scholarly journals Game Plan: What AI can do for Football, and What Football can do for AI

2021 ◽  
Vol 71 ◽  
pp. 41-88
Author(s):  
Karl Tuyls ◽  
Shayegan Omidshafiei ◽  
Paul Muller ◽  
Zhe Wang ◽  
Jerome Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Learning ◽  
New Developments ◽  
Game Theoretic Analysis ◽  
Learning Game ◽  
Game Theoretic ◽  
Game Plan ◽  
To Come ◽  
Individual Sports ◽  
Game Changer

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

Machine Learning, Data Mining for IoT-Based Systems

2019 ◽  
pp. 314-338 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Internet Of Things ◽  
The Internet ◽  
Learning Game ◽  
Security Challenges ◽  
Game Plan ◽  
The Internet Of Things ◽  
Learning Data ◽  
The Way

This chapter will addresses challenges with the internet of things (IoT) and machine learning (ML), how a bit of the trouble of machine learning executions are recorded here and should be recalled while arranging the game plan, and the decision of right figuring. Existing examination in ML and IoT was centered around discovering how garbage in will convey garbage out, which is extraordinarily suitable for the extent of the enlightening list for machine learning. The quality, aggregate, availability, and decision of data are essential to the accomplishment of a machine learning game plan. Therefore, the point of this section is to give an outline of how the framework can utilize advancements alongside machine learning and difficulties get a kick out of the chance to understand the security challenges IoT can be bolstered. There are a few extensively unmistakable counts open for ML use. In spite of the way that counts can work in any nonexclusive conditions, there are specific standards available about which figuring would work best under which conditions.

Influential individuals: Approach to modeling

2018 ◽  
pp. 114-131
Author(s):  
O. Yu. Bondarenko
Keyword(s):  
Social Interactions ◽  
Social Status ◽  
Experimental Approach ◽  
Expert Knowledge ◽  
Formal Models ◽  
Leader Influence ◽  
Learning Game ◽  
Game Theoretic ◽  
Asymmetric Interactions ◽  
Influential Individuals

his article explores theoretical and experimental approach to modeling social interactions. Communication and exchange of information with other people affect individual’s behavior in numerous areas. Generally, such influence is exerted by leaders, outstanding individuals who have a higher social status or expert knowledge. Social interactions are analyzed in the models of social learning, game theoretic models, conformity models, etc. However, there is a lack of formal models of asymmetric interactions. Such models could help elicit certain qualities characterizing higher social status and perception of status by other individuals, find the presence of leader influence and analyze its mechanism.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

scholarly journals A game theoretic approach reveals that discretizing clinical information can reduce antibiotic misuse

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Maya Diamant ◽  
Shoham Baruch ◽  
Eias Kassem ◽  
Khitam Muhsen ◽  
Dov Samet ◽  
...  
Keyword(s):  
Antibiotic Prescription ◽  
Clinical Information ◽  
Social Optimum ◽  
Theoretic Approach ◽  
Theoretic Analysis ◽  
Game Theoretic Analysis ◽  
Optimal Output ◽  
The Social ◽  
Game Theoretic ◽  
Game Theoretic Approach

AbstractThe overuse of antibiotics is exacerbating the antibiotic resistance crisis. Since this problem is a classic common-goods dilemma, it naturally lends itself to a game-theoretic analysis. Hence, we designed a model wherein physicians weigh whether antibiotics should be prescribed, given that antibiotic usage depletes its future effectiveness. The physicians’ decisions rely on the probability of a bacterial infection before definitive laboratory results are available. We show that the physicians’ equilibrium decision rule of antibiotic prescription is not socially optimal. However, we prove that discretizing the information provided to physicians can mitigate the gap between their equilibrium decisions and the social optimum of antibiotic prescription. Despite this problem’s complexity, the effectiveness of the discretization solely depends on the type of information available to the physician to determine the nature of infection. This is demonstrated on theoretic distributions and a clinical dataset. Our results provide a game-theory based guide for optimal output of current and future decision support systems of antibiotic prescription.

A Game-theoretic Analysis of Hybrid Threats

2021 ◽  
pp. 1-16
Author(s):  
Pieter Balcaen ◽  
Cind Du Bois ◽  
Caroline Buts
Keyword(s):  
Theoretic Analysis ◽  
Game Theoretic Analysis ◽  
Game Theoretic ◽  
Hybrid Threats

scholarly journals A data-driven approach to violin making

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sebastian Gonzalez ◽  
Davide Salvi ◽  
Daniel Baeza ◽  
Fabio Antonacci ◽  
Augusto Sarti
Keyword(s):  
Statistical Learning ◽  
Geometric Parameters ◽  
Data Driven ◽  
Vibrational Properties ◽  
Learning Tools ◽  
Predictive Tool ◽  
Complete Control ◽  
Data Driven Approach ◽  
To Come ◽  
Violin Making

AbstractOf all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the specific relations that exist between shape and vibrational properties is yet to come and sorely missed. In this article, using standard statistical learning tools, we show that the modal frequencies of violin tops can, in fact, be predicted from geometric parameters, and that artificial intelligence can be successfully applied to traditional violin making. We also study how modal frequencies vary with the thicknesses of the plate (a process often referred to as plate tuning) and discuss the complexity of this dependency. Finally, we propose a predictive tool for plate tuning, which takes into account material and geometric parameters.

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