Forward propagation closed loop learning

2019 ◽  
Vol 28 (3) ◽  
pp. 181-194
Author(s):  
Bernd Porr ◽  
Paul Miller
Keyword(s):  
Closed Loop ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Autonomous Agent ◽  
Sensory Inputs ◽  
Sensory Data ◽  
The World ◽  
Input Layer ◽  
Propagation Of Errors ◽  
Target Output

For an autonomous agent, the inputs are the sensory data that inform the agent of the state of the world, and the outputs are their actions, which act on the world and consequently produce new sensory inputs. The agent only knows of its own actions via their effect on future inputs; therefore desired states, and error signals, are most naturally defined in terms of the inputs. Most machine learning algorithms, however, operate in terms of desired outputs. For example, backpropagation takes target output values and propagates the corresponding error backwards through the network in order to change the weights. In closed loop settings, it is far more obvious how to define desired sensory inputs than desired actions, however. To train a deep network using errors defined in the input space would call for an algorithm that can propagate those errors forwards through the network, from input layer to output layer, in much the same way that activations are propagated. In this article, we present a novel learning algorithm which performs such ‘forward-propagation’ of errors. We demonstrate its performance, first in a simple line follower and then in a 1st person shooter game.

scholarly journals Learning Actions That Reduce Variation in Objects

2021 ◽  
Author(s):  
◽  
James Bebbington
Keyword(s):  
Real World ◽  
Relative Position ◽  
Focal Depth ◽  
Ground Truth ◽  
Direct Control ◽  
Relative Configuration ◽  
Sensory Inputs ◽  
Sensory Data ◽  
The World ◽  
Many Sources

<p>The variation in the data that a robot in the real world receives from its sensory inputs (i.e. its sensory data) will come from many sources. Much of this variation is the result of ground truths about the world, such as what class an object belongs to, its shape, its condition, and so on. Robots would like to infer this information so they can use it to reason. A considerable amount of additional variation in the data, however, arises as a result of the robot’s relative configuration compared to an object; that is, its relative position, orientation, focal depth, etc. Fortunately, a robot has direct control over this configural variation: it can perform actions such as tilting its head or shifting its gaze. The task of inferring ground truth from data is difficult, and is made much more difficult when data is affected by configural variation. This thesis explores an approach in which the robot learns to perform actions that minimize the amount of configural variation in its sensory data, making the task of inferring information about objects considerably easier. The value of this approach is demonstrated by classifying digits from the MNIST and USPS datasets that have been transformed in various ways so that they include various kinds of configural variation.</p>

scholarly journals Learning Actions That Reduce Variation in Objects

2021 ◽  
Author(s):  
◽  
James Bebbington
Keyword(s):  
Real World ◽  
Relative Position ◽  
Focal Depth ◽  
Ground Truth ◽  
Direct Control ◽  
Relative Configuration ◽  
Sensory Inputs ◽  
Sensory Data ◽  
The World ◽  
Many Sources

<p>The variation in the data that a robot in the real world receives from its sensory inputs (i.e. its sensory data) will come from many sources. Much of this variation is the result of ground truths about the world, such as what class an object belongs to, its shape, its condition, and so on. Robots would like to infer this information so they can use it to reason. A considerable amount of additional variation in the data, however, arises as a result of the robot’s relative configuration compared to an object; that is, its relative position, orientation, focal depth, etc. Fortunately, a robot has direct control over this configural variation: it can perform actions such as tilting its head or shifting its gaze. The task of inferring ground truth from data is difficult, and is made much more difficult when data is affected by configural variation. This thesis explores an approach in which the robot learns to perform actions that minimize the amount of configural variation in its sensory data, making the task of inferring information about objects considerably easier. The value of this approach is demonstrated by classifying digits from the MNIST and USPS datasets that have been transformed in various ways so that they include various kinds of configural variation.</p>

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Triggers of Thought: Impressions within Hume’s Theory of Mind

