OpenRaiser/Intern-Atlas huggingface dataset:
Implementing a Robot Intrusion Prevention System (RIPS) for ROS 2
Venue: arXiv cs.RO 2024
Year: 2024

It is imperative to develop an intrusion prevention system (IPS), specifically designed for autonomous robotic systems. This is due to the unique nature of these cyber-physical systems (CPS), which are not merely typical distributed systems. These systems employ their own systems software (i.e. robotic middleware and frameworks) and execute distinct components to facilitate interaction with various sensors and actuators, and other robotic components (e.g. cognitive subsystems). Furthermore, as cyber-physical systems, they engage in interactions with humans and their physical environment, as exemplified by social robots. These interactions can potentially lead to serious consequences, including physical damage. In response to this need, we have designed and implemented RIPS, an intrusion prevention system tailored for robotic applications based on ROS 2, the framework that has established itself as the de facto standard for developing robotic applications. This manuscript provides a comprehensive exposition of the issue, the security aspects of ROS 2 applications, and the key points of the threat model we created for our robotic environment. It also describes the architecture and the implementation of our initial research prototype and a language specifically designed for defining detection and prevention rules for diverse, real-world robotic scenarios. Moreover, the manuscript provides a comprehensive evaluation of the approach, that includes a set of experiments with a real social robot executing a well known testbed used in international robotic competitions.
Memory-Centric Computing: Recent Advances in Processing-in-DRAM
Venue: arXiv cs.AR 2024
Year: 2024

Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performance and energy impact of data access and data movement, by 1) fundamentally avoiding data movement, 2) reducing data access latency & energy, and 3) exploiting large parallelism of memory arrays. Many recent studies show that memory-centric computing can largely improve system performance & energy efficiency. Major industrial vendors and startup companies have recently introduced memory chips with sophisticated computation capabilities. Going forward, both hardware and software stack should be revisited and designed carefully to take advantage of memory-centric computing. This work describes several major recent advances in memory-centric computing, specifically in Processing-in-DRAM, a paradigm where the operational characteristics of a DRAM chip are exploited and enhanced to perform computation on data stored in DRAM. Specifically, we describe 1) new techniques that slightly modify DRAM chips to enable both enhanced computation capability and easier programmability, 2) new experimental studies that demonstrate the functionally-complete bulk-bitwise computational capability of real commercial off-the-shelf DRAM chips, without any modifications to the DRAM chip or the interface, and 3) new DRAM designs that improve access granularity & efficiency, unleashing the true potential of Processing-in-DRAM.
KALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing
Venue: arXiv cs.CV 2024
Year: 2024

Deep hashing has been widely used for large-scale approximate nearest neighbor search due to its storage and search efficiency. However, existing deep hashing methods predominantly rely on abundant training data, leaving the more challenging scenario of low-resource adaptation for deep hashing relatively underexplored. This setting involves adapting pre-trained models to downstream tasks with only an extremely small number of training samples available. Our preliminary benchmarks reveal that current methods suffer significant performance degradation due to the distribution shift caused by limited training samples. To address these challenges, we introduce Class-Calibration LoRA (CLoRA), a novel plug-and-play approach that dynamically constructs low-rank adaptation matrices by leveraging class-level textual knowledge embeddings. CLoRA effectively incorporates prior class knowledge as anchors, enabling parameter-efficient fine-tuning while maintaining the original data distribution. Furthermore, we propose Knowledge-Guided Discrete Optimization (KIDDO), a framework to utilize class knowledge to compensate for the scarcity of visual information and enhance the discriminability of hash codes. Extensive experiments demonstrate that our proposed method, Knowledge- Anchored Low-Resource Adaptation Hashing (KALAHash), significantly boosts retrieval performance and achieves a 4x data efficiency in low-resource scenarios.
A Prototype Unit for Image De-raining using Time-Lapse Data
Venue: arXiv cs.CV 2024
Year: 2024

We address the challenge of single-image de-raining, a task that involves recovering rain-free background information from a single rain image. While recent advancements have utilized real-world time-lapse data for training, enabling the estimation of consistent backgrounds and realistic rain streaks, these methods often suffer from computational and memory consumption, limiting their applicability in real-world scenarios. In this paper, we introduce a novel solution: the Rain Streak Prototype Unit (RsPU). The RsPU efficiently encodes rain streak-relevant features as real-time prototypes derived from time-lapse data, eliminating the need for excessive memory resources. Our de-raining network combines encoder-decoder networks with the RsPU, allowing us to learn and encapsulate diverse rain streak-relevant features as concise prototypes, employing an attention-based approach. To ensure the effectiveness of our approach, we propose a feature prototype loss encompassing cohesion and divergence components. This loss function captures both the compactness and diversity aspects of the prototypical rain streak features within the RsPU. Our method evaluates various de-raining benchmarks, accompanied by comprehensive ablation studies. We show that it can achieve competitive results in various rain images compared to state-of-the-art methods.
Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis
Venue: arXiv cs.CV 2024
Year: 2024

