OpenRaiser/Intern-Atlas huggingface dataset:
A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments
Venue: arXiv cs.AI 2024
Year: 2024

Investigating children's embodied learning in mixed-reality environments, where they collaboratively simulate scientific processes, requires analyzing complex multimodal data to interpret their learning and coordination behaviors. Learning scientists have developed Interaction Analysis (IA) methodologies for analyzing such data, but this requires researchers to watch hours of videos to extract and interpret students' learning patterns. Our study aims to simplify researchers' tasks, using Machine Learning and Multimodal Learning Analytics to support the IA processes. Our study combines machine learning algorithms and multimodal analyses to support and streamline researcher efforts in developing a comprehensive understanding of students' scientific engagement through their movements, gaze, and affective responses in a simulated scenario. To facilitate an effective researcher-AI partnership, we present an initial case study to determine the feasibility of visually representing students' states, actions, gaze, affect, and movement on a timeline. Our case study focuses on a specific science scenario where students learn about photosynthesis. The timeline allows us to investigate the alignment of critical learning moments identified by multimodal and interaction analysis, and uncover insights into students' temporal learning progressions.
MGS-SLAM: Monocular Sparse Tracking and Gaussian Mapping with Depth Smooth Regularization
Venue: arXiv cs.CV 2024
Year: 2024

This letter introduces a novel framework for dense Visual Simultaneous Localization and Mapping (VSLAM) based on Gaussian Splatting. Recently, SLAM based on Gaussian Splatting has shown promising results. However, in monocular scenarios, the Gaussian maps reconstructed lack geometric accuracy and exhibit weaker tracking capability. To address these limitations, we jointly optimize sparse visual odometry tracking and 3D Gaussian Splatting scene representation for the first time. We obtain depth maps on visual odometry keyframe windows using a fast Multi-View Stereo (MVS) network for the geometric supervision of Gaussian maps. Furthermore, we propose a depth smooth loss and Sparse-Dense Adjustment Ring (SDAR) to reduce the negative effect of estimated depth maps and preserve the consistency in scale between the visual odometry and Gaussian maps. We have evaluated our system across various synthetic and real-world datasets. The accuracy of our pose estimation surpasses existing methods and achieves state-of-the-art. Additionally, it outperforms previous monocular methods in terms of novel view synthesis and geometric reconstruction fidelities.
On Characterizations of Potential and Ordinal Potential Games
Venue: arXiv cs.GT 2024
Year: 2024

This paper investigates some necessary and sufficient conditions for a game to be a potential game. At first, we extend the classical results of Slade and Monderer and Shapley from games with one-dimensional action spaces to games with multi-dimensional action spaces, which require differentiable cost functions. Then, we provide a necessary and sufficient conditions for a game to have a potential function by investigating the structure of a potential function in terms of the players' cost differences, as opposed to differentials. This condition provides a systematic way for construction of a potential function, which is applied to network congestion games, as an example. Finally, we provide some sufficient conditions for a game to be ordinal potential and generalized ordinal potential.
Uncertainty-aware Semantic Mapping in Off-road Environments with Dempster-Shafer Theory of Evidence
Venue: arXiv cs.RO 2024
Year: 2024

Semantic mapping with Bayesian Kernel Inference (BKI) has shown promise in providing a richer understanding of environments by effectively leveraging local spatial information. However, existing methods face challenges in constructing accurate semantic maps or reliable uncertainty maps in perceptually challenging environments due to unreliable semantic predictions. To address this issue, we propose an evidential semantic mapping framework, which integrates the evidential reasoning of Dempster-Shafer Theory of Evidence (DST) into the entire mapping pipeline by adopting Evidential Deep Learning (EDL) and Dempster's rule of combination. Additionally, the extended belief is devised to incorporate local spatial information based on their uncertainty during the mapping process. Comprehensive experiments across various off-road datasets demonstrate that our framework enhances the reliability of uncertainty maps, consistently outperforming existing methods in scenes with high perceptual uncertainties while showing semantic accuracy comparable to the best-performing semantic mapping techniques.
A Multi-Channel Spatial-Temporal Transformer Model for Traffic Flow Forecasting
Venue: arXiv cs.AI 2024
Year: 2024

