pwc-archive/papers-with-abstracts huggingface dataset:
The importance of the clustering model to detect new types of intrusion in data traffic
Authors: Noor Saud Abd, Noor Walid Khalid, Basim Hussein Ali
Date: 2024-11-21 00:00:00
Tasks: Clustering, Intrusion Detection

In the current digital age, the volume of data generated by various cyber activities has become enormous and is constantly increasing. The data may contain valuable insights that can be harnessed to improve cyber security measures. However, much of this data is unclassified and qualitative, which poses significant challenges to traditional analysis methods. Clustering facilitates the identification of hidden patterns and structures in data through grouping similar data points, which makes it simpler to identify and address threats. Clustering can be defined as a data mining (DM) approach, which uses similarity calculations for dividing a data set into several categories. Hierarchical, density-based, along with partitioning clustering algorithms are typical. The presented work use K-means algorithm, which is a popular clustering technique. Utilizing K-means algorithm, we worked with two different types of data: first, we gathered data with the use of XG-boost algorithm following completing the aggregation with K-means algorithm. Data was gathered utilizing Kali Linux environment, cicflowmeter traffic, and Putty Software tools with the use of diverse and simple attacks. The concept could assist in identifying new attack types, which are distinct from the known attacks, and labeling them based on the characteristics they will exhibit, as the dynamic nature regarding cyber threats means that new attack types often emerge, for which labeled data might not yet exist. The model counted the attacks and assigned numbers to each one of them. Secondly, We tried the same work on the ready data inside the Kaggle repository called (Intrusion Detection in Internet of Things Network), and the clustering model worked well and detected the number of attacks correctly as shown in the results section.
Open Challenges in the Formal Verification of Autonomous Driving
Authors: Paolo Burgio, Angelo Ferrando, Marco Villani
Date: 2024-11-21 00:00:00
Tasks: Autonomous Driving, Diversity

In the realm of autonomous driving, the development and integration of highly complex and heterogeneous systems are standard practice. Modern vehicles are not monolithic systems; instead, they are composed of diverse hardware components, each running its own software systems. An autonomous vehicle comprises numerous independent components, often developed by different and potentially competing companies. This diversity poses significant challenges for the certification process, as it necessitates certifying components that may not disclose their internal behaviour (black-boxes). In this paper, we present a real-world case study of an autonomous driving system, identify key open challenges associated with its development and integration, and explore how formal verification techniques can address these challenges to ensure system reliability and safety.
Learn from Foundation Model: Fruit Detection Model without Manual Annotation
Authors: Yanan Wang, Zhenghao Fei, Ruichen Li, Yibin Ying
Date: 2024-11-25 00:00:00
Tasks: Instance Segmentation, Knowledge Distillation, model, object-detection, Object Detection, Segmentation, Semantic Segmentation

Recent breakthroughs in large foundation models have enabled the possibility of transferring knowledge pre-trained on vast datasets to domains with limited data availability. Agriculture is one of the domains that lacks sufficient data. This study proposes a framework to train effective, domain-specific, small models from foundation models without manual annotation. Our approach begins with SDM (Segmentation-Description-Matching), a stage that leverages two foundation models: SAM2 (Segment Anything in Images and Videos) for segmentation and OpenCLIP (Open Contrastive Language-Image Pretraining) for zero-shot open-vocabulary classification. In the second stage, a novel knowledge distillation mechanism is utilized to distill compact, edge-deployable models from SDM, enhancing both inference speed and perception accuracy. The complete method, termed SDM-D (Segmentation-Description-Matching-Distilling), demonstrates strong performance across various fruit detection tasks object detection, semantic segmentation, and instance segmentation) without manual annotation. It nearly matches the performance of models trained with abundant labels. Notably, SDM-D outperforms open-set detection methods such as Grounding SAM and YOLO-World on all tested fruit detection datasets. Additionally, we introduce MegaFruits, a comprehensive fruit segmentation dataset encompassing over 25,000 images, and all code and datasets are made publicly available at https://github.com/AgRoboticsResearch/SDM-D.git.
SAVEn-Vid: Synergistic Audio-Visual Integration for Enhanced Understanding in Long Video Context
Authors: Jungang Li, Sicheng Tao, Yibo Yan, Xiaojie Gu, Haodong Xu, Xu Zheng, Yuanhuiyi Lyu, Linfeng Zhang, Xuming Hu
Date: 2024-11-25 00:00:00
Tasks: Large Language Model, MME, Video MME, Video Understanding

