Methods for Sensor Based Farrowing Prediction and Floor-heat Regulation: The ...Aparna Udupi
Piglet mortality is a current issue in the pig production and the large variability between herds suggests a management component to the mortality. Studies show that the mortality may be reduced by the supervision of farrowing or through climate regulation in the farrowing pens. However, this is possible only if the farrowing time is known and thus provides sufficient time for the management to make and execute the decisions. The gestation period of a sow is approximately 115 (SD=2) days. However, an initial cost-benefit analysis recommended increased precision in the prediction of farrowing to make the increased management efforts cost-effective for the pig producer. Recently, a wide range of sensor technology have become available to monitor the behavioural and physiological changes of sow. Evidence show that appropriate utilization of sensor technology may increase the precision of prediction of onset of farrowing. Prediction is feasible only if the prediction is online and automated.
The thesis is focused on constructing a system that can give predictions about the expected time to farrowing of individual sows based on automatic sensor recordings such as water consumption, video based activity measurements, and photo-cells based activity measurements. The warnings could serve to activate the floor heating system to ensure a sufficiently high temperature for the new born piglets, as well as to help the farmer to organize extra surveillance around farrowing. The thesis is based on three submitted manuscripts, describing the system at different stages.
The kernel in the thesis is a Markov process with four subsequent states Before Nest-Building, Nest-Building, Resting and the absorbing state Farrowing; the states were selected based on ethological knowledge about sow behaviour. However, the sojourn time distribution in each state is not exponential. Therefore a continuous time discrete state semi-Markov process based on a Phase-Type distribution (in this case Erlang distributed) was formulated. Finally, the Markov process was transformed to a discrete time process. A Hidden Markov Model (HMM) was used for this process. The model is called Hidden Phase-type Markov Model (HPMM), and the time steps corresponded to each updating with sensor information at which the time to farrowing was predicted.
The POMDP model for optimizing the floor-heat regulation system was used to demonstrate the decision support tool as an extension to the prediction algorithm.
The tools for the prediction of onset of farrowing, estimation of HPMM and optimal decision making provides a framework for handling a large amount of sensor data available and gives an overview of how to integrate information from several sensors on the pen level. The complexity of the models imply that the prediction algorithm and decision tool may be run on the herd level computer; whereas the parameters were estimated on the central level computers.
Distributional Semantics and Unsupervised Clustering for Sensor Relevancy Pre...iammyr
The logging of Activities of Daily Living (ADLs) is becoming increasingly popular mainly thanks to wearable devices. Currently, most sensors used for ADLs logging are queried and filtered mainly by location and time. However, in an Internet of Things future, a query will return a large amount of sensor data. Therefore, existing approaches will not be feasible because of resource constraints and performance issues. Hence more fine-grained queries will be necessary. We propose to filter on the likelihood that a sensor is relevant for the currently sensed activity. Our aim is to improve system efficiency by reducing the amount of data to query, store and process by identifying which sensors are relevant for different activities during the ADLs logging by relying on Distributional Semantics over public text corpora and unsupervised hierarchical clustering. We have evaluated our system over a public dataset for activity recognition and compared our clusters of sensors with the sensors involved in the logging of manually-annotated activities. Our results show an average precision of 89% and an overall accuracy of 69%, thus outperforming the state of the art by 5% and 32% respectively. To support the uptake of our approach and to allow replication of our experiments, a Web service has been developed and open sourced.
Machine Learning (ML) in Wireless Sensor Networks (WSNs)mabualsh
Wireless sensor networks (WSNs) and the Internet of Things (IoT) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. WSNs and IoT often adopt machine learning to eliminate the need for unnecessary redesign. Machine learning inspires many practical solutions that maximize resource utilization and prolong the network's lifespan. These slides present an extensive literature review of machine learning methods to address common issues in WSNs and IoT.
DockerCon EU 2015: Containing IoT Sensor TelemetryDocker, Inc.
Presented by Samuel Cozannet, Strategic Program Manager, Canonical and Michael Schloh, Computer Scientist, Europalab
In this hour we consider benefits of interfacing docker with IoT systems using sensor telemetry and actuator telecommand technology. IoT has come a long way by embracing web interfaces like JavaScript and NodeJS, but lacks good packaging and container abstraction allowing for portability across hardware platforms. We bridge this inadequacy by introducing Docker and IoT to each other.
Real Time Semantic Analysis of Streaming Sensor DataHarshal Patni
Harshal Patni, "Real Time Semantic Analysis of Streaming Sensor Data," MS Thesis Defense, Kno.e.sis Center, Wright State University, Dayton OH, March 21, 2001.
