scholarly journals Joint Banknote Recognition and Counterfeit Detection Using Explainable Artificial Intelligence

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3607 ◽  
Author(s):  
Miseon Han ◽  
Jeongtae Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
European Union ◽  
Detection Method ◽  
Computation Time ◽  
Learning System ◽  
Detection Methods ◽  
The European Union ◽  
Explainable Artificial Intelligence ◽  
Banknote Recognition

We investigated machine learning-based joint banknote recognition and counterfeit detection method. Unlike existing methods, since the proposed method simultaneously recognize banknote type and detect counterfeit detection, it is significantly faster than existing serial banknote recognition and counterfeit detection methods. Furthermore, we propose an explainable artificial intelligence method for visualizing regions that contributed to the recognition and detection. Using the visualization, it is possible to understand the behavior of the trained machine learning system. In experiments using the United State Dollar and the European Union Euro banknotes, the proposed method shows significant improvement in computation time from conventional serial method.

The ‘Ethification’ of ICT Governance. Artificial Intelligence and Data Protection in the European Union

2021 ◽  
Vol 43 ◽  
pp. 105597
Author(s):  
Niels van Dijk ◽  
Simone Casiraghi ◽  
Serge Gutwirth
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Data Protection ◽  
The European Union

scholarly journals Artificial Cognition: How Experimental Psychology Can Help Generate Explainable Artificial Intelligence

2021 ◽  
Author(s):  
J. Eric T. Taylor ◽  
Graham Taylor
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Neural Networks ◽  
Experimental Approach ◽  
Well Being ◽  
Human Mind ◽  
Black Box ◽  
Psychological Science ◽  
Black Boxes ◽  
Explainable Artificial Intelligence

Artificial intelligence powered by deep neural networks has reached a levelof complexity where it can be difficult or impossible to express how a modelmakes its decisions. This black-box problem is especially concerning when themodel makes decisions with consequences for human well-being. In response,an emerging field called explainable artificial intelligence (XAI) aims to increasethe interpretability, fairness, and transparency of machine learning. In thispaper, we describe how cognitive psychologists can make contributions to XAI.The human mind is also a black box, and cognitive psychologists have overone hundred and fifty years of experience modeling it through experimentation.We ought to translate the methods and rigour of cognitive psychology to thestudy of artificial black boxes in the service of explainability. We provide areview of XAI for psychologists, arguing that current methods possess a blindspot that can be complemented by the experimental cognitive tradition. Wealso provide a framework for research in XAI, highlight exemplary cases ofexperimentation within XAI inspired by psychological science, and provide atutorial on experimenting with machines. We end by noting the advantages ofan experimental approach and invite other psychologists to conduct research inthis exciting new field.

scholarly journals Artificial intelligence, Digital Single Market and the proposal of a right to fair and reasonable inferences: a legal issue between ethics and techniques

2019 ◽  
Vol 5 (2) ◽  
pp. 75-91
Author(s):  
Alexandre Veronese ◽  
Alessandra Silveira ◽  
Amanda Nunes Lopes Espiñeira Lemos
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Data Protection ◽  
Personal Data ◽  
Legal Framework ◽  
Single Market ◽  
Legal Issue ◽  
The European Union ◽  
European Union Law ◽  
General Data Protection Regulation

The article discusses the ethical and technical consequences of Artificial intelligence (hereinafter, A.I) applications and their usage of the European Union data protection legal framework to enable citizens to defend themselves against them. This goal is under the larger European Union Digital Single Market policy, which has concerns about how this subject correlates with personal data protection. The article has four sections. The first one introduces the main issue by describing the importance of AI applications in the contemporary world scenario. The second one describes some fundamental concepts about AI. The third section has an analysis of the ongoing policies for AI in the European Union and the Council of Europe proposal about ethics applicable to AI in the judicial systems. The fourth section is the conclusion, which debates the current legal mechanisms for citizens protection against fully automated decisions, based on European Union Law and in particular the General Data Protection Regulation. The conclusion will be that European Union Law is still under construction when it comes to providing effective protection to its citizens against automated inferences that are unfair or unreasonable.

