pwc-archive/papers-with-abstracts huggingface dataset:
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-$k$ Multi-Label Learning
Authors: Yuchen Sun, Qianqian Xu, Zitai Wang, Qingming Huang
Date: 2023-07-27 00:00:00
Tasks: Adversarial Attack, Multi-Label Learning

With the great success of deep neural networks, adversarial learning has received widespread attention in various studies, ranging from multi-class learning to multi-label learning. However, existing adversarial attacks toward multi-label learning only pursue the traditional visual imperceptibility but ignore the new perceptible problem coming from measures such as Precision@$k$ and mAP@$k$. Specifically, when a well-trained multi-label classifier performs far below the expectation on some samples, the victim can easily realize that this performance degeneration stems from attack, rather than the model itself. Therefore, an ideal multi-labeling adversarial attack should manage to not only deceive visual perception but also evade monitoring of measures. To this end, this paper first proposes the concept of measure imperceptibility. Then, a novel loss function is devised to generate such adversarial perturbations that could achieve both visual and measure imperceptibility. Furthermore, an efficient algorithm, which enjoys a convex objective, is established to optimize this objective. Finally, extensive experiments on large-scale benchmark datasets, such as PASCAL VOC 2012, MS COCO, and NUS WIDE, demonstrate the superiority of our proposed method in attacking the top-$k$ multi-label systems.
Large-Scale Public Data Improves Differentially Private Image Generation Quality
Authors: Ruihan Wu, Chuan Guo, Kamalika Chaudhuri
Date: 2023-08-04 00:00:00
Tasks: Image Generation

Public data has been frequently used to improve the privacy-accuracy trade-off of differentially private machine learning, but prior work largely assumes that this data come from the same distribution as the private. In this work, we look at how to use generic large-scale public data to improve the quality of differentially private image generation in Generative Adversarial Networks (GANs), and provide an improved method that uses public data effectively. Our method works under the assumption that the support of the public data distribution contains the support of the private; an example of this is when the public data come from a general-purpose internet-scale image source, while the private data consist of images of a specific type. Detailed evaluations show that our method achieves SOTA in terms of FID score and other metrics compared with existing methods that use public data, and can generate high-quality, photo-realistic images in a differentially private manner.
Model Inversion Attack via Dynamic Memory Learning
Authors: Gege Qi, Yuefeng Chen, Xiaofeng Mao, Binyuan Hui, Xiaodan Li, Rong Zhang, Hui Xue
Date: 2023-08-24 00:00:00
Tasks: model

Model Inversion (MI) attacks aim to recover the private training data from the target model, which has raised security concerns about the deployment of DNNs in practice. Recent advances in generative adversarial models have rendered them particularly effective in MI attacks, primarily due to their ability to generate high-fidelity and perceptually realistic images that closely resemble the target data. In this work, we propose a novel Dynamic Memory Model Inversion Attack (DMMIA) to leverage historically learned knowledge, which interacts with samples (during the training) to induce diverse generations. DMMIA constructs two types of prototypes to inject the information about historically learned knowledge: Intra-class Multicentric Representation (IMR) representing target-related concepts by multiple learnable prototypes, and Inter-class Discriminative Representation (IDR) characterizing the memorized samples as learned prototypes to capture more privacy-related information. As a result, our DMMIA has a more informative representation, which brings more diverse and discriminative generated results. Experiments on multiple benchmarks show that DMMIA performs better than state-of-the-art MI attack methods.
Dual Radar SAR Controller
Authors: Josiah Smith
Date: 2023-06-27 00:00:00

