pwc-archive/papers-with-abstracts huggingface dataset:
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems
Authors: Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim, Priyadarshini Panda
Date: 2023-09-05 00:00:00
Tasks: Adversarial Robustness, Quantization

In practical cloud-edge scenarios, where a resource constrained edge performs data acquisition and a cloud system (having sufficient resources) performs inference tasks with a deep neural network (DNN), adversarial robustness is critical for reliability and ubiquitous deployment. Adversarial detection is a prime adversarial defence technique used in prior literature. However, in prior detection works, the detector is attached to the classifier model and both detector and classifier work in tandem to perform adversarial detection that requires a high computational overhead which is not available at the low-power edge. Therefore, prior works can only perform adversarial detection at the cloud and not at the edge. This means that in case of adversarial attacks, the unfavourable adversarial samples must be communicated to the cloud which leads to energy wastage at the edge device. Therefore, a low-power edge-friendly adversarial detection method is required to improve the energy efficiency of the edge and robustness of the cloud-based classifier. To this end, RobustEdge proposes Quantization-enabled Energy Separation (QES) training with "early detection and exit" to perform edge-based low cost adversarial detection. The QES-trained detector implemented at the edge blocks adversarial data transmission to the classifier model, thereby improving adversarial robustness and energy-efficiency of the Cloud-Edge system.
Performance Analysis of Various EfficientNet Based U-Net++ Architecture for Automatic Building Extraction from High Resolution Satellite Images
Authors: Tareque Bashar Ovi, Nomaiya Bashree, Protik Mukherjee, Shakil Mosharrof, Masuma Anjum Parthima
Date: 2023-09-05 00:00:00
Tasks: Semantic Segmentation

Building extraction is an essential component of study in the science of remote sensing, and applications for building extraction heavily rely on semantic segmentation of high-resolution remote sensing imagery. Semantic information extraction gap constraints in the present deep learning based approaches, however can result in inadequate segmentation outcomes. To address this issue and extract buildings with high accuracy, various efficientNet backbone based U-Net++ has been proposed in this study. The designed network, based on U-Net, can improve the sensitivity of the model by deep supervision, voluminous redesigned skip-connections and hence reducing the influence of irrelevant feature areas in the background. Various effecientNet backbone based encoders have been employed when training the network to enhance the capacity of the model to extract more relevant feature. According on the experimental findings, the suggested model significantly outperforms previous cutting-edge approaches. Among the 5 efficientNet variation Unet++ based on efficientb4 achieved the best result by scoring mean accuracy of 92.23%, mean iou of 88.32%, and mean precision of 93.2% on publicly available Massachusetts building dataset and thus showing the promises of the model for automatic building extraction from high resolution satellite images.
DeepTriNet: A Tri-Level Attention Based DeepLabv3+ Architecture for Semantic Segmentation of Satellite Images
Authors: Tareque Bashar Ovi, Shakil Mosharrof, Nomaiya Bashree, Md Shofiqul Islam, Muhammad Nazrul Islam
Date: 2023-09-05 00:00:00
Tasks: Change Detection, Image Segmentation, Management, Segmentation, Semantic Segmentation

The segmentation of satellite images is crucial in remote sensing applications. Existing methods face challenges in recognizing small-scale objects in satellite images for semantic segmentation primarily due to ignoring the low-level characteristics of the underlying network and due to containing distinct amounts of information by different feature maps. Thus, in this research, a tri-level attention-based DeepLabv3+ architecture (DeepTriNet) is proposed for the semantic segmentation of satellite images. The proposed hybrid method combines squeeze-and-excitation networks (SENets) and tri-level attention units (TAUs) with the vanilla DeepLabv3+ architecture, where the TAUs are used to bridge the semantic feature gap among encoders output and the SENets used to put more weight on relevant features. The proposed DeepTriNet finds which features are the more relevant and more generalized way by its self-supervision rather we annotate them. The study showed that the proposed DeepTriNet performs better than many conventional techniques with an accuracy of 98% and 77%, IoU 80% and 58%, precision 88% and 68%, and recall of 79% and 55% on the 4-class Land-Cover.ai dataset and the 15-class GID-2 dataset respectively. The proposed method will greatly contribute to natural resource management and change detection in rural and urban regions through efficient and semantic satellite image segmentation
DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials
Authors: Hirofumi Tsuruta, Yukari Katsura, Masaya Kumagai
Date: 2023-09-07 00:00:00
Tasks: Graph Neural Network

