pwc-archive/papers-with-abstracts huggingface dataset:
Clarifying the Path to User Satisfaction: An Investigation into Clarification Usefulness
Authors: Hossein A. Rahmani, Xi Wang, Mohammad Aliannejadi, Mohammadmehdi Naghiaei, Emine Yilmaz
Date: 2024-02-02 00:00:00
Tasks: Information Retrieval

Clarifying questions are an integral component of modern information retrieval systems, directly impacting user satisfaction and overall system performance. Poorly formulated questions can lead to user frustration and confusion, negatively affecting the system's performance. This research addresses the urgent need to identify and leverage key features that contribute to the classification of clarifying questions, enhancing user satisfaction. To gain deeper insights into how different features influence user satisfaction, we conduct a comprehensive analysis, considering a broad spectrum of lexical, semantic, and statistical features, such as question length and sentiment polarity. Our empirical results provide three main insights into the qualities of effective query clarification: (1) specific questions are more effective than generic ones; (2) the subjectivity and emotional tone of a question play a role; and (3) shorter and more ambiguous queries benefit significantly from clarification. Based on these insights, we implement feature-integrated user satisfaction prediction using various classifiers, both traditional and neural-based, including random forest, BERT, and large language models. Our experiments show a consistent and significant improvement, particularly in traditional classifiers, with a minimum performance boost of 45\%. This study presents invaluable guidelines for refining the formulation of clarifying questions and enhancing both user satisfaction and system performance.
Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
Authors: Yuhe Ke, Liyuan Jin, Kabilan Elangovan, Hairil Rizal Abdullah, Nan Liu, Alex Tiong Heng Sia, Chai Rick Soh, Joshua Yi Min Tung, Jasmine Chiat Ling Ong, Daniel Shu Wei Ting
Date: 2024-01-29 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Purpose: Large Language Models (LLMs) hold significant promise for medical applications. Retrieval Augmented Generation (RAG) emerges as a promising approach for customizing domain knowledge in LLMs. This case study presents the development and evaluation of an LLM-RAG pipeline tailored for healthcare, focusing specifically on preoperative medicine. Methods: We developed an LLM-RAG model using 35 preoperative guidelines and tested it against human-generated responses, with a total of 1260 responses evaluated. The RAG process involved converting clinical documents into text using Python-based frameworks like LangChain and Llamaindex, and processing these texts into chunks for embedding and retrieval. Vector storage techniques and selected embedding models to optimize data retrieval, using Pinecone for vector storage with a dimensionality of 1536 and cosine similarity for loss metrics. Human-generated answers, provided by junior doctors, were used as a comparison. Results: The LLM-RAG model generated answers within an average of 15-20 seconds, significantly faster than the 10 minutes typically required by humans. Among the basic LLMs, GPT4.0 exhibited the best accuracy of 80.1%. This accuracy was further increased to 91.4% when the model was enhanced with RAG. Compared to the human-generated instructions, which had an accuracy of 86.3%, the performance of the GPT4.0 RAG model demonstrated non-inferiority (p=0.610). Conclusions: In this case study, we demonstrated a LLM-RAG model for healthcare implementation. The pipeline shows the advantages of grounded knowledge, upgradability, and scalability as important aspects of healthcare LLM deployment.
AraCovTexFinder: Leveraging the transformer-based language model for Arabic COVID-19 text identification
Authors: Md. Rajib Hossain, Mohammed Moshiul Hoque, Nazmul Siddique
Date: 2024-01-04 00:00:00
Proceeding: Engineering Applications of Artificial Intelligence 2024 1
Tasks: Information Retrieval, Language Modeling, Language Modelling, Misinformation

