Combined Physics and Event Camera Simulator for Slip Detection

Oct 13, 2025 by pynkocean.com 37 views

Carelessness Detection using Performance Factor Analysis: A New Operationalization with Unexpectedly Different Relationship to Learning

Abstract

Detection of carelessness in digital learning platforms has relied on the contextual slip model, which leverages conditional probability and Bayesian Knowledge Tracing (BKT) to identify careless errors, where students make mistakes despite having the knowledge. However, this model cannot effectively assess carelessness in questions tagged with multiple skills due to the use of conditional probability. This limitation narrows the scope within which the model can be applied. Thus, we propose a novel model, the Beyond Knowledge Feature Carelessness (BKFC) model. The model detects careless errors using performance factor analysis (PFA) and behavioral features distilled from log data, controlling for knowledge when detecting carelessness. We applied the BKFC to detect carelessness in data from middle school students playing a learning game on decimal numbers and operations. We conducted analyses comparing the careless errors detected using contextual slip to the BKFC model. Unexpectedly, careless errors identified by these two approaches did not align. We found students' post-test performance was positively associated with the carelessness detected using the contextual slip model, while negatively associated with the carelessness detected using the BKFC model. These results highlight the complexity of carelessness and underline a broader challenge in operationalizing carelessness and careless errors.

Generative AI in Academic Writing: A Comparison of DeepSeek, Qwen, ChatGPT, Gemini, Llama, Mistral, and Gemma

Abstract

Deepseek and Qwen LLMs became popular at the beginning of 2025 with their low-cost and open-access LLM solutions. A company based in Hangzhou, Zhejiang, China, announced its new LLM, DeepSeek v3, in December 2024. Then, Alibaba released its AI model, Qwen 2.5 Max, on January 29, 2025. These tools, which are free and open-source have made a significant impact on the world. Deepseek and Qwen also have the potential to be used by many researchers and individuals around the world in academic writing and content creation. Therefore, it is important to determine the capacity of these new LLMs to generate high-quality academic content. This study aims to evaluate the academic writing performance of both Qwen 2.5 Max and DeepSeek v3 by comparing these models with popular systems such as ChatGPT, Gemini, Llama, Mistral, and Gemma. In this research, 40 articles on the topics of Digital Twin and Healthcare were used. The method of this study involves using generative AI tools to generate texts based on posed questions and paraphrased abstracts of these 40 articles. Then, the generated texts were evaluated through the plagiarism tool, AI detection tools, word count comparisons, semantic similarity tools and readability assessments.

Bangla Fake News Detection Based On Multichannel Combined CNN-LSTM

Abstract

There have recently been many cases of unverified or misleading information circulating quickly over bogus web networks and news portals. This false news creates big damage to society and misleads people. For Example, in 2019, there was a rumor that the Padma Bridge of Bangladesh needed 100,000 human heads for sacrifice. This rumor turns into a deadly position and this misleading information takes the lives of innocent people. There is a lot of work in English but a few works in Bangla. In this study, we are going to identify the fake news from the unconsidered news source to provide the newsreader with natural news or real news. The paper is based on the combination of convolutional neural network (CNN) and long short-term memory (LSTM), where CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature.

DA-STGCN: 4D Trajectory Prediction Based on Spatiotemporal Feature Extraction

Abstract

The importance of four-dimensional (4D) trajectory prediction within air traffic management systems is on the rise. Key operations such as conflict detection and resolution, aircraft anomaly monitoring, and the management of congested flight paths are increasingly reliant on this foundational technology, underscoring the urgent demand for intelligent solutions. The dynamics in airport terminal zones and crowded airspaces are intricate and ever-changing; however, current methodologies do not sufficiently account for the interactions among aircraft. To tackle these challenges, we propose DA-STGCN, an innovative spatiotemporal graph convolutional network that integrates a dual attention mechanism. Our model reconstructs the adjacency matrix through a self-attention approach, enhancing the capture of node correlations, and employs graph attention to distill spatiotemporal characteristics, thereby generating a probabilistic distribution of predicted trajectories.

