pwc-archive/papers-with-abstracts huggingface dataset:
Can LLMs Generate Visualizations with Dataless Prompts?
Authors: Darius Coelho, Harshit Barot, Naitik Rathod, Klaus Mueller
Date: 2024-06-22 00:00:00

Recent advancements in large language models have revolutionized information access, as these models harness data available on the web to address complex queries, becoming the preferred information source for many users. In certain cases, queries are about publicly available data, which can be effectively answered with data visualizations. In this paper, we investigate the ability of large language models to provide accurate data and relevant visualizations in response to such queries. Specifically, we investigate the ability of GPT-3 and GPT-4 to generate visualizations with dataless prompts, where no data accompanies the query. We evaluate the results of the models by comparing them to visualization cheat sheets created by visualization experts.
Understanding the Role of User Profile in the Personalization of Large Language Models
Authors: Bin Wu, Zhengyan Shi, Hossein A. Rahmani, Varsha Ramineni, Emine Yilmaz
Date: 2024-06-22 00:00:00

Utilizing user profiles to personalize Large Language Models (LLMs) has been shown to enhance the performance on a wide range of tasks. However, the precise role of user profiles and their effect mechanism on LLMs remains unclear. This study first confirms that the effectiveness of user profiles is primarily due to personalization information rather than semantic information. Furthermore, we investigate how user profiles affect the personalization of LLMs. Within the user profile, we reveal that it is the historical personalized response produced or approved by users that plays a pivotal role in personalizing LLMs. This discovery unlocks the potential of LLMs to incorporate a greater number of user profiles within the constraints of limited input length. As for the position of user profiles, we observe that user profiles integrated into different positions of the input context do not contribute equally to personalization. Instead, where the user profile that is closer to the beginning affects more on the personalization of LLMs. Our findings reveal the role of user profiles for the personalization of LLMs, and showcase how incorporating user profiles impacts performance providing insight to leverage user profiles effectively.
Boundary Detection Algorithm Inspired by Locally Linear Embedding
Authors: Pei-Cheng Kuo, Nan Wu
Date: 2024-06-26 00:00:00
Tasks: Boundary Detection

In the study of high-dimensional data, it is often assumed that the data set possesses an underlying lower-dimensional structure. A practical model for this structure is an embedded compact manifold with boundary. Since the underlying manifold structure is typically unknown, identifying boundary points from the data distributed on the manifold is crucial for various applications. In this work, we propose a method for detecting boundary points inspired by the widely used locally linear embedding algorithm. We implement this method using two nearest neighborhood search schemes: the $\epsilon$-radius ball scheme and the $K$-nearest neighbor scheme. This algorithm incorporates the geometric information of the data structure, particularly through its close relation with the local covariance matrix. We discuss the selection the key parameter and analyze the algorithm through our exploration of the spectral properties of the local covariance matrix in both neighborhood search schemes. Furthermore, we demonstrate the algorithm's performance with simulated examples.
Scalable Dual Coordinate Descent for Kernel Methods
Authors: Zishan Shao, Aditya Devarakonda
Date: 2024-06-26 00:00:00

Dual Coordinate Descent (DCD) and Block Dual Coordinate Descent (BDCD) are important iterative methods for solving convex optimization problems. In this work, we develop scalable DCD and BDCD methods for the kernel support vector machines (K-SVM) and kernel ridge regression (K-RR) problems. On distributed-memory parallel machines the scalability of these methods is limited by the need to communicate every iteration. On modern hardware where communication is orders of magnitude more expensive, the running time of the DCD and BDCD methods is dominated by communication cost. We address this communication bottleneck by deriving $s$-step variants of DCD and BDCD for solving the K-SVM and K-RR problems, respectively. The $s$-step variants reduce the frequency of communication by a tunable factor of $s$ at the expense of additional bandwidth and computation. The $s$-step variants compute the same solution as the existing methods in exact arithmetic. We perform numerical experiments to illustrate that the $s$-step variants are also numerically stable in finite-arithmetic, even for large values of $s$. We perform theoretical analysis to bound the computation and communication costs of the newly designed variants, up to leading order. Finally, we develop high performance implementations written in C and MPI and present scaling experiments performed on a Cray EX cluster. The new $s$-step variants achieved strong scaling speedups of up to $9.8\times$ over existing methods using up to $512$ cores.
The Effects of Data Split Strategies on the Offline Experiments for CTR Prediction
Authors: Ramazan Tarik Turksoy, Beyza Turkmen
Date: 2024-06-26 00:00:00
Tasks: Click-Through Rate Prediction, Prediction

