pwc-archive/papers-with-abstracts huggingface dataset:
PrivaScissors: Enhance the Privacy of Collaborative Inference through the Lens of Mutual Information
Authors: Lin Duan, Jingwei Sun, Yiran Chen, Maria Gorlatova
Date: 2023-05-17 00:00:00
Tasks: Collaborative Inference

Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep learning applications without disclosing their raw data to the cloud server, thus preserving privacy. Nevertheless, prior research has shown that collaborative inference still results in the exposure of data and predictions from edge devices. To enhance the privacy of collaborative inference, we introduce a defense strategy called PrivaScissors, which is designed to reduce the mutual information between a model's intermediate outcomes and the device's data and predictions. We evaluate PrivaScissors's performance on several datasets in the context of diverse attacks and offer a theoretical robustness guarantee.
Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain
Authors: Poupak Azad, Baris Coskunuzer, Murat Kantarcioglu, Cuneyt Gurcan Akcora
Date: 2023-05-18 00:00:00
Tasks: Graph Neural Network, Node Classification

The rise of cryptocurrencies like Bitcoin, which enable transactions with a degree of pseudonymity, has led to a surge in various illicit activities, including ransomware payments and transactions on darknet markets. These illegal activities often utilize Bitcoin as the preferred payment method. However, current tools for detecting illicit behavior either rely on a few heuristics and laborious data collection processes or employ computationally inefficient graph neural network (GNN) models that are challenging to interpret. To overcome the computational and interpretability limitations of existing techniques, we introduce an effective solution called Chainlet Orbits. This approach embeds Bitcoin addresses by leveraging their topological characteristics in transactions. By employing our innovative address embedding, we investigate e-crime in Bitcoin networks by focusing on distinctive substructures that arise from illicit behavior. The results of our node classification experiments demonstrate superior performance compared to state-of-the-art methods, including both topological and GNN-based approaches. Moreover, our approach enables the use of interpretable and explainable machine learning models in as little as 15 minutes for most days on the Bitcoin transaction network.
Coding for the Gaussian Channel in the Finite Blocklength Regime Using a CNN-Autoencoder
Authors: Nourhan Hesham, Mohamed Bouzid, Ahmad Abdel-Qader, Anas Chaaban
Date: 2023-05-25 00:00:00

The development of delay-sensitive applications that require ultra high reliability created an additional challenge for wireless networks. This led to Ultra-Reliable Low-Latency Communications, as a use case that 5G and beyond 5G systems must support. However, supporting low latency communications requires the use of short codes, while attaining vanishing frame error probability (FEP) requires long codes. Thus, developing codes for the finite blocklength regime (FBR) achieving certain reliability requirements is necessary. This paper investigates the potential of Convolutional Neural Networks autoencoders (CNN-AE) in approaching the theoretical maximum achievable rate over a Gaussian channel for a range of signal-to-noise ratios at a fixed blocklength and target FEP, which is a different perspective compared to existing works that explore the use of CNNs from bit-error and symbol-error rate perspectives. We explain the studied CNN-AE architecture, evaluate it numerically, and compare it to the theoretical maximum achievable rate and the achievable rates of polar coded quadrature amplitude modulation (QAM), Reed-Muller coded QAM, multilevel polar coded modulation, and a TurboAE-MOD scheme from the literature. Numerical results show that the CNN-AE outperforms these benchmark schemes and approaches the theoretical maximum rate, demonstrating the capability of CNN-AEs in learning good codes for delay-constrained applications.
Boosting multiple sclerosis lesion segmentation through attention mechanism
Authors: A. Rondinella, E. Crispino, F. Guarnera, O. Giudice, A. Ortis, G. Russo, C. Di Lorenzo, D. Maimone, F. Pappalardo, S. Battiato
Date: 2023-07-01 00:00:00
Proceeding: Computers in Biology and Medicine 2023 7
Tasks: Brain Image Segmentation, Lesion Segmentation, Medical Image Segmentation

Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts have been made to segment multiple sclerosis lesions using artificial intelligence, fully automated analysis is not yet available. State-of-the-art methods rely on slight variations in segmentation architectures (e.g. U-Net, etc.). However, recent research has demonstrated how exploiting temporal-aware features and attention mechanisms can provide a significant boost to traditional architectures. This paper proposes a framework that exploits an augmented U-Net architecture with a convolutional long short-term memory layer and attention mechanism which is able to segment and quantify multiple sclerosis lesions detected in magnetic resonance images. Quantitative and qualitative evaluation on challenging examples demonstrated how the method outperforms previous state-of-the-art approaches, reporting an overall Dice score of 89% and also demonstrating robustness and generalization ability on never seen new test samples of a new dedicated under construction dataset.
Deep learning for Background Replacement in Video Conferencing
Authors: Kiran Shahi, Yongmin Li
Date: 2023-06-12 00:00:00
Proceeding: International Journal of Network Dynamics and Intelligence 2023 6
Tasks: Deep Learning, Image Segmentation, Marketing, Semantic Segmentation, Video Background Subtraction

Background replacement is one of the most used features in video conferencing applications by many people, perhaps mainly for privacy protection, but also for other purposes such as branding, marketing and promoting professionalism. However, the existing applications in video conference tools have serious limitations. Most applications tend to generate strong artefacts (while there is a slight change in the perspective of the background), or require green screens to avoid such artefacts, which results in an unnatural background or even exposes the original background to other users in the video conference. In this work, we aim to study the relationship between the foreground and background in real-time videos. Three different methods are presented and evaluated, including the baseline U-Net, the lightweight U-Net MobileNet, and the U-Net MobileNet&ConvLSTM models. The above models are trained on public datasets for image segmentation. Experimental results show that both the lightweight U-Net MobileNet and the U-Net MobileNet& ConvLSTM models achieve superior performance as compared to the baseline U-Net model.
A Fast Palette Reordering Technique Based on GPU-Optimized Genetic Algorithms
Authors: Oliver Giudice, Dario Allegra, Filippo Stanco, Giorgio Grasso, Sebastiano Battiato
Date: 2018-09-08 00:00:00
Proceeding: IEEE International Conference on Image Processing (ICIP) 2018 9
Tasks: Clustering, GPU

Color re-indexing is one of main approaches for improving the loss-less compression of color indexed images. Zero-order entropy reduction of indexes matrix is the key to obtain high compression ratio. However, obtaining the optimal re-indexed palette is a challenging problem that cannot be solved by brute-force approaches. In this paper we propose a novel re-indexing approach where the Travelling Salesman Problem is solved through Ant Colony Optimization. Our method is proved to achieve high quality results by outperforming state-of-art ones in term of compression gain. Additionally, we exploit clustering and GPU computing to make our solution extremely fast.
ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation
Authors: Theodore J. Kataras, Tyler J. Jang, Jeffrey Koury, Hina Singh, Dominic Fok, Marcus Kaul
Date: 2023-05-22 00:00:00
Proceeding: Scientific Reports 2023 5
Tasks: Automatic Cell Counting, Image Segmentation, Semantic Segmentation

This dataset is a collection of fluorescent images from mice in order to test an automatic cell counting tool that we developed. 62 images viewed from 2 or 3 different fields of views are shown. In brief, the dataset was derived from brain sections of a model for HIV-induced brain injury (HIVgp120tg), which expresses soluble gp120 envelope protein in astrocytes under the control of a modified GFAP promoter. The mice were in a mixed C57BL/6.129/SJL genetic background, and two genotypes of 9 month old male mice were selected: wild type controls (Resting, n = 3) and transgenic littermates (HIVgp120tg, Activated, n = 3). No randomization was performed. HIVgp120tg mice show among other hallmarks of human HIV neuropathology an increase in microglia numbers which indicates activation of the cells compared to non-transgenic littermate controls.

