pwc-archive/papers-with-abstracts huggingface dataset:
Simulación de la distribución de alimento en el cultivo de camarón
Authors: Renato L. Conforme Rosado, Francisco C. Calderon Bocanegra
Date: 2024-09-16 00:00:00
Tasks: Decision Making

This document presents the experimentation of 4 cases of food distribution for shrimp farming. The distributions are based on the location of the automatic feeders. Three cases applied in reality and a fourth case where the food is irrigated on the crop simultaneously and uniformly. In a first stage, the simulation of the three distribution cases is successfully adjusted to reality, where the trend of the shrimp growth curve is correlated with the historical data curve. A second stage where you experiment in 16 configurations that are based on the amount of food, the density of biomass and the distribution of the food. The simulation adopts the concepts of genetic algorithms to improve the population and fuzzy logic as an agent evaluation technique for decision-making against the quality of physical-chemical parameters in the simulated environment. The results of these interactions reveal a reduction in the simulated total culture time from 22 weeks to 14 weeks.
SpikeGS: Learning 3D Gaussian Fields from Continuous Spike Stream
Authors: Jinze Yu, Xin Peng, Zhengda Lu, Laurent Kneip, Yiqun Wang
Date: 2024-09-23 00:00:00
Tasks: 3DGS, 3D Reconstruction, Novel View Synthesis

A spike camera is a specialized high-speed visual sensor that offers advantages such as high temporal resolution and high dynamic range compared to conventional frame cameras. These features provide the camera with significant advantages in many computer vision tasks. However, the tasks of novel view synthesis based on spike cameras remain underdeveloped. Although there are existing methods for learning neural radiance fields from spike stream, they either lack robustness in extremely noisy, low-quality lighting conditions or suffer from high computational complexity due to the deep fully connected neural networks and ray marching rendering strategies used in neural radiance fields, making it difficult to recover fine texture details. In contrast, the latest advancements in 3DGS have achieved high-quality real-time rendering by optimizing the point cloud representation into Gaussian ellipsoids. Building on this, we introduce SpikeGS, the method to learn 3D Gaussian fields solely from spike stream. We designed a differentiable spike stream rendering framework based on 3DGS, incorporating noise embedding and spiking neurons. By leveraging the multi-view consistency of 3DGS and the tile-based multi-threaded parallel rendering mechanism, we achieved high-quality real-time rendering results. Additionally, we introduced a spike rendering loss function that generalizes under varying illumination conditions. Our method can reconstruct view synthesis results with fine texture details from a continuous spike stream captured by a moving spike camera, while demonstrating high robustness in extremely noisy low-light scenarios. Experimental results on both real and synthetic datasets demonstrate that our method surpasses existing approaches in terms of rendering quality and speed. Our code will be available at https://github.com/520jz/SpikeGS.
BUPTCMCC-6G-CMG+: A GBSM-Based ISAC Standard Channel Model Generator
Authors: Changsheng Zhao, Jianhua Zhang, Yuxiang Zhang, Lei Tian, Heng Wang, Hanyuan Jiang, Yameng Liu, Wenjun Chen, Tao Jiang, Guangyi Liu
Date: 2024-09-22 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has been recognized as the key technology in the vision of the sixth generation (6G) era. With the emergence of new concepts in mobile communications, the channel model is the prerequisite for system design and performance evaluation. Currently, 3GPP Release 19 is advancing the standardization of ISAC channel models. Nevertheless, a unified modeling framework has yet to be established. This paper provides a simulation diagram of ISAC channel modeling extended based on the Geometry-Based Stochastic Model (GBSM), compatible with existing 5G channel models and the latest progress in the 3rd Generation Partnership Project (3GPP) standardization. We first introduce the progress of the ISAC channel model standardization in general. Then, a concatenated channel modeling approach is presented considering the team's standardization proposals, which is implemented on the BUPTCMCC-6G-CMG+ channel model generator. We validated the model in cumulative probability density function (CDF) in statistical extension of angle and delay, and radar cross section (RCS). Simulation results show that the proposed model can realistically characterize the feature of channel concatenation and RCS within the ISAC channel.
AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
Authors: Andrey Moskalenko, Alexey Bryncev, Dmitry Vatolin, Radu Timofte, Gen Zhan, Li Yang, Yunlong Tang, Yiting Liao, Jiongzhi Lin, Baitao Huang, Morteza Moradi, Mohammad Moradi, Francesco Rundo, Concetto Spampinato, Ali Borji, Simone Palazzo, Yuxin Zhu, Yinan Sun, Huiyu Duan, Yuqin Cao, Ziheng Jia, Qiang Hu, Xiongkuo Min, Guangtao Zhai, Hao Fang, Runmin Cong, Xiankai Lu, Xiaofei Zhou, Wei zhang, Chunyu Zhao, Wentao Mu, Tao Deng, Hamed R. Tavakoli
Date: 2024-09-23 00:00:00
Tasks: Saliency Detection, Saliency Prediction, Video Compression, Video Saliency Detection, Video Saliency Prediction

