pwc-archive/papers-with-abstracts huggingface dataset:
Augmented Balanced Image Dataset Generator Using AugStatic Library
Authors: Allena Venkata Sai Abhishek, Dr. Venkateswara Rao Gurrala
Date: 2022-05-01 00:00:00
Proceeding: International Journal Of Research And Analytical Reviews (IJRAR) 2022 5
Tasks: Classification, Data Augmentation, Data Visualization, Detecting Image Manipulation, Image Augmentation, Image Classification, Image Cropping, Image Denoising, Image Generation, Image Manipulation, Image Manipulation Detection, Image Matting, Image Morphing, image smoothing, Image Stitching, Image-Variation, ROI-based image generation

The mixed data consists of various structured and unstructured data. The exponential boom of the amount of data has made the datasets of varying samples. This paper focuses on the image dataset generator that balances an imbalanced dataset using the AugStatic augmentation library. The datasets, including various classes, are said to be balanced if the number of samples in the classes is equal. This gives a fair chance for the model to learn about all the classes. An augmented image dataset balances an imbalanced image dataset. It is useful when the data is less in a specific category, generating new data with it. There are multiple augmentation techniques supported by the AugStatic library that helps in developing the augmented balanced library by the iterative implementation of the augmentations on the generated dataset. It takes an input of the existing dataset, majority and minority classes sample count that returns a balanced image dataset by iteratively applying the augmentations on the generated augmented images in the minority class. This generator is efficient and can be used for any image dataset.
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation
Authors: Allena Venkata Sai Abhishek, Dr. Venkateswara Rao Gurrala
Date: 2022-05-01 00:00:00
Proceeding: International Journal of Advanced Research in Engineering and Technology (IJARET) 2022 5
Tasks: Classification, Data Augmentation, Data Visualization, Detecting Image Manipulation, Image Augmentation, Image Classification, Image Cropping, Image Denoising, Image Generation, Image Manipulation, Image Manipulation Detection, Image Matting, Image Morphing, image smoothing, Image Stitching, Image-Variation, ROI-based image generation

The data in the real world consists of various kinds of painful features. A majorly found one is the class imbalance in which the number of examples in different classes in a dataset is unequal. The class imbalance is being resolved using various sampling techniques on the data. Augmentation technique Augmentation is one of the essential steps in any machine learning pipeline and is used for oversampling the data of minority classes. This paper aims to improve the model performance is being enhanced with removing the class imbalance problem by using various Augmentation approaches to generate various balanced augmented datasets using various data augmentation techniques & random sampling. The accuracies are acquired for each augmentation technique using a RESNET18 model. The model is run up to 100 epochs for each case, and the best accuracies are compared. This iterative comparison of various augmentation techniques has shown stunning insights into the effectiveness of multiple datasets.
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset
Authors: Allena Venkata Sai Abhishek, Dr. S Phani Kumar, Anusha Kanukolanu
Date: 2022-05-01 00:00:00
Proceeding: International Journal of Innovative Research in Technology(IJIRT) 2022 5
Tasks: Classification, Data Augmentation, Data Visualization, Detecting Image Manipulation, Image Augmentation, Image Classification, Image Cropping, Image Denoising, Image Generation, Image Manipulation, Image Manipulation Detection, Image Matting, Image Morphing, image smoothing, Image Stitching, Image-Variation, ROI-based image generation

although best-in-class AI can deliver extraordinary outcomes in experimentation, data scientists struggle to duplicate these outcomes on actual-world data. It's nothing unexpected-actual data mirrors the messy world that made it, containing many biases and gaps. A painful element of real data is that it tends to be imbalanced. An imbalanced dataset is a dataset with a lot more examples in one class than others. This exploration features the broad study about taking care of class Imbalance issues utilizing Random Sampling and Data Augmentation Techniques. The critical angle featured is to grasp how Under-Sampling, Over-Sampling, and Data Augmentation use images and custom datasets. The model performance is improved with the expulsion of class imbalance issue utilizing different Augmentation approaches utilizing an augmentation library. The accuracy contrasts with taking care of the Class imbalance issue to boost accuracy, lessen error, and track down an ideal technique to tackle it.
Deep PCB To COCO Convertor
Authors: Allena Venkata Sai Abhishek, Genti Sreeja, Dr K S Sudeep
Date: 2022-05-01 00:00:00
Proceeding: International Journal for Research in Engineering Application & Management (IJREAM) 2022 5
Tasks: Classification, Data Augmentation, Data Visualization, Detecting Image Manipulation, Image Augmentation, Image Classification, Image Cropping, Image Denoising, Image Generation, Image Manipulation, Image Manipulation Detection, Image Matting, Image Morphing, image smoothing, Image Stitching, Image-Variation, object-detection, Object Detection, ROI-based image generation