More at: http://wiki.knoesis.org/index.php/SSW
Dissertation Advisor: Prof. Amit Sheth
The document discusses a sensor network project called InfraWatch that monitors a bridge in Holland. Over 145 sensors installed on the bridge collect stress, vibration, temperature and other data at 100Hz, generating over 5GB of data per day. The goals of the project are to model bridge behavior, identify effects of traffic, weather and decay on the bridge's structure over time, and determine an optimal sensor configuration for new bridges. The large volume of terabyte-scale sensor data will be analyzed using Hadoop and MapReduce since it is I/O bound. However, new building blocks are needed for Hadoop to perform signal processing tasks on the data like convolution, Fourier transforms and segmentation.
This document discusses using Hadoop to unify data management. It describes challenges with managing huge volumes of fast-moving machine data and outlines an overall architecture using Hadoop components like HDFS, HBase, Solr, Impala and OpenTSDB to store, search, analyze and build features from different types of data. Key aspects of the architecture include intelligent search, batch and real-time analytics, parsing, time series data and alerts.
Towards Automatic Composition of Multicomponent Predictive SystemsManuel Martín
Automatic composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. In this paper we propose and describe an extension to the Auto-WEKA software which now allows to compose and optimise such flexible MCPSs by using a sequence of WEKA methods. In the experimental analysis we focus on examining the impact of significantly extending the search space by incorporating additional hyperparameters of the models, on the quality of the found solutions. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs on 21 publicly available datasets. A comparison with previous work indicates that extending the search space improves the classification accuracy in the majority of the cases. The diversity of the found MCPSs are also an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. This can have a big impact on high quality predictive models development, maintenance and scalability aspects needed in modern application and deployment scenarios.
Methods for Sensor Based Farrowing Prediction and Floor-heat Regulation: The ...Aparna Udupi
Piglet mortality is a current issue in the pig production and the large variability between herds suggests a management component to the mortality. Studies show that the mortality may be reduced by the supervision of farrowing or through climate regulation in the farrowing pens. However, this is possible only if the farrowing time is known and thus provides sufficient time for the management to make and execute the decisions. The gestation period of a sow is approximately 115 (SD=2) days. However, an initial cost-benefit analysis recommended increased precision in the prediction of farrowing to make the increased management efforts cost-effective for the pig producer. Recently, a wide range of sensor technology have become available to monitor the behavioural and physiological changes of sow. Evidence show that appropriate utilization of sensor technology may increase the precision of prediction of onset of farrowing. Prediction is feasible only if the prediction is online and automated.
The thesis is focused on constructing a system that can give predictions about the expected time to farrowing of individual sows based on automatic sensor recordings such as water consumption, video based activity measurements, and photo-cells based activity measurements. The warnings could serve to activate the floor heating system to ensure a sufficiently high temperature for the new born piglets, as well as to help the farmer to organize extra surveillance around farrowing. The thesis is based on three submitted manuscripts, describing the system at different stages.
The kernel in the thesis is a Markov process with four subsequent states Before Nest-Building, Nest-Building, Resting and the absorbing state Farrowing; the states were selected based on ethological knowledge about sow behaviour. However, the sojourn time distribution in each state is not exponential. Therefore a continuous time discrete state semi-Markov process based on a Phase-Type distribution (in this case Erlang distributed) was formulated. Finally, the Markov process was transformed to a discrete time process. A Hidden Markov Model (HMM) was used for this process. The model is called Hidden Phase-type Markov Model (HPMM), and the time steps corresponded to each updating with sensor information at which the time to farrowing was predicted.
The POMDP model for optimizing the floor-heat regulation system was used to demonstrate the decision support tool as an extension to the prediction algorithm.
The tools for the prediction of onset of farrowing, estimation of HPMM and optimal decision making provides a framework for handling a large amount of sensor data available and gives an overview of how to integrate information from several sensors on the pen level. The complexity of the models imply that the prediction algorithm and decision tool may be run on the herd level computer; whereas the parameters were estimated on the central level computers.
Distributional Semantics and Unsupervised Clustering for Sensor Relevancy Pre...iammyr
The logging of Activities of Daily Living (ADLs) is becoming increasingly popular mainly thanks to wearable devices. Currently, most sensors used for ADLs logging are queried and filtered mainly by location and time. However, in an Internet of Things future, a query will return a large amount of sensor data. Therefore, existing approaches will not be feasible because of resource constraints and performance issues. Hence more fine-grained queries will be necessary. We propose to filter on the likelihood that a sensor is relevant for the currently sensed activity. Our aim is to improve system efficiency by reducing the amount of data to query, store and process by identifying which sensors are relevant for different activities during the ADLs logging by relying on Distributional Semantics over public text corpora and unsupervised hierarchical clustering. We have evaluated our system over a public dataset for activity recognition and compared our clusters of sensors with the sensors involved in the logging of manually-annotated activities. Our results show an average precision of 89% and an overall accuracy of 69%, thus outperforming the state of the art by 5% and 32% respectively. To support the uptake of our approach and to allow replication of our experiments, a Web service has been developed and open sourced.
