OpenRaiser/Intern-Atlas huggingface dataset:
Recommendation of data-free class-incremental learning algorithms by simulating future data
Venue: arXiv cs.LG 2024
Year: 2024

Class-incremental learning deals with sequential data streams composed of batches of classes. Various algorithms have been proposed to address the challenging case where samples from past classes cannot be stored. However, selecting an appropriate algorithm for a user-defined setting is an open problem, as the relative performance of these algorithms depends on the incremental settings. To solve this problem, we introduce an algorithm recommendation method that simulates the future data stream. Given an initial set of classes, it leverages generative models to simulate future classes from the same visual domain. We evaluate recent algorithms on the simulated stream and recommend the one which performs best in the user-defined incremental setting. We illustrate the effectiveness of our method on three large datasets using six algorithms and six incremental settings. Our method outperforms competitive baselines, and performance is close to that of an oracle choosing the best algorithm in each setting. This work contributes to facilitate the practical deployment of incremental learning.
Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) have gained popularity in numerous domains, yet they are vulnerable to backdoor attacks that can compromise their performance and ethical application. The detection of these attacks is crucial for maintaining the reliability and security of GNN classification tasks, but effective detection techniques are lacking. Recognizing the challenge in detecting such intrusions, we devised a novel detection method that creatively leverages graph-level explanations. By extracting and transforming secondary outputs from GNN explanation mechanisms, we developed seven innovative metrics for effective detection of backdoor attacks on GNNs. Additionally, we develop an adaptive attack to rigorously evaluate our approach. We test our method on multiple benchmark datasets and examine its efficacy against various attack models. Our results show that our method can achieve high detection performance, marking a significant advancement in safeguarding GNNs against backdoor attacks.
Eternagram: Probing Player Attitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure
Venue: arXiv cs.HC 2024
Year: 2024

Conventional methods of assessing attitudes towards climate change are limited in capturing authentic opinions, primarily stemming from a lack of context-specific assessment strategies and an overreliance on simplistic surveys. Game-based Assessments (GBA) have demonstrated the ability to overcome these issues by immersing participants in engaging gameplay within carefully crafted, scenario-based environments. Concurrently, advancements in AI and Natural Language Processing (NLP) show promise in enhancing the gamified testing environment, achieving this by generating context-aware, human-like dialogues that contribute to a more natural and effective assessment. Our study introduces a new technique for probing climate change attitudes by actualizing a GPT-driven chatbot system in harmony with a game design depicting a futuristic climate scenario. The correlation analysis reveals an assimilation effect, where players' post-game climate awareness tends to align with their in-game perceptions. Key predictors of pro-climate attitudes are identified as traits like 'Openness' and 'Agreeableness', and a preference for democratic values.
A Study of Three Influencer Archetypes for the Control of Opinion Spread in Time-Varying Social Networks
Venue: arXiv cs.SI 2024
Year: 2024

In this work we consider the impact of information spread in time-varying social networks, where agents request to follow other agents with aligned opinions while dropping ties to neighbors whose posts are too dissimilar to their own views. Opinion control and rhetorical influence has a very long history, employing various methods including education, persuasion, propaganda, marketing, and manipulation through mis-, dis-, and mal-information. The automation of opinion controllers, however, has only recently become easily deployable at a wide scale, with the advent of large language models (LLMs) and generative AI that can translate the quantified commands from opinion controllers into actual content with the appropriate nuance. Automated agents in social networks can be deployed for various purposes, such as breaking up echo chambers, bridging valuable new connections between agents, or shaping the opinions of a target population -- and all of these raise important ethical concerns that deserve serious attention and thoughtful discussion and debate. This paper attempts to contribute to this discussion by considering three archetypal influencing styles observed by human drivers in these settings, comparing and contrasting the impact of these different control methods on the opinions of agents in the network. We will demonstrate the efficacy of current generative AI for generating nuanced content consistent with the command signal from automatic opinion controllers like these, and we will report on frameworks for approaching the relevant ethical considerations.
Vision-Based Force Estimation for Minimally Invasive Telesurgery Through Contact Detection and Local Stiffness Models
Venue: arXiv cs.RO 2024
Year: 2024