Helmet detection is crucial for advancing protection levels in public road traffic dynamics. This problem statement translates to an object detection task. Therefore, this paper compares recent You Only Look Once (YOLO) models in the context of helmet detection in terms of reliability and computational load. Specifically, YOLOv8, YOLOv9, and the newly released YOLOv11 have been used. Besides, a modified architectural pipeline that remarkably improves the overall performance has been proposed in this manuscript. This hybridized YOLO model (h-YOLO) has been pitted against the independent models for analysis that proves h-YOLO is preferable for helmet detection over plain YOLO models. The models were tested using a range of standard object detection benchmarks such as recall, precision, and mAP (Mean Average Precision). In addition, training and testing times were recorded to provide the overall scope of the models in a real-time detection scenario.
Generative Adversarial Network on Motion-Blur Image Restoration
Venue: arXiv cs.CV 2024
Year: 2024

In everyday life, photographs taken with a camera often suffer from motion blur due to hand vibrations or sudden movements. This phenomenon can significantly detract from the quality of the images captured, making it an interesting challenge to develop a deep learning model that utilizes the principles of adversarial networks to restore clarity to these blurred pixels. In this project, we will focus on leveraging Generative Adversarial Networks (GANs) to effectively deblur images affected by motion blur. A GAN-based Tensorflow model is defined, training and evaluating by GoPro dataset which comprises paired street view images featuring both clear and blurred versions. This adversarial training process between Discriminator and Generator helps to produce increasingly realistic images over time. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are the two evaluation metrics used to provide quantitative measures of image quality, allowing us to evaluate the effectiveness of the deblurring process. Mean PSNR in 29.1644 and mean SSIM in 0.7459 with average 4.6921 seconds deblurring time are achieved in this project. The blurry pixels are sharper in the output of GAN model shows a good image restoration effect in real world applications.
Learning Radiance Fields from a Single Snapshot Compressive Image
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we explore the potential of Snapshot Compressive Imaging (SCI) technique for recovering the underlying 3D scene structure from a single temporal compressed image. SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging sensors. To achieve this, a series of specially designed 2D masks are usually employed, reducing storage and transmission requirements and offering potential privacy protection. Inspired by this, we take one step further to recover the encoded 3D scene information leveraging powerful 3D scene representation capabilities of neural radiance fields (NeRF). Specifically, we propose SCINeRF, in which we formulate the physical imaging process of SCI as part of the training of NeRF, allowing us to exploit its impressive performance in capturing complex scene structures. In addition, we further integrate the popular 3D Gaussian Splatting (3DGS) framework and propose SCISplat to improve 3D scene reconstruction quality and training/rendering speed by explicitly optimizing point clouds into 3D Gaussian representations. To assess the effectiveness of our method, we conduct extensive evaluations using both synthetic data and real data captured by our SCI system. Experimental results demonstrate that our proposed approach surpasses the state-of-the-art methods in terms of image reconstruction and novel view synthesis. Moreover, our method also exhibits the ability to render high frame-rate multi-view consistent images in real time by leveraging SCI and the rendering capabilities of 3DGS. Codes will be available at: https://github.com/WU- CVGL/SCISplat.
User Willingness-aware Sales Talk Dataset
Venue: arXiv cs.CL 2024
Year: 2024

User willingness is a crucial element in the sales talk process that affects the achievement of the salesperson's or sales system's objectives. Despite the importance of user willingness, to the best of our knowledge, no previous study has addressed the development of automated sales talk dialogue systems that explicitly consider user willingness. A major barrier is the lack of sales talk datasets with reliable user willingness data. Thus, in this study, we developed a user willingness-aware sales talk collection by leveraging the ecological validity concept, which is discussed in the field of human-computer interaction. Our approach focused on three types of user willingness essential in real sales interactions. We created a dialogue environment that closely resembles real-world scenarios to elicit natural user willingness, with participants evaluating their willingness at the utterance level from multiple perspectives. We analyzed the collected data to gain insights into practical user willingness-aware sales talk strategies. In addition, as a practical application of the constructed dataset, we developed and evaluated a sales dialogue system aimed at enhancing the user's intent to purchase.
Multi-label Classification using Deep Multi-order Context-aware Kernel Networks
Venue: arXiv cs.CV 2024
Year: 2024

