Tootfinder

Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy
Yunho Kim, Jeong Hyun Lee, Choongin Lee, Juhyeok Mun, Donghoon Youm, Jeongsoo Park, Jemin Hwangbo
https://arxiv.org/abs/2406.02989

Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy
For reliable autonomous robot navigation in urban settings, the robot must have the ability to identify semantically traversable terrains in the image based on the semantic understanding of the scene. This reasoning ability is based on semantic traversability, which is frequently achieved using semantic segmentation models fine-tuned on the testing domain. This fine-tuning process often involves manual data collection with the target robot and annotation by human labelers which is prohibitively…

Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation
Omar Alhussein, Ning Zhang, Sami Muhaidat, Weihua Zhuang
https://arxiv.org/abs/2406.03630

Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation
This paper explores the integration of active machine learning (ML) for 6G networks, an area that remains under-explored yet holds potential. Unlike passive ML systems, active ML can be made to interact with the network environment. It actively selects informative and representative data points for training, thereby reducing the volume of data needed while accelerating the learning process. While active learning research mainly focuses on data annotation, we call for a network-centric active le…

Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy
Yunho Kim, Jeong Hyun Lee, Choongin Lee, Juhyeok Mun, Donghoon Youm, Jeongsoo Park, Jemin Hwangbo
https://arxiv.org/abs/2406.02989

Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy
For reliable autonomous robot navigation in urban settings, the robot must have the ability to identify semantically traversable terrains in the image based on the semantic understanding of the scene. This reasoning ability is based on semantic traversability, which is frequently achieved using semantic segmentation models fine-tuned on the testing domain. This fine-tuning process often involves manual data collection with the target robot and annotation by human labelers which is prohibitively…

On Annotation-free Optimization of Video Coding for Machines
Marc Windsheimer, Fabian Brand, Andr\'e Kaup
https://arxiv.org/abs/2406.07938 https://

On Annotation-free Optimization of Video Coding for Machines
Today, image and video data is not only viewed by humans, but also automatically analyzed by computer vision algorithms. However, current coding standards are optimized for human perception. Emerging from this, research on video coding for machines tries to develop coding methods designed for machines as information sink. Since many of these algorithms are based on neural networks, most proposals for video coding for machines build upon neural compression. So far, optimizing the compression by …

MOSA: Music Motion with Semantic Annotation Dataset for Cross-Modal Music Processing
Yu-Fen Huang, Nikki Moran, Simon Coleman, Jon Kelly, Shun-Hwa Wei, Po-Yin Chen, Yun-Hsin Huang, Tsung-Ping Chen, Yu-Chia Kuo, Yu-Chi Wei, Chih-Hsuan Li, Da-Yu Huang, Hsuan-Kai Kao, Ting-Wei Lin, Li Su
https://arxiv.org/abs/2406.06375

MOSA: Music Motion with Semantic Annotation Dataset for Cross-Modal Music Processing
In cross-modal music processing, translation between visual, auditory, and semantic content opens up new possibilities as well as challenges. The construction of such a transformative scheme depends upon a benchmark corpus with a comprehensive data infrastructure. In particular, the assembly of a large-scale cross-modal dataset presents major challenges. In this paper, we present the MOSA (Music mOtion with Semantic Annotation) dataset, which contains high quality 3-D motion capture data, align…

reAnalyst: Scalable Analysis of Reverse Engineering Activities
Tab Zhang, Claire Taylor, Bart Coppens, Waleed Mebane, Christian Collberg, Bjorn De Sutter
https://arxiv.org/abs/2406.04427

reAnalyst: Scalable Analysis of Reverse Engineering Activities
This paper introduces reAnalyst, a scalable analysis framework designed to facilitate the study of reverse engineering (RE) practices through the semi-automated annotation of RE activities across various RE tools. By integrating tool-agnostic data collection of screenshots, keystrokes, active processes, and other types of data during RE experiments with semi-automated data analysis and annotation, reAnalyst aims to overcome the limitations of traditional RE studies that rely heavily on manual d…

This https://arxiv.org/abs/2406.00008 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

KnowledgeHub: An end-to-end Tool for Assisted Scientific Discovery
This paper describes the KnowledgeHub tool, a scientific literature Information Extraction (IE) and Question Answering (QA) pipeline. This is achieved by supporting the ingestion of PDF documents that are converted to text and structured representations. An ontology can then be constructed where a user defines the types of entities and relationships they want to capture. A browser-based annotation tool enables annotating the contents of the PDF documents according to the ontology. Named Entity …

JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models
Delong Ran, Jinyuan Liu, Yichen Gong, Jingyi Zheng, Xinlei He, Tianshuo Cong, Anyu Wang
https://arxiv.org/abs/2406.09321

JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models
Jailbreak attacks aim to induce Large Language Models (LLMs) to generate harmful responses for forbidden instructions, presenting severe misuse threats to LLMs. Up to now, research into jailbreak attacks and defenses is emerging, however, there is (surprisingly) no consensus on how to evaluate whether a jailbreak attempt is successful. In other words, the methods to assess the harmfulness of an LLM's response are varied, such as manual annotation or prompting GPT-4 in specific ways. Each approa…

This https://arxiv.org/abs/2309.05423 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Multi-Modal Automatic Prosody Annotation with Contrastive Pretraining of SSWP
In expressive and controllable Text-to-Speech (TTS), explicit prosodic features significantly improve the naturalness and controllability of synthesised speech. However, manual prosody annotation is labor-intensive and inconsistent. To address this issue, a two-stage automatic annotation pipeline is novelly proposed in this paper. In the first stage, we use contrastive pretraining of Speech-Silence and Word-Punctuation (SSWP) pairs to enhance prosodic information in latent representations. In t…

This https://arxiv.org/abs/2402.04673 has been replaced.
link: https://scholar.google.com/scholar?q=a

Streamlined Hybrid Annotation Framework using Scalable Codestream for Bandwidth-Restricted UAV Object Detection
Emergency response missions depend on the fast relay of visual information, a task to which unmanned aerial vehicles are well adapted. However, the effective use of unmanned aerial vehicles is often compromised by bandwidth limitations that impede fast data transmission, thereby delaying the quick decision-making necessary in emergency situations. To address these challenges, this paper presents a streamlined hybrid annotation framework that utilizes the JPEG 2000 compression algorithm to facil…

Advances in Machine Learning, Statistical Methods, and AI for Single-Cell RNA Annotation Using Raw Count Matrices in scRNA-seq Data
Megha Patel, Nimish Magre, Himanshi Motwani, Nik Bear Brown
https://arxiv.org/abs/2406.05258

Advances in Machine Learning, Statistical Methods, and AI for Single-Cell RNA Annotation Using Raw Count Matrices in scRNA-seq Data
Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the resolution of individual cells, providing unprecedented insights into cellular heterogeneity and complex biological systems. This paper reviews various advanced computational and machine learning techniques tailored for the analysis of scRNA-seq data, emphasizing their roles in different stages of the data processing pipeline.

AOC: Analysis of Orthologous Collections -- an application for the characterization of natural selection in protein-coding sequences
Alexander Lucaci, Sergei Pond
https://arxiv.org/abs/2406.09522

AOC: Analysis of Orthologous Collections -- an application for the characterization of natural selection in protein-coding sequences
Motivation Modern molecular sequence analysis increasingly relies on automated and robust software tools for interpretation, annotation, and biological insight. The Analysis of Orthologous Collections (AOC) application automates the identification of genomic sites and species/lineages influenced by natural selection in coding sequence analysis. AOC quantifies different types of selection: negative, diversifying or directional positive, or differential selection between groups of branches. We in…

This https://arxiv.org/abs/2402.00288 has been replaced.
link: https://scholar.google.com/scholar?q=a

Frame-Wise Breath Detection with Self-Training: An Exploration of Enhancing Breath Naturalness in Text-to-Speech
Developing Text-to-Speech (TTS) systems that can synthesize natural breath is essential for human-like voice agents but requires extensive manual annotation of breath positions in training data. To this end, we propose a self-training method for training a breath detection model that can automatically detect breath positions in speech. Our method trains the model using a large speech corpus and involves: 1) annotation of limited breath sounds utilizing a rule-based approach, and 2) iterative au…

This https://arxiv.org/abs/2207.10324 has been replaced.
link: https://scholar.google.com/scholar?q=a

Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation
Image translation based on a generative adversarial network (GAN-IT) is a promising method for the precise localization of abnormal regions in chest X-ray images (AL-CXR) even without the pixel-level annotation. However, heterogeneous unpaired datasets undermine existing methods to extract key features and distinguish normal from abnormal cases, resulting in inaccurate and unstable AL-CXR. To address this problem, we propose an improved two-stage GAN-IT involving registration and data augmentat…

This https://arxiv.org/abs/2211.16693 has been replaced.
link: https://scholar.google.com/scholar?q=a

Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds
The accurate detection and grasping of transparent objects are challenging but of significance to robots. Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is proposed, including the grasping position detection, tactile calibration, and visual-tactile fusion based classification. First, a multi-scene synthetic grasping dataset generation method with a Gaussian distribution based data annotation is proposed. Besides, a …