OpenRaiser/Intern-Atlas huggingface dataset:
Performance Evaluation of Real-Time Object Detection for Electric Scooters
Venue: arXiv cs.CV 2024
Year: 2024

Electric scooters (e-scooters) have rapidly emerged as a popular mode of transportation in urban areas, yet they pose significant safety challenges. In the United States, the rise of e-scooters has been marked by a concerning increase in related injuries and fatalities. Recently, while deep-learning object detection holds paramount significance in autonomous vehicles to avoid potential collisions, its application in the context of e-scooters remains relatively unexplored. This paper addresses this gap by assessing the effectiveness and efficiency of cutting-edge object detectors designed for e-scooters. To achieve this, the first comprehensive benchmark involving 22 state-of-the-art YOLO object detectors, including five versions (YOLOv3, YOLOv5, YOLOv6, YOLOv7, and YOLOv8), has been established for real-time traffic object detection using a self-collected dataset featuring e-scooters. The detection accuracy, measured in terms of mAP@0.5, ranges from 27.4% (YOLOv7-E6E) to 86.8% (YOLOv5s). All YOLO models, particularly YOLOv3-tiny, have displayed promising potential for real-time object detection in the context of e-scooters. Both the traffic scene dataset (https://zenodo.org/records/10578641) and software program codes (https://github.com/DongChen06/ScooterDet) for model benchmarking in this study are publicly available, which will not only improve e-scooter safety with advanced object detection but also lay the groundwork for tailored solutions, promising a safer and more sustainable urban micromobility landscape.
Tree-based Ensemble Learning for Out-of-distribution Detection
Venue: arXiv cs.LG 2024
Year: 2024

Being able to successfully determine whether the testing samples has similar distribution as the training samples is a fundamental question to address before we can safely deploy most of the machine learning models into practice. In this paper, we propose TOOD detection, a simple yet effective tree-based out-of-distribution (TOOD) detection mechanism to determine if a set of unseen samples will have similar distribution as of the training samples. The TOOD detection mechanism is based on computing pairwise hamming distance of testing samples' tree embeddings, which are obtained by fitting a tree-based ensemble model through in-distribution training samples. Our approach is interpretable and robust for its tree-based nature. Furthermore, our approach is efficient, flexible to various machine learning tasks, and can be easily generalized to unsupervised setting. Extensive experiments are conducted to show the proposed method outperforms other state-of-the-art out-of-distribution detection methods in distinguishing the in-distribution from out-of-distribution on various tabular, image, and text data.
AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection
Venue: arXiv cs.LG 2024
Year: 2024

Anomaly detection, a critical facet in data analysis, involves identifying patterns that deviate from expected behavior. This research addresses the complexities inherent in anomaly detection, exploring challenges and adapting to sophisticated malicious activities. With applications spanning cybersecurity, healthcare, finance, and surveillance, anomalies often signify critical information or potential threats. Inspired by the success of Anomaly Generative Adversarial Network (AnoGAN) in image domains, our research extends its principles to tabular data. Our contributions include adapting AnoGAN's principles to a new domain and promising advancements in detecting previously undetectable anomalies. This paper delves into the multifaceted nature of anomaly detection, considering the dynamic evolution of normal behavior, context-dependent anomaly definitions, and data-related challenges like noise and imbalances.
Research on Image Recognition Technology Based on Multimodal Deep Learning
Venue: arXiv cs.CV 2024
Year: 2024

This project investigates the human multi-modal behavior identification algorithm utilizing deep neural networks. According to the characteristics of different modal information, different deep neural networks are used to adapt to different modal video information. Through the integration of various deep neural networks, the algorithm successfully identifies behaviors across multiple modalities. In this project, multiple cameras developed by Microsoft Kinect were used to collect corresponding bone point data based on acquiring conventional images. In this way, the motion features in the image can be extracted. Ultimately, the behavioral characteristics discerned through both approaches are synthesized to facilitate the precise identification and categorization of behaviors. The performance of the suggested algorithm was evaluated using the MSR3D data set. The findings from these experiments indicate that the accuracy in recognizing behaviors remains consistently high, suggesting that the algorithm is reliable in various scenarios. Additionally, the tests demonstrate that the algorithm substantially enhances the accuracy of detecting pedestrian behaviors in video footage.
Determined Multichannel Blind Source Separation with Clustered Source Model
Venue: arXiv cs.SD 2024
Year: 2024