In minimally invasive telesurgery, obtaining accurate force information is difficult due to the complexities of in-vivo end effector force sensing. This constrains development and implementation of haptic feedback and force-based automated performance metrics, respectively. Vision-based force sensing approaches using deep learning are a promising alternative to intrinsic end effector force sensing. However, they have limited ability to generalize to novel scenarios, and require learning on high-quality force sensor training data that can be difficult to obtain. To address these challenges, this paper presents a novel vision-based contact-conditional approach for force estimation in telesurgical environments. Our method leverages supervised learning with human labels and end effector position data to train deep neural networks. Predictions from these trained models are optionally combined with robot joint torque information to estimate forces indirectly from visual data. We benchmark our method against ground truth force sensor data and demonstrate generality by fine-tuning to novel surgical scenarios in a data-efficient manner. Our methods demonstrated greater than 90% accuracy on contact detection and less than 10% force prediction error. These results suggest potential usefulness of contact-conditional force estimation for sensory substitution haptic feedback and tissue handling skill evaluation in clinical settings.
LLMs in HCI Data Work: Bridging the Gap Between Information Retrieval and Responsible Research Practices
Venue: arXiv cs.HC 2024
Year: 2024

Efficient and accurate information extraction from scientific papers is significant in the rapidly developing human-computer interaction research in the literature review process. Our paper introduces and analyses a new information retrieval system using state-of-the-art Large Language Models (LLMs) in combination with structured text analysis techniques to extract experimental data from HCI literature, emphasizing key elements. Then We analyze the challenges and risks of using LLMs in the world of research. We performed a comprehensive analysis on our conducted dataset, which contained the specified information of 300 CHI 2020-2022 papers, to evaluate the performance of the two large language models, GPT-3.5 (text-davinci-003) and Llama-2-70b, paired with structured text analysis techniques. The GPT-3.5 model gains an accuracy of 58\% and a mean absolute error of 7.00. In contrast, the Llama2 model indicates an accuracy of 56\% with a mean absolute error of 7.63. The ability to answer questions was also included in the system in order to work with streamlined data. By evaluating the risks and opportunities presented by LLMs, our work contributes to the ongoing dialogue on establishing methodological validity and ethical guidelines for LLM use in HCI data work.
EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications
Venue: arXiv cs.AI 2024
Year: 2024

Artificial intelligence (AI) techniques are widely applied in the life sciences. However, applying innovative AI techniques to understand and deconvolute biological complexity is hindered by the learning curve for life science scientists to understand and use computing languages. An open-source, user-friendly interface for AI models, that does not require programming skills to analyze complex biological data will be extremely valuable to the bioinformatics community. With easy access to different sequencing technologies and increased interest in different 'omics' studies, the number of biological datasets being generated has increased and analyzing these high-throughput datasets is computationally demanding. The majority of AI libraries today require advanced programming skills as well as machine learning, data preprocessing, and visualization skills. In this research, we propose a web-based end-to-end pipeline that is capable of preprocessing, training, evaluating, and visualizing machine learning (ML) models without manual intervention or coding expertise. By integrating traditional machine learning and deep neural network models with visualizations, our library assists in recognizing, classifying, clustering, and predicting a wide range of multi-modal, multi-sensor datasets, including images, languages, and one-dimensional numerical data, for drug discovery, pathogen classification, and medical diagnostics.
Exploring the Privacy Protection Capabilities of Chinese Large Language Models
Venue: arXiv cs.AI 2024
Year: 2024

Large language models (LLMs), renowned for their impressive capabilities in various tasks, have significantly advanced artificial intelligence. Yet, these advancements have raised growing concerns about privacy and security implications. To address these issues and explain the risks inherent in these models, we have devised a three-tiered progressive framework tailored for evaluating privacy in language systems. This framework consists of progressively complex and in-depth privacy test tasks at each tier. Our primary objective is to comprehensively evaluate the sensitivity of large language models to private information, examining how effectively they discern, manage, and safeguard sensitive data in diverse scenarios. This systematic evaluation helps us understand the degree to which these models comply with privacy protection guidelines and the effectiveness of their inherent safeguards against privacy breaches. Our observations indicate that existing Chinese large language models universally show privacy protection shortcomings. It seems that at the moment this widespread issue is unavoidable and may pose corresponding privacy risks in applications based on these models.
Sailing Through Point Clouds: Safe Navigation Using Point Cloud Based Control Barrier Functions
Venue: arXiv cs.RO 2024
Year: 2024