Click-through rate (CTR) prediction is a crucial task in online advertising to recommend products that users are likely to be interested in. To identify the best-performing models, rigorous model evaluation is necessary. Offline experimentation plays a significant role in selecting models for live user-item interactions, despite the value of online experimentation like A/B testing, which has its own limitations and risks. Often, the correlation between offline performance metrics and actual online model performance is inadequate. One main reason for this discrepancy is the common practice of using random splits to create training, validation, and test datasets in CTR prediction. In contrast, real-world CTR prediction follows a temporal order. Therefore, the methodology used in offline evaluation, particularly the data splitting strategy, is crucial. This study aims to address the inconsistency between current offline evaluation methods and real-world use cases, by focusing on data splitting strategies. To examine the impact of different data split strategies on offline performance, we conduct extensive experiments using both random and temporal splits on a large open benchmark dataset, Criteo.
Effects of Using Synthetic Data on Deep Recommender Models' Performance
Authors: Fatih Cihan Taskin, Ilknur Akcay, Muhammed Pesen, Said Aldemir, Ipek Iraz Esin, Furkan Durmus
Date: 2024-06-26 00:00:00
Tasks: Data Augmentation, Recommendation Systems, Synthetic Data Generation

Recommender systems are essential for enhancing user experiences by suggesting items based on individual preferences. However, these systems frequently face the challenge of data imbalance, characterized by a predominance of negative interactions over positive ones. This imbalance can result in biased recommendations favoring popular items. This study investigates the effectiveness of synthetic data generation in addressing data imbalances within recommender systems. Six different methods were used to generate synthetic data. Our experimental approach involved generating synthetic data using these methods and integrating the generated samples into the original dataset. Our results show that the inclusion of generated negative samples consistently improves the Area Under the Curve (AUC) scores. The significant impact of synthetic negative samples highlights the potential of data augmentation strategies to address issues of data sparsity and imbalance, ultimately leading to improved performance of recommender systems.
Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis
Authors: Lukas Bahr, Christoph Wehner, Judith Wewerka, José Bittencourt, Ute Schmid, Rüdiger Daub
Date: 2024-06-26 00:00:00
Tasks: Language Modeling, Language Modelling, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Failure mode and effects analysis (FMEA) is an essential tool for mitigating potential failures, particularly during the ramp-up phases of new products. However, its effectiveness is often limited by the reasoning capabilities of the FMEA tools, which are usually tabular structured. Meanwhile, large language models (LLMs) offer novel prospects for advanced natural language processing tasks. However, LLMs face challenges in tasks that require factual knowledge, a gap that retrieval-augmented generation (RAG) approaches aim to fill. RAG retrieves information from a non-parametric data store and uses a language model to generate responses. Building on this concept, we propose to enhance the non-parametric data store with a knowledge graph (KG). By integrating a KG into the RAG framework, we aim to leverage analytical and semantic question-answering capabilities for FMEA data. This paper contributes by presenting set-theoretic standardization and a schema for FMEA data, an algorithm for creating vector embeddings from the FMEA-KG, and a KG-enhanced RAG framework. Our approach is validated through a user experience design study, and we measure the precision and performance of the context retrieval recall.
Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification
Authors: Areeg Fahad Rasheed, M. Zarkoosh
Date: 2024-05-28 00:00:00
Tasks: Few-Shot Learning, In-Context Learning, text-classification, Text Classification