The independent low-rank matrix analysis (ILRMA) method stands out as a prominent technique for multichannel blind audio source separation. It leverages nonnegative matrix factorization (NMF) and nonnegative canonical polyadic decomposition (NCPD) to model source parameters. While it effectively captures the low-rank structure of sources, the NMF model overlooks inter-channel dependencies. On the other hand, NCPD preserves intrinsic structure but lacks interpretable latent factors, making it challenging to incorporate prior information as constraints. To address these limitations, we introduce a clustered source model based on nonnegative block-term decomposition (NBTD). This model defines blocks as outer products of vectors (clusters) and matrices (for spectral structure modeling), offering interpretable latent vectors. Moreover, it enables straightforward integration of orthogonality constraints to ensure independence among source images. Experimental results demonstrate that our proposed method outperforms ILRMA and its extensions in anechoic conditions and surpasses the original ILRMA in simulated reverberant environments.
DAFT-Spread Affine Frequency Division Multiple Access for Downlink Transmission
Venue: arXiv cs.IT 2024
Year: 2024

Affine frequency division multiplexing (AFDM) and orthogonal AFDM access (O-AFDMA) are promising techniques based on chirp signals, which are able to suppress the performance deterioration caused by Doppler shifts in high-mobility scenarios. However, the high peak-to-average power ratio (PAPR) in AFDM or O-AFDMA is still a crucial problem, which severely limits their practical applications. In this paper, we propose a discrete affine Fourier transform (DAFT)-spread AFDMA scheme based on the properties of the AFDM systems, named DAFT-s-AFDMA to significantly reduce the PAPR by resorting to the DAFT. We formulate the transmitted time-domain signals of the proposed DAFT-s-AFDMA schemes with localized and interleaved chirp subcarrier allocation strategies. Accordingly, we derive the guidelines for setting the DAFT parameters, revealing the insights of PAPR reduction. Finally, simulation results of PAPR comparison in terms of the complementary cumulative distribution function (CCDF) show that the proposed DAFT-s-AFDMA schemes with localized and interleaved strategies can both attain better PAPR performances than the conventional O-AFDMA scheme.
Automatic Retrieval-augmented Generation of 6G Network Specifications for Use Cases
Venue: arXiv cs.NI 2024
Year: 2024

6G Open Radio Access Networks (O-RAN) promises to open data interfaces to enable plug-and-play service Apps, many of which are consumer and business-facing. Opening up 6G access lowers the barrier to innovation but raises the challenge that the required communication specifications are not fully known to all service designers. As such, business innovators must either be familiar with 6G standards or consult with experts. Enabling consistent, unbiased, rapid, and low-cost requirement assessment and specification generation is crucial to the O-RAN innovation ecosystem. Here, we discuss our initiative to bridge service specification gaps between network service providers and business innovators leveraging Large Language Models (LLMs). We first review the state-of-the-art and motivation in 6G plug-and-play services, capabilities, potential use cases and LLMs. We identify an ample innovation space for hybrid use cases that may require diverse and variational wireless functionalities across its operating time. We show that the network specification can be automated and present the first automatic retrieval-augmented network service specification framework for 6G use cases. To enable public acceptance and feedback, a website interface is published for the research and industrial community to experiment with the framework. We hope this review highlights the need for emerging foundation models for this area and motivates researcher engagement and contribution to the community through our framework.
MambaJSCC: Deep Joint Source-Channel Coding with Visual State Space Model
Venue: arXiv cs.IT 2024
Year: 2024

Lightweight and efficient deep joint source-channel coding (JSCC) is a key technology for semantic communications. In this paper, we design a novel JSCC scheme named MambaJSCC, which utilizes a visual state space model with channel adaptation (VSSM-CA) block as its backbone for transmitting images over wireless channels. The VSSM-CA block utilizes VSSM to integrate two-dimensional images with the state space, enabling feature extraction and encoding processes to operate with linear complexity. It also incorporates channel state information (CSI) via a newly proposed CSI embedding method. This method deploys a shared CSI encoding module within both the encoder and decoder to encode and inject the CSI into each VSSM-CA block, improving the adaptability of a single model to varying channel conditions. Experimental results show that MambaJSCC not only outperforms Swin Transformer based JSCC (SwinJSCC) but also significantly reduces parameter size, computational overhead, and inference delay (ID). For example, with employing an equal number of the VSSM-CA blocks and the Swin Transformer blocks, MambaJSCC achieves a 0.48 dB gain in peak-signal-to-noise ratio (PSNR) over SwinJSCC while requiring only 53.3% multiply-accumulate operations, 53.8% of the parameters, and 44.9% of ID.
Time Series Stock Price Forecasting Based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) Optimization
Venue: arXiv cs.CE 2024
Year: 2024