Multi-label classification is a challenging task in pattern recognition. Many deep learning methods have been proposed and largely enhanced classification performance. However, most of the existing sophisticated methods ignore context in the models' learning process. Since context may provide additional cues to the learned models, it may significantly boost classification performances. In this work, we make full use of context information (namely geometrical structure of images) in order to learn better context-aware similarities (a.k.a. kernels) between images. We reformulate context-aware kernel design as a feed-forward network that outputs explicit kernel mapping features. Our obtained context-aware kernel network further leverages multiple orders of patch neighbors within different distances, resulting into a more discriminating Deep Multi-order Context-aware Kernel Network (DMCKN) for multi-label classification. We evaluate the proposed method on the challenging Corel5K and NUS-WIDE benchmarks, and empirical results show that our method obtains competitive performances against the related state-of-the-art, and both quantitative and qualitative performances corroborate its effectiveness and superiority for multi-label image classification.
Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation
Venue: arXiv cs.CL 2024
Year: 2024

Neural Machine Translation (NMT) systems built on multilingual sequence-to-sequence Language Models (msLMs) fail to deliver expected results when the amount of parallel data for a language, as well as the language's representation in the model are limited. This restricts the capabilities of domain-specific NMT systems for low-resource languages (LRLs). As a solution, parallel data from auxiliary domains can be used either to fine-tune or to further pre-train the msLM. We present an evaluation of the effectiveness of these two techniques in the context of domain-specific LRL-NMT. We also explore the impact of domain divergence on NMT model performance. We recommend several strategies for utilizing auxiliary parallel data in building domain-specific NMT models for LRLs.
Learning states enhanced knowledge tracing: Simulating the diversity in real-world learning process
Venue: arXiv cs.AI 2024
Year: 2024

The Knowledge Tracing (KT) task focuses on predicting a learner's future performance based on the historical interactions. The knowledge state plays a key role in learning process. However, considering that the knowledge state is influenced by various learning factors in the interaction process, such as the exercises similarities, responses reliability and the learner's learning state. Previous models still face two major limitations. First, due to the exercises differences caused by various complex reasons and the unreliability of responses caused by guessing behavior, it is hard to locate the historical interaction which is most relevant to the current answered exercise. Second, the learning state is also a key factor to influence the knowledge state, which is always ignored by previous methods. To address these issues, we propose a new method named Learning State Enhanced Knowledge Tracing (LSKT). Firstly, to simulate the potential differences in interactions, inspired by Item Response Theory~(IRT) paradigm, we designed three different embedding methods ranging from coarse-grained to fine-grained views and conduct comparative analysis on them. Secondly, we design a learning state extraction module to capture the changing learning state during the learning process of the learner. In turn, with the help of the extracted learning state, a more detailed knowledge state could be captured. Experimental results on four real-world datasets show that our LSKT method outperforms the current state-of-the-art methods.
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference
Venue: arXiv cs.CV 2024
Year: 2024

Image Quality Assessment (IQA) with references plays an important role in optimizing and evaluating computer vision tasks. Traditional methods assume that all pixels of the reference and test images are fully aligned. Such Aligned-Reference IQA (AR-IQA) approaches fail to address many real-world problems with various geometric deformations between the two images. Although significant effort has been made to attack Geometrically-Disparate-Reference IQA (GDR-IQA) problem, it has been addressed in a task-dependent fashion, for example, by dedicated designs for image super-resolution and retargeting, or by assuming the geometric distortions to be small that can be countered by translation-robust filters or by explicit image registrations. Here we rethink this problem and propose a unified, non-training-based Deep Structural Similarity (DeepSSIM) approach to address the above problems in a single framework, which assesses structural similarity of deep features in a simple but efficient way and uses an attention calibration strategy to alleviate attention deviation. The proposed method, without application-specific design, achieves state-of-the-art performance on AR-IQA datasets and meanwhile shows strong robustness to various GDR-IQA test cases. Interestingly, our test also shows the effectiveness of DeepSSIM as an optimization tool for training image super-resolution, enhancement and restoration, implying an even wider generalizability. \footnote{Source code will be made public after the review is completed.
Reinforced Label Denoising for Weakly-Supervised Audio-Visual Video Parsing
Venue: arXiv cs.CV 2024
Year: 2024