Millions of datasets and many models use the input datasets in COCO format. In this paper, we are converting the Deep PCB dataset to COCO format. The Deep PCB is a manufacturing defect data set. It has 1500 image pairs. Each has a template image & a test image. The template image has no defects & corresponding test image that has some defects with the annotations in a text file. The MS COCO (Microsoft Common Objects in Context) dataset is large-scale object detection, segmentation, key-point detection, and captioning. It is widely used for various models. We are trying to convert the deep PCB Manufacturing defect into COCO Format.
AMC-Net: An Effective Network for Automatic Modulation Classification
Authors: Jiawei Zhang, Tiantian Wang, Zhixi Feng, Shuyuan Yang
Date: 2023-04-02 00:00:00
Tasks: Classification, Denoising, Feature Engineering, Management

Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems. The accurate classification of the modulation format plays a vital role in the subsequent decoding of the transmitted data. End-to-end deep learning methods have been recently applied to AMC, outperforming traditional feature engineering techniques. However, AMC still has limitations in low signal-to-noise ratio (SNR) environments. To address the drawback, we propose a novel AMC-Net that improves recognition by denoising the input signal in the frequency domain while performing multi-scale and effective feature extraction. Experiments on two representative datasets demonstrate that our model performs better in efficiency and effectiveness than the most current methods.
Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties
Authors: Jingwei Sun, Zhixu Du, Anna Dai, Saleh Baghersalimi, Alireza Amirshahi, David Atienza, Yiran Chen
Date: 2023-03-28 00:00:00
Tasks: Federated Learning, Vertical Federated Learning

Vertical federated learning (VFL) enables a service provider (i.e., active party) who owns labeled features to collaborate with passive parties who possess auxiliary features to improve model performance. Existing VFL approaches, however, have two major vulnerabilities when passive parties unexpectedly quit in the deployment phase of VFL - severe performance degradation and intellectual property (IP) leakage of the active party's labels. In this paper, we propose \textbf{Party-wise Dropout} to improve the VFL model's robustness against the unexpected exit of passive parties and a defense method called \textbf{DIMIP} to protect the active party's IP in the deployment phase. We evaluate our proposed methods on multiple datasets against different inference attacks. The results show that Party-wise Dropout effectively maintains model performance after the passive party quits, and DIMIP successfully disguises label information from the passive party's feature extractor, thereby mitigating IP leakage.
Managing Cold-start in The Serverless Cloud with Temporal Convolutional Networks
Authors: Tam N. Nguyen
Date: 2023-04-01 00:00:00
Tasks: Management

Serverless cloud is an innovative cloud service model that frees customers from most cloud management duties. It also offers the same advantages as other cloud models but at much lower costs. As a result, the serverless cloud has been increasingly employed in high-impact areas such as system security, banking, and health care. A big threat to the serverless cloud's performance is cold-start, which is when the time of provisioning the needed cloud resource to serve customers' requests incurs unacceptable costs to the service providers and/or the customers. This paper proposes a novel low-coupling, high-cohesion ensemble policy that addresses the cold-start problem at infrastructure- and function-levels of the serverless cloud stack, while the state of the art policies have a more narrowed focus. This ensemble policy anchors on the prediction of function instance arrivals, 10 to 15 minutes into the future. It is achievable by using the temporal convolutional network (TCN) deep-learning method. Bench-marking results on a real-world dataset from a large-scale serverless cloud provider show that TCN out-performs other popular machine learning algorithms for time series. Going beyond cold-start management, the proposed policy and publicly available codes can be adopted in solving other cloud problems such as optimizing the provisioning of virtual software-defined network assets.
Rapid online solution of inverse heat transfer problem by ANN-based extended Kalman smoothing algorithm
Authors: Xinxin Zhang, Dike Li, Jianqin Zhu, Zhi Tao, Lu Qiu
Date: 2023-03-31 00:00:00

Digital twin is a modern technology for many advanced applications. To construct a digital twin of a thermal system, it is required to make online estimations of unknown time-varying boundary conditions from sensor measured data, which needs to solve inverse heat transfer problems (IHTPs). However, a fast and accurate solution is challenging since the measured data is normally contaminated with noise and the traditional method to solve IHTP involves significant amount of calculations. Therefore, in this work, a rapid yet robust inversion algorithm called ANN-based extended Kalman smoothing algorithm is developed to realize the online prediction of desired parameter based on the measurements. The fast prediction is realized by replacing the conventional CFD-based state transfer models in extended Kalman smoothing algorithm with pre-trained ANN. Then, a two-dimensional internal convective heat transfer problem was employed as the case study to test the algorithm. The results have proved that the proposed algorithm is a computational-light and robust approach for solving IHTPs. The proposed algorithm can achieve estimation of unknown boundary conditions with a dimensionless average error of 0.0580 under noisy temperature measurement with a standard deviation of 10 K with a drastic reduction of computational cost compared to the conventional approach. Moreover, the effects of training data, location of sensor, future time step selection on the performance of prediction are investigated.
Neural Volumetric Memory for Visual Locomotion Control
Authors: Ruihan Yang, Ge Yang, Xiaolong Wang
Date: 2023-04-03 00:00:00
Proceeding: CVPR 2023 1
Tasks: 3D geometry

