OpenRaiser/Intern-Atlas huggingface dataset:
Physics-Aware Iterative Learning and Prediction of Saliency Map for Bimanual Grasp Planning
Venue: arXiv cs.RO 2024
Year: 2024

Learning the skill of human bimanual grasping can extend the capabilities of robotic systems when grasping large or heavy objects. However, it requires a much larger search space for grasp points than single-hand grasping and numerous bimanual grasping annotations for network learning, making both data-driven or analytical grasping methods inefficient and insufficient. We propose a framework for bimanual grasp saliency learning that aims to predict the contact points for bimanual grasping based on existing human single-handed grasping data. We learn saliency corresponding vectors through minimal bimanual contact annotations that establishes correspondences between grasp positions of both hands, capable of eliminating the need for training a large-scale bimanual grasp dataset. The existing single-handed grasp saliency value serves as the initial value for bimanual grasp saliency, and we learn a saliency adjusted score that adds the initial value to obtain the final bimanual grasp saliency value, capable of predicting preferred bimanual grasp positions from single-handed grasp saliency. We also introduce a physics-balance loss function and a physics-aware refinement module that enables physical grasp balance, capable of enhancing the generalization of unknown objects. Comprehensive experiments in simulation and comparisons on dexterous grippers have demonstrated that our method can achieve balanced bimanual grasping effectively.
Timing Advance Estimation in Low Earth Orbit Satellite Networks
Venue: arXiv cs.SI 2024
Year: 2024

Low earth orbit (LEO) satellite communication based on 3GPP standard is seen as a promising solution to rolling out communication services in areas without terrestrial base stations. However, due to the fast movement of satellites and large beam footprint size, the existing 5G timing advance (TA) estimation mechanism cannot be directly applied when global navigation satellite system is unavailable. In this article, an enhanced TA estimation approach is proposed for LEO satellite communication networks. Specifically, a user-side time-frequency pre-compensation method is introduced at first, which leverages frequency offset measurement on synchronization signal blocks broadcasted by satellites in initial cell search phase. For the random access phase, the upper bound of inter-preamble interference incurred by partial-period cross-correlation operations is derived for a preamble format advised by 3GPP, and it is shown that the interference level is closely related to the square of the number of such operations. Inspired by this result, a cyclic prefix free preamble format is further designed, which features extended guard time, differential power allocation and flexible preamble structure. Numerical results show that our proposal can reduce the missed detection rate of preamble within a beam. Particularly, the missed detection rates of preamble under 32, 48, and 64 users are lower than 1% when SNR = -6 dB, which is a significant improvement compared to baselines. In addition, our proposal can limit the TA estimation error of the detected users to the time length of 25 time-domain sampling points when the subcarrier spacing is 30 kHz and operation frequency is 27 GHz.
LoopGaussian: Creating 3D Cinemagraph with Multi-view Images via Eulerian Motion Field
Venue: arXiv cs.CV 2024
Year: 2024

Cinemagraph is a unique form of visual media that combines elements of still photography and subtle motion to create a captivating experience. However, the majority of videos generated by recent works lack depth information and are confined to the constraints of 2D image space. In this paper, inspired by significant progress in the field of novel view synthesis (NVS) achieved by 3D Gaussian Splatting (3D-GS), we propose LoopGaussian to elevate cinemagraph from 2D image space to 3D space using 3D Gaussian modeling. To achieve this, we first employ the 3D-GS method to reconstruct 3D Gaussian point clouds from multi-view images of static scenes,incorporating shape regularization terms to prevent blurring or artifacts caused by object deformation. We then adopt an autoencoder tailored for 3D Gaussian to project it into feature space. To maintain the local continuity of the scene, we devise SuperGaussian for clustering based on the acquired features. By calculating the similarity between clusters and employing a two-stage estimation method, we derive an Eulerian motion field to describe velocities across the entire scene. The 3D Gaussian points then move within the estimated Eulerian motion field. Through bidirectional animation techniques, we ultimately generate a 3D Cinemagraph that exhibits natural and seamlessly loopable dynamics. Experiment results validate the effectiveness of our approach, demonstrating high-quality and visually appealing scene generation. The project is available at https://pokerlishao.github.io/LoopGaussian/.
Fast Gradient Computation for Gromov-Wasserstein Distance
Venue: arXiv cs.LG 2024
Year: 2024

