OpenRaiser/Intern-Atlas huggingface dataset:
Testing Spintronics Implemented Monte Carlo Dropout-Based Bayesian Neural Networks
Venue: arXiv cs.ET 2024
Year: 2024

Bayesian Neural Networks (BayNNs) can inherently estimate predictive uncertainty, facilitating informed decision-making. Dropout-based BayNNs are increasingly implemented in spintronics-based computation-in-memory architectures for resource-constrained yet high-performance safety-critical applications. Although uncertainty estimation is important, the reliability of Dropout generation and BayNN computation is equally important for target applications but is overlooked in existing works. However, testing BayNNs is significantly more challenging compared to conventional NNs, due to their stochastic nature. In this paper, we present for the first time the model of the non-idealities of the spintronics-based Dropout module and analyze their impact on uncertainty estimates and accuracy. Furthermore, we propose a testing framework based on repeatability ranking for Dropout-based BayNN with up to $100\%$ fault coverage while using only $0.2\%$ of training data as test vectors.
DedustNet: A Frequency-dominated Swin Transformer-based Wavelet Network for Agricultural Dust Removal
Venue: arXiv cs.CV 2024
Year: 2024

While dust significantly affects the environmental perception of automated agricultural machines, the existing deep learning-based methods for dust removal require further research and improvement in this area to improve the performance and reliability of automated agricultural machines in agriculture. We propose an end-to-end trainable learning network (DedustNet) to solve the real-world agricultural dust removal task. To our knowledge, DedustNet is the first time Swin Transformer-based units have been used in wavelet networks for agricultural image dusting. Specifically, we present the frequency-dominated block (DWTFormer block and IDWTFormer block) by adding a spatial features aggregation scheme (SFAS) to the Swin Transformer and combining it with the wavelet transform, the DWTFormer block and IDWTFormer block, alleviating the limitation of the global receptive field of Swin Transformer when dealing with complex dusty backgrounds. Furthermore, We propose a cross-level information fusion module to fuse different levels of features and effectively capture global and long-range feature relationships. In addition, we present a dilated convolution module to capture contextual information guided by wavelet transform at multiple scales, which combines the advantages of wavelet transform and dilated convolution. Our algorithm leverages deep learning techniques to effectively remove dust from images while preserving the original structural and textural features. Compared to existing state-of-the-art methods, DedustNet achieves superior performance and more reliable results in agricultural image dedusting, providing strong support for the application of agricultural machinery in dusty environments. Additionally, the impressive performance on real-world hazy datasets and application tests highlights DedustNet superior generalization ability and computer vision-related application performance.
Phishing Website Detection through Multi-Model Analysis of HTML Content
Venue: arXiv cs.CR 2024
Year: 2024

The way we communicate and work has changed significantly with the rise of the Internet. While it has opened up new opportunities, it has also brought about an increase in cyber threats. One common and serious threat is phishing, where cybercriminals employ deceptive methods to steal sensitive information.This study addresses the pressing issue of phishing by introducing an advanced detection model that meticulously focuses on HTML content. Our proposed approach integrates a specialized Multi-Layer Perceptron (MLP) model for structured tabular data and two pretrained Natural Language Processing (NLP) models for analyzing textual features such as page titles and content. The embeddings from these models are harmoniously combined through a novel fusion process. The resulting fused embeddings are then input into a linear classifier. Recognizing the scarcity of recent datasets for comprehensive phishing research, our contribution extends to the creation of an up-to-date dataset, which we openly share with the community. The dataset is meticulously curated to reflect real-life phishing conditions, ensuring relevance and applicability. The research findings highlight the effectiveness of the proposed approach, with the CANINE demonstrating superior performance in analyzing page titles and the RoBERTa excelling in evaluating page content. The fusion of two NLP and one MLP model,termed MultiText-LP, achieves impressive results, yielding a 96.80 F1 score and a 97.18 accuracy score on our research dataset. Furthermore, our approach outperforms existing methods on the CatchPhish HTML dataset, showcasing its efficacies.
Safe Low-Altitude Navigation in Steep Terrain with Fixed-Wing Aerial Vehicles
Venue: arXiv cs.RO 2024
Year: 2024