In light of the pandemic, the identification and processing of COVID-19-related text have emerged as critical research areas within the field of Natural Language Processing (NLP). With a growing reliance on online portals and social media for information exchange and interaction, a surge in online textual content, comprising disinformation, misinformation, fake news, and rumors has led to the phenomenon of an infodemic on the World Wide Web. Arabic, spoken by over 420 million people worldwide, stands as a significant low-resource language, lacking efficient tools or applications for the detection of COVID-19-related text. Additionally, the identification of COVID-19 text is an essential prerequisite task for detecting fake and toxic content associated with COVID-19. This gap hampers crucial COVID information retrieval and processing necessary for policymakers and health authorities. Addressing this issue, this paper introduces an intelligent Arabic COVID-19 text identification system named ‘AraCovTexFinder,’ leveraging a fine-tuned fusion-based transformer model. Recognizing the challenges posed by a scarcity of related text corpora, substantial morphological variations in the language, and a deficiency of well-tuned hyperparameters, the proposed system aims to mitigate these hurdles. To support the proposed method, two corpora are developed: an Arabic embedding corpus (AraEC) and an Arabic COVID-19 text identification corpus (AraCoV). The study evaluates the performance of six transformer-based language models (mBERT, XML-RoBERTa, mDeBERTa-V3, mDistilBERT, BERT-Arabic, and AraBERT), 12 deep learning models (combining Word2Vec, GloVe, and FastText embedding with CNN, LSTM, VDCNN, and BiLSTM), and the newly introduced model AraCovTexFinder. Through extensive evaluation, AraCovTexFinder achieves a high accuracy of 98.89 ± 0.001%, outperforming other baseline models, including transformer-based language and deep learning models. This research highlights the importance of specialized tools in low-resource languages to combat the infodemic relating to COVID-19, which can assist policymakers and health authorities in making informed decisions.
Accurate Frequency Estimator of Sinusoid Based on Interpolation of FFT and DTFT
Authors: Lei Fan, GUOQING QI2, Jun Xing, JIYU JIN, Jinyu Liu, AND ZHISEN WANG
Date: 2020-03-03 00:00:00
Proceeding: journal 2020 3

An accurate frequency estimator of complex sinusoid in additive white noise is proposed. It is
based on interpolation of Fast Fourier Transform (FFT) and Discrete-Time Fourier Transform (DTFT). Zeropadding
is rstly performed before the FFT of the sinusoid sampled data, and the coarse estimate is obtained
by searching the discrete frequency index of the maximum FFT spectrum line. Then the ne estimate is
obtained by employing the maximum FFT spectrum line and two DTFT sample values located on the left
and right side of the maximum spectrum line. The correlation coefcients between the Fourier Transform of
the noises on two arbitrarily spaced spectrum lines are derived, and the MSE calculation formula is derived in
additive white noise background based on the correlation coefcients. Simulations results demonstrate that
the proposed algorithm has lower MSE than the competing algorithms, and its signal-to-noise ratio (SNR)
threshold is lower compared with Candan algorithm, AM algorithm and Djukanovic algorithms.
User lung cancer classification using efficientnet from ct scan images
Authors: Rehan Raza a, B, Fatima Zulfiqar c, D, Muhammad Owais Khan b, C, Muhammad Arif e, Atif Alvi b, Muhammad Aksam Iftikhar c, Tanvir Alam e, *
Date: 2023-09-03 00:00:00
Proceeding: journal 2023 9
Tasks: Cancer Classification, Computed Tomography (CT), Data Augmentation, Lung Cancer Diagnosis, Transfer Learning