Legged robots have the potential to expand the reach of autonomy beyond paved roads. In this work, we consider the difficult problem of locomotion on challenging terrains using a single forward-facing depth camera. Due to the partial observability of the problem, the robot has to rely on past observations to infer the terrain currently beneath it. To solve this problem, we follow the paradigm in computer vision that explicitly models the 3D geometry of the scene and propose Neural Volumetric Memory (NVM), a geometric memory architecture that explicitly accounts for the SE(3) equivariance of the 3D world. NVM aggregates feature volumes from multiple camera views by first bringing them back to the ego-centric frame of the robot. We test the learned visual-locomotion policy on a physical robot and show that our approach, which explicitly introduces geometric priors during training, offers superior performance than more na\"ive methods. We also include ablation studies and show that the representations stored in the neural volumetric memory capture sufficient geometric information to reconstruct the scene. Our project page with videos is https://rchalyang.github.io/NVM .
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
Authors: Canwen Xu, Daya Guo, Nan Duan, Julian McAuley
Date: 2023-04-03 00:00:00
Tasks: Chatbot, Language Modeling, Language Modelling, Large Language Model

Chat models, such as ChatGPT, have shown impressive capabilities and have been rapidly adopted across numerous domains. However, these models are only accessible through a restricted API, creating barriers for new research and progress in the field. We propose a pipeline that can automatically generate a high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a conversation with itself. Subsequently, we employ parameter-efficient tuning to enhance LLaMA, an open-source large language model. The resulting model, named Baize, demonstrates good performance in multi-turn dialogues with guardrails that minimize potential risks. Furthermore, we propose a new technique called Self-Distill with Feedback, to further improve the performance of the Baize models with feedback from ChatGPT. The Baize models and data are released for research purposes only at https://github.com/project-baize/baize-chatbot. An online demo is also available at https://huggingface.co/spaces/project-baize/chat-with-baize.
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
Authors: Xiangyang Zhu, Renrui Zhang, Bowei He, Aojun Zhou, Dong Wang, Bin Zhao, Peng Gao
Date: 2023-04-03 00:00:00
Proceeding: ICCV 2023 1
Tasks: All, Computational Efficiency, Few-Shot Learning

The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its application to diverse downstream vision tasks. To improve its capacity on downstream tasks, few-shot learning has become a widely-adopted technique. However, existing methods either exhibit limited performance or suffer from excessive learnable parameters. In this paper, we propose APE, an Adaptive Prior rEfinement method for CLIP's pre-trained knowledge, which achieves superior accuracy with high computational efficiency. Via a prior refinement module, we analyze the inter-class disparity in the downstream data and decouple the domain-specific knowledge from the CLIP-extracted cache model. On top of that, we introduce two model variants, a training-free APE and a training-required APE-T. We explore the trilateral affinities between the test image, prior cache model, and textual representations, and only enable a lightweight category-residual module to be trained. For the average accuracy over 11 benchmarks, both APE and APE-T attain state-of-the-art and respectively outperform the second-best by +1.59% and +1.99% under 16 shots with x30 less learnable parameters.
Hate Speech Targets Detection in Parler using BERT
Authors: Nadav Schneider, Shimon Shouei, Saleem Ghantous, Elad Feldman
Date: 2023-04-03 00:00:00
Tasks: Hate Speech Detection

Online social networks have become a fundamental component of our everyday life. Unfortunately, these platforms are also a stage for hate speech. Popular social networks have regularized rules against hate speech. Consequently, social networks like Parler and Gab advocating and claiming to be free speech platforms have evolved. These platforms have become a district for hate speech against diverse targets. We present in our paper a pipeline for detecting hate speech and its targets and use it for creating Parler hate targets' distribution. The pipeline consists of two models; one for hate speech detection and the second for target classification, both based on BERT with Back-Translation and data pre-processing for improved results. The source code used in this work, as well as other relevant sources, are available at: https://github.com/NadavSc/HateRecognition.git
DribbleBot: Dynamic Legged Manipulation in the Wild
Authors: Yandong Ji, Gabriel B. Margolis, Pulkit Agrawal
Date: 2023-04-03 00:00:00
Tasks: reinforcement-learning