The capability to navigate safely in an unstructured environment is crucial when deploying robotic systems in real-world scenarios. Recently, control barrier function (CBF) based approaches have been highly effective in synthesizing safety-critical controllers. In this work, we propose a novel CBF-based local planner comprised of two components: Vessel and Mariner. The Vessel is a novel scaling factor based CBF formulation that synthesizes CBFs using only point cloud data. The Mariner is a CBF-based preview control framework that is used to mitigate getting stuck in spurious equilibria during navigation. To demonstrate the efficacy of our proposed approach, we first compare the proposed point cloud based CBF formulation with other point cloud based CBF formulations. Then, we demonstrate the performance of our proposed approach and its integration with global planners using experimental studies on the Unitree B1 and Unitree Go2 quadruped robots in various environments.
Preference-Based Planning in Stochastic Environments: From Partially-Ordered Temporal Goals to Most Preferred Policies
Venue: arXiv cs.RO 2024
Year: 2024

Human preferences are not always represented via complete linear orders: It is natural to employ partially-ordered preferences for expressing incomparable outcomes. In this work, we consider decision-making and probabilistic planning in stochastic systems modeled as Markov decision processes (MDPs), given a partially ordered preference over a set of temporally extended goals. Specifically, each temporally extended goal is expressed using a formula in Linear Temporal Logic on Finite Traces (LTL$_f$). To plan with the partially ordered preference, we introduce order theory to map a preference over temporal goals to a preference over policies for the MDP. Accordingly, a most preferred policy under a stochastic ordering induces a stochastic nondominated probability distribution over the finite paths in the MDP. To synthesize a most preferred policy, our technical approach includes two key steps. In the first step, we develop a procedure to transform a partially ordered preference over temporal goals into a computational model, called preference automaton, which is a semi-automaton with a partial order over acceptance conditions. In the second step, we prove that finding a most preferred policy is equivalent to computing a Pareto-optimal policy in a multi-objective MDP that is constructed from the original MDP, the preference automaton, and the chosen stochastic ordering relation. Throughout the paper, we employ running examples to illustrate the proposed preference specification and solution approaches. We demonstrate the efficacy of our algorithm using these examples, providing detailed analysis, and then discuss several potential future directions.
How is Testing Related to Single Statement Bugs?
Venue: arXiv cs.SE 2024
Year: 2024

In this study, we analyzed the correlation between unit test coverage and the occurrence of Single Statement Bugs (SSBs) in open-source Java projects. We analyzed data from the top 100 Maven-based projects on GitHub, which includes 7824 SSBs. Our preliminary findings suggest a weak to moderate correlation, indicating that increased test coverage is somewhat reduce the occurrence of SSBs. However, this relationship is not very strong, emphasizing the need for better tests. Our study contributes to the ongoing discussion on enhancing software quality and provides a basis for future research into effective testing practices aimed at mitigating SSBs.
Enhancing Metaphor Detection through Soft Labels and Target Word Prediction
Venue: arXiv cs.CL 2024
Year: 2024

Metaphors play a significant role in our everyday communication, yet detecting them presents a challenge. Traditional methods often struggle with improper application of language rules and a tendency to overlook data sparsity. To address these issues, we integrate knowledge distillation and prompt learning into metaphor detection. Our approach revolves around a tailored prompt learning framework specifically designed for metaphor detection. By strategically masking target words and providing relevant prompt data, we guide the model to accurately predict the contextual meanings of these words. This approach not only mitigates confusion stemming from the literal meanings of the words but also ensures effective application of language rules for metaphor detection. Furthermore, we've introduced a teacher model to generate valuable soft labels. These soft labels provide a similar effect to label smoothing and help prevent the model from becoming over confident and effectively addresses the challenge of data sparsity. Experimental results demonstrate that our model has achieved state-of-the-art performance, as evidenced by its remarkable results across various datasets.
GeNet: A Graph Neural Network-based Anti-noise Task-Oriented Semantic Communication Paradigm
Venue: arXiv cs.LG 2024
Year: 2024

Traditional approaches to semantic communication tasks rely on the knowledge of the signal-to-noise ratio (SNR) to mitigate channel noise. Moreover, these methods necessitate training under specific SNR conditions, entailing considerable time and computational resources. In this paper, we propose GeNet, a Graph Neural Network (GNN)-based paradigm for semantic communication aimed at combating noise, thereby facilitating Task-Oriented Communication (TOC). We propose a novel approach where we first transform the input data image into graph structures. Then we leverage a GNN-based encoder to extract semantic information from the source data. This extracted semantic information is then transmitted through the channel. At the receiver's end, a GNN-based decoder is utilized to reconstruct the relevant semantic information from the source data for TOC. Through experimental evaluation, we show GeNet's effectiveness in anti-noise TOC while decoupling the SNR dependency. We further evaluate GeNet's performance by varying the number of nodes, revealing its versatility as a new paradigm for semantic communication. Additionally, we show GeNet's robustness to geometric transformations by testing it with different rotation angles, without resorting to data augmentation.
HotStuff-2 vs. HotStuff: The Difference and Advantage
Venue: arXiv cs.CR 2024
Year: 2024