Machine learning (ML) is becoming increasingly popular for predicting material properties to accelerate materials discovery. Because material properties are strongly affected by its crystal structure, a key issue is converting the crystal structure into the features for input to the ML model. Currently, the most common method is to convert the crystal structure into a graph and predicting its properties using a graph neural network (GNN). Some GNN models, such as crystal graph convolutional neural network (CGCNN) and atomistic line graph neural network (ALIGNN), have achieved highly accurate predictions of material properties. Despite these successes, using a graph to represent a crystal structure has the notable limitation of losing the crystal structure's three-dimensional (3D) information. In this work, we propose DeepCrysTet, a novel deep learning approach for predicting material properties, which uses crystal structures represented as a 3D tetrahedral mesh generated by Delaunay tetrahedralization. DeepCrysTet provides a useful framework that includes a 3D mesh generation method, mesh-based feature design, and neural network design. The experimental results using the Materials Project dataset show that DeepCrysTet significantly outperforms existing GNN models in classifying crystal structures and achieves state-of-the-art performance in predicting elastic properties.
Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic Classification
Authors: Mahmoud Nazzal, Nura Aljaafari, Ahmed Sawalmeh, Abdallah Khreishah, Muhammad Anan, Abdulelah Algosaibi, Mohammed Alnaeem, Adel Aldalbahi, Abdulaziz Alhumam, Conrado P. Vizcarra, Shadan Alhamed
Date: 2023-09-27 00:00:00
Tasks: Backdoor Attack, Federated Learning, Traffic Classification

Federated learning enables multiple clients to collaboratively contribute to the learning of a global model orchestrated by a central server. This learning scheme promotes clients' data privacy and requires reduced communication overheads. In an application like network traffic classification, this helps hide the network vulnerabilities and weakness points. However, federated learning is susceptible to backdoor attacks, in which adversaries inject manipulated model updates into the global model. These updates inject a salient functionality in the global model that can be launched with specific input patterns. Nonetheless, the vulnerability of network traffic classification models based on federated learning to these attacks remains unexplored. In this paper, we propose GABAttack, a novel genetic algorithm-based backdoor attack against federated learning for network traffic classification. GABAttack utilizes a genetic algorithm to optimize the values and locations of backdoor trigger patterns, ensuring a better fit with the input and the model. This input-tailored dynamic attack is promising for improved attack evasiveness while being effective. Extensive experiments conducted over real-world network datasets validate the success of the proposed GABAttack in various situations while maintaining almost invisible activity. This research serves as an alarming call for network security experts and practitioners to develop robust defense measures against such attacks.
Learning with Noisy Labels for Human Fall Events Classification: Joint Cooperative Training with Trinity Networks
Authors: Leiyu Xie, Yang Sun, Syed Mohsen Naqvi
Date: 2023-09-27 00:00:00
Tasks: Learning with noisy labels

With the increasing ageing population, fall events classification has drawn much research attention. In the development of deep learning, the quality of data labels is crucial. Most of the datasets are labelled automatically or semi-automatically, and the samples may be mislabeled, which constrains the performance of Deep Neural Networks (DNNs). Recent research on noisy label learning confirms that neural networks first focus on the clean and simple instances and then follow the noisy and hard instances in the training stage. To address the learning with noisy label problem and protect the human subjects' privacy, we propose a simple but effective approach named Joint Cooperative training with Trinity Networks (JoCoT). To mitigate the privacy issue, human skeleton data are used. The robustness and performance of the noisy label learning framework is improved by using the two teacher modules and one student module in the proposed JoCoT. To mitigate the incorrect selections, the predictions from the teacher modules are applied with the consensus-based method to guide the student module training. The performance evaluation on the widely used UP-Fall dataset and comparison with the state-of-the-art, confirms the effectiveness of the proposed JoCoT in high noise rates. Precisely, JoCoT outperforms the state-of-the-art by 5.17% and 3.35% with the averaged pairflip and symmetric noises, respectively.
Computational models of object motion detectors accelerated using FPGA technology
Authors: Pedro Machado
Date: 2023-08-23 00:00:00
Tasks: Motion Detection, Object

