Ultra-strong machine learningcomprehensibility of programs learned with ILP
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In a recent paper [37] the authors introduced an operational definition for comprehensibility of logic programs and conducted human trials to determine how properties of a program affect its ease of comprehension. This paper builds on and extends the earlier work by

Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
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ABSTRACT The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning research as well as in many industrial settings. This article reviews different techniques that can be used for each of these three If it is true that there is always more than one way of construing a text, it is not true that all interpretations are equal.Paul Ricoeur The rich area of text analytics draws ideas from information retrieval, machine learning and natural language processing. Each of these

A functional perspective on machine learning via programmable induction and abduction
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ABSTRACT . We present a programming language for machine learning based on the concepts of inductionand abductionas encountered in Peirces logic of science. We consider the desirable features such a language must have, and we identify the abductive decouplingof

Investigating Chinas Urban Air Quality Using Big Data, Information Theory, and Machine Learning .
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ABSTRACT With the development of the economy and industrial construction, air quality deteriorates dramatically in China and seriously threatens peoples health. To investigate which factors most affect air quality and provide a useful tool to assist the prediction and

Practical Techniques for Interpreting Machine Learning Models: Introductory Open Source Examples Using Python, H2O, and XGBoost
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This series of Jupyter notebooks uses open source tools such as Python, H2O, XGBoost, GraphViz, Pandas, and NumPy to outline practical explanatory techniques for machine learning models and results. The notebooks cover the following modeling and explanatory

Data Infrastructure for Machine Learning
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ABSTRACT Data quality is critical for effective machine learning and this makes data a first- class citizen in the context of machine learning on par with algorithms, software, and infrastructure. As a result, machine learning platforms need to support data analysis and

3.6 Machine Learning and Algorithmic Model Theory
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After giving some general background in machine learning I introduced a declarative model theoretic framework for learning . Then I talked about recent positive and negative learnability results that we obtained within this framework for learning models defined in first

Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
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ABSTRACT Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot

How high will it be using machine learning models to predict branch coverage in automated testing
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ABSTRACT Software testing is a crucial component in modern continuous integration development environment. Ideally, at every commit, all the systems test cases should be executed and moreover, new test cases should be generated for the new code. This is

Revolutionising the detection and mapping of exotic weeds using UASs and machine learning
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Sandino, Juan, Harris, Stacy, Trotter, Peter, Shukla, Atharva,Gonzalez, Felipe (2018) Revolutionising the detection and mapping of exotic weeds using UASs and machine learning . In International Master Class in Plant Biosecurity, 14-26 January 2018, Den- pasar, Indonesia

Predicting Academic Achievement in Fundamentals of Thermodynamics using Supervised Machine Learning Techniques
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ABSTRACT Supervised machine learning techniques were used to answer the central question: does excelling in reading quizzes, a good predictor of accurately predicting the passing rate in MEEN241 Fundamentals of Thermodynamics Class assignments such as reading

Machine Learning and Algorithmic Model Theory
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Page 1. MachineLearning and Algorithmic Model Theory Martin Grohe RWTH Aachen Page 2. Part I: MachinelearningSome background on learning theory 2 Page 3. Part I: Machinelearning Some background on learning theory Part II: Finite and Algorithmic Model TheoryA

Different Strokes for Different Folks: Experimental Evidence on Complementarities Between Human Capital and Machine Learning
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The advent of artificial intelligence in the form of machine learning technologies ushers new questions regarding the pace at which it may substitute both older technology vintages and human capital. Rather than assuming superior productivity of one vintage over another, we

Forgotten Siblings: Unifying Attacks on Machine Learning and Digital Watermarking
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ABSTRACT Machine learning is increasingly used in securitycritical applications, such as autonomous driving, face recognition, and malware detection. Most learning methods, however, have not been designed with security in mind and thus are vulnerable to different

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Page 1. DIFFERENTIAL GEOMETRY APPROACH FOR UNSUPERVISED MACHINELEARNING ALGORITHMS by Nan Wu A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Mathemtics University of Toronto c Copyright

A Machine Learning Approach for Real-time Battery Optimal Operation Mode Prediction and Control
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ABSTRACT This paper introduces a machine learning approach for real-time battery optimal operation mode prediction in residential PV applications. First, from the historical data, the optimal battery operation mode for each operation interval is derived. Then, a best

Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects
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ABSTRACT Decision trees and related ensemble methods like random forest are state-of-theart tools in the field of machine learning for predictive regression and classification. However, they lack interpretability and can be less relevant in credit scoring applications, where

Simultaneous Timing Driven Tree Surgery in Routing with Machine Learningbased Acceleration
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ABSTRACT In global routing, both timing and routability are critical criterions to measure the performance of a design. However, these two objectives naturally conflict with each other during routing. In this paper, a tree surgery technique is presented to adjust routing tree

Declarative Metadata Management: A Missing Piece in End-To-End Machine Learning
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ABSTRACT We argue for the necessity of managing the metadata and lineage of common artifacts in machine learning (ML). We discuss a recently presented lightweight system built for this task, which accelerates users in their ML workflows, and provides a basis for

