scholarly journals Identification of token contracts on Ethereum: standard compliance and beyond

2021 ◽  
Author(s):  
Monika Di Angelo ◽  
Gernot Salzer
Keyword(s):  
Large Set ◽  
Smart Contracts ◽  
Automated Classification ◽  
Widespread Application ◽  
Transaction Data ◽  
Local Currency ◽  
Smart Contract ◽  
High Level ◽  
Digital Assets

AbstractNext to cryptocurrencies, tokens are a widespread application area of blockchains. Tokens are digital assets implemented as small programs on a blockchain. Being programmable makes them versatile and an innovative means for various purposes. Tokens can be used as investment, as a local currency in a decentralized application, or as a tool for building an ecosystem or a community. A high-level categorization of tokens differentiates between payment, security, and utility tokens. In most jurisdictions, security tokens are regulated, and hence, the distinction is of relevance. In this work, we discuss the identification of tokens on Ethereum, the most widely used token platform. The programs on Ethereum are called smart contracts, which—for the sake of interoperability—may provide standardized interfaces. In our approach, we evaluate the publicly available transaction data by first reconstructing interfaces in the low-level code of the smart contracts. Then, we not only check the compliance of a smart contract with an established interface standard for tokens, but also aim at identifying tokens that are not fully compliant. Thus, we discuss various heuristics for token identification in combination with possible definitions of a token. More specifically, we propose indicators for tokens and evaluate them on a large set of token and non-token contracts. Finally, we present first steps toward an automated classification of tokens regarding their purpose.

scholarly journals Genre of Bangla Music: A Machine Classification Learning Approach

2019 ◽  
Vol 18 (2) ◽  
pp. 66-72
Author(s):  
Abhijit Bhowmik ◽  
AZM Ehtesham Chowdhury
Keyword(s):  
Classification Systems ◽  
Learning Approach ◽  
Large Set ◽  
Automated Classification ◽  
Classification Learning ◽  
Genre Classification ◽  
Machine Learning Approach ◽  
Music Databases ◽  
East Asian Language

The necessity for designing autonomous indexing tools to establish expressive and efficient means of describing musical media content is well recognized. Music genre classification systems are significant to manage and use music databases. This research paper proposes an enhanced method to automatically classify music into different genre using a machine learning approach and presents the insight and results of the application of the proposed scheme to the classification of a large set of The Bangla music content, a South-East Asian language rich with a variety of music genres developed over many centuries. Building upon musical feature extraction and decision-making techniques, we propose new features and procedures to achieve enhanced accuracy. We demonstrate the efficacy of the proposed method by extracting features from a dataset of hundreds of The Bangla music pieces and testing the automatic classification decisions. This is the first development of an automated classification technique applied specifically to the Bangla music to the best of our knowledge, while the superior accuracy of the method makes it universally applicable.

Blockchain for Islamic Social Responsibility Institutions

2019 ◽  
pp. 221-240 ◽  
Author(s):  
Moutaz Abojeib ◽  
Farrukh Habib
Keyword(s):  
Social Responsibility ◽  
Transaction Cost ◽  
Economic Efficiency ◽  
Governance Structure ◽  
Innovative Technology ◽  
Islamic Finance ◽  
Smart Contracts ◽  
Smart Contract ◽  
Traditional Technologies ◽  
High Level

Blockchain and smart contracts are forming new systems to record and manage businesses with less need for intermediaries. The new systems are expected to offer high level of governance with lower cost as compared to the traditional technologies. While there is a continuous effort to apply this innovative technology in several businesses, Islamic finance in general—and Islamic social finance in particular—are facing few challenges that could be solved by such innovations. Islamic social finance institutions such as waqf are facing some challenges in enhancing its governance structure to ensure Shariah compliance as well as economic efficiency. This chapter explains how blockchain and smart contract technologies can help these institutions for better governance, lower transaction cost, more transparency, and higher trust, hence enhancing the business flexibility and market accessibility. It also presents some related cases that are currently under development as an evidence for the practicality of these technologies in the Islamic social finance arena.

Blockchain for Islamic Social Responsibility Institutions

2021 ◽  
pp. 1114-1128
Author(s):  
Moutaz Abojeib ◽  
Farrukh Habib
Keyword(s):  
Social Responsibility ◽  
Transaction Cost ◽  
Economic Efficiency ◽  
Governance Structure ◽  
Innovative Technology ◽  
Islamic Finance ◽  
Smart Contracts ◽  
Smart Contract ◽  
Traditional Technologies ◽  
High Level

Blockchain and smart contracts are forming new systems to record and manage businesses with less need for intermediaries. The new systems are expected to offer high level of governance with lower cost as compared to the traditional technologies. While there is a continuous effort to apply this innovative technology in several businesses, Islamic finance in general—and Islamic social finance in particular—are facing few challenges that could be solved by such innovations. Islamic social finance institutions such as waqf are facing some challenges in enhancing its governance structure to ensure Shariah compliance as well as economic efficiency. This chapter explains how blockchain and smart contract technologies can help these institutions for better governance, lower transaction cost, more transparency, and higher trust, hence enhancing the business flexibility and market accessibility. It also presents some related cases that are currently under development as an evidence for the practicality of these technologies in the Islamic social finance arena.