Fixed-wing aerial vehicles provide an efficient way to navigate long distances or cover large areas for environmental monitoring applications. By design, they also require large open spaces due to limited maneuverability. However, strict regulatory and safety altitude limits constrain the available space. Especially in complex, confined, or steep terrain, ensuring the vehicle does not enter an inevitable collision state(ICS) can be challenging. In this work, we propose a strategy to find safe paths that do not enter an ICS while navigating within tight altitude constraints. The method uses periodic paths to efficiently classify ICSs. A sampling-based planner creates collision-free and kinematically feasible paths that begin and end in safe periodic (circular) paths. We show that, in realistic terrain, using circular periodic paths can simplify the goal selection process by making it yaw agnostic and constraining yaw. We demonstrate our approach by dynamically planning safe paths in real-time while navigating steep terrain on a flight test in complex alpine terrain.
Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need
Venue: arXiv cs.CL 2024
Year: 2024

Automatic diacritization of Arabic text involves adding diacritical marks (diacritics) to the text. This task poses a significant challenge with noteworthy implications for computational processing and comprehension. In this paper, we introduce PTCAD (Pre-FineTuned Token Classification for Arabic Diacritization, a novel two-phase approach for the Arabic Text Diacritization task. PTCAD comprises a pre-finetuning phase and a finetuning phase, treating Arabic Text Diacritization as a token classification task for pre-trained models. The effectiveness of PTCAD is demonstrated through evaluations on two benchmark datasets derived from the Tashkeela dataset, where it achieves state-of-the-art results, including a 20\% reduction in Word Error Rate (WER) compared to existing benchmarks and superior performance over GPT-4 in ATD tasks.
FairQ: Fair and Fast Rate Allocation in Data Centers
Venue: arXiv cs.NI 2024
Year: 2024

The peculiar congestion patterns in data centers are caused by the bursty and composite nature of traffic, the small bandwidth-delay product, and the tiny switch buffers. It is not practical to modify TCP to adapt to data centers, especially in public clouds where multiple congestion control protocols coexist. In this work, we design a switch-based method to address such congestion issues; our approach does not require any modification to TCP, which enables easy and seamless deployment in public data centers via switch software update. We first present a simple analysis to demonstrate the stability and effectiveness of the scheme, and then we discuss a hardware NetFPGA switch-based prototype. The experimental results from real deployments in a small testbed cluster show the effectiveness of our approach.
A Good Score Does not Lead to A Good Generative Model
Venue: arXiv cs.LG 2024
Year: 2024

Score-based Generative Models (SGMs) is one leading method in generative modeling, renowned for their ability to generate high-quality samples from complex, high-dimensional data distributions. The method enjoys empirical success and is supported by rigorous theoretical convergence properties. In particular, it has been shown that SGMs can generate samples from a distribution that is close to the ground-truth if the underlying score function is learned well, suggesting the success of SGM as a generative model. We provide a counter-example in this paper. Through the sample complexity argument, we provide one specific setting where the score function is learned well. Yet, SGMs in this setting can only output samples that are Gaussian blurring of training data points, mimicking the effects of kernel density estimation. The finding resonates a series of recent finding that reveal that SGMs can demonstrate strong memorization effect and fail to generate.
Transportation Marketplace Rate Forecast Using Signature Transform
Venue: arXiv cs.LG 2024
Year: 2024