This PhD research introduces three key contributions in the domain of object motion detection: Multi-Hierarchical Spiking Neural Network (MHSNN): A specialized four-layer Spiking Neural Network (SNN) architecture inspired by vertebrate retinas. Trained on custom lab-generated images, it exhibited 6.75% detection error for horizontal and vertical movements. While non-scalable, MHSNN laid the foundation for further advancements. Hybrid Sensitive Motion Detector (HSMD): Enhancing Dynamic Background Subtraction (DBS) using a tailored three-layer SNN, stabilizing foreground data to enhance object motion detection. Evaluated on standard datasets, HSMD outperformed OpenCV-based methods, excelling in four categories across eight metrics. It maintained real-time processing (13.82-13.92 fps) on a high-performance computer but showed room for hardware optimisation. Neuromorphic Hybrid Sensitive Motion Detector (NeuroHSMD): Building upon HSMD, this adaptation implemented the SNN component on dedicated hardware (FPGA). OpenCL simplified FPGA design and enabled portability. NeuroHSMD demonstrated an 82% speedup over HSMD, achieving 28.06-28.71 fps on CDnet2012 and CDnet2014 datasets. These contributions collectively represent significant advancements in object motion detection, from a biologically inspired neural network design to an optimized hardware implementation that outperforms existing methods in accuracy and processing speed.
Deformation Monitoring of Tunnel using Phase-based Motion Magnification and Optical Flow
Authors: Kecheng Chen, Hiroshi Kogi, Kenichi Soga
Date: 2023-06-15 00:00:00
Tasks: GPU, Motion Magnification, Optical Flow Estimation

During construction, continuous monitoring of underground tunnels can mitigate potential hazards and facilitate an in-depth understanding of the ground-tunnel interaction behavior. Traditional vision-based monitoring can directly capture an extensive range of motion but cannot separate the tunnel's vibration and deformation mode. Phase-based motion magnification is one of the techniques to magnify the motion in target frequency bands and identify system dynamics. Optical flow is a popular method of calculating the motion of image intensities in computer vision and has a much lower computational cost than Digital Image Correlation. This study combines PMM and OF to quantify the underground tunnel scene's magnified deformation mode pixel displacements. As motion magnification artifacts may lead to inaccurate quantification, the 2D Wiener filter is used to smooth the high-frequency content. With GPU acceleration, a dense OF algorithm computing each pixel's displacement is adopted to derive the whole scene motion. A validation experiment is conducted between the amplification motion and the actual motion of prisms preinstalled in the tunnel.
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement Learning
Authors: Yucong Zhang, Chao Yu
Date: 2023-05-30 00:00:00
Proceeding: International Conference on Autonomous Agents and Multiagent Systems 2023 5
Tasks: Efficient Exploration, Multi-agent Reinforcement Learning, Starcraft, Starcraft II

Recently, Multi-Agent Reinforcement Learning (MARL) has been applied to a large number of scenarios and has shown promising performance. However, existing MARL algorithms still suffer from the severe exploration problem. In this paper, we propose EXploiting POlicy Discrepancy for efficient Exploration (EXPODE), a new multi-agent exploration framework that leverages discrepancy between two different policies to enable the agents to explore the environment more efficiently. In addition, to tackle the mutual influence issue caused by the concurrent exploration of the agents, we propose three different mechanisms to coordinate the agents’ exploration by taking the information of other agents’ states and policies into account when measuring the agent-wise policy discrepancies. Experimental results on three challenging tasks, i.e., Predator Prey, StarCraft II micromanagement tasks, and Google Research Football, demonstrate that EXPODE achieves the state-of-the-art performance.
DNN-Based Map Deviation Detection in LiDAR Point Clouds
Authors: Christopher Plachetka, Benjamin Sertolli, Jenny Fricke, Marvin Klingner, Tim Fingscheidt
Date: 2023-07-11 00:00:00
Proceeding: Open Journal on ITS 2023 7
Tasks: object-detection, Object Detection