On Machine Learning and Programming Languages
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ABSTRACT The complexity of Machine Learning (ML) models and the frameworks people are using to build them has exploded along with ML itself. State-of-the-art models are increasingly programs, with support for programming constructs like loops and recursion

Detection Over Unknown Channels via Machine Learning
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One of the important modules in reliable recovery of data sent over a communication channel is the detection algorithm, where the transmitted signal is estimated from a noisy and corrupted version observed at the receiver. The design and analysis of this module has

Introduction to the Minitrack on Data Analytics, Data Mining and Machine Learning for Social Media
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Transforming data from social media into useful information, or knowledge, is the focus of this minitrack. The papers at HICSS in 2018 remind our attendees and readers of the many real-world applications of data analytics, data mining, and machine learning for social

Machine Learning Based Prediction of Consumer Purchasing Decisions: The Evidence and Its Significance
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ABSTRACT Every day consumers make decisions on whether or not to buy a product. In some cases the decision is based solely on price but in many instances the purchasing decision is more complex, and many more factors might be considered before the final commitment is

Privacy-preserving Machine Learning Based Data Analytics on Edge Devices
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ABSTRACT Emerging Machine Learning (ML) techniques, such as Deep Neural Network, are widely used in todays applications and services. However, with social awareness of privacy and personal data rapidly rising, it becomes a pressing and challenging societal issue to ABSTRACT Hand motion tracking traditionally re-quires highly complex and expensive systems in terms of energy and computational demands. A low-power, low-cost system could lead to a revolution in this field as it would not require complex hardware while

Detecting code smells using machine learning techniques: are we there yet
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ABSTRACT Code smells are symptoms of poor design and implementation choices weighing heavily on the quality of produced source code. During the last decades several code smell detection tools have been proposed. However, the literature shows that the results of these

Wearable Sensors and Machine Learning Diagnose Anxiety and Depression in Young Children
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ABSTRACT This paper describes a new approach for diagnosing anxiety and depression in young children. Currently, diagnosis in this population requires hours of structured clinical interviews spread over days and weeks. In contrast, we propose the use of a 90-second fear

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ABSTRACT Security of valuable information is always a very essential issue for modern digital world. Intrusion Detection System (IDS) and many security techniques is widely used against cyber attacks. Data mining and machine learning methods have also been used by

Towards Generalization in QBF Solving via Machine Learning
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ABSTRACT There are well known cases of Quantified Boolean Formulas (QBFs) that have short winning strategies (Skolem/Herbrand functions) but that are hard to solve by nowadays solvers. This paper argues that a solver benefits from generalizing a set of individual wins

Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies
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ABSTRACT In road traffic, critical situations pass by as quickly as they appear. Within the blink of an eye, one has to come to a decision, which can make the difference between a low severity, high severity or fatal crash. Because time is important, a machine learning driven

Bone Tumor Detection from MRI Images Using Machine Learning : A Review
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ABSTRACT -Image processing have a vast area under research, in which Medical Imaging is the most significant area to work in. As in biological cases such as fractures, tumors, ulcers, etc image processing made it more easy to find out the exact cause and the best fitted solution

Investigating How Experienced UX Designers Effectively Work with Machine Learning
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ABSTRACT Machine learning (ML) plays an increasingly important role in improving a users experience. However, most UX practitioners face challenges in understanding MLs capabilities or envisioning what it might be. We interviewed 13 designers who had many

Prospects for Machine Learning for Shell Midden Analysis
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This paper describes using machine learning (ML) techniques to assist in analysis of shellfish midden remains from archaeological sites in New Zealand. Midden analysis is the most common post-excavation analysis undertaken in prehistoric archaeological projects

Machine Translation Using Deep Learning : A Survey
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ABSTRACT Machine Translation using Deep Learning (Neural Machine Translation) is a newly proposed approach to machine translation. The term Machine Translation is used in the sense of translation of one language to another, with no human improvement. It can also

Norms, Rewards, and the Intentional Stance: Comparing Machine Learning Approaches to Ethical Training
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ABSTRACT The challenge of training AI systems to perform responsibly and beneficially has inspired different approaches for teaching a system what people want and how it is acceptable to attain that in the world. In this paper we compare work in reinforcement

Non-Discriminatory Machine Learning through Convex Fairness Criteria
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ABSTRACT Biased decision making by machine learning systems is increasingly recognized as an important issue. Recently, techniques have been proposed to learn non-discriminatory classifiers by enforcing constraints in the training phase. Such constraints are either non

Hybrid Approach for Software Defect Prediction Using Machine Learning with Optimization Technique
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ABSTRACT Technology is growing rapidly which lead to the industrial growth for various manufacturing and software industries. Now-a-days, demand and use of software based application has attracted users and adopted widely in daily routine or business work

Prediction of Crop Yield using Machine Learning
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ABSTRACT -Looking at the current situation faced by farmers in Maharashtra, we have observed that there is an increase in suicide rate over the years. The reasons behind this includes weather conditions, debt, family issues and frequent change in Indian government norms