Freight transportation marketplace rates are typically challenging to forecast accurately. In this work, we have developed a novel statistical technique based on signature transforms and have built a predictive and adaptive model to forecast these marketplace rates. Our technique is based on two key elements of the signature transform: one being its universal nonlinearity property, which linearizes the feature space and hence translates the forecasting problem into linear regression, and the other being the signature kernel, which allows for comparing computationally efficiently similarities between time series data. Combined, it allows for efficient feature generation and precise identification of seasonality and regime switching in the forecasting process. An algorithm based on our technique has been deployed by Amazon trucking operations, with far superior forecast accuracy and better interpretability versus commercially available industry models, even during the COVID-19 pandemic and the Ukraine conflict. Furthermore, our technique is able to capture the influence of business cycles and the heterogeneity of the marketplace, improving prediction accuracy by more than fivefold, with an estimated annualized saving of \$50MM.
Modality-Aware Representation Learning for Zero-shot Sketch-based Image Retrieval
Venue: arXiv cs.CV 2024
Year: 2024

Zero-shot learning offers an efficient solution for a machine learning model to treat unseen categories, avoiding exhaustive data collection. Zero-shot Sketch-based Image Retrieval (ZS-SBIR) simulates real-world scenarios where it is hard and costly to collect paired sketch-photo samples. We propose a novel framework that indirectly aligns sketches and photos by contrasting them through texts, removing the necessity of access to sketch-photo pairs. With an explicit modality encoding learned from data, our approach disentangles modality-agnostic semantics from modality-specific information, bridging the modality gap and enabling effective cross-modal content retrieval within a joint latent space. From comprehensive experiments, we verify the efficacy of the proposed model on ZS-SBIR, and it can be also applied to generalized and fine-grained settings.
Cyclic and Negacyclic Sum-Rank Codes
Venue: arXiv cs.IT 2024
Year: 2024

Sum-rank codes have known applications in the multishot network coding, the distributed storage and the construction of space-time codes. U. Mart\'ınez-Peñas introduced the cyclic-skew-cyclic sum-rank codes and proposed the BCH bound on the cyclic-skew-cyclic sum-rank codes in his paper published in IEEE Trans. Inf. Theory, vol. 67, no. 8, 2021. Afterwards, many sum-rank BCH codes with lower bounds on their dimensions and minimum sum-rank distances were constructed. Sum-rank Hartmann-Tzeng bound and sum-rank Roos bound on cyclic-skew-cyclic codes were proposed and proved by G. N. Alfarano, F. J. Lobillo, A. Neri, and A. Wachter-Zeh in 2022. In this paper, cyclic, negacyclic and constacyclic sum-rank codes are introduced and a direct construction of cyclic, negacyclic and constacyclic sum-rank codes of the matrix size $m \times m$ from cyclic, negacyclic and constacyclic codes over ${\bf F}_{q^m}$ in the Hamming metric is proposed. The cyclic-skew-cylic sum-rank codes are special cyclic sum-rank codes. In addition, BCH and Hartmann-Tzeng bounds for a type of cyclic sum-rank codes are developed. Specific constructions of cyclic, negacyclic and constacyclic sum-rank codes with known dimensions and controllable minimum sum-rank distances are proposed. Moreover, many distance-optimal binary sum-rank codes and an infinite family of distance-optimal binary cyclic sum-rank codes with minimum sum-rank distance four are constructed. This is the first infinite family of distance-optimal sum-rank codes with minimum sum-rank distance four in the literature.
SnapCap: Efficient Snapshot Compressive Video Captioning
Venue: arXiv cs.CV 2024
Year: 2024