Lung cancer (LC) remains a leading cause of death worldwide. Early diagnosis is critical to protect innocent 
human lives. Computed tomography (CT) scans are one of the primary imaging modalities for lung cancer 
diagnosis. However, manual CT scan analysis is time-consuming and prone to errors/not accurate. Considering 
these shortcomings, computational methods especially machine learning and deep learning algorithms are 
leveraged as an alternative to accelerate the accurate detection of CT scans as cancerous, and non-cancerous. In 
the present article, we proposed a novel transfer learning-based predictor called, Lung-EffNet for lung cancer 
classification. Lung-EffNet is built based on the architecture of EfficientNet and further modified by adding top 
layers in the classification head of the model. Lung-EffNet is evaluated by utilizing five variants of EfficientNet i. 
e., B0–B4. The experiments are conducted on the benchmark dataset “IQ-OTH/NCCD” for lung cancer patients 
grouped as benign, malignant, or normal based on the presence or absence of lung cancer. The class imbalance 
issue was handled through multiple data augmentation methods to overcome the biases. The developed model 
Lung-EffNet attained 99.10% of accuracy and a score of 0.97 to 0.99 of ROC on the test set. We compared the 
efficacy of the proposed fine-tuned pre-trained EfficientNet with other pre-trained CNN architectures. The predicted outcomes demonstrate that EfficientNetB1 based Lung-EffNet outperforms other CNNs in terms of both 
accuracy and efficiency. Moreover, it is faster and requires fewer parameters to train than other CNN based 
models, making it a good choice for large-scale deployment in clinical settings and a promising tool for automated lung cancer diagnosis from CT scan images.
Deep Learning Based Amharic Chatbot for FAQs in Universities
Authors: Goitom Ybrah Hailu, Hadush Hailu, Shishay Welay
Date: 2024-01-26 00:00:00
Tasks: Chatbot, Deep Learning

University students often spend a considerable amount of time seeking answers to common questions from administrators or teachers. This can become tedious for both parties, leading to a need for a solution. In response, this paper proposes a chatbot model that utilizes natural language processing and deep learning techniques to answer frequently asked questions (FAQs) in the Amharic language. Chatbots are computer programs that simulate human conversation through the use of artificial intelligence (AI), acting as a virtual assistant to handle questions and other tasks. The proposed chatbot program employs tokenization, normalization, stop word removal, and stemming to analyze and categorize Amharic input sentences. Three machine learning model algorithms were used to classify tokens and retrieve appropriate responses: Support Vector Machine (SVM), Multinomial Na\"ive Bayes, and deep neural networks implemented through TensorFlow, Keras, and NLTK. The deep learning model achieved the best results with 91.55% accuracy and a validation loss of 0.3548 using an Adam optimizer and SoftMax activation function. The chatbot model was integrated with Facebook Messenger and deployed on a Heroku server for 24-hour accessibility. The experimental results demonstrate that the chatbot framework achieved its objectives and effectively addressed challenges such as Amharic Fidel variation, morphological variation, and lexical gaps. Future research could explore the integration of Amharic WordNet to narrow the lexical gap and support more complex questions.
Nature's Brewery to Bedtime: The Role of Hops in GABAA Receptor Modulation and Sleep Promotion
Authors: Ali Y. Benkherouf
Date: 2024-02-03 00:00:00
Tasks: Molecular Docking

Insomnia often requires pharmacological interventions, with benzodiazepines and Z-drugs enhancing GABA's inhibitory effects by stabilizing GABAA receptor chloride ion channels. Prolonged use, however, raises dependency and cognitive concerns. Humulus lupulus (hops) is gaining attention as a natural relaxant and sleep aid, potentially modulating GABAA receptors differently. This study explores hops' neuroactive phytochemicals and their therapeutic mechanisms. The alpha-acid humulone and hop prenylflavonoids affect GABA-induced displacement of [3H]EBOB in the GABAA receptor, showing flumazenil-insensitive and subtype-selective effects. Molecular docking identifies binding sites, with humulone's activity confirmed electrophysiologically and in mouse studies, impacting sleep onset and duration. These findings suggest hops as positive modulators of GABAA receptors, offering insights for sleep aid optimization.
Unveiling Molecular Moieties through Hierarchical Grad-CAM Graph Explainability
Authors: Salvatore Contino, Paolo Sortino, Maria Rita Gulotta, Ugo Perricone, Roberto Pirrone
Date: 2024-01-29 00:00:00
Tasks: Drug Discovery, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), valid