Traffic flow forecasting is a crucial task in transportation management and planning. The main challenges for traffic flow forecasting are that (1) as the length of prediction time increases, the accuracy of prediction will decrease; (2) the predicted results greatly rely on the extraction of temporal and spatial dependencies from the road networks. To overcome the challenges mentioned above, we propose a multi-channel spatial-temporal transformer model for traffic flow forecasting, which improves the accuracy of the prediction by fusing results from different channels of traffic data. Our approach leverages graph convolutional network to extract spatial features from each channel while using a transformer-based architecture to capture temporal dependencies across channels. We introduce an adaptive adjacency matrix to overcome limitations in feature extraction from fixed topological structures. Experimental results on six real-world datasets demonstrate that introducing a multi-channel mechanism into the temporal model enhances performance and our proposed model outperforms state-of-the-art models in terms of accuracy.
Smooth Sensitivity for Geo-Privacy
Venue: arXiv cs.CR 2024
Year: 2024

Suppose each user $i$ holds a private value $x_i$ in some metric space $(U, \mathrm{dist})$, and an untrusted data analyst wishes to compute $\sum_i f(x_i)$ for some function $f : U \rightarrow \mathbb{R}$ by asking each user to send in a privatized $f(x_i)$. This is a fundamental problem in privacy-preserving population analytics, and the local model of differential privacy (LDP) is the predominant model under which the problem has been studied. However, LDP requires any two different $x_i, x'_i$ to be $\varepsilon$-distinguishable, which can be overly strong for geometric/numerical data. On the other hand, Geo-Privacy (GP) stipulates that the level of distinguishability be proportional to $\mathrm{dist}(x_i, x_i')$, providing an attractive alternative notion of personal data privacy in a metric space. However, existing GP mechanisms for this problem, which add a uniform noise to either $x_i$ or $f(x_i)$, are not satisfactory. In this paper, we generalize the smooth sensitivity framework from Differential Privacy to Geo-Privacy, which allows us to add noise tailored to the hardness of the given instance. We provide definitions, mechanisms, and a generic procedure for computing the smooth sensitivity under GP equipped with a general metric. Then we present three applications: one-way and two-way threshold functions, and Gaussian kernel density estimation, to demonstrate the applicability and utility of our smooth sensitivity framework.
Distinguishing articles in questionable and non-questionable journals using quantitative indicators associated with quality
Venue: arXiv cs.DL 2024
Year: 2024

This study investigates the viability of distinguishing articles in questionable journals (QJs) from those in non-QJs on the basis of quantitative indicators typically associated with quality. Subsequently, I examine what can be deduced about the quality of articles in QJs based on the differences observed. I contrast the length of abstracts and full-texts, prevalence of spelling errors, text readability, number of references and citations, the size and internationality of the author team, the documentation of ethics and informed consent statements, and the presence erroneous decisions based on statistical errors in 1,714 articles from 31 QJs, 1,691 articles from 16 journals indexed in Web of Science (WoS), and 1,900 articles from 45 mid-tier journals, all in the field of psychology. The results suggest that QJ articles do diverge from the disciplinary standards set by peer-reviewed journals in psychology on quantitative indicators of quality that tend to reflect the effect of peer review and editorial processes. However, mid-tier and WoS journals are also affected by potential quality concerns, such as under-reporting of ethics and informed consent processes and the presence of errors in interpreting statistics. Further research is required to develop a comprehensive understanding of the quality of articles in QJs.
Interpretable Multi-task Learning with Shared Variable Embeddings
Venue: arXiv cs.LG 2024
Year: 2024

This paper proposes a general interpretable predictive system with shared information. The system is able to perform predictions in a multi-task setting where distinct tasks are not bound to have the same input/output structure. Embeddings of input and output variables in a common space are obtained, where the input embeddings are produced through attending to a set of shared embeddings, reused across tasks. All the embeddings are treated as model parameters and learned. Specific restrictions on the space of shared embedings and the sparsity of the attention mechanism are considered. Experiments show that the introduction of shared embeddings does not deteriorate the results obtained from a vanilla variable embeddings method. We run a number of further ablations. Inducing sparsity in the attention mechanism leads to both an increase in accuracy and a significant decrease in the number of training steps required. Shared embeddings provide a measure of interpretability in terms of both a qualitative assessment and the ability to map specific shared embeddings to pre-defined concepts that are not tailored to the considered model. There seems to be a trade-off between accuracy and interpretability. The basic shared embeddings method favors interpretability, whereas the sparse attention method promotes accuracy. The results lead to the conclusion that variable embedding methods may be extended with shared information to provide increased interpretability and accuracy.
KeepOriginalAugment: Single Image-based Better Information-Preserving Data Augmentation Approach
Venue: arXiv cs.CV 2024
Year: 2024