scholarly journals Automated Classification of the Tympanic Membrane Using a Convolutional Neural Network

2019 ◽  
Vol 9 (9) ◽  
pp. 1827 ◽  
Author(s):  
Je Yeon Lee ◽  
Seung-Ho Choi ◽  
Jong Woo Chung
Keyword(s):  
Tympanic Membrane ◽  
Automated Classification ◽  
Model Accuracy ◽  
Test Dataset ◽  
Real Time Analysis ◽  
Medical Image Recognition ◽  
Test Sets ◽  
High Level ◽  
Level Analysis

Precise evaluation of the tympanic membrane (TM) is required for accurate diagnosis of middle ear diseases. However, making an accurate assessment is sometimes difficult. Artificial intelligence is often employed for image processing, especially for performing high level analysis such as image classification, segmentation and matching. In particular, convolutional neural networks (CNNs) are increasingly used in medical image recognition. This study demonstrates the usefulness and reliability of CNNs in recognizing the side and perforation of TMs in medical images. CNN was constructed with typically six layers. After random assignment of the available images to the training, validation and test sets, training was performed. The accuracy of the CNN model was consequently evaluated using a new dataset. A class activation map (CAM) was used to evaluate feature extraction. The CNN model accuracy of detecting the TM side in the test dataset was 97.9%, whereas that of detecting the presence of perforation was 91.0%. The side of the TM and the presence of a perforation affect the activation sites. The results show that CNNs can be a useful tool for classifying TM lesions and identifying TM sides. Further research is required to consider real-time analysis and to improve classification accuracy.

scholarly journals Rich specifications for Ethereum smart contract verification

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-30
Author(s):  
Christian Bräm ◽  
Marco Eilers ◽  
Peter Müller ◽  
Robin Sierra ◽  
Alexander J. Summers
Keyword(s):  
Smart Contracts ◽  
Domain Specific ◽  
Smart Contract ◽  
Verification Tool ◽  
Resource Transfers ◽  
Core Functionality ◽  
Reasoning Technique ◽  
Financial Losses ◽  
Digital Assets ◽  
Specification Methodology

Smart contracts are programs that execute in blockchains such as Ethereum to manipulate digital assets. Since bugs in smart contracts may lead to substantial financial losses, there is considerable interest in formally proving their correctness. However, the specification and verification of smart contracts faces challenges that rarely arise in other application domains. Smart contracts frequently interact with unverified, potentially adversarial outside code, which substantially weakens the assumptions that formal analyses can (soundly) make. Moreover, the core functionality of smart contracts is to manipulate and transfer resources; describing this functionality concisely requires dedicated specification support. Current reasoning techniques do not fully address these challenges, being restricted in their scope or expressiveness (in particular, in the presence of re-entrant calls), and offering limited means of expressing the resource transfers a contract performs. In this paper, we present a novel specification methodology tailored to the domain of smart contracts. Our specifications and associated reasoning technique are the first to enable: (1) sound and precise reasoning in the presence of unverified code and arbitrary re-entrancy, (2) modular reasoning about collaborating smart contracts, and (3) domain-specific specifications for resources and resource transfers, expressing a contract's behaviour in intuitive and concise ways and excluding typical errors by default. We have implemented our approach in 2vyper, an SMT-based automated verification tool for Ethereum smart contracts written in Vyper, and demonstrated its effectiveness for verifying strong correctness guarantees for real-world contracts.

scholarly journals Research on Human Activity Identification Based on Image Processing and Artificial Intelligence

2018 ◽  
Vol 7 (3.27) ◽  
pp. 174 ◽  
Author(s):  
Praywin Moses Dass Alex ◽  
Akash Ravikumar ◽  
Jerritta Selvaraj ◽  
Arun Sahayadhas
Keyword(s):  
Patient Monitoring ◽  
Fall Detection ◽  
Machine Learning Algorithms ◽  
Large Set ◽  
Human Computer Interface ◽  
Comparison Study ◽  
Needed Information ◽  
And Performance ◽  
High Level

Recognizing the activities of humans through computer vision techniques is an important area of research. This area of research leads to various applications such as patient monitoring, fall detection, surveillance and human-computer interface. The capability for recognizing these acts lays foundation for developing highly intelligent and decision making systems. Generally, most of the mentioned applications requires automatic recognition of high-level activities, consisting of simple actions of multiple persons. Usually, the intelligence to the system is delivered only if these activities are properly classified. This paper addresses various machine learning algorithms used in classifying various activities such as Multi-Layer Perceptron, Random Forest, Naïve Bayes and SVM algorithms. This paper provides classification of general to complex human activities through comparison study and performance evaluation of these mentioned algorithms using very large set of images. This review will provide much needed information for further research in more productive areas. 

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