In this work we present a novel deep learning-based approach to detect and specify map deviations in erroneous or outdated high-definition (HD) maps using both sensor and map data as input to a deep neural network (DNN). We first present our proposed reference method for map deviation detection (MDD) utilizing a sensor-only DNN detecting traffic signs, traffic lights, and pole-like objects in LiDAR data, with deviations obtained by subsequently comparing detected objects and examined map. Second, we facilitate the object detection task by using the examined map as additional input to the network. Third, we employ a specialized MDD network to directly infer the correctness of the map input. Finally, we demonstrate the robustness of our approach for challenging scenes featuring occlusions and a reduced point density, e.g., due to heavy rain. Our code is available at https://github.com/Volkswagen/3dhd_devkit.
Semantic-Forward Relaying: A Novel Framework Towards 6G Cooperative Communications
Authors: Wensheng Lin, Yuna Yan, Lixin Li, Zhu Han, Tad Matsumoto
Date: 2023-10-12 00:00:00

This letter proposes a novel relaying framework, semantic-forward (SF), for cooperative communications towards the sixth-generation (6G) wireless networks. The SF relay extracts and transmits the semantic features, which reduces forwarding payload, and also improves the network robustness against intra-link errors. Based on the theoretical basis for cooperative communications with side information and the turbo principle, we design a joint source-channel coding algorithm to iteratively exchange the extrinsic information for enhancing the decoding gains at the destination. Surprisingly, simulation results indicate that even in bad channel conditions, SF relaying can still effectively improve the recovered information quality.
Interpretable Diffusion via Information Decomposition
Authors: Xianghao Kong, Ollie Liu, Han Li, Dani Yogatama, Greg Ver Steeg
Date: 2023-10-12 00:00:00
Tasks: Image Generation, Vision-Language Segmentation

Denoising diffusion models enable conditional generation and density modeling of complex relationships like images and text. However, the nature of the learned relationships is opaque making it difficult to understand precisely what relationships between words and parts of an image are captured, or to predict the effect of an intervention. We illuminate the fine-grained relationships learned by diffusion models by noticing a precise relationship between diffusion and information decomposition. Exact expressions for mutual information and conditional mutual information can be written in terms of the denoising model. Furthermore, pointwise estimates can be easily estimated as well, allowing us to ask questions about the relationships between specific images and captions. Decomposing information even further to understand which variables in a high-dimensional space carry information is a long-standing problem. For diffusion models, we show that a natural non-negative decomposition of mutual information emerges, allowing us to quantify informative relationships between words and pixels in an image. We exploit these new relations to measure the compositional understanding of diffusion models, to do unsupervised localization of objects in images, and to measure effects when selectively editing images through prompt interventions.
Self-supervised Representation Learning From Random Data Projectors
Authors: Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs
Date: 2023-10-11 00:00:00
Tasks: Data Augmentation, Representation Learning

Self-supervised representation learning~(SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations. While augmentation-based SSRL algorithms push the boundaries of performance in computer vision and natural language processing, they are often not directly applicable to other data modalities, and can conflict with application-specific data augmentation constraints. This paper presents an SSRL approach that can be applied to any data modality and network architecture because it does not rely on augmentations or masking. Specifically, we show that high-quality data representations can be learned by reconstructing random data projections. We evaluate the proposed approach on a wide range of representation learning tasks that span diverse modalities and real-world applications. We show that it outperforms multiple state-of-the-art SSRL baselines. Due to its wide applicability and strong empirical results, we argue that learning from randomness is a fruitful research direction worthy of attention and further study.
Improving Zero-shot Relation Classification via Automatically-acquired Entailment Templates
Authors: Mahdi Rahimi, Mihai Surdeanu
Date: 2023-07-13 00:00:00
Proceeding: Proceedings of the 8th Workshop on Representation Learning for NLP 2023 7
Tasks: Natural Language Inference, Relation, Relation Classification, Zero-shot Relation Classification