Brain sections were obtained using a vibratome (Leica VT1000S, Leica Biosystems, Buffalo Grove, IL) and cerebral cortex in 40 μm thick sagittal sections spaced 320 μm apart medial to lateral from brains of each genotype. Staining was performed with rabbit anti-ionized calcium-binding adaptor molecule 1 (Iba-1) IgG (1:125; Wako) with secondary antibody Fluorescein isothiocyanate (FITC). For quantification of Iba-1 stained microglia, cell bodies were counted in the cerebral cortex from three fields of view for three sections each per animal. Between 2 and 3 images were collected per field of view to capture as many cells as possible in sufficient focus for identification. Images were acquired at 10X magnification and pixel resolution 1280x1280 and cropped to 1280x733 pixel area to exclude irregular tissue edges. For more details, please refer to Kataras et al. at https://doi.org/10.1038/s41598-023-34943-w.

We note that this Github links to some images gathered in the fluocells (Fluorescent Neuronal Cells) dataset introduced by Morelli et al. which can be found here: https://paperswithcode.com/dataset/fluocells. 

We provide a link to our automatic cell counting tool that this dataset was used for here at the following Github link:https://github.com/tkataras/Automatic-Cell-Counting-with-TWS
Enhancing social network hate detection using back translation and GPT-3 augmentations during training and test-time
Authors: Seffi Cohen, Dan Presil, Or Katz, Ofir Arbili, Shvat Messica, Lior Rokach
Date: 2023-06-17 00:00:00
Proceeding: Information Fusion 2023 6
Tasks: Hate Speech Detection

Social media platforms have become an essential means of communication, but they also serve as a breeding ground for hateful content. Detecting hate speech accurately is challenging due to factors such as slang and implicit hate speech. In response to these challenges, this paper presents a novel ensemble approach utilizing DeBERTa models, integrating back-translation and GPT-3 augmentation techniques during both training and test time. This method aims to address the complexities associated with detecting hate speech, resulting in more robust and accurate results. Our findings indicate that the proposed approach significantly enhances hate speech detection performance across various metrics and models in both the Parler and GAB datasets. For reproducibility and further exploration, our code is publicly available at https://github.com/OrKatz7/parler-hate-speech.
Improving Local Features with Relevant Spatial Information by Vision Transformer for Crowd Counting
Authors: Nguyen H. Tran, Ta Duc Huy, Soan T. M. Duong, Phan Nguyen, Dao Huu Hung, Chanh D. Tr. Nguyen, Trung Bui, Steven Q.H. Truong
Date: 2022-09-30 00:00:00
Proceeding: British Machine Vision Conference (BMVC) 2022 9
Tasks: Crowd Counting

Vision Transformer (ViT) variants have demonstrated state-of-the-art performances in plenty of computer vision benchmarks, including crowd counting. Although Transformer based models have shown breakthroughs in crowd counting, existing methods
have some limitations. Global embeddings extracted from ViTs do not encapsulate finegrained local features and, thus, are prone to errors in crowded scenes with diverse human scales and densities. In this paper, we propose LoViTCrowd with the argument that: LOcal features with spatial information from relevant regions via the attention mechanism of ViT can effectively reduce the crowd counting error. To this end, we divide each image into a cell grid. Considering patches of 3 × 3 cells, in which the main parts
of the human body are encapsulated, the surrounding cells provide meaningful cues for crowd estimation. ViT is adapted on each patch to employ the attention mechanism across the 3 × 3 cells to count the number of people in the central cell. The number
of people in the image is obtained by summing up the counts of its non-overlapping cells. Extensive experiments on four public datasets of sparse and dense scenes, i.e., Mall, ShanghaiTech Part A, ShanghaiTech Part B, and UCF-QNRF, demonstrate our
method’s state-of-the-art performance. Compared to TransCrowd, LoViTCrowd reduces the root mean square errors (RMSE) and the mean absolute errors (MAE) by an average of 14.2% and 9.7%, respectively. The source is available at https://github.com/nguyen1312/LoViTCrowd
GCRDN: Global Context-Driven Residual Dense Network for Remote Sensing Image Superresolution
Authors: Jialu Sui, Xianping Ma, Xiaokang Zhang, Man-on Pun
Date: 2023-05-04 00:00:00
Proceeding: Jounal 2023 5
Tasks: Decoder, Image Reconstruction, Super-Resolution

