Search Results for author:
Found 25 papers, 9 papers with code
STAPI: An Automatic Scraper for Extracting Iterative Title-Text Structure from Web Documents
Therefore, we propose the first title-text dataset on web documents that incorporates a wide variety of domains to facilitate downstream training.
Evaluating LLMs at Detecting Errors in LLM Responses
This work introduces ReaLMistake, the first error detection benchmark consisting of objective, realistic, and diverse errors made by LLMs.
PEaCE: A Chemistry-Oriented Dataset for Optical Character Recognition on Scientific Documents
To mitigate this gap, we present the Printed English and Chemical Equations (PEaCE) dataset, containing both synthetic and real-world records, and evaluate the efficacy of transformer-based OCR models when trained on this resource.
Optical Character Recognition
Optical Character Recognition (OCR)
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
We categorize methods based on their optimization focus: computational, memory, energy, financial, and network resources and their applicability across various stages of an LLM's lifecycle, including architecture design, pretraining, finetuning, and system design.
Adaptive Annotation Distribution for Weakly Supervised Point Cloud Semantic Segmentation
Based on our analysis, we propose a label-aware point cloud downsampling strategy to increase the proportion of annotations involved in the training stage.
Fair Abstractive Summarization of Diverse Perspectives
However, current work in summarization metrics and Large Language Models (LLMs) evaluation has not explored fair abstractive summarization.
FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
In this paper, we investigate and improve faithfulness in summarization on a broad range of medical summarization tasks.
A Digital Twin Approach for Adaptive Compliance in Cyber-Physical Systems: Case of Smart Warehouse Logistics
Engineering regulatory compliance in complex Cyber-Physical Systems (CPS), such as smart warehouse logistics, is challenging due to the open and dynamic nature of these systems, scales, and unpredictable modes of human-robot interactions that can be best learnt at runtime.
EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices
In this paper, we introduce EdgeMA, a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time, addressing the data drift problem.
Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning
This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for boosting inference performance on real-time video of changing scenes.
Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering
Recently, self-attention networks achieve impressive performance in point cloud segmentation due to their superiority in modeling long-range dependencies.
Coordinating Cross-modal Distillation for Molecular Property Prediction
The existing graph methods have demonstrated that 3D geometric information is significant for better performance in MPP.
CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors
This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop at the European Conference on Computer Vision (ECCV 2022).
Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification
In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading.
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins
DT can leverage fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional symbiotic sensing feedback loops for its continuous updates.
DT-SV: A Transformer-based Time-domain Approach for Speaker Verification
Therefore, we propose an approach to derive utterance-level speaker embeddings via a Transformer architecture that uses a novel loss function named diffluence loss to integrate the feature information of different Transformer layers.
Knowledge Equivalence in Digital Twins of Intelligent Systems
Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous.
Super Reinforcement Bros: Playing Super Mario Bros with Reinforcement Learning
We plan to apply and adjust some well-known reinforcement learning (RL) algorithms to train an automatic agent to play the 1985 Nintendo game Super Mario Bros under a speedrun rule.
For Intelligent and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-than-Nyquist and Deep Learning
With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable.
Leveraging History for Faster Sampling of Online Social Networks
Random walk fits naturally with this problem because, for most online social networks, the only query we can issue through the interface is to retrieve the neighbors of a given node (i. e., no access to the full graph topology).
Real Time 3D Indoor Human Image Capturing Based on FMCW Radar
Compared to traditional camera-based computer vision and imaging, radio imaging based on wireless sensing does not require lighting and is friendly to privacy.
Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records
Both unsupervised machine learning approaches could be leveraged to discover patient subgroups using EHRs but with different foci.
Understanding and Mitigating the Security Risks of Voice-Controlled Third-Party Skills on Amazon Alexa and Google Home
The significance of our findings have already been acknowledged by Amazon and Google, and further evidenced by the risky skills discovered on Alexa and Google markets by the new detection systems we built.
Cryptography and Security
Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be
We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas.
Cryptography and Security
Fine-Grained Change Detection of Misaligned Scenes With Varied Illuminations
To guarantee detection sensitivity and accuracy of minute changes, in an observation, we capture a group of images under multiple illuminations, which need only to be roughly aligned to the last time lighting conditions.