Audio-visual video parsing (AVVP) aims to recognize audio and visual event labels with precise temporal boundaries, which is quite challenging since audio or visual modality might include only one event label with only the overall video labels available. Existing label denoising models often treat the denoising process as a separate preprocessing step, leading to a disconnect between label denoising and AVVP tasks. To bridge this gap, we present a novel joint reinforcement learning-based label denoising approach (RLLD). This approach enables simultaneous training of both label denoising and video parsing models through a joint optimization strategy. We introduce a novel AVVP-validation and soft inter-reward feedback mechanism that directly guides the learning of label denoising policy. Extensive experiments on AVVP tasks demonstrate the superior performance of our proposed method compared to label denoising techniques. Furthermore, by incorporating our label denoising method into other AVVP models, we find that it can further enhance parsing results.
Goal-oriented Communications based on Recursive Early Exit Neural Networks
Venue: arXiv cs.LG 2024
Year: 2024

This paper presents a novel framework for goal-oriented semantic communications leveraging recursive early exit models. The proposed approach is built on two key components. First, we introduce an innovative early exit strategy that dynamically partitions computations, enabling samples to be offloaded to a server based on layer-wise recursive prediction dynamics that detect samples for which the confidence is not increasing fast enough over layers. Second, we develop a Reinforcement Learning-based online optimization framework that jointly determines early exit points, computation splitting, and offloading strategies, while accounting for wireless conditions, inference accuracy, and resource costs. Numerical evaluations in an edge inference scenario demonstrate the method's adaptability and effectiveness in striking an excellent trade-off between performance, latency, and resource efficiency.
Machine Generated Product Advertisements: Benchmarking LLMs Against Human Performance
Venue: arXiv cs.CL 2024
Year: 2024

This study compares the performance of AI-generated and human-written product descriptions using a multifaceted evaluation model. We analyze descriptions for 100 products generated by four AI models (Gemma 2B, LLAMA, GPT2, and ChatGPT 4) with and without sample descriptions, against human-written descriptions. Our evaluation metrics include sentiment, readability, persuasiveness, Search Engine Optimization(SEO), clarity, emotional appeal, and call-to-action effectiveness. The results indicate that ChatGPT 4 performs the best. In contrast, other models demonstrate significant shortcomings, producing incoherent and illogical output that lacks logical structure and contextual relevance. These models struggle to maintain focus on the product being described, resulting in disjointed sentences that do not convey meaningful information. This research provides insights into the current capabilities and limitations of AI in the creation of content for e-Commerce.
Xmodel-2 Technical Report
Venue: arXiv cs.AI 2024
Year: 2024

Xmodel-2 is a 1.2-billion-parameter large language model designed specifically for reasoning tasks. Its architecture enables different model scales to share a unified set of hyperparameters, allowing for extensive experimentation on smaller models and seamless transfer of optimal configurations to larger models. To maximize training efficiency and stability, Xmodel-2 employs the WSD learning rate scheduler from MiniCPM. Pretrained on 1.5 trillion tokens from diverse sources, Xmodel-2 achieves state-of-the-art performance in complex reasoning and agent-based tasks, while maintaining low training costs. These results highlight the potential of efficient model design and training strategies in advancing reasoning capabilities. Model checkpoints and code are publicly available on GitHub at https://github.com/XiaoduoAILab/Xmodel-2
Toward Scalable Multirobot Control: Fast Policy Learning in Distributed MPC
Venue: arXiv cs.RO 2024
Year: 2024

Distributed model predictive control (DMPC) is promising in achieving optimal cooperative control in multirobot systems (MRS). However, real-time DMPC implementation relies on numerical optimization tools to periodically calculate local control sequences online. This process is computationally demanding and lacks scalability for large-scale, nonlinear MRS. This article proposes a novel distributed learning-based predictive control (DLPC) framework for scalable multirobot control. Unlike conventional DMPC methods that calculate open-loop control sequences, our approach centers around a computationally fast and efficient distributed policy learning algorithm that generates explicit closed-loop DMPC policies for MRS without using numerical solvers. The policy learning is executed incrementally and forward in time in each prediction interval through an online distributed actor-critic implementation. The control policies are successively updated in a receding-horizon manner, enabling fast and efficient policy learning with the closed-loop stability guarantee. The learned control policies could be deployed online to MRS with varying robot scales, enhancing scalability and transferability for large-scale MRS. Furthermore, we extend our methodology to address the multirobot safe learning challenge through a force field-inspired policy learning approach. We validate our approach's effectiveness, scalability, and efficiency through extensive experiments on cooperative tasks of large-scale wheeled robots and multirotor drones. Our results demonstrate the rapid learning and deployment of DMPC policies for MRS with scales up to 10,000 units.