Machine Learning (ML) in Wireless Sensor Networks (WSNs)mabualsh
Wireless sensor networks (WSNs) and the Internet of Things (IoT) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. WSNs and IoT often adopt machine learning to eliminate the need for unnecessary redesign. Machine learning inspires many practical solutions that maximize resource utilization and prolong the network's lifespan. These slides present an extensive literature review of machine learning methods to address common issues in WSNs and IoT.
DockerCon EU 2015: Containing IoT Sensor TelemetryDocker, Inc.
Presented by Samuel Cozannet, Strategic Program Manager, Canonical and Michael Schloh, Computer Scientist, Europalab
In this hour we consider benefits of interfacing docker with IoT systems using sensor telemetry and actuator telecommand technology. IoT has come a long way by embracing web interfaces like JavaScript and NodeJS, but lacks good packaging and container abstraction allowing for portability across hardware platforms. We bridge this inadequacy by introducing Docker and IoT to each other.
Real Time Semantic Analysis of Streaming Sensor DataHarshal Patni
Harshal Patni, "Real Time Semantic Analysis of Streaming Sensor Data," MS Thesis Defense, Kno.e.sis Center, Wright State University, Dayton OH, March 21, 2001.
More at: http://wiki.knoesis.org/index.php/SSW
Dissertation Advisor: Prof. Amit Sheth
The document discusses a sensor network project called InfraWatch that monitors a bridge in Holland. Over 145 sensors installed on the bridge collect stress, vibration, temperature and other data at 100Hz, generating over 5GB of data per day. The goals of the project are to model bridge behavior, identify effects of traffic, weather and decay on the bridge's structure over time, and determine an optimal sensor configuration for new bridges. The large volume of terabyte-scale sensor data will be analyzed using Hadoop and MapReduce since it is I/O bound. However, new building blocks are needed for Hadoop to perform signal processing tasks on the data like convolution, Fourier transforms and segmentation.
This document discusses using Hadoop to unify data management. It describes challenges with managing huge volumes of fast-moving machine data and outlines an overall architecture using Hadoop components like HDFS, HBase, Solr, Impala and OpenTSDB to store, search, analyze and build features from different types of data. Key aspects of the architecture include intelligent search, batch and real-time analytics, parsing, time series data and alerts.
Towards Automatic Composition of Multicomponent Predictive SystemsManuel Martín
Automatic composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. In this paper we propose and describe an extension to the Auto-WEKA software which now allows to compose and optimise such flexible MCPSs by using a sequence of WEKA methods. In the experimental analysis we focus on examining the impact of significantly extending the search space by incorporating additional hyperparameters of the models, on the quality of the found solutions. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs on 21 publicly available datasets. A comparison with previous work indicates that extending the search space improves the classification accuracy in the majority of the cases. The diversity of the found MCPSs are also an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. This can have a big impact on high quality predictive models development, maintenance and scalability aspects needed in modern application and deployment scenarios.
Collecting and analyzing sensor data with hadoop or other no sql databasesMatteo Redaelli
This document discusses using Hadoop and other NoSQL databases like HDFS, Hive, Spark and Storm to collect, store, and analyze sensor data from devices in real-time. It provides an overview of collecting data using Flume, storing it in HDFS, and performing analysis on the data using tools like Hive, Pig, and Spark. It also discusses using cloud services from Amazon and Google with Hadoop and alternatives to Hadoop like Cassandra, MongoDB, Riak, Kafka and Storm.
IoT Sensor Sensibility - Hull Digital - C4Di - Feb 2016Glynn Bird
An Introduction to IoT. What is it, why does it matter and how can I get started. Introduces MQTT and talks about offline-first data collection using CouchDB and Cloudant replication. Hardware such as Raspberry Pis and SensorTags are also discussed.
Quick presentation for the OpenML workshop in Eindhoven 2014Manuel Martín
This document summarizes Manuel Martín Salvador's background and research interests in automated and adaptive data pre-processing for building predictive models. It discusses how data pre-processing makes up a large portion of the data mining process but is labor intensive. The document also outlines OpenML, a scientific workflow platform and repository for machine learning experiments, and highlights opportunities to increase the number and types of pre-processing methods available on the platform as well as improve flow representation and recommendation.
Mobile Sensor Data, Machine Learning and Context (Strata 2014)Argus Labs
Sensors are everywhere, and context detection can improve almost any service, from media recommendations and advertising, to mobility management and mHealth. These two use cases show what mobile sensor data and machine learning can do to help make technologies more 'smart' and context-aware.