The following is a user guide for the Dual Radar SAR Controller graphical user interface (GUI) to operate the dual radar synthetic aperture radar (SAR) scanner. The scanner was designed in the Spring semester of 2022 by Josiah Smith (RA), Yusef Alimam (UG), and Geetika Vedula (UG) with multiple axes of motion for the radar and target under test. The system is operated by a personal computer (PC) running MATLAB. An AMC4030 motion controller is employed to control the mechanical system. An ESP32 microcontroller synchronizes the mechanical motion and radar frame firing to achieving precise positioning at high movement speeds; the software was designed by Josiah Smith (RA) and Benjamin Roy (UG). A second system is designed that employs 3-axes of motion (X-Y + rotation) for fine control over the location of the target under test. The entire system is capable of efficiently collecting data from colocated and non-colocated radars for multiband fusion imaging in addition to simple single radar imaging.
The effect of facets reflectivity on the static characteristics of (DFB) semiconductor laser
Authors: Mohammed Mehdi Bouchene, Rachid Hamd
Date: 2019-01-17 00:00:00
Proceeding: International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM) 2019 1

In this paper, we present a rigorous approach based on split-step time-domain dynamic modeling (SS-TDM) algorithm using to solve the time-dependent coupled wave equations of the time-domain traveling-wave (TDTW) model to investigate the effect of facets reflectivity on the Light-Current characteristics of (DFB) semiconductor laser for different values the coupling coefficient. The simulation results show that using high reflection coating on the front facet and low reflection coating on the back facet with a proper diffraction grating can significantly improve the power-conversion efficiency of the (DFB) semiconductor laser.
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognition
Authors: Mohammed Mehdi Bouchene, Abdelhak BoukharoubaAbdelhak Boukharouba
Date: 2022-05-30 00:00:00
Proceeding: Iran Journal of Computer Science 2022 5
Tasks: Bayesian Optimization, Computational Efficiency, Dimensionality Reduction, Handwritten Digit Recognition

Recognizing handwritten digits is one of the most active research areas in computer vision, as there are a variety of applications, such as automatic identification of digits in bank checks and vehicle numbers. In the last 3 decades, much effort has been devoted to recognizing handwritten Latin digits, while much less attention has been paid to the recognition of Persian and Arabic handwritten digits. For this reason, we will focus on the problem of recognizing Persian and Arabic handwritten numerals. We present an efficient and robust low-dimensional representation of the digit image based on an improved version of the histogram of oriented gradient (HOG) as a feature descriptor. A principal component analysis (PCA)-based dimensionality reduction strategy is also proposed to obtain a subset of features that optimizes classification accuracy. The selected sets of features were fed into a radial basis function (RBF)-based support vector machine (SVM) algorithm that performs classification, and the hyperparameters C and γ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma $$\end{document} were optimized using a Bayesian optimization (BO) algorithm. Extensive experimental results with 80,000 handwritten samples of Persian/Arabic numerals show that our method outperforms the current state-of-the-art classification accuracy and computational efficiency. This trade-off between accuracy and time complexity is highly beneficial for the real-time performance of handwritten digit recognition applications.
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
Authors: Runlong Yu, Xiang Xu, Yuyang Ye, Qi Liu, Enhong Chen
Date: 2023-08-01 00:00:00
Proceeding: KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2023 8
Tasks: Click-Through Rate Prediction, Evolutionary Algorithms, Feature Engineering, feature selection, Marketing, Recommendation Systems

Click-Through Rate (CTR) prediction of intelligent marketing systems is of great importance, in which feature interaction selection plays a key role. Most approaches model interactions of features by the same pre-defined operation under expert guidance, among which improper interactions may bring unnecessary noise and complicate the training process. To that end, in this paper, we aim to adaptively evolve the model to select proper operations to interact on feature pairs under task guidance. Inspired by natural evolution, we propose a general Cognitive EvoLutionary Search (CELS) framework, where cognitive ability refers to the malleability of organisms to orientate to the environment. Specifically, we conceptualize interactions as genomes, models as organisms, and tasks as natural environments. Mirroring how genetic malleability develops environmental adaptability, we thus diagnose the fitness of models to simulate the survival rates of organisms for natural selection, thereby an evolution path can be planned and visualized, offering an intuitive interpretation of the mechanisms underlying interaction modeling and selection. Based on the CELS framework, we develop four instantiations including individual-based search and population-based search. We demonstrate how individual mutation and population crossover enable CELS to evolve into diverse models suitable for various tasks and data, providing ready-to-use models. Extensive experiments on real-world datasets demonstrate that CELS significantly outperforms state-of-the-art approaches.
Graph isomorphism UNet
Authors: Alireza Amouzad, Zahra Dehghanian, Saeed Saravani, Maryam Amirmazlaghani, Behnam Roshanfekr
Date: 2023-08-23 00:00:00
Proceeding: Expert Systems with Applications 2023 8
Tasks: Decoder, Graph Classification, Graph Embedding

