A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper proposes auction method for smart derivatives to avoid disputes.
In this note we describe the application of existing smart contract technologies with the aim to construct a new digital representation of a financial derivative contract. We compare several existing DLT based technologies. We provide a detailed description of two separate prototypes which are able to be executed on a …
New model assesses risks of staking and borrowing in smart contracts.
Smart reply systems have been developed for various messaging platforms. In this paper, we introduce Uber's smart reply system: one-click-chat (OCC), which is a key enhanced feature on top of the Uber in-app chat system. It enables driver-partners to quickly respond to rider messages using smart replies. The smart repl…
SMART-FAN-Lasso fine-tunes neural networks for high-dimensional nonparametric regression.
This paper examines the quantitative finance aspects of AMMs in decentralized finance.
Smart beta, also known as strategic beta or factor investing, is the idea of selecting an investment portfolio in a simple rule-based manner that systematically captures market inefficiencies, thereby enhancing risk-adjusted returns above capitalization-weighted benchmarks. We explore the idea of applying a smart strat…
Study analyzes smart contract adoption under bounded risk, showing stable adoption but fragile financial outcomes.
Proposes a probabilistic framework for smart contract risk quantification.
Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elder…
FinTech uses data science and AI to transform finance.
The paper categorizes four types of scale-up: smart, dumb, forced, and fumbled.
Improved security of smart contracts by classifying them into four categories.
Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart build…
Study develops smart contract framework for procurement under demand variability.
A new method streamlines digital payment programming using smart contracts.
Derives metrics for DeFi vaults, addressing credit risk.
In this paper, we propose a game theoretical adversarial intervention detection mechanism for reliable smart road signs. A future trend in intelligent transportation systems is ``smart road signs" that incorporate smart codes (e.g., visible at infrared) on their surface to provide more detailed information to smart veh…
SMART is an open source web application designed to help data scientists and research teams efficiently build labeled training data sets for supervised machine learning tasks. SMART provides users with an intuitive interface for creating labeled data sets, supports active learning to help reduce the required amount of …
Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. Combined with the Internet of Things (IoT), a smart MG can leverage the sensory data and machine learning techniques for intelligent energy management. This paper focuses on deep reinforcement learning (DRL)-based…
Paper assesses the market value of sharing privacy-protected smart meter data.
Hybrid approach protects privacy while analyzing smart meter data.
Smart Close-out Netting aims to automate close-out netting processes.
Model predicts missing boarding stops in smart card data.
The implementation of smart building technology in the form of smart infrastructure applications has great potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. However, human preference in regard to living conditions is usually unknown and heterogeneous in its manifestati…
Paper addresses class imbalance in disk SMART dataset using GANs and genetic algorithms.
Smart contracts are a digital technology with potential but also flaws.
Deep learning enhances smart fish farming through automated feature extraction.
SMART combines decision trees and MARS for better regression modeling.
We propose a new model of minority game with so-called smart agents such that the standard deviation and the total loss in this model reach the theoretical minimum values in the limit of long time. The smart agents use trail and error method to make a choice but bring global optimization to the system, which suggests t…
Some general Finsler connections are defined. Emphasis is being made on the Cartan tensor and its derivatives. Vanishing of the hv-curvature tensors of these connections characterizes Landsbergian, Berwaldian as well as Riemannian structures. This view point makes it possible to give a smart representation of connectio…
This paper proposes a joint energy and data market to handle uncertainty in energy procurement.
Smart grid is an emerging and promising technology. It uses the power of information technologies to deliver intelligently the electrical power to customers, and it allows the integration of the green technology to meet the environmental requirements. Unfortunately, information technologies have its inherent vulnerabil…
FedRule uses graph neural networks to recommend rules for smart homes without centralizing data.
Smart watches can identify smoking gestures with high accuracy.
SFC aims to protect the Amazon with a digital currency and smart contracts.
SMART transfers knowledge across related studies for multi-task learning.
A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN). There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional met…
Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data scenarios pose a new challenge to traditional imbalanced classification algorithms, since they are not prepared to work with such amount of data. Split data strategies and lack o…
Detecting inaccurate smart meters and targeting them for replacement can save significant resources. For this purpose, a novel deep-learning method was developed based on long short-term memory (LSTM) and a modified convolutional neural network (CNN) to predict electricity usage trajectories based on historical data. F…
Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learnin…
New method identifies distribution grid outages using smart meter data.
Wearable computing is one of the fastest growing technologies today. Smart watches are poised to take over at least of half the wearable devices market in the near future. Smart watch screen size, however, is a limiting factor for growth, as it restricts practical text input. On the other hand, wearable devices have so…
Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models have been proposed. In this paper, we formulate the online attack/anomaly detection…
In a voice-controlled smart-home, a controller must respond not only to user's requests but also according to the interaction context. This paper describes Arcades, a system which uses deep reinforcement learning to extract context from a graphical representation of home automation system and to update continuously its…
Paper predicts in-situ metro passenger density using smart card data.
Smart grid uses deep learning to optimize household energy use.
Study optimizes smart contract adoption under high demand variability using Negative Binomial models.