Presented at Strata Hadoop Barcelona, Internet of Things track, November 2014
This document provides an overview of big data analysis tools and methods presented by Ehsan Derakhshan of innfinision. It discusses what data and big data are, important questions about database selection, and several tools and solutions offered by innfinision including MongoDB, PyTables, Blosc, and Blaze. MongoDB is highlighted as a scalable and high performance document database. The advantages of these tools include optimized memory usage, rich queries, fast updates, and the ability to analyze and optimize queries.
From sensor readings to prediction: on the process of developing practical so...Manuel Martín
Automatic data acquisition systems provide large amounts of streaming data generated by physical sensors. This data forms an input to computational models (soft sensors) routinely used for monitoring and control of industrial processes, traffic patterns, environment and natural hazards, and many more. The majority of these models assume that the data comes in a cleaned and pre-processed form, ready to be fed directly into a predictive model. In practice, to ensure appropriate data quality, most of the modelling efforts concentrate on preparing data from raw sensor readings to be used as model inputs. This study analyzes the process of data preparation for predictive models with streaming sensor data. We present the challenges of data preparation as a four-step process, identify the key challenges in each step, and provide recommendations for handling these issues. The discussion is focused on the approaches that are less commonly used, while, based on our experience, may contribute particularly well to solving practical soft sensor tasks. Our arguments are illustrated with a case study in the chemical production industry.
In this presentation, I address two major questions:
1. What is Intelligent Agent Perception?
2. How do Intelligent Agents Perceive?
In addressing the meaning of agent perception, I highlight impediments to the perceptual process and the process of situation assessment.
In addressing how agents perceive, I highlight traditional approaches to robotic perception and then the next step after sensor input, which is how sensor data (vision sensor, tactile, olfactory, haptic, etc.) is interpreted. Methods for interpretation include solutions based on Bayes' Theorem, the underpinning of many robotics algorithms; Probabilistic Algorithms; and Artificial Neural Networks.
I also discuss a current system for robotic perception, designed to accommodate more robust and complex robotic needs: using sensors in tandem with machine learning. This method is closer to mimicking the human perceptual process than previous methods. I discuss some examples of this: 1) a study in which researchers used visual sensors and an artificial neural network (ANN) for robotic perception; 2) a study in which researchers used haptic sensors and a classification algorithm, called a boosting algorithm for robotic perception; and 3) a study in which researchers used pressure sensors, an ANN, and intended to add pre-programmed models in order to facilitate robotic perception.
A Brief Study on Different Intrusions and Machine Learning-based Anomaly Dete...Eswar Publications
Wireless Sensor Networks (WSN) consist of a number of resource constrained sensors to collect and monitor data from unattended environments. Hence, security is a crucial task as the nodes are not provided with tamper-resistance hardware. Provision for secured communication in WSN is a challenging task especially due to the environment in which they are deployed. One of the main challenges is detection of intrusions. Intrusion detection system gathers and analyzes information from various areas within a computer or a network to identify possible security breaches. Different intrusion detection methods have been proposed in the literature to identify attacks in the network. Out of these detection methods, machine-learning based methods are observed to be efficient in terms of detection accuracy and alert generations for the system to act immediately. A brief study on different intrusions along with the machine learning based anomaly detection methods are reviewed in this work. The study also classifies the machine learning algorithms into supervised, unsupervised and semi-supervised learning–based anomaly detection. The performances
of the algorithms are compared and efficient methods are identified.
Watson Analytics is a smart data discovery service that guides data exploration, automates predictive analytics, and enables dashboard and infographic creation without complex modeling. The document demonstrates Watson Analytics using a bike sharing case study to understand trends, predict future bike rental demand, and create a dashboard. Key factors like temperature, humidity, season, and time of day that influence ridership are identified. Decision rules are generated to predict 499 riders for a sample Friday in January based on those factors.
Machine Learning Challenges For Automated Prompting In Smart HomesBarnan Das
As the world's population ages, there is an increased prevalence of diseases related to aging, such as dementia. Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions or prompts. This dissertation focuses on addressing machine learning challenges that arise while devising an effective automated prompting system.
Our first goal is to emulate natural interventions provided by a caregiver to individuals with memory impairments, by using a supervised machine learning approach to classify pre-segmented activity steps into prompt or no-prompt classes. However, the lack of training examples representing prompt situations causes imbalanced class distribution. We proposed two probabilistic oversampling techniques, RACOG and wRACOG, that help in better learning of the``prompt'' class. Moreover, there are certain prompt situations where the sensor triggering signature is quite similar to the situations when the participant would probably need no prompt. The absence of sufficient data attributes to differentiate between prompt and no-prompt classes causes class overlap. We propose ClusBUS, a clustering-based under-sampling technique that identifies ambiguous data regions. ClusBUS preprocesses the data in order to give more importance to the minority class during classification.