Superresolution (SR) of remote sensing images aims to restore high-quality information from low-resolution images. Recently, it has witnessed great strides with the rapid development of deep learning (DL) techniques. Despite their good performance, these DL-based models are often ineffective in balancing global and local feature extraction. Moreover, they are usually hindered by the poor image reconstruction capability of the decoder inside their SR models. To cope with this problem, this work proposes a novel global context-driven residual dense network (GCRDN) for satellite image SR based on the encoder and decoder architecture. In particular, the proposed encoder is endowed with nonlocal sparse attention modules incorporated into the residual dense network to learn robust representations from global features. Furthermore, a decoder equipped with back-sampling blocks is devised to fully exploit the feature maps extracted from the encoder. Extensive experimental comparisons based on two multisensor satellite remote sensing datasets confirm that the proposed GCRDN achieves impressive performance in terms of perceptual quality and fidelity.
Event-Aware Video Deraining via Multi-Patch Progressive Learning
Authors: Sun Shangquan, Ren Wenqi, Li Jingzhi, Zhang Kaihao, Liang Meiyu, Cao Xiaochun
Date: 2023-05-10 00:00:00
Proceeding: journal 2023 5
Tasks: Rain Removal, Video deraining

In this paper, we address the problem of video-based rain streak removal by developing an event-aware multi-patch progressive neural network. Rain streaks in video exhibit correlations in both temporal and spatial dimensions. Existing methods have difficulties in modeling the characteristics. Based on the observation, we propose to develop a module encoding events from neuromorphic cameras to facilitate deraining. Events are captured asynchronously at pixel-level only when intensity changes by a margin exceeding a certain threshold. Due to this property, events contain considerable information about moving objects including rain streaks passing though the camera across adjacent frames. Thus we suggest that utilizing it properly facilitates deraining performance non-trivially. In addition, we develop a multi-patch progressive neural network. The multi-patch manner enables various receptive fields by partitioning patches and the progressive learning in different patch levels makes the model emphasize each patch level to a different extent. Extensive experiments show that our method guided by events outperforms the state-of-the-art methods by a large margin in synthetic and real-world datasets.
Multi-encoder parse-decoder network for sequential medical image segmentation
Authors: Dachuan Shi, Ruiyang Liu, Linmi Tao, Zuoxiang He, Li Huo
Date: 2021-09-19 00:00:00
Proceeding: International Conference on Image Processing 2021 9
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Deep learning models, especially U-Net and its derivate models, have been widely used in medical image segmentation. These approaches have achieved promising results in many medical image segmentation tasks with a limited number of training samples. We aim on enhancing medical image segmentation by using spatial continuity information in a proposed Multi-Encoder Parse-Decoder Network (MEPDNet) based on the fact that most of the medical images are sampled continuously. Sequential images are input into parameter-shared encoders for getting feature maps, which are then fused by a fusion block. A $\textbf{V}\mathbf{\Lambda}$-block is structured to parse the fused feature map to extract the hidden continuity information. The reconstructed feature map is fed into a decoder for generating segmentation masks. Experiments on three datasets show MEPDNet outperforms other state-of-the-art segmentation models while using the least parameters.
Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models
Authors: Dachuan Shi, Ruiyang Liu, Linmi Tao, Chun Yuan
Date: 2022-05-23 00:00:00
Proceeding: International Conference on Acoustics, Speech and Signal Processing 2022 5
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

