Deep learning enhances smart fish farming through automated feature extraction.
problem Challenges in obtaining labeled data limit DL applications in aquaculture.
method Application of DL techniques in aquaculture, including live fish identification, species classification, and feeding decision-making.
result Deep learning's ability to automatically extract features is a significant contribution.
Survey of yield farming protocols in DeFi.
problem Understanding and evaluating yield farming mechanisms in DeFi.
method Analyzed smart contracts, performed simulations, reviewed literature.
result Characterized major yield aggregators and identified risks.
Compound examines decentralized lending users and their short loan durations.
problem Systemic risk in decentralized finance due to concentration and interconnection.
method Analysis of on-chain transaction data and smart contract programming.
result Many users borrow for yield farming, not for traditional lending.
Predicting illegal fishing on Patagonian Shelf using machine learning.
problem Improving detection of illegal fishing to conserve fish stocks.
method Combining vessel tracking data with oceanographic seascapes and machine learning models.
result Machine learning models predict illegal fishing with 69-96% confidence.
Precision farming uses data analysis to optimize crop management.
problem Meeting increased food demand with sustainable practices.
method Statistical tools and machine learning for pattern recognition and prediction.
result Optimized farming processes in real-time reduce resource use.
Method reconstructs missing wind farm data using graph theory and nearest neighbors.
problem Missing data in wind farm records due to sensor failures.
method Combines spectral graph theory and k-Nearest Neighbors to estimate missing data.
result Significant improvement in data reconstruction over existing methods.
This study examines yield aggregators in DeFi, summarizing strategies and analyzing performance.
problem Understanding and optimizing yield farming strategies in DeFi.
method Summarizes yield farming protocols and tokens, analyzes performance through simulations and empirical data.
result Plausible connection between data anomalies and historical events in yield aggregators.
Wind farm layout optimisation tackles space constraints with Bayesian multi-objective approach.
problem Optimizing wind farm layout due to limited space and conflicting objectives.
method Set-based multi-objective Bayesian optimisation using Gaussian process.
result Demonstrates potential of set-based Bayesian multi-objective optimisation for wind farm layout.
New method predicts wind farm power and wakes using weather patterns.
problem Inefficient and computationally intensive wind energy resource assessment.
method Unsupervised clustering of ERA5 data on wind velocity, WRF simulations at cluster centers, and post-processing.
result Accurate long-term predictions of power and wakes with reduced computational time.
According to Cobanoglu et al and Murphy, it is now widely acknowledged that the single target paradigm (one protein or target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous - that is, i…
Bayesian method models multivalued power data from wind farms.
problem Accurate modeling of power curves with multivalued relationships.
method Overlapping mixture of probabilistic regression models.
result Model accurately represents practical power data.
Optimal wind farm placement using quantile constraints for better power output.
problem Optimizing wind farm placement to maximize power output considering spatial and temporal wind speed correlations.
method Used a probabilistic neural network with ReLU activation functions to reformulate constraints as linear ones, embedding them into a two-stage stochastic optimization problem.
result The constraint learning approach outperforms classical methods, especially for risk-averse investors.
The study optimizes wind farm yaw control using Gaussian process regression and high-fidelity simulations.
problem Improving yaw control inputs for maximum power production in wind farms.
method Gaussian process regression and modifier adaptation scheme based on high-fidelity simulation data.
result Both modifier adaptation and Bayesian optimization improve power production with smaller yaw misalignments.
In order to face the expected increasing demand of energy crops without creating conflicts of land occupation sustainability, farmers need to find reliable alternatives in marginal agricultural areas where the production of food hardly ever is economically and environmentally sustainable. The purpose of this work was t…
There is a small number of case studies of automatic land cover classification on the coastal area. Here, I test extraction of seagrass beds, sandy area, oyster farming rafts at Mangoku-ura Lagoon, Miyagi, Japan by comparing manual tracing, simple image segmentation, and image transformation using deep learning. The re…
Recent introduction of ICT in agriculture has brought a number of changes in the way farming is done. This means use of Internet of Things(IoT), Cloud Computing(CC), Big Data (BD) and automation to gain better control over the process of farming. As the use of these technologies in farms has grown exponentially with ma…
Derives a fluid model for fish swarming in arbitrary dimensions.
problem Modeling fish swarming behavior in various dimensions.
method Develops a novel n-dimensional model using bundle geometry and variational formulation. result Derives the self-organized hydrodynamic model from the interacting particle system.
The values that the first two Vassiliev invariants take on prime knots with up to fourteen crossings are considered. This leads to interesting fish-like graphs. Several results about the values taken on torus knots are proved.
Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means an…
The study visualizes Spanish fish and meat processing companies using financial, environmental, and social ratios.
problem Mapping financial, environmental, and social performance of Spanish processing companies.
method Used compositional data and principal-component analysis biplot for statistical analysis.
result Identified clusters of companies with similar financial, environmental, and social performance.
We introduce a measure for estimating the best risk-return relation of power production in wind farms within a given time-lag, conditioned to the velocity field. The velocity field is represented by a scalar that weighs the influence of the velocity at each wind turbine at present and previous time-steps for the presen…
Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish. The ability to automatically detect and remember these types of interactions is not only beneficial for individuals interested in their behavior in crowded situations, but also of interest to those who analyz…
Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this regard is that agents in the real world often only directly affect a limited set of neighbouring agents. Leveraging such loose couplings amon…
The installation process of offshore wind turbines requires the use of expensive jack-up vessels. These vessels regularly report their position via the Automatic Identification System (AIS). This paper introduces a novel approach of applying machine learning to AIS data from jack-up vessels. We apply the new method to …
Proposes a probabilistic framework for smart contract risk quantification.
problem Quantifying financial risk of smart contract cyber attacks and failures.
method Probabilistic graph-theoretical framework using bond percolation models.
result Analytical results and numerical examples for aggregate loss distribution.