Video Captioning (VC) is a challenging multi-modal task since it requires describing the scene in language by understanding various and complex videos. For machines, the traditional VC follows the "imaging-compression-decoding-and-then-captioning" pipeline, where compression is pivot for storage and transmission. However, in such a pipeline, some potential shortcomings are inevitable, i.e., information redundancy resulting in low efficiency and information loss during the sampling process for captioning. To address these problems, in this paper, we propose a novel VC pipeline to generate captions directly from the compressed measurement, which can be captured by a snapshot compressive sensing camera and we dub our model SnapCap. To be more specific, benefiting from the signal simulation, we have access to obtain abundant measurement-video-annotation data pairs for our model. Besides, to better extract language-related visual representations from the compressed measurement, we propose to distill the knowledge from videos via a pre-trained CLIP with plentiful language-vision associations to guide the learning of our SnapCap. To demonstrate the effectiveness of SnapCap, we conduct experiments on two widely-used VC datasets. Both the qualitative and quantitative results verify the superiority of our pipeline over conventional VC pipelines. In particular, compared to the "caption-after-reconstruction" methods, our SnapCap can run at least 3$\times$ faster, and achieve better caption results.
On Achieving High-Fidelity Grant-free Non-Orthogonal Multiple Access
Venue: arXiv cs.IT 2024
Year: 2024

Grant-free access (GFA) has been envisioned to play an active role in massive Machine Type Communication (mMTC) under 5G and Beyond mobile systems, which targets at achieving significant reduction of signaling overhead and access latency in the presence of sporadic traffic and small-size data. The paper focuses on a novel K-repetition GFA (K-GFA) scheme by incorporating Reed-Solomon (RS) code with the contention resolution diversity slotted ALOHA (CRDSA), aiming to achieve high-reliability and low-latency access in the presence of massive uncoordinated MTC devices (MTCDs). We firstly defines a MAC layer transmission structure at each MTCD for supporting message-level RS coding on a data message of $Q$ packets, where a RS code of $KQ$ packets is generated and sent in a super time frame (STF) that is composed of $Q$ time frames. The access point (AP) can recover the original $Q$ packets of the data message if at least $Q$ out of the $KQ$ packets of the RS code are successfully received. The AP buffers the received MTCD signals of each resource block (RB) within an STF and exercises the CRDSA based multi-user detection (MUD) by exploring signal-level inter-RB correlation via iterative interference cancellation (IIC). With the proposed CRDSA based K-GFA scheme, we provide the complexity analysis, and derive a closed-form analytical model on the access probability for each MTCD as well as its simplified approximate form. Extensive numerical experiments are conducted to validate its effectiveness on the proposed CRDSA based K-GFA scheme and gain deep understanding on its performance regarding various key operational parameters.
DualVAE: Dual Disentangled Variational AutoEncoder for Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

Learning precise representations of users and items to fit observed interaction data is the fundamental task of collaborative filtering. Existing studies usually infer entangled representations to fit such interaction data, neglecting to model the diverse matching relationships between users and items behind their interactions, leading to limited performance and weak interpretability. To address this problem, we propose a Dual Disentangled Variational AutoEncoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data. Specifically, we first implement the disentangling concept by unifying an attention-aware dual disentanglement and disentangled variational autoencoder to infer the disentangled latent representations of users and items. Further, to encourage the correspondence and independence of disentangled representations of users and items, we design a neighborhood-enhanced representation constraint with a customized contrastive mechanism to improve the representation quality. Extensive experiments on three real-world benchmarks show that our proposed model significantly outperforms several recent state-of-the-art baselines. Further empirical experimental results also illustrate the interpretability of the disentangled representations learned by DualVAE.
Autonomous Navigation of Tractor-Trailer Vehicles through Roundabout Intersections
Venue: arXiv cs.RO 2024
Year: 2024

In recent years, significant advancements have been made in the field of autonomous driving with the aim of increasing safety and efficiency. However, research that focuses on tractor-trailer vehicles is relatively sparse. Due to the physical characteristics and articulated joints, such vehicles require tailored models. While turning, the back wheels of the trailer turn at a tighter radius and the truck often has to deviate from the centre of the lane to accommodate this. Due to the lack of publicly available models, this work develops truck and trailer models using the high-fidelity simulation software CARLA, together with several roundabout scenarios, to establish a baseline dataset for benchmarks. Using a twin-q soft actor-critic algorithm, we train a quasi-end-to-end autonomous driving model which is able to achieve a 73% success rate on different roundabouts.
Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Vision
Venue: arXiv cs.CV 2024
Year: 2024