Within few-shot learning, in-context learning (ICL) has become a potential method for leveraging contextual information to improve model performance on small amounts of data or in resource-constrained environments where training models on large datasets is prohibitive. However, the quality of the selected sample in a few shots severely limits the usefulness of ICL. The primary goal of this paper is to enhance the performance of evaluation metrics for in-context learning by selecting high-quality samples in few-shot learning scenarios. We employ the chi-square test to identify high-quality samples and compare the results with those obtained using low-quality samples. Our findings demonstrate that utilizing high-quality samples leads to improved performance with respect to all evaluated metrics.
Claude 3.5 Sonnet Model Card Addendum
Authors: Anthropic
Date: 2024-06-24 00:00:00
Proceeding: Preprint 2024 6
Tasks: Code Generation, MMR total, model, Multi-task Language Understanding, Question Answering, Visual Question Answering

This addendum to our Claude 3 Model Card describes Claude 3.5 Sonnet, a new model which outperforms
our previous most capable model, Claude 3 Opus, while operating faster and at a lower cost. Claude 3.5
Sonnet offers improved capabilities, including better coding and visual processing. Since it is an evolution of
the Claude 3 model family, we are providing an addendum rather than a new model card. We provide updated
key evaluations and results from our safety testing.
Optimal Driving Model for Connected and Automated Electric Freight Vehicles in a Wireless Charging Scenario at Signalised Intersections
Authors: Wenbo Wang, Songhua Fan, Zijian Wang, Xinpeng Yao, Kenan Mu
Date: 2023-05-21 00:00:00
Proceeding: Appl. Sci. 2023 5

Electric freight vehicles have become an important means of transportation in connected and automated environments owing to their numerous advantages. However, the generally short driving range of connected and automated electric freight vehicles (CAEFVs) does not satisfy the growing transport demand. In this study, wireless charging technology is employed to construct a complex driving scenario including urban roads and dynamic wireless charging facilities. A combination of variable-scale elements consisting of vehicles, roads, and the environment is analysed hierarchically to develop a wireless charging scheme for urban transport systems. Using passage efficiency, energy consumption, and passenger comfort as the joint optimisation objectives, an optimal driving model for CAEFVs in wireless charging scenarios at signalised intersections combining scenario boundaries and vehicle dynamic constraints is proposed. Considering the differentiated charging needs of vehicles, this model is divided into a time priority strategy (TPS), balance priority strategy (BPS), and charging priority strategy (CPS). The obtained results reveal that the CPS is superior to the TPS in terms of the charging benefits but requires a longer travel time. Meanwhile, the BPS increases the charging benefits and passing efficiency. This study provides guidance for the deployment of wireless charging lanes with a high application value.
Domain generalization for retinal vessel segmentation via Hessian-based vector field
Authors: Hu, DW (Hu, Dewei), Li, H (Li, Hao), Liu, H (Liu, Han), Oguz, I (Oguz, Ipek)
Date: 2024-05-31 00:00:00
Proceeding: Medical Image Analysis 2024 5
Tasks: Data Augmentation, Domain Generalization, Medical Image Analysis, Retinal Vessel Segmentation

Blessed by vast amounts of data, learning -based methods have achieved remarkable performance in countless tasks in computer vision and medical image analysis. Although these deep models can simulate highly nonlinear mapping functions, they are not robust with regard to the domain shift of input data. This is a significant concern that impedes the large-scale deployment of deep models in medical images since they have inherent variation in data distribution due to the lack of imaging standardization. Therefore, researchers have explored many domain generalization (DG) methods to alleviate this problem. In this work, we introduce a Hessianbased vector field that can effectively model the tubular shape of vessels, which is an invariant feature for data across various distributions. The vector field serves as a good embedding feature to take advantage of the self -attention mechanism in a vision transformer. We design paralleled transformer blocks that stress the local features with different scales. Furthermore, we present a novel data augmentation method that introduces perturbations in image style while the vessel structure remains unchanged. In experiments conducted on public datasets of different modalities, we show that our model achieves superior generalizability compared with the existing algorithms. Our code and trained model are publicly available at https://hfbicf65f60ce88954b40s605cb0xkpf0p6qqqfiac.eds.tju.edu.cn/MedICLVU/Vector-Field-Transformer.
DIAT-RadHARNet: A lightweight DCNN for radar based classification of human suspicious activities
Authors: Mainak Chakraborty, Harish C. Kumawat, Sunita Vikrant Dhavale, Arockia Bazil Raj A
Date: 2022-02-01 00:00:00
Proceeding: IEEE Transactions on Instrumentation and Measurement 2022 2
Tasks: Human Activity Recognition