The European Union Artificial Intelligence Act: Potential implications for Australia

2021 ◽  
pp. 1037969X2110523
Author(s):  
Dan Svantesson
Keyword(s):  
Artificial Intelligence ◽  
European Union ◽  
Early Stage ◽  
The European Union ◽  
Key Features ◽  
Proposed Regulation

The European Union (EU) published its proposed Regulation laying down harmonised rules for Artificial Intelligence (the Artificial Intelligence Act) on 21 April 2021. Once it comes into force, this Act will impact upon Australia. It is therefore important that Australians take note of the proposal at this relatively early stage. This article brings attention to the key features of the EU’s proposed Artificial Intelligence Act. However, the main aim is to highlight why it is important for Australia and to examine, in some detail, the rules that will determine when the Act applies to Australians.

scholarly journals An API Semantics-Aware Malware Detection Method Based on Deep Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xin Ma ◽  
Shize Guo ◽  
Wei Bai ◽  
Jun Chen ◽  
Shiming Xia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection Method ◽  
Malware Detection ◽  
Poor Performance ◽  
Detection Methods ◽  
Interference Detection ◽  
Machine Learning Method ◽  
Accuracy Rate ◽  
Malware Classification ◽  
Self Protection

The explosive growth of malware variants poses a continuously and deeply evolving challenge to information security. Traditional malware detection methods require a lot of manpower. However, machine learning has played an important role on malware classification and detection, and it is easily spoofed by malware disguising to be benign software by employing self-protection techniques, which leads to poor performance for existing techniques based on the machine learning method. In this paper, we analyze the local maliciousness about malware and implement an anti-interference detection framework based on API fragments, which uses the LSTM model to classify API fragments and employs ensemble learning to determine the final result of the entire API sequence. We present our experimental results on Ali-Tianchi contest API databases. By comparing with the experiments of some common methods, it is proved that our method based on local maliciousness has better performance, which is a higher accuracy rate of 0.9734.

scholarly journals Implementation of modified SARSA learning technique in EMCAP

2017 ◽  
Vol 7 (1.5) ◽  
pp. 274
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Learning System ◽  
State Action ◽  
Learning Technique ◽  
Markov Decision ◽  
Experiment Analysis

This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm.  State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance. This modified   SARSA learning algorithm can   be more suitable in EMCAP architecture.  The experiments are conducted the modified   SARSA Learning system gets   more rewards compare to existing  SARSA algorithm.

scholarly journals Explainable artificial intelligence and machine learning: A reality rooted perspective

2020 ◽  
Vol 10 (6) ◽  
Author(s):  
Frank Emmert‐Streib ◽  
Olli Yli‐Harja ◽  
Matthias Dehmer
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Explainable Artificial Intelligence

scholarly journals Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods

Plants ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 593 ◽  
Author(s):  
Péter Szilassi ◽  
Gábor Szatmári ◽  
László Pásztor ◽  
Mátyás Árvai ◽  
József Szatmári ◽  
...  
Keyword(s):  
Machine Learning ◽  
Invasive Species ◽  
European Union ◽  
Land Cover ◽  
Invasive Plant ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Invasive Plant Species ◽  
The European Union ◽  
Asclepias Syriaca

For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (Asclepias syriaca L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as well as an EU-scale land cover databases (CORINE) for the analyses. For the abundance data of A. syriaca, we applied the field survey photos from the Land Use and Coverage Area Frame Survey (LUCAS) Land Cover database for the European Union. With machine learning algorithm methods, we quantified the relative weight of the environmental variables on the abundance of common milkweed. According to our findings, soil texture and soil type (sandy soils) were the most important variables determining the occurrence of this species. We could exactly identify the actual land cover types and the recent land cover changes that have a significant role in the occurrence the common milkweed in Europe. We could also show the role of climatic conditions of the study area in the occurrence of this species, and we could prepare the potential distribution map of common milkweed for the study area.

Evaluation of maker space index system based on machine learning and intelligent interactive system

2020 ◽  
Vol 39 (4) ◽  
pp. 5941-5952
Author(s):  
Yang Chunhe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Index System ◽  
Evaluation System ◽  
Learning System ◽  
Interactive System ◽  
Learning Technology ◽  
Integrated Learning System ◽  
Performance Evaluation System ◽  
Innovation Space

Machine learning technology is the core of artificial intelligence and the basis of computer intelligence. In recent years, machine learning technology has integrated and developed different learning methods, and the research of integrated learning system with more flexible and efficient form is also emerging. In this paper, the authors analyze the maker space index system based on machine learning and intelligent interactive system. As a comprehensive innovation and entrepreneurship platform, mass innovation space has the characteristics of both existing entrepreneurship service system and knowledge innovation driven. Through the index score calculation, the related evaluation system is constructed, the final score of social support system is 61.4.Multi-factor performance evaluation system based on machine learning and artificial intelligence,this paper reveals the development and change law of maker space, and provides theoretical basis for the future operation and decision-making of maker space.

Sign in / Sign up

Export Citation Format

Share Document