This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences. Saliency maps are widely exploited in various applications, including video compression, quality assessment, visual perception studies, the advertising industry, etc. For this competition, a previously unused large-scale audio-visual mouse saliency (AViMoS) dataset of 1500 videos with more than 70 observers per video was collected using crowdsourced mouse tracking. The dataset collection methodology has been validated using conventional eye-tracking data and has shown high consistency. Over 30 teams registered in the challenge, and there are 7 teams that submitted the results in the final phase. The final phase solutions were tested and ranked by commonly used quality metrics on a private test subset. The results of this evaluation and the descriptions of the solutions are presented in this report. All data, including the private test subset, is made publicly available on the challenge homepage - https://challenges.videoprocessing.ai/challenges/video-saliency-prediction.html.
Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely
Authors: Siyun Zhao, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K. Qiu, Lili Qiu
Date: 2024-09-23 00:00:00
Tasks: RAG, Retrieval-augmented Generation

Large language models (LLMs) augmented with external data have demonstrated remarkable capabilities in completing real-world tasks. Techniques for integrating external data into LLMs, such as Retrieval-Augmented Generation (RAG) and fine-tuning, are gaining increasing attention and widespread application. Nonetheless, the effective deployment of data-augmented LLMs across various specialized fields presents substantial challenges. These challenges encompass a wide range of issues, from retrieving relevant data and accurately interpreting user intent to fully harnessing the reasoning capabilities of LLMs for complex tasks. We believe that there is no one-size-fits-all solution for data-augmented LLM applications. In practice, underperformance often arises from a failure to correctly identify the core focus of a task or because the task inherently requires a blend of multiple capabilities that must be disentangled for better resolution. In this survey, we propose a RAG task categorization method, classifying user queries into four levels based on the type of external data required and primary focus of the task: explicit fact queries, implicit fact queries, interpretable rationale queries, and hidden rationale queries. We define these levels of queries, provide relevant datasets, and summarize the key challenges and most effective techniques for addressing these challenges. Finally, we discuss three main forms of integrating external data into LLMs: context, small model, and fine-tuning, highlighting their respective strengths, limitations, and the types of problems they are suited to solve. This work aims to help readers thoroughly understand and decompose the data requirements and key bottlenecks in building LLM applications, offering solutions to the different challenges and serving as a guide to systematically developing such applications.
Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors
Authors: Shida Sun, Yue Li, Yueyi Zhang, Zhiwei Xiong
Date: 2024-09-21 00:00:00

Non-line-of-sight (NLOS) imaging, recovering the hidden volume from indirect reflections, has attracted increasing attention due to its potential applications. Despite promising results, existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors, e.g., single fixed path compensation. Moreover, these approaches still possess limited generalization ability, particularly when dealing with scenes at a low signal-to-noise ratio (SNR). To overcome the above problems, we introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF). The LPC applies tailored path compensation coefficients to adapt to different objects in the scene, effectively reducing light wave attenuation, especially in distant regions. Meanwhile, the APF learns the precise Gaussian window of the illumination function for the phasor field, dynamically selecting the relevant spectrum band of the transient measurement. Experimental validations demonstrate that our proposed approach, only trained on synthetic data, exhibits the capability to seamlessly generalize across various real-world datasets captured by different imaging systems and characterized by low SNRs.
More Consideration for the Perceptron
Authors: Slimane Larabi
Date: 2024-09-20 00:00:00
Tasks: Multi-class Classification

In this paper, we introduce the gated perceptron, an enhancement of the conventional perceptron, which incorporates an additional input computed as the product of the existing inputs. This allows the perceptron to capture non-linear interactions between features, significantly improving its ability to classify and regress on complex datasets. We explore its application in both linear and non-linear regression tasks using the Iris dataset, as well as binary and multi-class classification problems, including the PIMA Indian dataset and Breast Cancer Wisconsin dataset. Our results demonstrate that the gated perceptron can generate more distinct decision regions compared to traditional perceptrons, enhancing its classification capabilities, particularly in handling non-linear data. Performance comparisons show that the gated perceptron competes with state-of-the-art classifiers while maintaining a simple architecture.
Lightweight Transducer Based on Frame-Level Criterion
Authors: Genshun Wan, Mengzhi Wang, Tingzhi Mao, Hang Chen, Zhongfu Ye
Date: 2024-09-05 00:00:00
Tasks: Decoder, imbalanced classification, Speech Recognition