Combined Physics and Event Camera Simulator for Slip Detection

Abstract

Robot manipulation is a common task in fields like industrial manufacturing. Detecting when objects slip from a robot's grasp is crucial for safe and reliable operation. Event cameras, which register pixel-level brightness changes at high temporal resolution (called ``events''), offer an elegant feature when mounted on a robot's end effector: since they only detect motion relative to their viewpoint, a properly grasped object produces no events, while a slipping object immediately triggers them. To research this feature, representative datasets are essential, both for analytic approaches and for training machine learning models. The majority of current research on slip detection with event-based data is done on real-world scenarios and manual data collection, as well as additional setups for data labeling.

Honest to a Fault: Root-Causing Fault Attacks with Pre-Silicon RISC Pipeline Characterization

Abstract

Fault injection attacks represent a class of threats that can compromise embedded systems across multiple layers of abstraction, such as system software, instruction set architecture (ISA), microarchitecture, and physical implementation. Early detection of these vulnerabilities and understanding their root causes along with their propagation from the physical layer to the system software is critical to secure the cyberinfrastructure. This present presents a comprehensive methodology for conducting controlled fault injection attacks at the pre-silicon level and an analysis of the underlying system for root-causing behavior.

Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain

Abstract

We identify the slow liquidity drain (SLID) scam, an insidious and highly profitable threat to decentralized finance (DeFi), posing a large-scale, persistent, and growing risk to the ecosystem. Unlike traditional scams such as rug pulls or honeypots (USENIX Sec'19, USENIX Sec'23), SLID gradually siphons funds from liquidity pools over extended periods, making detection significantly more challenging. In this paper, we conducted the first large-scale empirical analysis of 319,166 liquidity pools across six major decentralized exchanges (DEXs) since 2018. We identified 3,117 SLID affected liquidity pools, resulting in cumulative losses of more than US$103 million.

From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints

Abstract

For the first time, we unveil discernible temporal (or historical) trajectory imprints resulting from adversarial example (AE) attacks. Standing in contrast to existing studies all focusing on spatial (or static) imprints within the targeted underlying victim models, we present a fresh temporal paradigm for understanding these attacks. Of paramount discovery is that these imprints are encapsulated within a single loss metric, spanning universally across diverse tasks such as classification and regression, and modalities including image, text, and audio.

End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems

Abstract

Full 3D human pose reconstruction is a critical enabler for extended reality (XR) applications in future sixth generation (6G) networks, supporting immersive interactions in gaming, virtual meetings, and remote collaboration. However, achieving accurate pose reconstruction over wireless networks remains challenging due to channel impairments, bit errors, and quantization effects. Existing approaches often assume error-free transmission in indoor settings, limiting their applicability to real-world scenarios. To address these challenges, we propose a novel deep learning-based framework for human pose reconstruction over orthogonal frequency-division multiplexing (OFDM) systems.

Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders

Abstract

This paper presents a novel approach to radar target detection using Variational AutoEncoders (VAEs). Known for their ability to learn complex distributions and identify out-of-distribution samples, the proposed VAE architecture effectively distinguishes radar targets from various noise types, including correlated Gaussian and compound Gaussian clutter, often combined with additive white Gaussian thermal noise.

Privacy in Responsible AI: Approaches to Facial Recognition from Cloud Providers

Abstract

As the use of facial recognition technology is expanding in different domains, ensuring its responsible use is gaining more importance. This paper conducts a comprehensive literature review of existing studies on facial recognition technology from the perspective of privacy, which is one of the key Responsible AI principles. Cloud providers, such as Microsoft, AWS, and Google, are at the forefront of delivering facial-related technology services, but their approaches to responsible use of these technologies vary significantly.

Maximizing Signal in Human-Model Preference Alignment

Abstract

The emergence of powerful LLMs has led to a paradigm shift in Natural Language Understanding and Natural Language Generation. The properties that make LLMs so valuable for these tasks -- creativity, ability to produce fluent speech, and ability to quickly and effectively abstract information from large corpora -- also present new challenges to evaluating their outputs. The rush to market has led teams to fall back on quick, cost-effective automatic evaluations which offer value, but do not obviate the need for human judgments in model training and evaluation.