Our second goal is to automatically detect activity errors in real time, while an individual performs an activity. We propose a collection of one-class classification-based algorithms, known as DERT, that learns only from the normal activity patterns and without using any training examples for the activity errors. When evaluated on unseen activity data, DERT is able to identify abnormalities or errors, which can be potential prompt situations.
We validate the effectiveness of the proposed algorithms in predicting potential prompt situations on the sensor data of ten activities of daily living, collected from 580 participants, who were part of two smart home studies.
マイクロソフトは より効率的、かつ大量のデータを使ったデータ分析のための基盤を急ピッチで拡充しています。
分析自体やデータ準備の前処理における手段の１つとして使って頂くことを想定している各種製品・サービスについて説明します。
具体的には、R の並列実行環境である Microsoft R Server、Power BI、並列処理基盤である Azure Data Lake Analytics、Azure Machine Learning を取り上げます。
Watson Analytics is a cloud-based analytics tool from IBM that leverages Watson technology to accelerate data discovery for business users. It provides semantic recognition of data concepts, identifies analysis starting points, and allows natural language interaction. The tool automates tasks like data preparation, generates insights and visualizations, and enables predictive analytics. It aims to make analytics more self-service, collaborative, and accessible to non-experts.
Development of Software for scalable anomaly detection modeling of time-series data using Apache Spark.
私たちはこれまで、様々な機器類を監視するセンサーの時系列データを分析し、異常を検知する手法およびソフトウェアの研究開発を行ってきた。
今回紹介するソフトウェアでは、バッチ処理で複数のセンサーから得られた高次元の時系列データから線形のLASSO回帰により学習、モデル化し、異常時を識別する。
しかし学習時間やメモリー使用量の増大が課題になってきたため、Sparkを活用し並列分散化を行った。
SparkにはMLlibという汎用的な機械学習ライブラリが存在するが、今回は使用するアルゴリズムの特殊性を考慮し、既存実装を基に新規に開発した。
本講演では当開発におけるデザインチョイスや性能計測結果について報告する。
a
Collecting and analyzing sensor data with hadoop or other no sql databasesMatteo Redaelli
This document discusses using Hadoop and other NoSQL databases like HDFS, Hive, Spark and Storm to collect, store, and analyze sensor data from devices in real-time. It provides an overview of collecting data using Flume, storing it in HDFS, and performing analysis on the data using tools like Hive, Pig, and Spark. It also discusses using cloud services from Amazon and Google with Hadoop and alternatives to Hadoop like Cassandra, MongoDB, Riak, Kafka and Storm.
IoT Sensor Sensibility - Hull Digital - C4Di - Feb 2016Glynn Bird
An Introduction to IoT. What is it, why does it matter and how can I get started. Introduces MQTT and talks about offline-first data collection using CouchDB and Cloudant replication. Hardware such as Raspberry Pis and SensorTags are also discussed.
Quick presentation for the OpenML workshop in Eindhoven 2014Manuel Martín
This document summarizes Manuel Martín Salvador's background and research interests in automated and adaptive data pre-processing for building predictive models. It discusses how data pre-processing makes up a large portion of the data mining process but is labor intensive. The document also outlines OpenML, a scientific workflow platform and repository for machine learning experiments, and highlights opportunities to increase the number and types of pre-processing methods available on the platform as well as improve flow representation and recommendation.
Mobile Sensor Data, Machine Learning and Context (Strata 2014)Argus Labs
Sensors are everywhere, and context detection can improve almost any service, from media recommendations and advertising, to mobility management and mHealth. These two use cases show what mobile sensor data and machine learning can do to help make technologies more 'smart' and context-aware.
Presented at Strata Hadoop Barcelona, Internet of Things track, November 2014
This document provides an overview of big data analysis tools and methods presented by Ehsan Derakhshan of innfinision. It discusses what data and big data are, important questions about database selection, and several tools and solutions offered by innfinision including MongoDB, PyTables, Blosc, and Blaze. MongoDB is highlighted as a scalable and high performance document database. The advantages of these tools include optimized memory usage, rich queries, fast updates, and the ability to analyze and optimize queries.