Recognizing suspicious human activities is one of the critical requirements for national security considerations. Nowadays, designing the deep convolution neural network (DCNN) models suitable for micro-Doppler (m-D) signature-based human activity classification is rapidly growing. However, high computation cost and a huge number of parameters limit their direct/effective usability in field applications. This article introduces an m-D signatures’ dataset “DIAT- μ RadHAR” covering army crawling, boxing, jumping while holding a gun, army jogging, army marching, and stone-pelting/grenade-throwing, generated using an X -band continuous wave (CW) radar. This article also introduces a lightweight DCNN model, “DIAT-RadHARNet,” designed for those human suspicious activity classification. To reduce the computation cost and to improve the generalization ability, DIAT-RadHARNet is designed with four design principles: depthwise separable convolutions, channel weighting (CHW) based on the importance, different size filters in the depthwise part, and operating different size kernels on the same input tensor. The network has 213 793 parameters with a total of 55 layers. Our extensive experimental analysis demonstrates that the DIAT-RadHARNet model efficiently classifies the activities with 99.22% accuracy, giving minimal false positive and false negative outcomes. The time complexity of the proposed DCNN model observed during the testing phase is 0.35 s. The same accuracy and time complexity are obtained even at adverse weather conditions, low-lighting environments, and long-range operations.
DIAT-μ RadHAR (micro-doppler signature dataset) & μ RadNet (a lightweight DCNN)—For human suspicious activity recognition
Authors: Mainak Chakraborty, Harish C. Kumawat, Sunita Vikrant Dhavale, Arockia Bazil Raj A
Date: 2022-02-01 00:00:00
Proceeding: IEEE Sensors Journal 2022 2
Tasks: Activity Recognition, Human Activity Recognition

In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory. Designing an automated human suspicious activities: army crawling, army jogging, jumping with holding a gun, army marching, boxing, and stone-pelting/grenades-throwing, recognition system using a suitable deep convolutional neural network (DCNN) model is rapidly growing due to its inherent in-depth features extraction capability. As a value addition to this research, an X-band continuous wave (CW) 10 GHz radar has been developed at our radar systems laboratory and used to acquire the m-D signatures, to prepare a dataset (DIAT- μ RadHAR) corresponding to above mentioned suspicious activities. In order to prepare a realistic dataset, human targets of different heights, weights, and gender are directed to perform the suspicious activities in front of the radar at different ranges between 10 m - 0.5 km and at different target aspect angles (0°, ±15°, ±30° and ±45°). A lightweight DCNN architecture ( μ RadNet) is also designed and trained with the prepared DIAT- μ RadHAR dataset comprising 3780 samples. The performance and recognition accuracy of μ RadNet is statistically computed, and the results are compared to the state-of-the-art (SOTA) CNN models. The μ RadNet DCNN model outperforms the SOTA CNN models, giving 99.22% of overall classification accuracy, 0.09M parameters, and 0.40G floating point operations (FLOPs) with minimal false negative/positive rates. The time-complexity of the designed lightweight μ RadNet DCNN model is 0.12 s, which evidences the suitability of our DCNN model for the on-device implementation.
Corona-Nidaan: lightweight deep convolutional neural network for chest X-Ray based COVID-19 infection detection
Authors: Mainak Chakraborty, Sunita Vikrant Dhavale
Date: 2021-02-01 00:00:00
Proceeding: Applied Intelligence 2021 2
Tasks: COVID-19 Diagnosis, Transfer Learning