For medical image segmentation in a real scenario, the amount of accurate annotation data at the pixel level is typically small, which tends to cause an overfitting problem. This manuscript goes deep into the research of the Dropout algorithm, which is commonly used in neural networks to alleviate the overfitting problem. From the perspective of solving the co-adaptation problem, this manuscript explains the basic principles of the Dropout algorithm and discusses the existing limitations of its derivative methods. Furthermore, we propose a novel Heuristic Dropout algorithm to address these limitations. The proposed algorithm takes information entropy and variance as heuristic rules. It guides our algorithm to drop features suffering from co-adaptation problem more efficiently and thus can better alleviate the overfitting problem of small-scale medical image segmentation datasets. Experiments on medical image segmentation datasets and models show that the proposed algorithm significantly improves the performance of these models.
Representation Learning through Multimodal Attention and Time-Sync Comments for Affective Video Content Analysis
Authors: Jicai Pan, Shangfei Wang, Lin Fang
Date: 2022-10-14 00:00:00
Proceeding: ACM MM22 2022 10
Tasks: Representation Learning, Video Emotion Recognition

Although temporal patterns inherent in visual and audio signals are crucial for affective video content analysis, they have not been thoroughly explored yet. In this paper, we propose a novel Temporal-Aware Multimodal (TAM) method to fully capture the temporal information. Specifically, we design a cross-temporal multimodal fusion module that applies attention-based fusion to different modalities within and across video segments. As a result, it fully captures the temporal relations between different modalities. Furthermore, a single emotion label lacks supervision for learning representation of each segment, making temporal pattern mining difficult. We leverage time-synchronized comments (TSCs) as auxiliary supervision, since these comments are easily accessible and contain rich emotional cues. Two TSC-based self-supervised tasks are designed: the first aims to predict the emotional words in a TSC from video representation and TSC contextual semantics, and the second predicts the segment in which the TSC appears by calculating the correlation between video representation and TSC embedding. These self-supervised tasks are used to pre-train the cross-temporal multimodal fusion module on a large-scale video-TSC dataset, which is crawled from the web without labeling costs. These self-supervised pre-training tasks prompt the fusion module to perform representation learning on segments including TSC, thus capturing more temporal affective patterns. Experimental results on three benchmark datasets show that the proposed fusion module achieves state-of-the-art results in affective video content analysis. Ablation studies verify that after TSC-based pre-training, the fusion module learns more segments' affective patterns and achieves better performance.
Anomaly Detection via oversampling Principal Component Analysis
Authors: Yi-Ren Yeh, Zheng-Yi Lee, Yuh-Jye Lee
Date: 2013-07-01 00:00:00
Proceeding: 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems 2013 7
Tasks: Anomaly Detection, Outlier Detection

Abstract Outlier detection is an important issue in data mining and has been studied
in different research areas. It can be used for detecting the small amount of deviated
data. In this article, we use “Leave One Out” procedure to check each individual
point the “with or without” effect on the variation of principal directions. Based on
this idea, an over-sampling principal component analysis outlier detection method
is proposed for emphasizing the influence of an abnormal instance (or an outlier).
Except for identifying the suspicious outliers, we also design an on-line anomaly
detection to detect the new arriving anomaly. In addition, we also study the quick
updating of the principal directions for the effective computation and satisfying the
on-line detecting demand. Numerical experiments show that our proposed method
is effective in computation time and anomaly detection.
Enhancing Social Network Hate Detection Using Back Translation and GPT-3 Augmentations During Training and Test-Time
Authors: S Cohen, D Presil, O Katz, O Arbili, S Messica, L Rokach
Date: 2023-06-13 00:00:00
Proceeding: Information Fusion 2023 6
Tasks: Hate Speech Detection, Hate Speech Normalization

Social media platforms have become an essential means of communication, but they also serve as a breeding ground for hateful content. Detecting hate speech accurately is challenging due to factors such as slang and implicit hate speech. In response to these challenges, this paper presents a novel ensemble approach utilizing DeBERTa models, integrating back-translation and GPT-3 augmentation techniques during both training and test time. This method aims to address the complexities associated with detecting hate speech, resulting in more robust and accurate results. Our findings indicate that the proposed approach significantly enhances hate speech detection performance across various metrics and models in both the Parler and GAB datasets. For reproducibility and further exploration, our code is publicly available at https://github.com/OrKatz7/parler-hate-speech.