Graph embedding learning is a fundamental task when dealing with diverse datasets. While encoder–decoder architectures, such as U-Nets, have shown great success in image pixel-wise prediction tasks, applying similar methods to graph data poses challenges due to the lack of natural pooling and up-sampling operations for graphs. Recent methods leverage learnable parameters to extract structural information from neural networks and extend pooling and unpooling to graphs using node features and graph structural information. This paper proposes a novel model called GIUNet (Graph Isomorphism U-Net) for the graph classification task. The proposed Graph U-Net structure is based on graph isomorphism convolution while using a comprehensive pqPooling layer. The pqPooling layer in our approach effectively combines node features and graph structure information during the graph down-sampling stage. To incorporate graph structure information, we utilize both the spectral representation and node centrality measurements. Node centrality measurements capture various structural aspects of nodes in the graph, while the spectral representation helps us focus on the informative low-frequency components of the graph structure. Through ablation studies, we have demonstrated that leveraging the GIUNet model leads to significant improvements compared to state-of-the-art methods across multiple benchmark datasets.
Anomaly Detection in File Fragment Classification of Image File Formats
Authors: Zahra Seyedghorban, Mehdi Teimouri
Date: 2022-02-28 00:00:00
Proceeding: 11th International Conference on Computer Engineering and Knowledge (ICCKE) 2022 2
Tasks: Anomaly Detection, Classification, File Type Identification, Semi-supervised Anomaly Detection, Supervised Anomaly Detection

File fragment classification is an important and challenging problem in many domains. Among all the different file formats in computer networks and storage devices, image file formats are of much importance and interest. However, image formats form only a small portion of the datasets used in the previous works in the field of file fragment classification. Additionally, the proposed methods in the literature are concentrated on limited feature sets, which are suitable only for the task at hand. A challenge in the area of image type classification is to distinguish image file fragments from any other file format. To date, all presented approaches share the same hypothesis that each sample belongs to one of the training class labels. In most applications, this assumption is wrong, since a variety of file formats are transferred or being stored in a given environment. This proves the importance of discriminating image formats from other file types. In this paper, using a feature vector with length 577, we have classified image fragments of 10 different file formats. We have proposed to include an anomaly detection step before the classification phase to determine the non-image file fragments. Semi-supervised anomaly detection methods from other research domains are used as image discriminators. To evaluate the proposed method, three different anomaly detection methods are explained and designed. The result is evaluated with three different file format families considered anomalies: audio, video, and text.
Fragments Expert A Graphical User Interface MATLAB Toolbox for Classification of File Fragments
Authors: Mehdi Teimouri, Zahra Seyedghorban, Fatemeh Amirjani
Date: 2020-12-23 00:00:00
Proceeding: Concurrency and Computation Practice and Experience 2020 12
Tasks: Classification, Computer Security, File Type Identification, Intrusion Detection