The transducer model trained based on sequence-level criterion requires a lot of memory due to the generation of the large probability matrix. We proposed a lightweight transducer model based on frame-level criterion, which uses the results of the CTC forced alignment algorithm to determine the label for each frame. Then the encoder output can be combined with the decoder output at the corresponding time, rather than adding each element output by the encoder to each element output by the decoder as in the transducer. This significantly reduces memory and computation requirements. To address the problem of imbalanced classification caused by excessive blanks in the label, we decouple the blank and non-blank probabilities and truncate the gradient of the blank classifier to the main network. Experiments on the AISHELL-1 demonstrate that this enables the lightweight transducer to achieve similar results to transducer. Additionally, we use richer information to predict the probability of blank, achieving superior results to transducer.
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models
Authors: Sven Kruschel, Nico Hambauer, Sven Weinzierl, Sandra Zilker, Mathias Kraus, Patrick Zschech
Date: 2024-09-22 00:00:00
Tasks: Additive models, Interpretable Machine Learning

Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models are often associated with inferior predictive qualities. More recently, however, a new generation of generalized additive models (GAMs) has been proposed that offer promising properties for capturing complex, non-linear patterns while remaining fully interpretable. To uncover the merits and limitations of these models, this study examines the predictive performance of seven different GAMs in comparison to seven commonly used machine learning models based on a collection of twenty tabular benchmark datasets. To ensure a fair and robust model comparison, an extensive hyperparameter search combined with cross-validation was performed, resulting in 68,500 model runs. In addition, this study qualitatively examines the visual output of the models to assess their level of interpretability. Based on these results, the paper dispels the misconception that only black-box models can achieve high accuracy by demonstrating that there is no strict trade-off between predictive performance and model interpretability for tabular data. Furthermore, the paper discusses the importance of GAMs as powerful interpretable models for the field of information systems and derives implications for future work from a socio-technical perspective.
A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders
Authors: David Chanin, James Wilken-Smith, Tomáš Dulka, Hardik Bhatnagar, Joseph Bloom
Date: 2024-09-22 00:00:00

Sparse Autoencoders (SAEs) have emerged as a promising approach to decompose the activations of Large Language Models (LLMs) into human-interpretable latents. In this paper, we pose two questions. First, to what extent do SAEs extract monosemantic and interpretable latents? Second, to what extent does varying the sparsity or the size of the SAE affect monosemanticity / interpretability? By investigating these questions in the context of a simple first-letter identification task where we have complete access to ground truth labels for all tokens in the vocabulary, we are able to provide more detail than prior investigations. Critically, we identify a problematic form of feature-splitting we call feature absorption where seemingly monosemantic latents fail to fire in cases where they clearly should. Our investigation suggests that varying SAE size or sparsity is insufficient to solve this issue, and that there are deeper conceptual issues in need of resolution.
Digital Image Forensics: A quantitative & qualitative comparison between State-of-the-art-AI and Traditional Techniques for detection and localization of image manipulations
Authors: UHstudent
Date: 2024-09-01 00:00:00
Proceeding: None 2024 9
Tasks: Detecting Image Manipulation, Image Forensics, Image Forgery Detection, Image Manipulation, Image Manipulation Detection, Image Manipulation Localization, Jpeg Forgery Localization, Philosophy

With the rise of realistic AI-generated images and continuously advancing photo-editing software, it has become increasingly difficult to reliably distinguish between authentic and manipulated images. Using Digital Image Forensics, the primary objective of this work is to conduct a comprehensive quantitative and qualitative study that compares traditional forensic techniques to a state-of-the-art AI based approach. A practical underpinning for this work is the development of a toolset capable of providing reliable and transparent evidence regarding the authenticity of an image.

Results indicate that the developed algorithms were implemented correctly. The quantitative comparison suggests that the combined traditional techniques outperform a recent state-of-the-art AI network by an average of 17% more detections in scenarios where 0% False Positives are allowed. However, the study acknowledges potential biases in the validation process and further experimentation is necessary to ascertain the reliability of these findings. A qualitative comparison suggests that traditional techniques are more dependable than AI.