Background: Virtual Screening (VS) has become an essential tool in drug discovery, enabling the rapid and cost-effective identification of potential bioactive molecules. Among recent advancements, Graph Neural Networks (GNNs) have gained prominence for their ability to model complex molecular structures using graph-based representations. However, the integration of explainable methods to elucidate the specific contributions of molecular substructures to biological activity remains a significant challenge. This limitation hampers both the interpretability of predictive models and the rational design of novel therapeutics. Results: We trained 20 GNN models on a dataset of small molecules with the goal of predicting their activity on 20 distinct protein targets from the Kinase family. These classifiers achieved state-of-the-art performance in virtual screening tasks, demonstrating high accuracy and robustness on different targets. Building upon these models, we implemented the Hierarchical Grad-CAM graph Explainer (HGE) framework, enabling an in-depth analysis of the molecular moieties driving protein-ligand binding stabilization. HGE exploits Grad-CAM explanations at the atom, ring, and whole-molecule levels, leveraging the message-passing mechanism to highlight the most relevant chemical moieties. Validation against experimental data from the literature confirmed the ability of the explainer to recognize a molecular pattern of drugs and correctly annotate them to the known target. Conclusion: Our approach may represent a valid support to shorten both the screening and the hit discovery process. Detailed knowledge of the molecular substructures that play a role in the binding process can help the computational chemist to gain insights into the structure optimization, as well as in drug repurposing tasks.
Open Heterogeneous Data for Condition Monitoring of Multi Faults in Rotating Machines Used in Different Operating Conditions
Authors: Moncef Soualhi, Abdenour Soualhi, Khanh TP Nguyen, Kamal Medjaher, Guy Clerc, Hubert Razik
Date: 2023-08-24 00:00:00
Proceeding: International Journal of Prognostics and Health Management 2023 8
Tasks: Diversity, Management

Rotating machines are widely used in several fields such as railways, renewable energies, robotics, etc. This diversity of application implies a large variety of faults of critical components susceptible to fail. For this purpose, prognostics and health management (PHM) is deployed to effectively monitor these components through the detection, diagnostics as well as prognostics of faults. In the literature, there exist numerous methods to ensure the above monitoring activities. However, few of them consider different failure types using heterogeneous data and various operating conditions. Also, there are no dominant methods that can be generalized for monitoring. For this reason, the genericity of these methods and their applicability in several systems is a crucial issue. To help researchers to achieve the above challenges, this paper presents a detailed description of data sources from experimental test benches. These data-sets correspond to different case studies that monitor the health states of multiple critical components in various operating conditions using numerous sensors.
Handling Delayed Feedback in Distributed Online Optimization : A Projection-Free Approach
Authors: Tuan-Anh Nguyen, Nguyen Kim Thang, Denis Trystram
Date: 2024-02-03 00:00:00

Learning at the edges has become increasingly important as large quantities of data are continually generated locally. Among others, this paradigm requires algorithms that are simple (so that they can be executed by local devices), robust (again uncertainty as data are continually generated), and reliable in a distributed manner under network issues, especially delays. In this study, we investigate the problem of online convex optimization under adversarial delayed feedback. We propose two projection-free algorithms for centralised and distributed settings in which they are carefully designed to achieve a regret bound of O(\sqrt{B}) where B is the sum of delay, which is optimal for the OCO problem in the delay setting while still being projection-free. We provide an extensive theoretical study and experimentally validate the performance of our algorithms by comparing them with existing ones on real-world problems.
Neural option pricing for rough Bergomi model
Authors: Changqing Teng, Guanglian Li
Date: 2024-02-05 00:00:00
Tasks: model