The classification of file fragments of various file formats is an essential task in various applications such as firewalls, intrusion detection systems, anti-viruses, web content filtering, and digital forensics. However, the community lacks a suitable software tool that can integrate major methods for feature extraction from file fragments and classification among various file formats. In this paper, we present Fragments-Expert that is a graphical user interface MATLAB toolbox for the classification of file fragments. It provides users with 22 categories of features extracted from file fragments. These features can be employed by 7 categories of machine learning algorithms for the task of classification among various file formats.
Integration of Vision-based Object Detection and Grasping for Articulated Manipulator in Lunar Conditions
Authors: Camille Boucher, Gustavo H. Diaz, Shreya Santra, Kentaro Uno, Kazuya Yoshida
Date: 2023-09-03 00:00:00
Tasks: Instance Segmentation, object-detection, Object Detection, Semantic Segmentation

The integration of vision-based frameworks to achieve lunar robot applications faces numerous challenges such as terrain configuration or extreme lighting conditions. This paper presents a generic task pipeline using object detection, instance segmentation and grasp detection, that can be used for various applications by using the results of these vision-based systems in a different way. We achieve a rock stacking task on a non-flat surface in difficult lighting conditions with a very good success rate of 92%. Eventually, we present an experiment to assemble 3D printed robot components to initiate more complex tasks in the future.
Improving Capital Efficiency and Impermanent Loss: Multi-Token Proactive Market Maker
Authors: Wayne Chen, Songwei Chen, Preston Rozwood
Date: 2023-08-17 00:00:00

Current approaches to the cryptocurrency automated market makers result in poor impermanent loss and capital efficiency. We analyze the mechanics underlying DODO Exchange's proactive market maker (PMM) to probe for solutions to these issues, leading to our key insight of multi-token trading pools. We explore this paradigm primarily through the construction of a generalization of PMM, the multi-token token proactive market maker (MPMM). We show via simulations that MPMM has better impermanent loss and capital efficiency than comparable market makers under a variety of market scenarios. We also test multi-token generalizations of other common 2-token pool market makers. Overall, this work demonstrates several advantages of multi-token pools and introduces a novel multi-token pool market maker.
DCP-Net: A Distributed Collaborative Perception Network for Remote Sensing Semantic Segmentation
Authors: Zhechao Wang, Peirui Cheng, Shujing Duan, Kaiqiang Chen, Zhirui Wang, Xinming Li, Xian Sun
Date: 2023-09-05 00:00:00
Tasks: Semantic Segmentation

Onboard intelligent processing is widely applied in emergency tasks in the field of remote sensing. However, it is predominantly confined to an individual platform with a limited observation range as well as susceptibility to interference, resulting in limited accuracy. Considering the current state of multi-platform collaborative observation, this article innovatively presents a distributed collaborative perception network called DCP-Net. Firstly, the proposed DCP-Net helps members to enhance perception performance by integrating features from other platforms. Secondly, a self-mutual information match module is proposed to identify collaboration opportunities and select suitable partners, prioritizing critical collaborative features and reducing redundant transmission cost. Thirdly, a related feature fusion module is designed to address the misalignment between local and collaborative features, improving the quality of fused features for the downstream task. We conduct extensive experiments and visualization analyses using three semantic segmentation datasets, including Potsdam, iSAID and DFC23. The results demonstrate that DCP-Net outperforms the existing methods comprehensively, improving mIoU by 2.61%~16.89% at the highest collaboration efficiency, which promotes the performance to a state-of-the-art level.
TemporalNet: Real-time 2D-3D Video Object Detection
Authors: Meihong Chen，Jochen Lang
Date: 2022-08-29 00:00:00
Proceeding: Conference on Robots and Vision 2022 8
Tasks: GPU, Object, object-detection, Object Detection, Video Object Detection