2010 ◽  
Vol 13 (1) ◽  
pp. 105-121
Author(s):  
Anik Waldow
Keyword(s):  
Theory Of Mind ◽  
Associative Processes ◽  
Sensory Inputs ◽  
The World ◽  
Theories Of Meaning ◽  
Conceptual Resources

This essay argues that Humean impressions are triggers of associative processes, which enable us to form stable patterns of thought that co-vary with our experiences of the world. It will thus challenge the importance of the Copy Principle by claiming that it is the regularity with which certain kinds of sensory inputs motivate certain sets of complex ideas that matters for the discrimination of ideas. This reading is conducive to Hume’s account of perception, because it avoids the impoverishment of conceptual resources so typical for empiricist theories of meaning and explains why ideas should be based on impressions, although impressions cannot be known to mirror matters of fact. Dieser Aufsatz argumentiert dafür, dass humesche Eindrücke („impressions“) Auslöser von assoziativen Prozessen sind, welche es uns ermöglichen, stabile Denkmuster zu bilden, die mit unseren Erfahrungen der Welt kovariant sind. Der Aufsatz stellt somit die Wichtigkeit des Kopien-Prinzips in Frage, nämlich dadurch, dass behauptet wird, für die Unterscheidung der Ideen sei die Regelmäßigkeit maßgeblich, mit der gewisse Arten von sensorischen Eingaben gewisse Mengen von komplexen Ideen motivieren. Diese Lesart trägt zu einem Verständnis von Humes Auffassung der Wahrnehmung bei, da sie die Verarmung der begrifflichen Mittel, die für empiristische Theorien der Bedeutung so typisch ist, vermeidet und erklärt, warum Ideen auf Eindrücken basieren sollten, obwohl Eindrücke nicht als Abbildungen von Tatsachen erkannt werden können.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 617
Author(s):  
Umer Saeed ◽  
Young-Doo Lee ◽  
Sana Ullah Jan ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Context Aware ◽  
Sensor Faults ◽  
Training Time ◽  
Low Intensity ◽  
Fault Diagnostic

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

scholarly journals Machine Learning Algorithms, Applied to Intact Islets of Langerhans, Demonstrate Significantly Enhanced Insulin Staining at the Capillary Interface of Human Pancreatic β Cells

Metabolites ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 363
Author(s):  
Louise Cottle ◽  
Ian Gilroy ◽  
Kylie Deng ◽  
Thomas Loudovaris ◽  
Helen E. Thomas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Islets Of Langerhans ◽  
Learning Algorithm ◽  
Contact Point ◽  
Machine Learning Algorithms ◽  
Automated Image Analysis ◽  
Β Cells ◽  
Pancreatic Β Cells ◽  
Blood Glucose Concentrations ◽  
Insulin Staining

Pancreatic β cells secrete the hormone insulin into the bloodstream and are critical in the control of blood glucose concentrations. β cells are clustered in the micro-organs of the islets of Langerhans, which have a rich capillary network. Recent work has highlighted the intimate spatial connections between β cells and these capillaries, which lead to the targeting of insulin secretion to the region where the β cells contact the capillary basement membrane. In addition, β cells orientate with respect to the capillary contact point and many proteins are differentially distributed at the capillary interface compared with the rest of the cell. Here, we set out to develop an automated image analysis approach to identify individual β cells within intact islets and to determine if the distribution of insulin across the cells was polarised. Our results show that a U-Net machine learning algorithm correctly identified β cells and their orientation with respect to the capillaries. Using this information, we then quantified insulin distribution across the β cells to show enrichment at the capillary interface. We conclude that machine learning is a useful analytical tool to interrogate large image datasets and analyse sub-cellular organisation.

scholarly journals Automation of surgical skill assessment using a three-stage machine learning algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Joël L. Lavanchy ◽  
Joel Zindel ◽  
Kadir Kirtac ◽  
Isabell Twick ◽  
Enes Hosgor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Surgical Skills ◽  
Adverse Outcomes ◽  
Machine Learning Algorithms ◽  
Surgical Instruments ◽  
Skill Assessment ◽  
Surgical Skill ◽  
Surgical Skill Assessment ◽  
Motion Features

AbstractSurgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment.

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