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Found 26 papers, 9 papers with code
Improving Robustness and Accuracy of Ponzi Scheme Detection on Ethereum Using Time-Dependent Features
The contract-code-based approach, while achieving very high accuracy, is not robust: first, the source codes of a majority of contracts on Ethereum are not available, and second, a Ponzi developer can fool a contract-code-based detection model by obfuscating the opcode or inventing a new profit distribution logic that cannot be detected (since these models were trained on existing Ponzi logics only).
Action Pick-up in Dynamic Action Space Reinforcement Learning
Then, we design two different AP methods: frequency-based global method and state clustering-based local method, based on the prior optimal policy.
Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization
Designing an intelligent volume-weighted average price (VWAP) strategy is a critical concern for brokers, since traditional rule-based strategies are relatively static that cannot achieve a lower transaction cost in a dynamic market.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Knowledge Graph Quality Evaluation under Incomplete Information
Winner of the game is thus considered to have better qualities.
Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling
Finally, we demonstrate that hybridising the machine learning-based variable ordering methods with traditional domain-based methods is beneficial.
Adaptive Population-based Simulated Annealing for Uncertain Resource Constrained Job Scheduling
While a number of optimisation methods have been proposed to tackle the deterministic problem, the uncertainty associated with resource availability, an inevitable challenge in mining operations, has received less attention.
Network medicine framework reveals generic herb-symptom effectiveness of Traditional Chinese Medicine
We validate our framework with real-world hospital patient data by showing that (1) shorter network distance between symptoms of inpatients correlates with higher relative risk (co-occurrence), and (2) herb-symptom network proximity is indicative of patients' symptom recovery rate after herbal treatment.
Efficient Joint DOA and TOA Estimation for Indoor Positioning with 5G Picocell Base Stations
Secondly, based on the deployment reality that 5G picocell gNBs only have a small-scale antenna array but have a large signal bandwidth, the proposed scheme decouples the estimation of time-of-arrival (TOA) and direction-of-arrival (DOA) to reduce the huge complexity induced by two-dimensional joint processing.
Low-latency Monaural Speech Enhancement with Deep Filter-bank Equalizer
To improve the performance of traditional low-latency speech enhancement algorithms, a deep filter-bank equalizer (FBE) framework was proposed, which integrated a deep learning-based subband noise reduction network with a deep learning-based shortened digital filter mapping network.
A deep complex multi-frame filtering network for stereophonic acoustic echo cancellation
In hands-free communication system, the coupling between loudspeaker and microphone generates echo signal, which can severely influence the quality of communication.
Pseudo-labelling and Meta Reweighting Learning for Image Aesthetic Quality Assessment
In the tasks of image aesthetic quality evaluation, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets.
Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems
Topology optimisation of trusses can be formulated as a combinatorial and multi-modal problem in which locating distinct optimal designs allows practitioners to choose the best design based on their preferences.
Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring
In each iteration of CG, our MLPH leverages an ML model to predict the optimal solution of the pricing problem, which is then used to guide a sampling method to efficiently generate multiple high-quality columns.
Unbiased IoU for Spherical Image Object Detection
In this paper, we first identify that spherical rectangles are unbiased bounding boxes for objects in spherical images, and then propose an analytical method for IoU calculation without any approximations.
Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies
Classification sub-module supplies classifying of images according to the eras, nationalities and garment types; Parsing sub-network supplies the semantic for person contours, clothing and background in the image to achieve more accurate colorization of clothes and persons and prevent color overflow.
Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark
This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems.
Learning Linear Polytree Structural Equation Models
Under the Gaussian polytree models, we study sufficient conditions on the sample sizes for the well-known Chow-Liu algorithm to exactly recover both the skeleton and the equivalence class of the polytree, which is uniquely represented by a CPDAG.
Learning Primal Heuristics for Mixed Integer Programs
This paper proposes a novel primal heuristic for Mixed Integer Programs, by employing machine learning techniques.
IEEE CEC 2022 Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark
This document describes the Generalized Moving Peaks Benchmark (GMPB) that generates continuous dynamic optimization problem instances.
A Robust Maximum Likelihood Distortionless Response Beamformer based on a Complex Generalized Gaussian Distribution
The proposed beamformer can be regarded as a generalization of the minimum power distortionless response beamformer and its improved variations.
Confused Modulo Projection based Somewhat Homomorphic Encryption -- Cryptosystem, Library and Applications on Secure Smart Cities
To make this algorithm easy to use, we also designed and implemented an efficient general blind computing library based on CMP-SWHE.
Instance Space Analysis for the Car Sequencing Problem
We investigate an important research question for solving the car sequencing problem, that is, which characteristics make an instance hard to solve?
A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction
In this paper, we propose a Deep Drift-Diffusion (DDD) model inspired by psychologists to predict aesthetic score distribution from images.
Consistency of Spectral Clustering on Hierarchical Stochastic Block Models
We study the hierarchy of communities in real-world networks under a generic stochastic block model, in which the connection probabilities are structured in a binary tree.
Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization
To the best of our knowledge, our algorithm is the first blind deconvolution algorithm that is numerically efficient, robust against noise, and comes with rigorous recovery guarantees under certain subspace conditions.
Information Theory Information Theory
Stable Principal Component Pursuit
We further prove that the solution to a related convex program (a relaxed PCP) gives an estimate of the low-rank matrix that is simultaneously stable to small entrywise noise and robust to gross sparse errors.
Information Theory Information Theory