Advanced image data augmentation techniques play a pivotal role in enhancing the training of models for diverse computer vision tasks. Notably, SalfMix and KeepAugment have emerged as popular strategies, showcasing their efficacy in boosting model performance. However, SalfMix reliance on duplicating salient features poses a risk of overfitting, potentially compromising the model's generalization capabilities. Conversely, KeepAugment, which selectively preserves salient regions and augments non-salient ones, introduces a domain shift that hinders the exchange of crucial contextual information, impeding overall model understanding. In response to these challenges, we introduce KeepOriginalAugment, a novel data augmentation approach. This method intelligently incorporates the most salient region within the non-salient area, allowing augmentation to be applied to either region. Striking a balance between data diversity and information preservation, KeepOriginalAugment enables models to leverage both diverse salient and non-salient regions, leading to enhanced performance. We explore three strategies for determining the placement of the salient region minimum, maximum, or random and investigate swapping perspective strategies to decide which part (salient or non-salient) undergoes augmentation. Our experimental evaluations, conducted on classification datasets such as CIFAR-10, CIFAR-100, and TinyImageNet, demonstrate the superior performance of KeepOriginalAugment compared to existing state-of-the-art techniques.
How to Augment for Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models?
Venue: arXiv cs.CV 2024
Year: 2024

Atmospheric turbulence poses a significant challenge to the performance of object detection models. Turbulence causes distortions, blurring, and noise in images by bending and scattering light rays due to variations in the refractive index of air. This results in non-rigid geometric distortions and temporal fluctuations in the electromagnetic radiation received by optical systems. This paper explores the effectiveness of turbulence image augmentation techniques in improving the accuracy and robustness of thermal-adapted and deep learning-based object detection models under atmospheric turbulence. Three distinct approximation-based turbulence simulators (geometric, Zernike-based, and P2S) are employed to generate turbulent training and test datasets. The performance of three state-of-the-art deep learning-based object detection models: RTMDet-x, DINO-4scale, and YOLOv8-x, is employed on these turbulent datasets with and without turbulence augmentation during training. The results demonstrate that utilizing turbulence-specific augmentations during model training can significantly improve detection accuracy and robustness against distorted turbulent images. Turbulence augmentation enhances performance even for a non-turbulent test set.
Inclusive content reduces racial and gender biases, yet non-inclusive content dominates popular culture
Venue: arXiv cs.CY 2024
Year: 2024

Images are often termed as representations of perceived reality. As such, racial and gender biases in popular culture and visual media could play a critical role in shaping people's perceptions of society. While previous research has made significant progress in exploring the frequency and discrepancies in racial and gender group appearances in visual media, it has largely overlooked important nuances in how these groups are portrayed, as it lacked the ability to systematically capture such complexities at scale over time. To address this gap, we examine two media forms of varying target audiences, namely fashion magazines and movie posters. Accordingly, we collect a large dataset comprising over 300,000 images spanning over five decades and utilize state-of-the-art machine learning models to classify not only race and gender but also the posture, expressed emotional state, and body composition of individuals featured in each image. We find that racial minorities appear far less frequently than their White counterparts, and when they do appear, they are portrayed less prominently. We also find that women are more likely to be portrayed with their full bodies, whereas men are more frequently presented with their faces. Finally, through a series of survey experiments, we find evidence that exposure to inclusive content can help reduce biases in perceptions of minorities, while racially and gender-homogenized content may reinforce and amplify such biases. Taken together, our findings highlight that racial and gender biases in visual media remain pervasive, potentially exacerbating existing stereotypes and inequalities.
I3DGS: Improve 3D Gaussian Splatting from Multiple Dimensions
Venue: arXiv cs.CV 2024
Year: 2024

3D Gaussian Splatting is a novel method for 3D view synthesis, which can gain an implicit neural learning rendering result than the traditional neural rendering technology but keep the more high-definition fast rendering speed. But it is still difficult to achieve a fast enough efficiency on 3D Gaussian Splatting for the practical applications. To Address this issue, we propose the I3DS, a synthetic model performance improvement evaluation solution and experiments test. From multiple and important levels or dimensions of the original 3D Gaussian Splatting, we made more than two thousand various kinds of experiments to test how the selected different items and components can make an impact on the training efficiency of the 3D Gaussian Splatting model. In this paper, we will share abundant and meaningful experiences and methods about how to improve the training, performance and the impacts caused by different items of the model. A special but normal Integer compression in base 95 and a floating-point compression in base 94 with ASCII encoding and decoding mechanism is presented. Many real and effective experiments and test results or phenomena will be recorded. After a series of reasonable fine-tuning, I3DS can gain excellent performance improvements than the previous one. The project code is available as open source.
E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple Prediction
Venue: arXiv cs.CL 2024
Year: 2024