Endeavors have been made to explore Large Language Models for video analysis (Video-LLMs), particularly in understanding and interpreting long videos. However, existing Video-LLMs still face challenges in effectively integrating the rich and diverse audio-visual information inherent in long videos, which is crucial for comprehensive understanding. This raises the question: how can we leverage embedded audio-visual information to enhance long video understanding? Therefore, (i) we introduce SAVEn-Vid, the first-ever long audio-visual video dataset comprising over 58k audio-visual instructions. (ii) From the model perspective, we propose a time-aware Audio-Visual Large Language Model (AV-LLM), SAVEnVideo, fine-tuned on SAVEn-Vid. (iii) Besides, we present AVBench, a benchmark containing 2,500 QAs designed to evaluate models on enhanced audio-visual comprehension tasks within long video, challenging their ability to handle intricate audio-visual interactions. Experiments on AVBench reveal the limitations of current AV-LLMs. Experiments also demonstrate that SAVEnVideo outperforms the best Video-LLM by 3.61% on the zero-shot long video task (Video-MME) and surpasses the leading audio-visual LLM by 1.29% on the zero-shot audio-visual task (Music-AVQA). Consequently, at the 7B parameter scale, SAVEnVideo can achieve state-of-the-art performance. Our dataset and code will be released at https://ljungang.github.io/SAVEn-Vid/ upon acceptance.
OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining
Authors: Ming Hu, Kun Yuan, Yaling Shen, Feilong Tang, Xiaohao Xu, Lin Zhou, Wei Li, Ying Chen, Zhongxing Xu, Zelin Peng, Siyuan Yan, Vinkle Srivastav, Diping Song, Tianbin Li, Danli Shi, Jin Ye, Nicolas Padoy, Nassir Navab, Junjun He, ZongYuan Ge
Date: 2024-11-23 00:00:00
Tasks: Representation Learning, Retrieval

Surgical practice involves complex visual interpretation, procedural skills, and advanced medical knowledge, making surgical vision-language pretraining (VLP) particularly challenging due to this complexity and the limited availability of annotated data. To address the gap, we propose OphCLIP, a hierarchical retrieval-augmented vision-language pretraining framework specifically designed for ophthalmic surgical workflow understanding. OphCLIP leverages the OphVL dataset we constructed, a large-scale and comprehensive collection of over 375K hierarchically structured video-text pairs with tens of thousands of different combinations of attributes (surgeries, phases/operations/actions, instruments, medications, as well as more advanced aspects like the causes of eye diseases, surgical objectives, and postoperative recovery recommendations, etc). These hierarchical video-text correspondences enable OphCLIP to learn both fine-grained and long-term visual representations by aligning short video clips with detailed narrative descriptions and full videos with structured titles, capturing intricate surgical details and high-level procedural insights, respectively. Our OphCLIP also designs a retrieval-augmented pretraining framework to leverage the underexplored large-scale silent surgical procedure videos, automatically retrieving semantically relevant content to enhance the representation learning of narrative videos. Evaluation across 11 datasets for phase recognition and multi-instrument identification shows OphCLIP's robust generalization and superior performance.
Stop Playing the Guessing Game! Target-free User Simulation for Evaluating Conversational Recommender Systems
Authors: Sunghwan Kim, Tongyoung Kim, Kwangwook Seo, Jinyoung Yeo, Dongha Lee
Date: 2024-11-25 00:00:00
Tasks: Recommendation Systems, User Simulation