From sensor readings to prediction: on the process of developing practical so...Manuel Martín
Automatic data acquisition systems provide large amounts of streaming data generated by physical sensors. This data forms an input to computational models (soft sensors) routinely used for monitoring and control of industrial processes, traffic patterns, environment and natural hazards, and many more. The majority of these models assume that the data comes in a cleaned and pre-processed form, ready to be fed directly into a predictive model. In practice, to ensure appropriate data quality, most of the modelling efforts concentrate on preparing data from raw sensor readings to be used as model inputs. This study analyzes the process of data preparation for predictive models with streaming sensor data. We present the challenges of data preparation as a four-step process, identify the key challenges in each step, and provide recommendations for handling these issues. The discussion is focused on the approaches that are less commonly used, while, based on our experience, may contribute particularly well to solving practical soft sensor tasks. Our arguments are illustrated with a case study in the chemical production industry.
In this presentation, I address two major questions:
1. What is Intelligent Agent Perception?
2. How do Intelligent Agents Perceive?
In addressing the meaning of agent perception, I highlight impediments to the perceptual process and the process of situation assessment.
In addressing how agents perceive, I highlight traditional approaches to robotic perception and then the next step after sensor input, which is how sensor data (vision sensor, tactile, olfactory, haptic, etc.) is interpreted. Methods for interpretation include solutions based on Bayes' Theorem, the underpinning of many robotics algorithms; Probabilistic Algorithms; and Artificial Neural Networks.
I also discuss a current system for robotic perception, designed to accommodate more robust and complex robotic needs: using sensors in tandem with machine learning. This method is closer to mimicking the human perceptual process than previous methods. I discuss some examples of this: 1) a study in which researchers used visual sensors and an artificial neural network (ANN) for robotic perception; 2) a study in which researchers used haptic sensors and a classification algorithm, called a boosting algorithm for robotic perception; and 3) a study in which researchers used pressure sensors, an ANN, and intended to add pre-programmed models in order to facilitate robotic perception.
A Brief Study on Different Intrusions and Machine Learning-based Anomaly Dete...Eswar Publications
Wireless Sensor Networks (WSN) consist of a number of resource constrained sensors to collect and monitor data from unattended environments. Hence, security is a crucial task as the nodes are not provided with tamper-resistance hardware. Provision for secured communication in WSN is a challenging task especially due to the environment in which they are deployed. One of the main challenges is detection of intrusions. Intrusion detection system gathers and analyzes information from various areas within a computer or a network to identify possible security breaches. Different intrusion detection methods have been proposed in the literature to identify attacks in the network. Out of these detection methods, machine-learning based methods are observed to be efficient in terms of detection accuracy and alert generations for the system to act immediately. A brief study on different intrusions along with the machine learning based anomaly detection methods are reviewed in this work. The study also classifies the machine learning algorithms into supervised, unsupervised and semi-supervised learning–based anomaly detection. The performances
of the algorithms are compared and efficient methods are identified.
Watson Analytics is a smart data discovery service that guides data exploration, automates predictive analytics, and enables dashboard and infographic creation without complex modeling. The document demonstrates Watson Analytics using a bike sharing case study to understand trends, predict future bike rental demand, and create a dashboard. Key factors like temperature, humidity, season, and time of day that influence ridership are identified. Decision rules are generated to predict 499 riders for a sample Friday in January based on those factors.
Machine Learning Challenges For Automated Prompting In Smart HomesBarnan Das
As the world's population ages, there is an increased prevalence of diseases related to aging, such as dementia. Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions or prompts. This dissertation focuses on addressing machine learning challenges that arise while devising an effective automated prompting system.
Our first goal is to emulate natural interventions provided by a caregiver to individuals with memory impairments, by using a supervised machine learning approach to classify pre-segmented activity steps into prompt or no-prompt classes. However, the lack of training examples representing prompt situations causes imbalanced class distribution. We proposed two probabilistic oversampling techniques, RACOG and wRACOG, that help in better learning of the``prompt'' class. Moreover, there are certain prompt situations where the sensor triggering signature is quite similar to the situations when the participant would probably need no prompt. The absence of sufficient data attributes to differentiate between prompt and no-prompt classes causes class overlap. We propose ClusBUS, a clustering-based under-sampling technique that identifies ambiguous data regions. ClusBUS preprocesses the data in order to give more importance to the minority class during classification.
Our second goal is to automatically detect activity errors in real time, while an individual performs an activity. We propose a collection of one-class classification-based algorithms, known as DERT, that learns only from the normal activity patterns and without using any training examples for the activity errors. When evaluated on unseen activity data, DERT is able to identify abnormalities or errors, which can be potential prompt situations.
We validate the effectiveness of the proposed algorithms in predicting potential prompt situations on the sensor data of ten activities of daily living, collected from 580 participants, who were part of two smart home studies.