Event-based vision is an emerging research field involving processing data generated by Dynamic Vision Sensors (neuromorphic cameras). One of the latest proposals in this area are Graph Convolutional Networks (GCNs), which allow to process events in its original sparse form while maintaining high detection and classification performance. In this paper, we present the hardware implementation of a~graph generation process from an event camera data stream, taking into account both the advantages and limitations of FPGAs. We propose various ways to simplify the graph representation and use scaling and quantisation of values. We consider both undirected and directed graphs that enable the use of PointNet convolution. The results obtained show that by appropriately modifying the graph representation, it is possible to create a~hardware module for graph generation. Moreover, the proposed modifications have no significant impact on object detection performance, only 0.08% mAP less for the base model and the N-Caltech data set.Finally, we describe the proposed hardware architecture of the graph generation module.
AdvMT: Adversarial Motion Transformer for Long-term Human Motion Prediction
Venue: arXiv cs.CV 2024
Year: 2024

To achieve seamless collaboration between robots and humans in a shared environment, accurately predicting future human movements is essential. Human motion prediction has traditionally been approached as a sequence prediction problem, leveraging historical human motion data to estimate future poses. Beginning with vanilla recurrent networks, the research community has investigated a variety of methods for learning human motion dynamics, encompassing graph-based and generative approaches. Despite these efforts, achieving accurate long-term predictions continues to be a significant challenge. In this regard, we present the Adversarial Motion Transformer (AdvMT), a novel model that integrates a transformer-based motion encoder and a temporal continuity discriminator. This combination effectively captures spatial and temporal dependencies simultaneously within frames. With adversarial training, our method effectively reduces the unwanted artifacts in predictions, thereby ensuring the learning of more realistic and fluid human motions. The evaluation results indicate that AdvMT greatly enhances the accuracy of long-term predictions while also delivering robust short-term predictions
Discrete-Time Stress Matrix-Based Formation Control of General Linear Multi-Agent Systems
Venue: arXiv cs.RO 2024
Year: 2024

This paper considers the distributed leader-follower stress-matrix-based affine formation control problem of discrete-time linear multi-agent systems with static and dynamic leaders. In leader-follower multi-agent formation control, the aim is to drive a set of agents comprising leaders and followers to form any desired geometric pattern and simultaneously execute any required manoeuvre by controlling only a few agents denoted as leaders. Existing works in literature are mostly limited to the cases where the agents' inter-agent communications are either in the continuous-time settings or the sampled-data cases where the leaders are constrained to constant (or zero) velocities or accelerations. Here, we relax these constraints and study the discrete-time cases where the leaders can have stationary or time-varying velocities. We propose control laws in the study of different situations and provide some sufficient conditions to guarantee the overall system stability. Simulation study is used to demonstrate the efficacy of our proposed control laws.
Neural Population Learning beyond Symmetric Zero-sum Games
Venue: arXiv cs.AI 2024
Year: 2024

We study computationally efficient methods for finding equilibria in n-player general-sum games, specifically ones that afford complex visuomotor skills. We show how existing methods would struggle in this setting, either computationally or in theory. We then introduce NeuPL-JPSRO, a neural population learning algorithm that benefits from transfer learning of skills and converges to a Coarse Correlated Equilibrium (CCE) of the game. We show empirical convergence in a suite of OpenSpiel games, validated rigorously by exact game solvers. We then deploy NeuPL-JPSRO to complex domains, where our approach enables adaptive coordination in a MuJoCo control domain and skill transfer in capture-the-flag. Our work shows that equilibrium convergent population learning can be implemented at scale and in generality, paving the way towards solving real-world games between heterogeneous players with mixed motives.