The rough Bergomi (rBergomi) model can accurately describe the historical and implied volatilities, and has gained much attention in the past few years. However, there are many hidden unknown parameters or even functions in the model. In this work, we investigate the potential of learning the forward variance curve in the rBergomi model using a neural SDE. To construct an efficient solver for the neural SDE, we propose a novel numerical scheme for simulating the volatility process using the modified summation of exponentials. Using the Wasserstein 1-distance to define the loss function, we show that the learned forward variance curve is capable of calibrating the price process of the underlying asset and the price of the European-style options simultaneously. Several numerical tests are provided to demonstrate its performance.
Automatic Detection of Congestive Heart Failure Based on a Hybrid Deep Learning Algorithm in the Internet of Medical Things
Authors: Wenlong Ning, Shuhua Li, Dongmei Wei, Long Zhe Guo, and Hong Chen
Date: 2020-09-10 00:00:00
Proceeding: journal 2020 9
Tasks: Electrocardiography (ECG), Specificity

Congestive heart failure (CHF) is a chronic heart condition with heart function decline caused by various heart diseases that requires long-term treatment and affects personal life safety. Presently, CHF diagnosis is being conducted by experts, which is a nonspecific mode that is time consuming and depends on experience. Therefore, it is of great clinical value to conduct CHF recognition through automatic detection. This article proposed an automatic CHF detection model based on a hybrid deep learning algorithm that is composed of a convolutional neural network (CNN) and a recursive neural network (RNN). We also classified normal sinus heart rate signals and CHF signals based on electrocardiography (ECG) and time-frequency spectra during the RR interval. The accuracy of this algorithm was 99.93%, the sensitivity was 99.85%, and the specificity was 100% when 5-min ECG signals were analyzed. It showed a certain improvement over previous studies. We also investigated the detection of CHF patients from healthy subjects by ultrashort-term ECG, and good performance was obtained. The hybrid deep learning algorithm can make objective, accurate classifications of CHF signals and serve as an effective auxiliary tool for the clinical detection of CHF patients.
Embedding Ontologies via Incorporating Extensional and Intensional Knowledge
Authors: Keyu Wang, Guilin Qi, Jiaoyan Chen, Yi Huang, Tianxing Wu
Date: 2024-01-20 00:00:00
Tasks: Language Modeling, Language Modelling, Link Prediction, Ontology Embedding, Triple Classification

Ontologies contain rich knowledge within domain, which can be divided into two categories, namely extensional knowledge and intensional knowledge. Extensional knowledge provides information about the concrete instances that belong to specific concepts in the ontology, while intensional knowledge details inherent properties, characteristics, and semantic associations among concepts. However, existing ontology embedding approaches fail to take both extensional knowledge and intensional knowledge into fine consideration simultaneously. In this paper, we propose a novel ontology embedding approach named EIKE (Extensional and Intensional Knowledge Embedding) by representing ontologies in two spaces, called extensional space and intensional space. EIKE presents a unified framework for embedding instances, concepts and their relations in an ontology, applying a geometry-based method to model extensional knowledge and a pretrained language model to model intensional knowledge, which can capture both structure information and textual information. Experimental results show that EIKE significantly outperforms state-of-the-art methods in three datasets for both triple classification and link prediction, indicating that EIKE provides a more comprehensive and representative perspective of the domain.
Rethinking Interpretability in the Era of Large Language Models
Authors: Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, Jianfeng Gao
Date: 2024-01-30 00:00:00
Tasks: Interpretable Machine Learning

Interpretable machine learning has exploded as an area of interest over the last decade, sparked by the rise of increasingly large datasets and deep neural networks. Simultaneously, large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks, offering a chance to rethink opportunities in interpretable machine learning. Notably, the capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. However, these new capabilities raise new challenges, such as hallucinated explanations and immense computational costs. In this position paper, we start by reviewing existing methods to evaluate the emerging field of LLM interpretation (both interpreting LLMs and using LLMs for explanation). We contend that, despite their limitations, LLMs hold the opportunity to redefine interpretability with a more ambitious scope across many applications, including in auditing LLMs themselves. We highlight two emerging research priorities for LLM interpretation: using LLMs to directly analyze new datasets and to generate interactive explanations.