マイクロソフトは より効率的、かつ大量のデータを使ったデータ分析のための基盤を急ピッチで拡充しています。
分析自体やデータ準備の前処理における手段の１つとして使って頂くことを想定している各種製品・サービスについて説明します。
具体的には、R の並列実行環境である Microsoft R Server、Power BI、並列処理基盤である Azure Data Lake Analytics、Azure Machine Learning を取り上げます。
Watson Analytics is a cloud-based analytics tool from IBM that leverages Watson technology to accelerate data discovery for business users. It provides semantic recognition of data concepts, identifies analysis starting points, and allows natural language interaction. The tool automates tasks like data preparation, generates insights and visualizations, and enables predictive analytics. It aims to make analytics more self-service, collaborative, and accessible to non-experts.
Development of Software for scalable anomaly detection modeling of time-series data using Apache Spark.
私たちはこれまで、様々な機器類を監視するセンサーの時系列データを分析し、異常を検知する手法およびソフトウェアの研究開発を行ってきた。
今回紹介するソフトウェアでは、バッチ処理で複数のセンサーから得られた高次元の時系列データから線形のLASSO回帰により学習、モデル化し、異常時を識別する。
しかし学習時間やメモリー使用量の増大が課題になってきたため、Sparkを活用し並列分散化を行った。
SparkにはMLlibという汎用的な機械学習ライブラリが存在するが、今回は使用するアルゴリズムの特殊性を考慮し、既存実装を基に新規に開発した。
本講演では当開発におけるデザインチョイスや性能計測結果について報告する。
a
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Вся информация, которая уходит в облако, остается
там навсегда
Каждый день облако накапливает все больше
приватной информации
Технологичная проблема №1 – Анализ
неструктурированных данных
Сбор информации в облаке проводится с целью
продажи товаров и услуг
Контролировать свои личные данные в 21 веке
НЕВОЗМОЖНО
10. GOOGLE HEALTH
Создавать медицинские профили
онлайн.
Импортировать медицинские
записи из больниц и аптек
Больше узнать о здоровье и найти
полезные ресурсы
Находить больницы и докторов
Получить доступ к интернет-
службам посвящённым здоровью
17. СЕРВИСЫ GOOGLE
• Google AdSense — сервис контекстной рекламы, позволяющий заработать • Gmail — бесплатная электронная почта с большим объѐмом места для хранения
хозяевам страниц с большой посещаемостью. Программа автоматически сообщений (более 7,2 Гб), с доступом по POP3 и удобным веб-интерфейсом. Также
доставляет текстовые и графические объявления, рассчитанные на веб-сайт и его является OpenID-провайдером для всех служб Google.
содержание. • Google Groups — архив конференций Usenet.
• Google AdWords — сервис контекстной рекламы, работает с ключевыми словами. • Google Health — представляет собой онлайновую личную медицинскую карту.
• Google Alerts — отправление на почту результатов поиска с заданной • Google Knol — вики-энциклопедия, состоящая из авторских статей по заданным
периодичностью. темам.
• Google Analytics — бесплатный сервис, предоставляющий детальную статистику • Google Labs Google Labs — инкубатор идей для новых сервисов,
по трафику веб-сайта. предназначенный для тестирования интерфейса и т. п.
• Google ArtProject — интерактивно-представленные популярные музеи мира. • Google Maps — набор карт, построенных на основе бесплатного
• Google App Engine — платформа для создания и хостинга масштабируемых веб- картографического сервиса.
приложений на серверах компании Google. • Google Maps API — интерфейс, позволяющий встраивать карты на внешние сайты
• Google Apps — сервис для использования служб Google вместе со своим доменом. с помощью JavaScript.
• Google Merchant Center (ранее Google Base) — позволяет владельцам контента • Google Mars — карты Марса.
помещать структурированную информацию в хранилище, автоматически получая • Google Moon — карты Луны.
возможность поиска по этой информации. • Google Mobile — интерфейс для использования приложений Google с помощью
• Blogger — это сервис для ведения блогов, позволяющий держать на своѐм мобильных устройств.
хостинге не только программное обеспечение, а всю информацию: записи, • Google News — автоматически создаваемый новостной сайт, на котором собраны
комментарии и персональные страницы в СУБД на серверах Google. заголовки более чем из 400 источников новостей по всему миру: похожие статьи
• Google Bookmarks — позволяет отмечать сайты закладками, добавлять к ним группируются, а затем показываются в соответствии с личными интересами каждого
ярлыки и примечания. По ярлыкам и примечаниям можно делать поиск, закладки читателя.
хранятся на сервере и доступны с любого компьютера. • Google Notebook — веб-приложение, позволяющее создавать, хранить и
• Google Buzz — инструмент социальной сети, разработанный компанией Google и редактировать заметки на сервере. Текст в заметках может содержать URL, а также
интегрированный в Gmail. содержать разметку. Частично закрыт в январе 2009 года.