Fine-Tuning Florence2 for Enhanced Object Detection in Un-constructed Environments: Vision-Language Model Approach

Abstract

Artificial intelligence has progressed through the development of Vision-Language Models (VLMs), which integrate text and visual inputs to achieve comprehensive understanding and interaction in various
Q&A: Exploring the Latest Developments in AI and Machine Learning

Q: What is the main focus of the paper "Carelessness Detection using Performance Factor Analysis: A New Operationalization with Unexpectedly Different Relationship to Learning"?

A: The main focus of the paper is to propose a novel model, the Beyond Knowledge Feature Carelessness (BKFC) model, which detects careless errors using performance factor analysis (PFA) and behavioral features distilled from log data, controlling for knowledge when detecting carelessness.

Q: How does the BKFC model differ from the contextual slip model?

A: The BKFC model differs from the contextual slip model in that it uses performance factor analysis (PFA) and behavioral features distilled from log data, whereas the contextual slip model uses conditional probability and Bayesian Knowledge Tracing (BKT).

Q: What is the significance of the paper "Generative AI in Academic Writing: A Comparison of DeepSeek, Qwen, ChatGPT, Gemini, Llama, Mistral, and Gemma"?

A: The paper is significant because it evaluates the academic writing performance of two new LLMs, DeepSeek v3 and Qwen 2.5 Max, and compares them with popular systems such as ChatGPT, Gemini, Llama, Mistral, and Gemma.

Q: What is the main contribution of the paper "Bangla Fake News Detection Based On Multichannel Combined CNN-LSTM"?

A: The main contribution of the paper is the proposal of a novel model that combines convolutional neural network (CNN) and long short-term memory (LSTM) to detect fake news in Bangla.

Q: What is the significance of the paper "DA-STGCN: 4D Trajectory Prediction Based on Spatiotemporal Feature Extraction"?

A: The paper is significant because it proposes a novel spatiotemporal graph convolutional network, DA-STGCN, which integrates a dual attention mechanism to predict 4D trajectories in air traffic management systems.

Q: What is the main focus of the paper "Combined Physics and Event Camera Simulator for Slip Detection"?

A: The main focus of the paper is to propose a simulation pipeline for generating slip data using the described camera-gripper configuration in a robot arm.

Q: What is the significance of the paper "Honest to a Fault: Root-Causing Fault Attacks with Pre-Silicon RISC Pipeline Characterization"?

A: The paper is significant because it presents a comprehensive methodology for conducting controlled fault injection attacks at the pre-silicon level and analyzing the underlying system for root-causing behavior.

Q: What is the main contribution of the paper "Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain"?

A: The main contribution of the paper is the identification of the slow liquidity drain (SLID) scam, an insidious and highly profitable threat to decentralized finance (DeFi), and the analysis of 319,166 liquidity pools across six major decentralized exchanges (DEXs) since 2018.

Q: What is the significance of the paper "From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints"?

A: The paper is significant because it presents a fresh temporal paradigm for understanding adversarial example (AE) attacks and proposes a novel approach to detect AEs using temporal imprints.

Q: What is the main focus of the paper "End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems"?

A: The main focus of the paper is to propose a novel deep learning-based framework for human pose reconstruction over orthogonal frequency-division multiplexing (OFDM) systems.

Q: What is the significance of the paper "Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders"?

A: The paper is significant because it presents a novel approach to radar target detection using Variational AutoEncoders (VAEs) and evaluates its performance in detecting out-of-distribution samples.

Q: What is the main contribution of the paper "Privacy in Responsible AI: Approaches to Facial Recognition from Cloud Providers"?

A: The main contribution of the paper is the comprehensive literature review of existing studies on facial recognition technology from the perspective of privacy and the comparison of approaches to responsible use of facial recognition technologies from cloud providers.

Q: What is the significance of the paper "Maximizing Signal in Human-Model Preference Alignment"?

A: The paper is significant because it highlights the challenges of evaluating the outputs of powerful LLMs and proposes a framework for maximizing signal in human-model preference alignment.

Q: What is the main focus of the paper "Fine-Tuning Florence2 for Enhanced Object Detection in Un-constructed Environments: Vision-Language Model Approach"?