The Gromov-Wasserstein distance is a notable extension of optimal transport. In contrast to the classic Wasserstein distance, it solves a quadratic assignment problem that minimizes the pair-wise distance distortion under the transportation of distributions and thus could apply to distributions in different spaces. These properties make Gromov-Wasserstein widely applicable to many fields, such as computer graphics and machine learning. However, the computation of the Gromov-Wasserstein distance and transport plan is expensive. The well-known Entropic Gromov-Wasserstein approach has a cubic complexity since the matrix multiplication operations need to be repeated in computing the gradient of Gromov-Wasserstein loss. This becomes a key bottleneck of the method. Currently, existing methods accelerate the computation focus on sampling and approximation, which leads to low accuracy or incomplete transport plan. In this work, we propose a novel method to accelerate accurate gradient computation by dynamic programming techniques, reducing the complexity from cubic to quadratic. In this way, the original computational bottleneck is broken and the new entropic solution can be obtained with total quadratic time, which is almost optimal complexity. Furthermore, it can be extended to some variants easily. Extensive experiments validate the efficiency and effectiveness of our method.
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild
Venue: arXiv cs.CV 2024
Year: 2024

Dynamic Facial Expression Recognition (DFER) has received significant interest in the recent years dictated by its pivotal role in enabling empathic and human-compatible technologies. Achieving robustness towards in-the-wild data in DFER is particularly important for real-world applications. One of the directions aimed at improving such models is multimodal emotion recognition based on audio and video data. Multimodal learning in DFER increases the model capabilities by leveraging richer, complementary data representations. Within the field of multimodal DFER, recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders. Another line of research has focused on adapting pre-trained static models for DFER. In this work, we propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders. We identify main challenges associated with this task, namely, intra-modality adaptation, cross-modal alignment, and temporal adaptation, and propose solutions to each of them. As a result, we demonstrate improvement over current state-of-the-art on two popular DFER benchmarks, namely DFEW and MFAW.
Three Disclaimers for Safe Disclosure: A Cardwriter for Reporting the Use of Generative AI in Writing Process
Venue: arXiv cs.CY 2024
Year: 2024

Generative artificial intelligence (AI) and large language models (LLMs) are increasingly being used in the academic writing process. This is despite the current lack of unified framework for reporting the use of machine assistance. In this work, we propose "Cardwriter", an intuitive interface that produces a short report for authors to declare their use of generative AI in their writing process. The demo is available online, at https://cardwriter.vercel.app
Multilingual Evaluation of Semantic Textual Relatedness
Venue: arXiv cs.CL 2024
Year: 2024

The explosive growth of online content demands robust Natural Language Processing (NLP) techniques that can capture nuanced meanings and cultural context across diverse languages. Semantic Textual Relatedness (STR) goes beyond superficial word overlap, considering linguistic elements and non-linguistic factors like topic, sentiment, and perspective. Despite its pivotal role, prior NLP research has predominantly focused on English, limiting its applicability across languages. Addressing this gap, our paper dives into capturing deeper connections between sentences beyond simple word overlap. Going beyond English-centric NLP research, we explore STR in Marathi, Hindi, Spanish, and English, unlocking the potential for information retrieval, machine translation, and more. Leveraging the SemEval-2024 shared task, we explore various language models across three learning paradigms: supervised, unsupervised, and cross-lingual. Our comprehensive methodology gains promising results, demonstrating the effectiveness of our approach. This work aims to not only showcase our achievements but also inspire further research in multilingual STR, particularly for low-resourced languages.
GView: A Versatile Assistant for Security Researchers
Venue: arXiv cs.CR 2024
Year: 2024

Cyber security attacks have become increasingly complex over time, with various phases of their kill chain, involving binaries, scripts, documents, executed commands, vulnerabilities, or network traffic. We propose a tool, GView, that is designed to investigate possible attacks by providing guided analysis for various file types using automatic artifact identification, extraction, coherent correlation &,inference, and meaningful & intuitive views at different levels of granularity w.r.t. revealed information. The concept behind GView simplifies navigation through all payloads in a complex attack, streamlining the process for security researchers, and Increasing the quality of analysis. GView is generic in the sense it supports a variety of file types and has multiple visualization modes that can be automatically adjusted for each file type alone. Our evaluation shows that GView significantly improves the analysis time of an attack compared to conventional tools used in forensics.
Exploring Explainability in Video Action Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Image Classification and Video Action Recognition are perhaps the two most foundational tasks in computer vision. Consequently, explaining the inner workings of trained deep neural networks is of prime importance. While numerous efforts focus on explaining the decisions of trained deep neural networks in image classification, exploration in the domain of its temporal version, video action recognition, has been scant. In this work, we take a deeper look at this problem. We begin by revisiting Grad-CAM, one of the popular feature attribution methods for Image Classification, and its extension to Video Action Recognition tasks and examine the method's limitations. To address these, we introduce Video-TCAV, by building on TCAV for Image Classification tasks, which aims to quantify the importance of specific concepts in the decision-making process of Video Action Recognition models. As the scalable generation of concepts is still an open problem, we propose a machine-assisted approach to generate spatial and spatiotemporal concepts relevant to Video Action Recognition for testing Video-TCAV. We then establish the importance of temporally-varying concepts by demonstrating the superiority of dynamic spatiotemporal concepts over trivial spatial concepts. In conclusion, we introduce a framework for investigating hypotheses in action recognition and quantitatively testing them, thus advancing research in the explainability of deep neural networks used in video action recognition.
EGGS: Edge Guided Gaussian Splatting for Radiance Fields
Venue: arXiv cs.CV 2024
Year: 2024