Designing a video detection network based on state-of-the-art single-image object detectors may seem like an obvious choice. However, video object detection has extra challenges due to the lower quality of individual frames in a video, and hence the need to include temporal information for high-quality detection results. We design a novel interleaved architecture combining a 2D convolutional network and a 3D temporal network. To explore inter-frame information, we propose feature aggregation based on a temporal network. Our TemporalNet utilizes Appearance-preserving 3D convolution (AP3D) for extracting aligned features in the temporal dimension. Our temporal network functions at multiple scales for better performance, which allows communication between 2D and 3D blocks at each scale and also across scales. Our TemporalNet is a plug-and-play block that can be added to a multi-scale single-image detection network without any adjustments in the network architecture. When TemporalNet is applied to Yolov3 it is real-time with a running time of 35ms/frame on a low-end GPU. Our real-time approach achieves 77.1 % mAP (mean Average Precision) on ImageNet VID 2017 dataset with TemporalNet-4, where TemporalNet-16 achieves 80.9 % mAP which is a competitive result.
STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation
Authors: Shaohua Liu, Yu Qi, Gen Li, Mingjian Chen, Teng Zhang, Jia Cheng, Jun Lei
Date: 2023-09-05 00:00:00
Tasks: graph construction, Graph Sampling

In Location-Based Services, Point-Of-Interest(POI) recommendation plays a crucial role in both user experience and business opportunities. Graph neural networks have been proven effective in providing personalized POI recommendation services. However, there are still two critical challenges. First, existing graph models attempt to capture users' diversified interests through a unified graph, which limits their ability to express interests in various spatial-temporal contexts. Second, the efficiency limitations of graph construction and graph sampling in large-scale systems make it difficult to adapt quickly to new real-time interests. To tackle the above challenges, we propose a novel Spatial-Temporal Graph Interaction Network. Specifically, we construct subgraphs of spatial, temporal, spatial-temporal, and global views respectively to precisely characterize the user's interests in various contexts. In addition, we design an industry-friendly framework to track the user's latest interests. Extensive experiments on the real-world dataset show that our method outperforms state-of-the-art models. This work has been successfully deployed in a large e-commerce platform, delivering a 1.1% CTR and 6.3% RPM improvement.
SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction
Authors: Xu Si, Xinming Wu, Hanlin Sheng, Jun Zhu, Zefeng Li
Date: 2023-09-05 00:00:00
Tasks: Contrastive Learning

Training specific deep learning models for particular tasks is common across various domains within seismology. However, this approach encounters two limitations: inadequate labeled data for certain tasks and limited generalization across regions. To address these challenges, we develop SeisCLIP, a seismology foundation model trained through contrastive learning from multi-modal data. It consists of a transformer encoder for extracting crucial features from time-frequency seismic spectrum and an MLP encoder for integrating the phase and source information of the same event. These encoders are jointly pre-trained on a vast dataset and the spectrum encoder is subsequently fine-tuned on smaller datasets for various downstream tasks. Notably, SeisCLIP's performance surpasses that of baseline methods in event classification, localization, and focal mechanism analysis tasks, employing distinct datasets from different regions. In conclusion, SeisCLIP holds significant potential as a foundational model in the field of seismology, paving the way for innovative directions in foundation-model-based seismology research.
Instant Continual Learning of Neural Radiance Fields
Authors: Ryan Po, Zhengyang Dong, Alexander W. Bergman, Gordon Wetzstein
Date: 2023-09-04 00:00:00
Tasks: 3D Scene Reconstruction, Continual Learning, NeRF, Novel View Synthesis

Neural radiance fields (NeRFs) have emerged as an effective method for novel-view synthesis and 3D scene reconstruction. However, conventional training methods require access to all training views during scene optimization. This assumption may be prohibitive in continual learning scenarios, where new data is acquired in a sequential manner and a continuous update of the NeRF is desired, as in automotive or remote sensing applications. When naively trained in such a continual setting, traditional scene representation frameworks suffer from catastrophic forgetting, where previously learned knowledge is corrupted after training on new data. Prior works in alleviating forgetting with NeRFs suffer from low reconstruction quality and high latency, making them impractical for real-world application. We propose a continual learning framework for training NeRFs that leverages replay-based methods combined with a hybrid explicit--implicit scene representation. Our method outperforms previous methods in reconstruction quality when trained in a continual setting, while having the additional benefit of being an order of magnitude faster.