A: The main focus of the paper is to propose a fine-tuning approach for the Florence2 model to enhance object detection in un-constructed environments using a vision-language model approach.

Q: What is the significance of the paper "AUTOFRAME -- A Software-driven Integration Framework for Automotive Systems"?

A: The paper is significant because it proposes a novel framework for integrating software and hardware in automotive systems and demonstrates its effectiveness in a simulated environment.

Q: What is the main contribution of the paper "Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems"?

A: The main contribution of the paper is the proposal of a deliberation-enhancing chatbot, DeepFakeDeLiBot, which supports groups in detecting deepfake text and enhances group dynamics.

Q: What is the significance of the paper "ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects"?

A: The paper is significant because it proposes a large-scale real-world dataset for advancing industrial anomaly detection with synthetic and real defects and evaluates the performance of various anomaly detection methods on this dataset.

Q: What is the main focus of the paper "Adaptive-LIO: Enhancing Robustness and Precision through Environmental Adaptation in LiDAR Inertial Odometry"?

A: The main focus of the paper is to propose a novel approach to LiDAR inertial odometry that adapts to environmental changes and enhances robustness and precision.

Q: What is the significance of the paper "AutoTestForge: A Multidimensional Automated Testing Framework for Natural Language Processing Models"?

A: The paper is significant because it proposes a novel framework for automated testing of natural language processing models and evaluates its effectiveness in detecting errors in NLP models.

Q: What is the main contribution of the paper "HexPlane Representation for 3D Semantic Scene Understanding"?

A: The main contribution of the paper is the proposal of a novel representation, HexPlane, for 3D semantic scene understanding and the evaluation of its effectiveness in 3D scene understanding tasks.

Q: What is the significance of the paper "Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History"?

A: The paper is significant because it proposes a new task for proactive dialogue systems that utilizes conversational history and evaluates the effectiveness of this approach in improving dialogue systems.

Q: What is the main focus of the paper "Vbox: Efficient Black-Box Serializability Verification"?

A: The main focus of the paper is to propose a novel approach to black-box serializability verification and evaluate its effectiveness in detecting data anomalies.

Q: What is the significance of the paper "phepy: Visual Benchmarks and Improvements for Out-of-Distribution Detectors"?

A: The paper is significant because it proposes a novel benchmark for out-of-distribution detectors and evaluates the effectiveness of various out-of-distribution detection methods on this benchmark.

Q: What is the main contribution of the paper "Spectral-Spatial Extraction through Layered Tensor Decomposition for Hyperspectral Anomaly Detection"?

A: The main contribution of the paper is the proposal of a novel approach to hyperspectral anomaly detection using layered tensor decomposition and the evaluation of its effectiveness in detecting anomalies in hyperspectral images.

Q: What is the significance of the paper "Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation"?

A: The paper is significant because it proposes a novel approach to out-of-distribution detection in deep reinforcement learning using transition estimation and evaluates its effectiveness in detecting out-of-distribution samples.

Q: What is the main focus of the paper "ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spaces"?

A: The main focus of the paper is to propose a novel approach to finger photo presentation attack detection using window-attention on color spaces and evaluate its effectiveness in detecting presentation attacks.

Q: What is the significance of the paper "A Map-free Deep Learning-based Framework for Gate-to-Gate Monocular Visual Navigation aboard Miniaturized Aerial Vehicles"?

A: The paper is significant because it proposes a novel framework for gate-to-gate monocular visual navigation aboard miniaturized aerial vehicles and evaluates its effectiveness in navigating through gates.

Q: What is the main contribution of the paper "Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing"?

A: The main contribution of the paper is the proposal of a novel approach to cross-country content moderation using natural language processing and the evaluation of its effectiveness in detecting content moderation decisions.

Q: What is the significance of the paper "Robust Intrusion Detection System with Explainable Artificial Intelligence"?

A: The paper is significant because it proposes a novel approach to intrusion detection using explainable artificial intelligence and evaluates its effectiveness in detecting intrusions.

Q: What is the main focus of the paper "Environment-Aware Scheduling of URLLC and Sensing Services for Smart Industries"?

A: The main focus of the paper is to propose a novel approach