In this paper, a time series algorithm based on Genetic Algorithm (GA) and Long Short-Term Memory Network (LSTM) optimization is used to forecast stock prices effectively, taking into account the trend of the big data era. The data are first analyzed by descriptive statistics, and then the model is built and trained and tested on the dataset. After optimization and adjustment, the mean absolute error (MAE) of the model gradually decreases from 0.11 to 0.01 and tends to be stable, indicating that the model prediction effect is gradually close to the real value. The results on the test set show that the time series algorithm optimized based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) is able to accurately predict the stock prices, and is highly consistent with the actual price trends and values, with strong generalization ability. The MAE on the test set is 2.41, the MSE is 9.84, the RMSE is 3.13, and the R2 is 0.87. This research result not only provides a novel stock price prediction method, but also provides a useful reference for financial market analysis using computer technology and big data.
An Efficient All-to-All GCD Algorithm for Low Entropy RSA Key Factorization
Venue: arXiv cs.CR 2024
Year: 2024

RSA is an incredibly successful and useful asymmetric encryption algorithm. One of the types of implementation flaws in RSA is low entropy of the key generation, specifically the prime number creation stage. This can occur due to flawed usage of random prime number generator libraries, or on computers where there is a lack of a source of external entropy. These implementation flaws result in some RSA keys sharing prime factors, which means that the full factorization of the public modulus can be recovered incredibly efficiently by performing a computation GCD between the two public key moduli that share the prime factor. However, since one does not know which of the composite moduli share a prime factor a-priori, to determine if any such shared prime factors exist, an all-to-all GCD attack (also known as a batch GCD attack, or a bulk GCD attack) can be performed on the available public keys so as to recover any shared prime factors. This study describes a novel all-to-all batch GCD algorithm, which will be referred to as the binary tree batch GCD algorithm, that is more efficient than the current best batch GCD algorithm (the remainder tree batch GCD algorithm). A comparison against the best existing batch GCD method (which is a product tree followed by a remainder tree computation) is given using a dataset of random RSA moduli that are constructed such that some of the moduli share prime factors. This proposed binary tree batch GCD algorithm has better runtime than the existing remainder tree batch GCD algorithm, although asymptotically it has nearly identical scaling and its complexity is dependent on how many shared prime factors exist in the set of RSA keys. In practice, the implementation of the proposed binary tree batch GCD algorithm has a roughly 6x speedup compared to the standard remainder tree batch GCD approach.
Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner
Venue: arXiv cs.LG 2024
Year: 2024

Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or source-dependent patterns, restricting them from unifying representations. Here, we present a novel paradigm to address the STTD learning problem by parameterizing STTD as an implicit neural representation. To discern the underlying dynamics in low-dimensional regimes, coordinate-based neural networks that can encode high-frequency structures are employed to directly map coordinates to traffic variables. To unravel the entangled spatial-temporal interactions, the variability is decomposed into separate processes. We further enable modeling in irregular spaces such as sensor graphs using spectral embedding. Through continuous representations, our approach enables the modeling of a variety of STTD with a unified input, thereby serving as a generalized learner of the underlying traffic dynamics. It is also shown that it can learn implicit low-rank priors and smoothness regularization from the data, making it versatile for learning different dominating data patterns. We validate its effectiveness through extensive experiments in real-world scenarios, showcasing applications from corridor to network scales. Empirical results not only indicate that our model has significant superiority over conventional low-rank models, but also highlight that the versatility of the approach extends to different data domains, output resolutions, and network topologies. Comprehensive model analyses provide further insight into the inductive bias of STTD. We anticipate that this pioneering modeling perspective could lay the foundation for universal representation of STTD in various real-world tasks. Code is available at https://github.com/tongnie/traffic_dynamics.
Exploiting Matrix Information Geometry for Integrated Decoding of Massive Uncoupled Unsourced Random Access
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we explore an efficient uncoupled unsourced random access (UURA) scheme for 6G massive communication. UURA is a typical framework of unsourced random access that addresses the problems of codeword detection and message stitching, without the use of check bits. Firstly, we establish a framework for UURA, allowing for immediate decoding of sub-messages upon arrival. Thus, the processing delay is effectively reduced due to the decreasing waiting time. Next, we propose an integrated decoding algorithm for sub-messages by leveraging matrix information geometry (MIG) theory. Specifically, MIG is applied to measure the feature similarities of codewords belonging to the same user equipment, and thus sub-message can be stitched once it is received. This enables the timely recovery of a portion of the original message by simultaneously detecting and stitching codewords within the current sub-slot. Furthermore, we analyze the performance of the proposed integrated decoding-based UURA scheme in terms of computational complexity and convergence rate. Finally, we present extensive simulation results to validate the effectiveness of the proposed scheme in 6G wireless networks.
QuadraNet V2: Efficient and Sustainable Training of High-Order Neural Networks with Quadratic Adaptation
Venue: arXiv cs.LG 2024
Year: 2024