Generative approaches have significantly influenced Aspect-Based Sentiment Analysis (ABSA), garnering considerable attention. However, existing studies often predict target text components monolithically, neglecting the benefits of utilizing single elements for tuple prediction. In this paper, we introduce Element to Tuple Prompting (E2TP), employing a two-step architecture. The former step focuses on predicting single elements, while the latter step completes the process by mapping these predicted elements to their corresponding tuples. E2TP is inspired by human problem-solving, breaking down tasks into manageable parts, using the first step's output as a guide in the second step. Within this strategy, three types of paradigms, namely E2TP($diet$), E2TP($f_1$), and E2TP($f_2$), are designed to facilitate the training process. Beyond dataset-specific experiments, our paper addresses cross-domain scenarios, demonstrating the effectiveness and generalizability of the approach. By conducting a comprehensive analysis on various benchmarks, we show that E2TP achieves new state-of-the-art results in nearly all cases.
SketchDream: Sketch-based Text-to-3D Generation and Editing
Venue: arXiv cs.GR 2024
Year: 2024

Existing text-based 3D generation methods generate attractive results but lack detailed geometry control. Sketches, known for their conciseness and expressiveness, have contributed to intuitive 3D modeling but are confined to producing texture-less mesh models within predefined categories. Integrating sketch and text simultaneously for 3D generation promises enhanced control over geometry and appearance but faces challenges from 2D-to-3D translation ambiguity and multi-modal condition integration. Moreover, further editing of 3D models in arbitrary views will give users more freedom to customize their models. However, it is difficult to achieve high generation quality, preserve unedited regions, and manage proper interactions between shape components. To solve the above issues, we propose a text-driven 3D content generation and editing method, SketchDream, which supports NeRF generation from given hand-drawn sketches and achieves free-view sketch-based local editing. To tackle the 2D-to-3D ambiguity challenge, we introduce a sketch-based multi-view image generation diffusion model, which leverages depth guidance to establish spatial correspondence. A 3D ControlNet with a 3D attention module is utilized to control multi-view images and ensure their 3D consistency. To support local editing, we further propose a coarse-to-fine editing approach: the coarse phase analyzes component interactions and provides 3D masks to label edited regions, while the fine stage generates realistic results with refined details by local enhancement. Extensive experiments validate that our method generates higher-quality results compared with a combination of 2D ControlNet and image-to-3D generation techniques and achieves detailed control compared with existing diffusion-based 3D editing approaches.
Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): an NLP-based Method for Evaluating Chess Strategies from Textbooks
Venue: arXiv cs.CL 2024
Year: 2024

The chess domain is well-suited for creating an artificial intelligence (AI) system that mimics real-world challenges, including decision-making. Throughout the years, minimal attention has been paid to investigating insights derived from unstructured chess data sources. In this study, we examine the complicated relationships between multiple referenced moves in a chess-teaching textbook, and propose a novel method designed to encapsulate chess knowledge derived from move-action phrases. This study investigates the feasibility of using a modified sentiment analysis method as a means for evaluating chess moves based on text. Our proposed Aspect-Based Sentiment Analysis (ABSA) method represents an advancement in evaluating the sentiment associated with referenced chess moves. By extracting insights from move-action phrases, our approach aims to provide a more fine-grained and contextually aware `chess move'-based sentiment classification. Through empirical experiments and analysis, we evaluate the performance of our fine-tuned ABSA model, presenting results that confirm the efficiency of our approach in advancing aspect-based sentiment classification within the chess domain. This research contributes to the area of game-playing by machines and shows the practical applicability of leveraging NLP techniques to understand the context of strategic games.
Hal: A Language-General Framework for Analysis of User-Specified Monotone Frameworks [DRAFT]
Venue: arXiv cs.PL 2024
Year: 2024

Writing dataflow analyzers requires both language and domain-specificity. That is to say, each programming language and each program property requires its own analyzer. To enable a streamlined, user-driven approach to dataflow analyzers, we introduce the theoretical framework for a user-specified dataflow analysis. This framework is constructed in such a way that the user has to specify as little as possible, while the analyzer infers and computes everything else, including interprocedural embellishments. This theoretical framework was also implemented in Java, where users can specify a program property alongside minimal extra information to induce a dataflow analysis. This framework (both theoretical and in implementation) is language-general, meaning that it is independent of syntax and semantics (as all necessary syntactic and semantic information is provided by the user, and this information is provided only once for a given language). In this paper, we introduce basic notions of intraprocedural and interprocedural dataflow analyses, the proposed "Implicit Monotone Framework," and a rigorous framework for partial functions as a property space.