The coronavirus COVID-19 pandemic is today’s major public health crisis, we have faced since the Second World War. The pandemic is spreading around the globe like a wave, and according to the World Health Organization’s recent report, the number of confirmed cases and deaths are rising rapidly. COVID-19 pandemic has created severe social, economic, and political crises, which in turn will leave long-lasting scars. One of the countermeasures against controlling coronavirus outbreak is specific, accurate, reliable, and rapid detection technique to identify infected patients. The availability and affordability of RT-PCR kits remains a major bottleneck in many countries, while handling COVID-19 outbreak effectively. Recent findings indicate that chest radiography anomalies can characterize patients with COVID-19 infection. In this study, Corona-Nidaan, a lightweight deep convolutional neural network (DCNN), is proposed to detect COVID-19, Pneumonia, and Normal cases from chest X-ray image analysis; without any human intervention. We introduce a simple minority class oversampling method for dealing with imbalanced dataset problem. The impact of transfer learning with pre-trained CNNs on chest X-ray based COVID-19 infection detection is also investigated. Experimental analysis shows that Corona-Nidaan model outperforms prior works and other pre-trained CNN based models. The model achieved 95% accuracy for three-class classification with 94% precision and recall for COVID-19 cases. While studying the performance of various pre-trained models, it is also found that VGG19 outperforms other pre-trained CNN models by achieving 93% accuracy with 87% recall and 93% precision for COVID-19 infection detection. The model is evaluated by screening the COVID-19 infected Indian Patient chest X-ray dataset with good accuracy.
A Review of Electromagnetic Elimination Methods for low-field portable MRI scanner
Authors: Wanyu Bian, Panfeng Li, Mengyao Zheng, Chihang Wang, Anying Li, Ying Li, Haowei Ni, Zixuan Zeng
Date: 2024-06-22 00:00:00
Tasks: Deep Learning, Diagnostic

This paper analyzes conventional and deep learning methods for eliminating electromagnetic interference (EMI) in MRI systems. We compare traditional analytical and adaptive techniques with advanced deep learning approaches. Key strengths and limitations of each method are highlighted. Recent advancements in active EMI elimination, such as external EMI receiver coils, are discussed alongside deep learning methods, which show superior EMI suppression by leveraging neural networks trained on MRI data. While deep learning improves EMI elimination and diagnostic capabilities, it introduces security and safety concerns, particularly in commercial applications. A balanced approach, integrating conventional reliability with deep learning's advanced capabilities, is proposed for more effective EMI suppression in MRI systems.
AutoPureData: Automated Filtering of Undesirable Web Data to Update LLM Knowledge
Authors: Praneeth Vadlapati
Date: 2024-06-27 00:00:00
Tasks: RAG

Up-to-date and reliable language models are consistently sought after and are essential in various applications. Typically, models are trained on a fixed dataset and then deployed globally. However, the knowledge of the models becomes outdated. Enabling automatic updation of AI knowledge using web data involves significant concerns regarding the model's safety and quality due to a threat from unsafe and undesirable text across the web. The purity of new data was essential for updating knowledge of language models to maintain their reliability. This paper proposes AutoPureData, a system that automatically collects and purifies web data. The system loaded a sample of web data. Utilizing existing trusted AI models, it successfully eliminated unsafe text with an accuracy of 97% and undesirable text with an accuracy of 86%, demonstrating the system's effectiveness in purifying the data. The system ensures that only meaningful and safe text can be used to update LLM knowledge. The pure text was then optimized and stored in a vector database for future querying. It was found that LLM can fetch new data from the vector DB. The LLM writes the RAG query in English, even if the user's query is in another language, proving that the system can perform cross-lingual retrieval. This paper proposes a method to maintain the accuracy and relevance of up-to-date language models by ensuring that only purified data was used to update LLM knowledge. This work contributes to updating knowledge of chatbots using meaningful and safe text, enhancing their utility across various industries, and potentially reducing the risks associated with outputs caused by unsafe or impure data. Code is available at github.com/Pro-GenAI/AutoPureData.