Byzantine consensus protocols are essential in blockchain technology. The widely recognized HotStuff protocol uses cryptographic measures for efficient view changes and reduced communication complexity. Recently, the main authors of HotStuff introduced an advanced iteration named HotStuff-2. This paper aims to compare the principles and analyze the effectiveness of both protocols, hoping to depict their key differences and assess the potential enhancements offered by HotStuff-2.
Comparison of different methods for identification of dominant oscillation mode
Venue: arXiv cs.CE 2024
Year: 2024

This paper introduces and compares the various techniques for identification and analysis of low frequency oscillations in a power system. Inter-area electromechanical oscillations are the focus of this paper. After multiresolution decomposition of characteristic signals, physical characteristics of system oscillations in signal components are identified and presented using the Fourier transform, Prony method, Matrix Pencil Analysis Method, S-transform, Global Wavelet Spectrum and Hilbert Huang transform (Hilbert Marginal Spectrum) in time-frequency domain representation. The analyses were performed on real frequency signals obtained from FNET GridEye system during the earthquake that triggered the shutdown of the North Anna Nuclear Generating Station in the east coast of the United States. In addition, according to the obtained results the proposed methods have proven to be reliable for identification of the model parameters of low-frequency oscillation in power systems. The relevant analyses are carried out in MATLAB coding environment.
Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons
Venue: arXiv cs.LG 2024
Year: 2024

Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA), suffer from their computationally demanding nature, becoming advisable for their implementation onto high-performance computer architectures for applications under strict latency constraints. This work presents the implementation of the PCA algorithm onto two different high-performance devices, namely, an NVIDIA Graphics Processing Unit (GPU) and a Kalray manycore, uncovering a highly valuable set of tips and tricks in order to take full advantage of the inherent parallelism of these high-performance computing platforms, and hence, reducing the time that is required to process a given hyperspectral image. Moreover, the achieved results obtained with different hyperspectral images have been compared with the ones that were obtained with a field programmable gate array (FPGA)-based implementation of the PCA algorithm that has been recently published, providing, for the first time in the literature, a comprehensive analysis in order to highlight the pros and cons of each option.
How to Cache Important Contents for Multi-modal Service in Dynamic Networks: A DRL-based Caching Scheme
Venue: arXiv cs.NI 2024
Year: 2024

With the continuous evolution of networking technologies, multi-modal services that involve video, audio, and haptic contents are expected to become the dominant multimedia service in the near future. Edge caching is a key technology that can significantly reduce network load and content transmission latency, which is critical for the delivery of multi-modal contents. However, existing caching approaches only rely on a limited number of factors, e.g., popularity, to evaluate their importance for caching, which is inefficient for caching multi-modal contents, especially in dynamic network environments. To overcome this issue, we propose a content importance-based caching scheme which consists of a content importance evaluation model and a caching model. By leveraging dueling double deep Q networks (D3QN) model, the content importance evaluation model can adaptively evaluate contents' importance in dynamic networks. Based on the evaluated contents' importance, the caching model can easily cache and evict proper contents to improve caching efficiency. The simulation results show that the proposed content importance-based caching scheme outperforms existing caching schemes in terms of caching hit ratio (at least 15% higher), reduced network load (up to 22% reduction), average number of hops (up to 27% lower), and unsatisfied requests ratio (more than 47% reduction).
Merits of Time-Domain Computing for VMM -- A Quantitative Comparison
Venue: arXiv cs.AR 2024
Year: 2024

Vector-matrix-multiplication (VMM) accel-erators have gained a lot of traction, especially due to therise of convolutional neural networks (CNNs) and the desireto compute them on the edge. Besides the classical digitalapproach, analog computing has gone through a renais-sance to push energy efficiency further. A more recent ap-proach is called time-domain (TD) computing. In contrastto analog computing, TD computing permits easy technol-ogy as well as voltage scaling. As it has received limitedresearch attention, it is not yet clear which scenarios aremost suitable to be computed in the TD. In this work, weinvestigate these scenarios, focussing on energy efficiencyconsidering approximative computations that preserve ac-curacy. Both goals are addressed by a novel efficiency met-ric, which is used to find a baseline design. We use SPICEsimulation data which is fed into a python framework toevaluate how performance scales for VMM computation.We see that TD computing offers best energy efficiency forsmall to medium sized arrays. With throughput and sili-con footprint we investigate two additional metrics, givinga holistic comparison.