The Gaussian splatting methods are getting popular. However, their loss function only contains the $\ell_1$ norm and the structural similarity between the rendered and input images, without considering the edges in these images. It is well-known that the edges in an image provide important information. Therefore, in this paper, we propose an Edge Guided Gaussian Splatting (EGGS) method that leverages the edges in the input images. More specifically, we give the edge region a higher weight than the flat region. With such edge guidance, the resulting Gaussian particles focus more on the edges instead of the flat regions. Moreover, such edge guidance does not crease the computation cost during the training and rendering stage. The experiments confirm that such simple edge-weighted loss function indeed improves about $1\sim2$ dB on several difference data sets. With simply plugging in the edge guidance, the proposed method can improve all Gaussian splatting methods in different scenarios, such as human head modeling, building 3D reconstruction, etc.
Private Multiple Linear Computation: A Flexible Communication-Computation Tradeoff
Venue: arXiv cs.IT 2024
Year: 2024

We consider the problem of private multiple linear computation (PMLC) over a replicated storage system with colluding and unresponsive constraints. In this scenario, the user wishes to privately compute $P$ linear combinations of $M$ files from a set of $N$ replicated servers without revealing any information about the coefficients of these linear combinations to any $T$ colluding servers, in the presence of $S$ unresponsive servers that do not provide any information in response to user queries. Our focus is on more general performance metrics where the communication and computational overheads incurred by the user are not neglected. Additionally, the communication and computational overheads for servers are also taken into consideration. Unlike most previous literature that primarily focused on download cost from servers as a performance metric, we propose a novel PMLC scheme to establish a flexible tradeoff between communication costs and computational complexities.
Exploring and Improving Drafts in Blockwise Parallel Decoding
Venue: arXiv cs.CL 2024
Year: 2024

Despite the remarkable strides made by autoregressive language models, their potential is often hampered by the slow inference speeds inherent in sequential token generation. Blockwise parallel decoding (BPD) was proposed by Stern et al. as a method to improve inference speed of language models by simultaneously predicting multiple future tokens, termed block drafts, which are subsequently verified and conditionally accepted by the autoregressive model. This paper contributes to the understanding and improvement of block drafts in two ways. First, we analyze the token distributions produced by multiple prediction heads. Secondly, we leverage this analysis to develop algorithms to improve BPD inference speed by refining the block drafts using n-gram and neural language models. Experiments demonstrate that refined block drafts yield a +5-21% increase in block efficiency (i.e., the number of accepted tokens from the block draft) across diverse datasets.
An Empirical Evaluation of Manually Created Equivalent Mutants
Venue: arXiv cs.SE 2024
Year: 2024

Mutation testing consists of evaluating how effective test suites are at detecting artificially seeded defects in the source code, and guiding the improvement of the test suites. Although mutation testing tools are increasingly adopted in practice, equivalent mutants, i.e., mutants that differ only in syntax but not semantics, hamper this process. While prior research investigated how frequently equivalent mutants are produced by mutation testing tools and how effective existing methods of detecting these equivalent mutants are, it remains unclear to what degree humans also create equivalent mutants, and how well they perform at identifying these. We therefore study these questions in the context of Code Defenders, a mutation testing game, in which players competitively produce mutants and tests. Using manual inspection as well as automated identification methods we establish that less than 10 % of manually created mutants are equivalent. Surprisingly, our findings indicate that a significant portion of developers struggle to accurately identify equivalent mutants, emphasizing the need for improved detection mechanisms and developer training in mutation testing.
Artificial Intelligence enhanced Security Problems in Real-Time Scenario using Blowfish Algorithm
Venue: arXiv cs.CR 2024
Year: 2024