This work emphasizes the importance of developing reliable digital image forensic tools and outlines a future vision where AI can be utilized in a key support role to assist forensic analysts. Guided by an open-source philosophy, each algorithm was successfully integrated into Sherloq, an established open source image forensic toolset, which has garnered positive feedback from the community.
Detection of pulmonary pathologies using convolutional neural networks, Data Augmentation, ResNet50 and Vision Transformers
Authors: Pablo Ramirez Amador, Dinarle Milagro Ortega, Arnold Cesarano
Date: 2024-09-22 00:00:00
Tasks: Data Augmentation, Diagnostic, Specificity

Pulmonary diseases are a public health problem that requires accurate and fast diagnostic techniques. In this paper, a method based on convolutional neural networks (CNN), Data Augmentation, ResNet50 and Vision Transformers (ViT) is proposed to detect lung pathologies from medical images. A dataset of X-ray images and CT scans of patients with different lung diseases, such as cancer, pneumonia, tuberculosis and fibrosis, is used. The results obtained by the proposed method are compared with those of other existing methods, using performance metrics such as accuracy, sensitivity, specificity and area under the ROC curve. The results show that the proposed method outperforms the other methods in all metrics, achieving an accuracy of 98% and an area under the ROC curve of 99%. It is concluded that the proposed method is an effective and promising tool for the diagnosis of pulmonary pathologies by medical imaging.
Enhancing Semi-supervised Domain Adaptation via Effective Target Labeling
Authors: Jiujun He1, Bin Liu1, Guosheng Yin
Date: 2024-03-24 00:00:00
Proceeding: AAAI Conference on Artificial Intelligence 2024 3
Tasks: Active Learning, Domain Adaptation, Semi-supervised Domain Adaptation

Existing semi-supervised domain adaptation (SSDA) models have exhibited impressive performance on the target domain
 by effectively utilizing few labeled target samples per class (e.g., 3 samples per class). To guarantee an equal number of
 labeled target samples for each class, however, they require domain experts to manually recognize a considerable amount
 of the unlabeled target data. Moreover, as the target samples are not equally informative for shaping the decision bound
aries of the learning models, it is crucial to select the most informative target samples for labeling, which is, however, impossible for human selectors. As a remedy, we propose an EFfective Target Labeling (EFTL) framework that harnesses active learning and pseudo-labeling strategies to automatically select some informative target samples to annotate. Con
cretely, we introduce a novel sample query strategy, called non-maximal degree node suppression (NDNS), that itera
tively performs maximal degree node query and non-maximal degree node removal to select representative and diverse target samples for labeling. To learn target-specific characteristics, we propose a novel pseudo-labeling strategy that attempts to label low-confidence target samples accurately via clustering consistency (CC), and then inject information of
 the model uncertainty into our query process. CC enhances the utilization of the annotation budget and increases the
 number of “labeled” target samples while requiring no additional manual effort. Our proposed EFTL framework can
 be easily coupled with existing SSDA models, showing significant improvements on three benchmarks.
ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
Authors: Arnab Kumar Roy, Hemant Kumar Kathania, Adhitiya Sharma, Abhishek Dey, Md. Sarfaraj Alam Ansari
Date: 2024-09-01 00:00:00
Tasks: Emotion Recognition, Facial Emotion Recognition, Facial Expression Recognition (FER)

The human face is a silent communicator, expressing emotions and thoughts through its facial expressions. With the advancements in computer vision in recent years, facial emotion recognition technology has made significant strides, enabling machines to decode the intricacies of facial cues. In this work, we propose ResEmoteNet, a novel deep learning architecture for facial emotion recognition designed with the combination of Convolutional, Squeeze-Excitation (SE) and Residual Networks. The inclusion of SE block selectively focuses on the important features of the human face, enhances the feature representation and suppresses the less relevant ones. This helps in reducing the loss and enhancing the overall model performance. We also integrate the SE block with three residual blocks that help in learning more complex representation of the data through deeper layers. We evaluated ResEmoteNet on four open-source databases: FER2013, RAF-DB, AffectNet-7 and ExpW, achieving accuracies of 79.79%, 94.76%, 72.39% and 75.67% respectively. The proposed network outperforms state-of-the-art models across all four databases. The source code for ResEmoteNet is available at https://github.com/ArnabKumarRoy02/ResEmoteNet.
Investigating Robot Dogs for Construction Monitoring: A Comparative Analysis of Specifications and On-site Requirements
Authors: Miguel Arturo Vega Torres, Fabian Pfitzner
Date: 2024-09-23 00:00:00

Robot dogs are receiving increasing attention in various fields of research. However, the number of studies investigating their potential usability on construction sites is scarce. The construction industry implies several human resource-demanding tasks such as safety monitoring, material transportation, and site inspections. Robot dogs can address some of these challenges by providing automated support and lowering manual effort. In this paper, we investigate the potential usability of currently available robot dogs on construction sites in terms of focusing on their different specifications and on-site requirements to support data acquisition. In addition, we conducted a real-world experiment on a large-scale construction site using a quadruped robot. In conclusion, we consider robot dogs to be a valuable asset for monitoring intricate construction environments in the future, particularly as their limitations are mitigated through technical advancements.