DribbleBot (Dexterous Ball Manipulation with a Legged Robot) is a legged robotic system that can dribble a soccer ball under the same real-world conditions as humans (i.e., in-the-wild). We adopt the paradigm of training policies in simulation using reinforcement learning and transferring them into the real world. We overcome critical challenges of accounting for variable ball motion dynamics on different terrains and perceiving the ball using body-mounted cameras under the constraints of onboard computing. Our results provide evidence that current quadruped platforms are well-suited for studying dynamic whole-body control problems involving simultaneous locomotion and manipulation directly from sensory observations.
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition
Authors: F. O. de Franca, M. Virgolin, M. Kommenda, M. S. Majumder, M. Cranmer, G. Espada, L. Ingelse, A. Fonseca, M. Landajuela, B. Petersen, R. Glatt, N. Mundhenk, C. S. Lee, J. D. Hochhalter, D. L. Randall, P. Kamienny, H. Zhang, G. Dick, A. Simon, B. Burlacu, Jaan Kasak, Meera Machado, Casper Wilstrup, W. G. La Cava
Date: 2023-04-03 00:00:00
Tasks: Bayesian Optimization, Evolutionary Algorithms, regression, Symbolic Regression

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate models.We present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions.
Changes to Captions: An Attentive Network for Remote Sensing Change Captioning
Authors: Shizhen Chang, Pedram Ghamisi
Date: 2023-04-03 00:00:00

In recent years, advanced research has focused on the direct learning and analysis of remote sensing images using natural language processing (NLP) techniques. The ability to accurately describe changes occurring in multi-temporal remote sensing images is becoming increasingly important for geospatial understanding and land planning. Unlike natural image change captioning tasks, remote sensing change captioning aims to capture the most significant changes, irrespective of various influential factors such as illumination, seasonal effects, and complex land covers. In this study, we highlight the significance of accurately describing changes in remote sensing images and present a comparison of the change captioning task for natural and synthetic images and remote sensing images. To address the challenge of generating accurate captions, we propose an attentive changes-to-captions network, called Chg2Cap for short, for bi-temporal remote sensing images. The network comprises three main components: 1) a Siamese CNN-based feature extractor to collect high-level representations for each image pair; 2) an attentive decoder that includes a hierarchical self-attention block to locate change-related features and a residual block to generate the image embedding; and 3) a transformer-based caption generator to decode the relationship between the image embedding and the word embedding into a description. The proposed Chg2Cap network is evaluated on two representative remote sensing datasets, and a comprehensive experimental analysis is provided. The code and pre-trained models will be available online at https://github.com/ShizhenChang/Chg2Cap.
Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts?
Authors: Wen Shen, Lei Cheng, Yuxiao Yang, Mingjie Li, Quanshi Zhang
Date: 2023-04-03 00:00:00
Tasks: Sentence

In this paper, we explain the inference logic of large language models (LLMs) as a set of symbolic concepts. Many recent studies have discovered that traditional DNNs usually encode sparse symbolic concepts. However, because an LLM has much more parameters than traditional DNNs, whether the LLM also encodes sparse symbolic concepts is still an open problem. Therefore, in this paper, we propose to disentangle the inference score of LLMs for dialogue tasks into a small number of symbolic concepts. We verify that we can use those sparse concepts to well estimate all inference scores of the LLM on all arbitrarily masking states of the input sentence. We also evaluate the transferability of concepts encoded by an LLM and verify that symbolic concepts usually exhibit high transferability across similar input sentences. More crucially, those symbolic concepts can be used to explain the exact reasons accountable for the LLM's prediction errors.
Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet Loss
Authors: Jeffrey Lu, Ivan Rodriguez
Date: 2023-04-03 00:00:00
Tasks: Logical Reasoning, Reading Comprehension, Transfer Learning

The current trend in developing machine learning models for reading comprehension and logical reasoning tasks is focused on improving the models' abilities to understand and utilize logical rules. This work focuses on providing a novel loss function and accompanying model architecture that has more interpretable components than some other models by representing a common strategy employed by humans when given reading comprehension and logical reasoning tasks. Our strategy involves emphasizing relative accuracy over absolute accuracy and can theoretically produce the correct answer with incomplete knowledge. We examine the effectiveness of this strategy to solve reading comprehension and logical reasoning questions. The models were evaluated on the ReClor dataset, a challenging reading comprehension and logical reasoning benchmark. We propose the polytuplet loss function, which forces prioritization of learning the relative correctness of answer choices over learning the true accuracy of each choice. Our results indicate that models employing polytuplet loss outperform existing baseline models, though further research is required to quantify the benefits it may present.