Recent approaches in Conversational Recommender Systems (CRSs) have tried to simulate real-world users engaging in conversations with CRSs to create more realistic testing environments that reflect the complexity of human-agent dialogue. Despite the significant advancements, reliably evaluating the capability of CRSs to elicit user preferences still faces a significant challenge. Existing evaluation metrics often rely on target-biased user simulators that assume users have predefined preferences, leading to interactions that devolve into simplistic guessing game. These simulators typically guide the CRS toward specific target items based on fixed attributes, limiting the dynamic exploration of user preferences and struggling to capture the evolving nature of real-user interactions. Additionally, current evaluation metrics are predominantly focused on single-turn recall of target items, neglecting the intermediate processes of preference elicitation. To address this, we introduce PEPPER, a novel CRS evaluation protocol with target-free user simulators constructed from real-user interaction histories and reviews. PEPPER enables realistic user-CRS dialogues without falling into simplistic guessing games, allowing users to gradually discover their preferences through enriched interactions, thereby providing a more accurate and reliable assessment of the CRS's ability to elicit personal preferences. Furthermore, PEPPER presents detailed measures for comprehensively evaluating the preference elicitation capabilities of CRSs, encompassing both quantitative and qualitative measures that capture four distinct aspects of the preference elicitation process. Through extensive experiments, we demonstrate the validity of PEPPER as a simulation environment and conduct a thorough analysis of how effectively existing CRSs perform in preference elicitation and recommendation.
Revisiting Marr in Face: The Building of 2D--2.5D--3D Representations in Deep Neural Networks
Authors: Xiangyu Zhu, Chang Yu, Jiankuo Zhao, Zhaoxiang Zhang, Stan Z. Li, Zhen Lei
Date: 2024-11-25 00:00:00

David Marr's seminal theory of vision proposes that the human visual system operates through a sequence of three stages, known as the 2D sketch, the 2.5D sketch, and the 3D model. In recent years, Deep Neural Networks (DNN) have been widely thought to have reached a level comparable to human vision. However, the mechanisms by which DNNs accomplish this and whether they adhere to Marr's 2D--2.5D--3D construction theory remain unexplored. In this paper, we delve into the perception task to explore these questions and find evidence supporting Marr's theory. We introduce a graphics probe, a sub-network crafted to reconstruct the original image from the network's intermediate layers. The key to the graphics probe is its flexible architecture that supports image in both 2D and 3D formats, as well as in a transitional state between them. By injecting graphics probes into neural networks, and analyzing their behavior in reconstructing images, we find that DNNs initially encode images as 2D representations in low-level layers, and finally construct 3D representations in high-level layers. Intriguingly, in mid-level layers, DNNs exhibit a hybrid state, building a geometric representation that s sur normals within a narrow depth range, akin to the appearance of a low-relief sculpture. This stage resembles the 2.5D representations, providing a view of how DNNs evolve from 2D to 3D in the perception process. The graphics probe therefore serves as a tool for peering into the mechanisms of DNN, providing empirical support for Marr's theory.
A Simple and Efficient Approach to Batch Bayesian Optimization
Authors: Dawei Zhan, Zhaoxi Zeng, Shuoxiao Wei, Ping Wu
Date: 2024-11-25 00:00:00
Tasks: Bayesian Optimization