The paper defines price sensitivity and liquidity in CFMMs and links it to curvature.
problem Understanding the relationship between CFMM curvature and market performance.
method Proposes a definition of price sensitivity and liquidity, and links it to CFMM curvature.
result Curvature of CFMMs affects market performance and liquidity provider incentives.
Paper proposes auction method for smart derivatives to avoid disputes.
problem Disputes over derivative liquidation processes in smart contracts.
method Defines an auction type resolution for smart derivatives.
result Proposes a beneficial method for smart derivatives participants.
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…
SAGDA generates synthetic agricultural datasets to improve ML in African farming.
problem Data scarcity in African agriculture limits machine learning innovations.
method SAGDA is an open-source Python toolkit that generates, augments, and validates synthetic agricultural datasets.
result SAGDA enhances ML applications in agriculture, such as yield prediction and fertilizer recommendation.
Gradient matching method improves domain generalization across various datasets.
problem Machine learning's inability to generalize to unseen domains.
method Inter-domain gradient matching objective and first-order algorithm Fish.
result Fish method produces competitive results and surpasses baselines on 4 datasets.
FinTech uses data science and AI to transform finance.
problem Transforming finance with data science and AI.
method DSAI techniques including complex system methods, quantitative methods, etc.
result DSAI enables smart FinTech for various financial sectors.
Unified model learns from proteins and ligands for drug design.
problem Disjoint data sources and modeling assumptions limit joint use of structure- and ligand-based drug design.
method Contrastive Geometric Learning for Unified Computational Drug Design (ConGLUDe)
result Unified model achieves competitive zero-shot virtual screening performance and state-of-the-art ligand-conditioned pocket selection.
The paper categorizes four types of scale-up: smart, dumb, forced, and fumbled.
problem Growing ventures in size and maintaining efficiency.
method Identifying modularity and speed as key factors, categorizing four types of scale-up.
result Modularity and speed are crucial for successful scale-up.
Improved security of smart contracts by classifying them into four categories.
problem Detecting and classifying vulnerabilities in smart contracts efficiently.
method Used AWD-LSTM for multi-class classification, addressing class imbalance.
result Achieved a weighted average Fbeta score of 90.0%.
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.
problem Operational and economic implications of smart contract adoption under moderate uncertainty.
method Multi-supplier model with endogenized adoption costs, supplier readiness, and inventory penalties; analytical and numerical results.
result Partial adoption strategies support moderate demand variability, while excessive digital investment reduces profitability.
A new method streamlines digital payment programming using smart contracts.
problem High costs and security challenges in programming smart contracts for digital payments.
method Transforming digital currencies into token streams and using configurable templates to generate specialized smart contracts.
result Reduces payment programming costs and enhances security, self-enforcement, adaptability, and controllability.
Proposes a model combining graph networks and variational Bayes for graph data.
problem Probabilistic modeling of graph structured data.
method Combines graph networks and variational Bayes for probabilistic modeling of graph data.
result Demonstrates effectiveness on wind farm monitoring and Gaussian Process data.
Wind power, as an alternative to burning fossil fuels, is abundant and inexhaustible. To fully utilize wind power, wind farms are usually located in areas of high altitude and facing serious ice conditions, which can lead to serious consequences. Quick detection of blade ice accretion is crucial for the maintenance of …
GeoStat simplifies time series classification with fast, intuitive features.
problem Efficiently classify time series data without high computational costs.
method GeoStat representations based on differential geometric statistics.
result Simple KNN and SVM classifiers achieve state-of-the-art performance.
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…
Study on stability of network flow shrinkers with findings on instability of specific shapes.
problem Stability of regular shrinkers in network flow.
method Analysis of self-similarly shrinking solutions called regular shrinkers.
result All regular shrinkers with two or more enclosed regions can be perturbed away. Specific shapes like 4-ray star, 5-ray star, fish, and rocket are unstable among those with one enclosed region.
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 …
Poultry farms are an important contributor to the human food chain. Worldwide, humankind keeps an enormous number of domesticated birds (e.g. chickens) for their eggs and their meat, providing rich sources of low-fat protein. However, around the world, there have been growing concerns about the quality of life for the …
Paper assesses the market value of sharing privacy-protected smart meter data.
problem Value of sharing privacy-protected smart meter data between consumers and load serving entities.
method Discounted differential privacy model, ANN-based load forecasting, optimal procurement problem.
result Significant value in sharing smart meter data while retaining individual consumer privacy.
Smart Close-out Netting aims to automate close-out netting processes.
problem Inefficiencies in close-out netting processes for financial institutions.
method Standardisation and automation of legal and regulatory processes using a data-driven framework and controlled natural language.
result Standardisation and automation can improve close-out netting processes for prudentially regulated financial institutions.
Model predicts missing boarding stops in smart card data.
problem Missing boarding stop information in public transport datasets.
method Supervised machine learning with ordinal classification.
result Proposed method significantly outperforms existing imputation methods.
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…