Search Results for author:
Found 19 papers, 1 papers with code
Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation
Previous in-context learning (ICL) research has focused on tasks such as classification, machine translation, text2table, etc., while studies on whether ICL can improve human-like dialogue generation are scarce.
From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrency
An ideal detection model is expected to achieve all three critical properties of (I) early detection, (II) good interpretability, and (III) versatility for various illicit activities.
Examining the Effect of Pre-training on Time Series Classification
(iv) Adding more pre-training data does not improve generalization, but it can strengthen the advantage of pre-training on the original data volume, such as faster convergence.
Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency
To detect fraud behaviors of malicious addresses in the early stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor.
Toward Intention Discovery for Early Malice Detection in Bitcoin
% With the type-dependent selection strategy and global status vectors, our model can be applied to detect various illicit activities with strong interpretability.
Geometry-Entangled Visual Semantic Transformer for Image Captioning
However, those fusion-based models, they are still criticized for the lack of geometry information for inter and intra attention refinement.
Self-Synchronising On-Off-Keying Visible Light Communication System For Intra and Inter-Vehicle Data Transmission
Consequently, the deployment of the protocol in both inter and intra-vehicle communication presents numerous advantages over existing data transmission processes.
Solving MKP Applied to IoT in Smart Grid Using Meta-heuristics Algorithms: A Parallel Processing Perspective
Increasing electricity prices in South Africa and the imminent threat of load shedding due to the overloaded power grid has led to a need for Demand Side Management (DSM) devices like smart grids.
Low-Complexity Detection of Multiweight Permutation Modulation Space-Time Block Codes for Indoor Visible Light Communication
The BER results also show a close match with the ML detection in some codebooks and the performance of the soft-decision decoder is evaluated for different positions of the receiver in an indoor environment.
Optimization Algorithms for Improving the Performance of Permutation Trellis Codes
SD decoding can then be designed accordingly, using the presented, low-complexity optimization-based schemes.
A multivariate water quality parameter prediction model using recurrent neural network
The global degradation of water resources is a matter of great concern, especially for the survival of humanity.
Surface Damage Detection Scheme using Convolutional Neural Network and Artificial Neural Network
The ANN performs feature-based classification, in which the features are extracted from the detected edges within the image.
Optimal DG allocation and sizing in power system networks using swarm-based algorithms
The WOA real power loss reduction considering techno-economic factors in the IEEE 14-bus and 30-bus test system are 6. 14 MW and 10. 77 MW, compared to the PSOs' 6. 47 MW and 11. 73 MW respectively.
Short sighted deep learning
This idea has been explored in the setting of a local (nearest neighbor interactions) Ising spin lattice.
Trends in the optimal location and sizing of electrical units in smart grids using meta-heuristic algorithms
Techniques such as voltage stability index, power loss index, and loss sensitivity factors have been implemented alongside the meta-heuristic optimization algorithms to reduce the search space of solutions for objective functions.
Stack-VS: Stacked Visual-Semantic Attention for Image Caption Generation
Recently, automatic image caption generation has been an important focus of the work on multimodal translation task.
Is Deep Learning a Renormalization Group Flow?
Although we are motivated by the connection between deep learning and RG flow, in this study we focus mainly on comparing a single layer of a deep network to a single step in the RG flow.
Long-Bone Fracture Detection using Artificial Neural Networks based on Contour Features of X-ray Images
The difference between the two schemes is the removal of the surrounding detected flesh contours from the leg region in the improved CHFB scheme.
Long-Bone Fracture Detection using Artificial Neural Networks based on Line Features of X-ray Images
The purpose for the two line-based fracture detection schemes is to detect fractured lines from X-ray images using extracted features based on recognised patterns to differentiate fractured lines from non-fractured lines.