Extending Bayesian optimization to batch evaluation can enable the designer to make the most use of parallel computing technology. However, most of current batch approaches do not scale well with the batch size. That is, their performances deteriorate dramatically as the batch size increases. To address this issue, we propose a simple and efficient approach to extend Bayesian optimization to large-scale batch evaluation in this work. Different from existing batch approaches, the idea of the new approach is to draw a batch of axis-aligned subspaces of the original problem and select one acquisition point from each subspace. To achieve this, we propose the expected subspace improvement criterion to measure the amount of the improvement that a candidate point can achieve within a certain axis-aligned subspace. By optimizing these expected subspace improvement functions simultaneously, we can get a batch of query points for parallel evaluation. Numerical experiments show that our proposed approach can speedup the convergence significantly when compared with the sequential Bayesian optimization algorithm, and performs very competitively when compared with seven batch Bayesian optimization algorithms. A Matlab implementation of the proposed approach is available at https://github.com/zhandawei/Expected_Subspace_Improvement_Batch_Bayesian_Optimization.
When Spatial meets Temporal in Action Recognition
Authors: Huilin Chen, Lei Wang, Yifan Chen, Tom Gedeon, Piotr Koniusz
Date: 2024-11-22 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Video action recognition has made significant strides, but challenges remain in effectively using both spatial and temporal information. While existing methods often focus on either spatial features (e.g., object appearance) or temporal dynamics (e.g., motion), they rarely address the need for a comprehensive integration of both. Capturing the rich temporal evolution of video frames, while preserving their spatial details, is crucial for improving accuracy. In this paper, we introduce the Temporal Integration and Motion Enhancement (TIME) layer, a novel preprocessing technique designed to incorporate temporal information. The TIME layer generates new video frames by rearranging the original sequence, preserving temporal order while embedding $N^2$ temporally evolving frames into a single spatial grid of size $N \times N$. This transformation creates new frames that balance both spatial and temporal information, making them compatible with existing video models. When $N=1$, the layer captures rich spatial details, similar to existing methods. As $N$ increases ($N\geq2$), temporal information becomes more prominent, while the spatial information decreases to ensure compatibility with model inputs. We demonstrate the effectiveness of the TIME layer by integrating it into popular action recognition models, such as ResNet-50, Vision Transformer, and Video Masked Autoencoders, for both RGB and depth video data. Our experiments show that the TIME layer enhances recognition accuracy, offering valuable insights for video processing tasks.
Stability of Brain Functional Network During Working Memory Using Structural Balance Theory
Authors: Sepehr Gourabi, Masoud Lotfalipour, Reza Khosrowabadi, Reza Jafari
Date: 2024-11-25 00:00:00

Working memory plays a crucial role in various aspects of human life. Therefore, it has been an area of interest in different research studies, especially neuroscience. The neuroscientists investigating working memory have primarily emphasized the brain's functional modularity. At the same time, a holistic perspective is still required to investigate the brain as an integrated and unified system. We hypothesized that the brain should shift towards a more stable state during working memory than the resting state. Therefore, based on the Structural Balance Theory (SBT), we aimed to address this process. To achieve this, we examined triadic associations in signed fMRI networks in healthy individuals using the N-back as the working memory task. We demonstrated that the number of balanced triads increased during the working memory task compared to the resting state, while the opposite is true for imbalanced triads. The increase of balanced triads forced the network to a more stable state with a lower balance energy level. The increase of balanced triads was crucially related to changes in anti-synchrony to synchronous activities between the Temporal Cortex, the Prefrontal Cortex, and the Parietal Cortex, which are known to be involved in various aspects of working memory, during the working memory process. We hope these findings pave the way to a better understanding the working memory process.
An AutoML-based approach for Network Intrusion Detection
Authors: Nana Kankam Gyimah, Judith Mwakalonge, Gurcan Comert, Saidi Siuhi, Robert Akinie, Methusela Sulle, Denis Ruganuza, Benibo Izison, Arthur Mukwaya
Date: 2024-11-24 00:00:00
Tasks: AutoML, Feature Engineering, Intrusion Detection, Model Selection, Network Intrusion Detection

In this paper, we present an automated machine learning (AutoML) approach for network intrusion detection, leveraging a stacked ensemble model developed using the MLJAR AutoML framework. Our methodology combines multiple machine learning algorithms, including LightGBM, CatBoost, and XGBoost, to enhance detection accuracy and robustness. By automating model selection, feature engineering, and hyperparameter tuning, our approach reduces the manual overhead typically associated with traditional machine learning methods. Extensive experimentation on the NSL-KDD dataset demonstrates that the stacked ensemble model outperforms individual models, achieving high accuracy and minimizing false positives. Our findings underscore the benefits of using AutoML for network intrusion detection, as the AutoML-driven stacked ensemble achieved the highest performance with 90\% accuracy and an 89\% F1 score, outperforming individual models like Random Forest (78\% accuracy, 78\% F1 score), XGBoost and CatBoost (both 80\% accuracy, 80\% F1 score), and LightGBM (78\% accuracy, 78\% F1 score), providing a more adaptable and efficient solution for network security applications.
CIA: Controllable Image Augmentation Framework Based on Stable Diffusion
Authors: Mohamed Benkedadra, Dany Rimez, Tiffanie Godelaine, Natarajan Chidambaram, Hamed Razavi Khosroshahi, Horacio Tellez, Matei Mancas, Benoit Macq, Sidi Ahmed Mahmoudi
Date: 2024-11-25 00:00:00
Tasks: Image Augmentation, Object, object-detection, Object Detection

Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeline, for (1) generating synthetic images for dataset augmentation using Stable Diffusion, (2) filtering out low quality samples using defined quality metrics, (3) forcing the existence of specific patterns in generated images using accurate prompting and ControlNet. In order to show how CIA can be used to search for an optimal augmentation pipeline of training data, we study human object detection in a data constrained scenario, using YOLOv8n on COCO and Flickr30k datasets. We have recorded significant improvement using CIA-generated images, approaching the performances obtained when doubling the amount of real images in the dataset. Our findings suggest that our modular framework can significantly enhance object detection systems, and make it possible for future research to be done on data-constrained scenarios. The framework is available at: github.com/multitel-ai/CIA.
Efficient Video Face Enhancement with Enhanced Spatial-Temporal Consistency
Authors: Yutong Wang, Jiajie Teng, Jiajiong Cao, Yuming Li, Chenguang Ma, Hongteng Xu, Dixin Luo
Date: 2024-11-25 00:00:00
Proceeding: CVPR 2025 1
Tasks: Quantization, Video Restoration

As a very common type of video, face videos often appear in movies, talk shows, live broadcasts, and other scenes. Real-world online videos are often plagued by degradations such as blurring and quantization noise, due to the high compression ratio caused by high communication costs and limited transmission bandwidth. These degradations have a particularly serious impact on face videos because the human visual system is highly sensitive to facial details. Despite the significant advancement in video face enhancement, current methods still suffer from $i)$ long processing time and $ii)$ inconsistent spatial-temporal visual effects (e.g., flickering). This study proposes a novel and efficient blind video face enhancement method to overcome the above two challenges, restoring high-quality videos from their compressed low-quality versions with an effective de-flickering mechanism. In particular, the proposed method develops upon a 3D-VQGAN backbone associated with spatial-temporal codebooks recording high-quality portrait features and residual-based temporal information. We develop a two-stage learning framework for the model. In Stage \Rmnum{1}, we learn the model with a regularizer mitigating the codebook collapse problem. In Stage \Rmnum{2}, we learn two transformers to lookup code from the codebooks and further update the encoder of low-quality videos. Experiments conducted on the VFHQ-Test dataset demonstrate that our method surpasses the current state-of-the-art blind face video restoration and de-flickering methods on both efficiency and effectiveness. Code is available at \url{https://github.com/Dixin-Lab/BFVR-STC}.
TreeFormer: Single-view Plant Skeleton Estimation via Tree-constrained Graph Generation
Authors: Xinpeng Liu, Hiroaki Santo, Yosuke Toda, Fumio Okura
Date: 2024-11-25 00:00:00
Tasks: Graph Generation

Accurate estimation of plant skeletal structure (e.g., branching structure) from images is essential for smart agriculture and plant science. Unlike human skeletons with fixed topology, plant skeleton estimation presents a unique challenge, i.e., estimating arbitrary tree graphs from images. While recent graph generation methods successfully infer thin structures from images, it is challenging to constrain the output graph strictly to a tree structure. To this problem, we present TreeFormer, a plant skeleton estimator via tree-constrained graph generation. Our approach combines learning-based graph generation with traditional graph algorithms to impose the constraints during the training loop. Specifically, our method projects an unconstrained graph onto a minimum spanning tree (MST) during the training loop and incorporates this prior knowledge into the gradient descent optimization by suppressing unwanted feature values. Experiments show that our method accurately estimates target plant skeletal structures for multiple domains: Synthetic tree patterns, real botanical roots, and grapevine branches. Our implementations are available at https://github.com/huntorochi/TreeFormer/.