In a nutshell, "the cloud" refers to a collection of interconnected computing resources made possible by an extensive, real-time communication network like the internet. Because of its potential to reduce processing costs, the emerging paradigm of cloud computing has recently attracted a large number of academics. The exponential expansion of cloud computing has made the rapid expansion of cloud services very remarkable. Ensuring the security of personal information in today's interconnected world is no easy task. These days, security is really crucial. Models of security that are relevant to cloud computing include confidentiality, authenticity, accessibility, data integrity, and recovery. Using the Hybrid Encryption this study, we cover all the security issues and leaks in cloud infrastructure.
Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT
Venue: arXiv cs.CL 2024
Year: 2024

In today's rapidly evolving landscape of Artificial Intelligence, large language models (LLMs) have emerged as a vibrant research topic. LLMs find applications in various fields and contribute significantly. Despite their powerful language capabilities, similar to pre-trained language models (PLMs), LLMs still face challenges in remembering events, incorporating new information, and addressing domain-specific issues or hallucinations. To overcome these limitations, researchers have proposed Retrieval-Augmented Generation (RAG) techniques, some others have proposed the integration of LLMs with Knowledge Graphs (KGs) to provide factual context, thereby improving performance and delivering more accurate feedback to user queries. Education plays a crucial role in human development and progress. With the technology transformation, traditional education is being replaced by digital or blended education. Therefore, educational data in the digital environment is increasing day by day. Data in higher education institutions are diverse, comprising various sources such as unstructured/structured text, relational databases, web/app-based API access, etc. Constructing a Knowledge Graph from these cross-data sources is not a simple task. This article proposes a method for automatically constructing a Knowledge Graph from multiple data sources and discusses some initial applications (experimental trials) of KG in conjunction with LLMs for question-answering tasks.
Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers
Venue: arXiv cs.CV 2024
Year: 2024

Few-shot knowledge distillation recently emerged as a viable approach to harness the knowledge of large-scale pre-trained models, using limited data and computational resources. In this paper, we propose a novel few-shot feature distillation approach for vision transformers. Our approach is based on two key steps. Leveraging the fact that vision transformers have a consistent depth-wise structure, we first copy the weights from intermittent layers of existing pre-trained vision transformers (teachers) into shallower architectures (students), where the intermittence factor controls the complexity of the student transformer with respect to its teacher. Next, we employ an enhanced version of Low-Rank Adaptation (LoRA) to distill knowledge into the student in a few-shot scenario, aiming to recover the information processing carried out by the skipped teacher layers. We present comprehensive experiments with supervised and self-supervised transformers as teachers, on six data sets from various domains (natural, medical and satellite images) and tasks (classification and segmentation). The empirical results confirm the superiority of our approach over state-of-the-art competitors. Moreover, the ablation results demonstrate the usefulness of each component of the proposed pipeline. We release our code at https://github.com/dianagrigore/WeCoLoRA.
Self-Selected Attention Span for Accelerating Large Language Model Inference
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) can solve challenging tasks. However, their inference computation on modern GPUs is highly inefficient due to the increasing number of tokens they must attend to as they generate new ones. To address this inefficiency, we capitalize on LLMs' problem-solving capabilities to optimize their own inference-time efficiency. We demonstrate with two specific tasks: (a) evaluating complex arithmetic expressions and (b) summarizing news articles. For both tasks, we create custom datasets to fine-tune an LLM. The goal of fine-tuning is twofold: first, to make the LLM learn to solve the evaluation or summarization task, and second, to train it to identify the minimal attention spans required for each step of the task. As a result, the fine-tuned model is able to convert these self-identified minimal attention spans into sparse attention masks on-the-fly during inference. We develop a custom CUDA kernel to take advantage of the reduced context to attend to. We demonstrate that using this custom CUDA kernel improves the throughput of LLM inference by 28%. Our work presents an end-to-end demonstration showing that training LLMs to self-select their attention spans speeds up autoregressive inference in solving real-world tasks.
Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination -- generating content ungrounded in the realities of training data. Recent work has focused on decoding techniques to improve factuality during inference by leveraging LLMs' hierarchical representation of factual knowledge, manipulating the predicted distributions at inference time. Current state-of-the-art approaches refine decoding by contrasting early-exit distributions from a lower layer with the final layer to exploit information related to factuality within the model forward procedure. However, such methods often assume the final layer is the most reliable and the lower layer selection process depends on it. In this work, we first propose extrapolation of critical token probabilities beyond the last layer for more accurate contrasting. We additionally employ layer-wise entropy-guided lower layer selection, decoupling the selection process from the final layer. Experiments demonstrate strong performance - surpassing state-of-the-art on multiple different datasets by large margins. Analyses show different kinds of prompts respond to different selection strategies.