• Google Calendar — онлайновый сервис для планирования встреч, событий и дел с • Google Orkut — социальная сеть, в которой пользователи могут указывать свою
привязкой к календарю. Возможно совместное использование календаря группой персональную и профессиональную информацию, создавать связи с друзьями и
пользователей. Кроме того, сервис интегрирован с Gmail. объединяться в сообщества по интересам.
• Google Checkout — сервис обработки онлайновых платежей, имеющий целью • Google Picasa Web — персональные галереи фотографий.
упростить процесс оплаты онлайновых покупок. Веб-мастера могут использовать • Google Public DNS — альтернативный DNS-сервер Google.
данный сервис в качестве одной из форм оплаты. Работает по всему миру.
• Google Docs — веб-ориентированное приложение для работы с документами,
• Google Reader — RSS-агрегатор, позволяющий читать потоки новостей в форматах
Atom и RSS.
допускающее совместное использование документа.
• Google Directory (ранее Catalogs) — содержимое сети, организованное по
• Google Talk — программа для обмена мгновенными сообщениями (на основе
протокола XMPP) и интернет-телефон.
разделам в категориях.
• Google Dictionary — сервис для перевода отдельных слов на другие языки.
• Google Search History — история поисковых запросов пользователя.
• Google Finance — сайт-агрегатор биржевой информации.
• Google Sites — бесплатный хостинг, использующий вики-технологию.
• iGoogle (ранее Google Portal, Google Fusion и Personalized Homepage) — сервис
• Google Translate — система статистического машинного перевода слов, текстов,
фраз, веб-страниц между любыми парами языков.
для создания персональных страниц, использующих AJAX.
• Google One Pass — онлайн-магазин, где издатели могут продавать доступ к своему
• Google Voice — передача голоса по протоколу VoIP.
контенту. • Google Wave — сайт, объединяющий в себе функции электронной почты, вики,
социальной сети, системы мгновенных сообщений. Закрылся.
• Picnik — онлайн-сервис для редактирования фотографий.
• Google Webmasters — инструменты для вебмастеров.
• YouTube — видеохостинг.
19. FACEBOOK
Изначально
разрабатывался
для сбора
пользовательских
данных
Бизнес модель
изначально
ориентирована на
рост рекламного
потенциала
Компания
убыточна, но
стоит 87,5 млрд. $
24. TWITTER
Twitter сохраняет от 20 до 37 атрибутов, за каждое
действие пользователя
• Время
• Страна
• Местоположение
• Ссылка
• Скорость действия
• и т.п.
Информация о пользователе:
• Интересы
• Увлечения
• Хобби
• Предпочтения
• Желания Именно эта информация создает их
• И т.д.
капитализацию
27. TWITTER
Десятки тысяч партнеров. Создано
объединение Twitter
Partners, продвигающее платформу
Twitter
Спецслужбы США имеют прямой
доступ к аналитике Twitter
30. ПЕРВАЯ «ВЕЩЬ» - HDD
Рост объема HDD 1000% за каждые 5 лет на протяжении 30 лет
Стоимость хранения 1 Мб информации падает на 1000% за каждые 5 лет на
протяжении 30 лет
31. ПЕРВАЯ «ВЕЩЬ» - HDD
Рост объема HDD 1000% за каждые 5 лет на протяжении 30 лет
Стоимость хранения 1 Мб информации падает на 1000% за каждые 5 лет на
протяжении 30 лет
Информация хранится
ВЕЧНО
32. БЫСТРЫЙ И ДОСТУПНЫЙ ИНТЕРНЕТ
2-я вещь, которая уничтожила тайну личной жизни
33. БЫСТРЫЙ И ДОСТУПНЫЙ ИНТЕРНЕТ
2000 год, 1 час интернета 60руб., скорость до 3
кбит/сек.
…
2011 год, безлимитный интернет 600руб, 10240
кбит/сек. В ценах 2000
года на
2,5 часа
35. МОБИЛЬНЫЙ ТЕЛЕФОН
Обычный мобильный телефон дает информацию о
владельце:
• Местоположение
• Социальные связи
• и т.п.
• Номер телефона –
главный идентификатор
• Voicemail на почту
• регистрация на 3-е лицо;
• 2-3 симкарты;
• и т.п. Какие возможности дает смартфон?
Он у вас есть? ;-)
НЕ РАБОТАЕТ
36. ПОЛОЖЕНИЯ
Вся информация, которая уходит в облако, остается
там навсегда
Каждый день облако накапливает все больше
приватной информации
Технологичная проблема №1 – Анализ
неструктурированных данных
Сбор информации в облаке проводится с целью
продажи товаров и услуг
Контролировать свои личные данные в 21 веке
НЕВОЗМОЖНО
37. ВЫВОД
Изменились лишь правила игры в
конфиденциальность, но игра осталась!