While fully supervised relation classification (RC) models perform well on large-scale datasets, their performance drops drastically in low-resource settings. As generating annotated examples are expensive, recent zero-shot methods have been proposed that reformulate RC into other NLP tasks for which supervision exists such as textual entailment. However, these methods rely on templates that are manually created which is costly and requires domain expertise. In this paper, we present a novel strategy for template generation for relation classification, which is based on adapting Harris’ distributional similarity principle to templates encoded using contextualized representations. Further, we perform empirical evaluation of different strategies for combining the automatically acquired templates with manual templates. The experimental results on TACRED show that our approach not only performs better than the zero-shot RC methods that only use manual templates, but also that it achieves state-of-the-art performance for zero-shot TACRED at 64.3 F1 score.
Concealed Electronic Countermeasures of Radar Signal with Adversarial Examples
Authors: Ruinan Ma, Canjie Zhu, Mingfeng Lu, Yunjie Li, Yu-an Tan, Ruibin Zhang, Ran Tao
Date: 2023-10-12 00:00:00

Electronic countermeasures involving radar signals are an important aspect of modern warfare. Traditional electronic countermeasures techniques typically add large-scale interference signals to ensure interference effects, which can lead to attacks being too obvious. In recent years, AI-based attack methods have emerged that can effectively solve this problem, but the attack scenarios are currently limited to time domain radar signal classification. In this paper, we focus on the time-frequency images classification scenario of radar signals. We first propose an attack pipeline under the time-frequency images scenario and DITIMI-FGSM attack algorithm with high transferability. Then, we propose STFT-based time domain signal attack(STDS) algorithm to solve the problem of non-invertibility in time-frequency analysis, thus obtaining the time-domain representation of the interference signal. A large number of experiments show that our attack pipeline is feasible and the proposed attack method has a high success rate.
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes
Authors: Ziyang Song, Ruijie Zhu, Chuxin Wang, Jiacheng Deng, Jianfeng He, Tianzhu Zhang
Date: 2023-10-12 00:00:00
Tasks: Autonomous Driving, Depth Estimation, Depth Prediction, Monocular Depth Estimation, Pseudo Label, Pseudo Label Filtering, Unsupervised Monocular Depth Estimation

Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics. However, existing methods are typically trained and tested on standard datasets, overlooking the impact of various adverse conditions prevalent in real-world applications, such as rainy days. As a result, it is commonly observed that these methods struggle to handle these challenging scenarios. To address this issue, we present EC-Depth, a novel self-supervised two-stage framework to achieve a robust depth estimation. In the first stage, we propose depth consistency regularization to propagate reliable supervision from standard to challenging scenes. In the second stage, we adopt the Mean Teacher paradigm and propose a novel consistency-based pseudo-label filtering strategy to improve the quality of pseudo-labels, further improving both the accuracy and robustness of our model. Extensive experiments demonstrate that our method achieves accurate and consistent depth predictions in both standard and challenging scenarios, surpassing existing state-of-the-art methods on KITTI, KITTI-C, DrivingStereo, and NuScenes-Night benchmarks.
Implementation of Fuzzy Control Algorithm in Two-Wheeled Differential Drive Platform
Authors: Guoyi Chen
Date: 2023-10-11 00:00:00

Designing and developing Artificial Intelligence controllers on separately dedicated chips have many advantages. This report reviews the development of a real-time fuzzy logic controller for optimizing locomotion control of a two-wheeled differential drive platform using an Arduino Uno board. Based on the Raspberry Pi board, fuzzy sets are used to optimize color recognition, enabling the color sensor to correctly recognize color at long distances, across a wide range of light intensity, and with high fault tolerance.