Machine learning is evolving towards high-order models that necessitate pre-training on extensive datasets, a process associated with significant overheads. Traditional models, despite having pre-trained weights, are becoming obsolete due to architectural differences that obstruct the effective transfer and initialization of these weights. To address these challenges, we introduce a novel framework, QuadraNet V2, which leverages quadratic neural networks to create efficient and sustainable high-order learning models. Our method initializes the primary term of the quadratic neuron using a standard neural network, while the quadratic term is employed to adaptively enhance the learning of data non-linearity or shifts. This integration of pre-trained primary terms with quadratic terms, which possess advanced modeling capabilities, significantly augments the information characterization capacity of the high-order network. By utilizing existing pre-trained weights, QuadraNet V2 reduces the required GPU hours for training by 90\% to 98.4\% compared to training from scratch, demonstrating both efficiency and effectiveness.
CityLLaVA: Efficient Fine-Tuning for VLMs in City Scenario
Venue: arXiv cs.CV 2024
Year: 2024

In the vast and dynamic landscape of urban settings, Traffic Safety Description and Analysis plays a pivotal role in applications ranging from insurance inspection to accident prevention. This paper introduces CityLLaVA, a novel fine-tuning framework for Visual Language Models (VLMs) designed for urban scenarios. CityLLaVA enhances model comprehension and prediction accuracy through (1) employing bounding boxes for optimal visual data preprocessing, including video best-view selection and visual prompt engineering during both training and testing phases; (2) constructing concise Question-Answer sequences and designing textual prompts to refine instruction comprehension; (3) implementing block expansion to fine-tune large VLMs efficiently; and (4) advancing prediction accuracy via a unique sequential questioning-based prediction augmentation. Demonstrating top-tier performance, our method achieved a benchmark score of 33.4308, securing the leading position on the leaderboard. The code can be found: https://github.com/alibaba/AICITY2024_Track2_AliOpenTrek_CityLLaVA
Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth
Venue: arXiv cs.LG 2024
Year: 2024

Federated learning can train models without directly providing local data to the server. However, the frequent updating of the local model brings the problem of large communication overhead. Recently, scholars have achieved the communication efficiency of federated learning mainly by model compression. But they ignore two problems: 1) network state of each client changes dynamically; 2) network state among clients is not the same. The clients with poor bandwidth update local model slowly, which leads to low efficiency. To address this challenge, we propose a communication-efficient federated learning algorithm with adaptive compression under dynamic bandwidth (called AdapComFL). Concretely, each client performs bandwidth awareness and bandwidth prediction. Then, each client adaptively compresses its local model via the improved sketch mechanism based on his predicted bandwidth. Further, the server aggregates sketched models with different sizes received. To verify the effectiveness of the proposed method, the experiments are based on real bandwidth data which are collected from the network topology we build, and benchmark datasets which are obtained from open repositories. We show the performance of AdapComFL algorithm, and compare it with existing algorithms. The experimental results show that our AdapComFL achieves more efficient communication as well as competitive accuracy compared to existing algorithms.
MARE: Multi-Agents Collaboration Framework for Requirements Engineering
Venue: arXiv cs.SE 2024
Year: 2024

Requirements Engineering (RE) is a critical phase in the software development process that generates requirements specifications from stakeholders' needs. Recently, deep learning techniques have been successful in several RE tasks. However, obtaining high-quality requirements specifications requires collaboration across multiple tasks and roles. In this paper, we propose an innovative framework called MARE, which leverages collaboration among large language models (LLMs) throughout the entire RE process. MARE divides the RE process into four tasks: elicitation, modeling, verification, and specification. Each task is conducted by engaging one or two specific agents and each agent can conduct several actions. MARE has five agents and nine actions. To facilitate collaboration between agents, MARE has designed a workspace for agents to upload their generated intermediate requirements artifacts and obtain the information they need. We conduct experiments on five public cases, one dataset, and four new cases created by this work. We compared MARE with three baselines using three widely used metrics for the generated requirements models. Experimental results show that MARE can generate more correct requirements models and outperform the state-of-the-art approaches by 15.4%. For the generated requirements specifications, we conduct a human evaluation in three aspects and provide insights about the quality
Evaluating Eye Movement Biometrics in Virtual Reality: A Comparative Analysis of VR Headset and High-End Eye-Tracker Collected Dataset
Venue: arXiv cs.HC 2024
Year: 2024

Previous studies have shown that eye movement data recorded at 1000 Hz can be used to authenticate individuals. This study explores the effectiveness of eye movement-based biometrics (EMB) by utilizing data from an eye-tracking (ET)-enabled virtual reality (VR) headset (GazeBaseVR) and compares it to the performance using data from a high-end eye tracker (GazeBase) that has been downsampled to 250 Hz. The research also aims to assess the biometric potential of both binocular and monocular eye movement data. GazeBaseVR dataset achieves an equal error rate (EER) of 1.67% and a false rejection rate (FRR) at 10^-4 false acceptance rate (FAR) of 22.73% in a binocular configuration. This study underscores the biometric viability of data obtained from eye-tracking-enabled VR headset.