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Found 18 papers, 3 papers with code
Pixie: Preference in Implicit and Explicit Comparisons
We present Pixie, a manually annotated dataset for preference classification comprising 8, 890 sentences drawn from app reviews.
Uncertainty-Aware Explainable Recommendation with Large Language Models
Providing explanations within the recommendation system would boost user satisfaction and foster trust, especially by elaborating on the reasons for selecting recommended items tailored to the user.
Object-Driven One-Shot Fine-tuning of Text-to-Image Diffusion with Prototypical Embedding
Our proposed method aims to address the challenges of generalizability and fidelity in an object-driven way, using only a single input image and the object-specific regions of interest.
FGSI: Distant Supervision for Relation Extraction method based on Fine-Grained Semantic Information
We propose the hypothesis that the key semantic information inside the sentence plays a key role in entity relationship extraction.
Real-time Path Planning of Driver-less Mining Trains with Time-dependent Physical Constraints
While the increased automation levels of production and operation equipment have led to improved productivity of mining activity in open pit mines, the capacity of mine transport system become a bottleneck.
Detection of Real-time DeepFakes in Video Conferencing with Active Probing and Corneal Reflection
Specifically, we authenticate video calls by displaying a distinct pattern on the screen and using the corneal reflection extracted from the images of the call participant's face.
Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces
In this work, we develop an online platform called Open-eye to study the human performance of AI-synthesized face detection.
GAN-generated Faces Detection: A Survey and New Perspectives
Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters that can generate profound impacts.
Discovery methods for systematic analysis of causal molecular networks in modern omics datasets
With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology and genetics.
Robust Attentive Deep Neural Network for Exposing GAN-generated Faces
However, images from existing public datasets do not represent real-world scenarios well enough in terms of view variations and data distributions (where real faces largely outnumber synthetic faces).
Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Faces
Generative adversary network (GAN) generated high-realistic human faces have been used as profile images for fake social media accounts and are visually challenging to discern from real ones.
Holmes: An Efficient and Lightweight Semantic Based Anomalous Email Detector
Email threat is a serious issue for enterprise security, which consists of various malicious scenarios, such as phishing, fraud, blackmail and malvertisement.
Lin: Unsupervised Extraction of Tasks from Textual Communication
Commitments and requests are a hallmark of collaborative communication, especially in team settings.
DualNet: Locate Then Detect Effective Payload with Deep Attention Network
Network intrusion detection (NID) is an essential defense strategy that is used to discover the trace of suspicious user behaviour in large-scale cyberspace, and machine learning (ML), due to its capability of automation and intelligence, has been gradually adopted as a mainstream hunting method in recent years.
Densely Connected Residual Network for Attack Recognition
High false alarm rate and low detection rate are the major sticking points for unknown threat perception.
Pelican: A Deep Residual Network for Network Intrusion Detection
To address this problem, the network intrusion detection system (NIDS) has been used.
LuNet: A Deep Neural Network for Network Intrusion Detection
Our experiments on two network traffic datasets show that compared to the state-of-the-art network intrusion detection techniques, LuNet not only offers a high level of detection capability but also has a much low rate of false positive-alarm.
A Transfer Learning Approach for Network Intrusion Detection
ConvNet can also be applied to network intrusion detection.
