this large calibrated data obtained the corner stone of the data mining. In this study, assignment of the most compatible machinelearning classification techniques for road accidents estimation by data mining has been intended.
Introduction of fluorescence-based Real-Time PCR (RT-PCR) is increasingly used to detect multiple pathogens simultaneously and rapidly by gene expression analysis of PCR amplification data. PCR data is analyzed often ...
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ISBN:
(纸本)9781509061679
Introduction of fluorescence-based Real-Time PCR (RT-PCR) is increasingly used to detect multiple pathogens simultaneously and rapidly by gene expression analysis of PCR amplification data. PCR data is analyzed often by setting an arbitrary threshold that intersect the signal curve in its exponential phase if it exists. the point at which the curve crosses the threshold is called threshold Cycle (CT) for positive samples. On the other, when such cross of threshold does not occur, the sample is identified as negative. this simple and arbitrary however not an elegant definition of CT value sometimes leads to conclusions that are either false positive or negative. therefore, the purpose of this paper is to present a stable and consistent alternative approach that is based on machinelearning for the definition and determination of CT values.
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