Reviewerly/exHarmony huggingface dataset:
Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways

Authors:
raw_author_name: Hui Lin
raw_affiliation_string: UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and San Francisco, San Francisco, CA, USA
raw_author_name: Julian Hong
raw_affiliation_string: Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA; Joint Program in Computational Precision Health, University of California, Berkeley and San Francisco, Berkeley, CA, USA

Abstract: Natural language processing (NLP), a technology that translates human language into machine-readable data, is revolutionizing numerous sectors, including cancer care. This review outlines the evolution of NLP and its potential for crafting personalized treatment pathways for cancer patients. Leveraging NLP's ability to transform unstructured medical data into structured learnable formats, researchers can tap into the potential of big data for clinical and research applications. Significant advancements in NLP have spurred interest in developing tools that automate information extraction from clinical text, potentially transforming medical research and clinical practices in radiation oncology. Applications discussed include symptom and toxicity monitoring, identification of social determinants of health, improving patient-physician communication, patient education, and predictive modeling. However, several challenges impede the full realization of NLP's benefits, such as privacy and security concerns, biases in NLP models, and the interpretability and generalizability of these models. Overcoming these challenges necessitates a collaborative effort between computer scientists and the radiation oncology community. This paper serves as a comprehensive guide to understanding the intricacies of NLP algorithms, their performance assessment, past research contributions, and the future of NLP in radiation oncology research and clinics.
Supervised learning for improving the accuracy of robot-mounted 3D camera applied to human gait analysis.

Authors:
raw_author_name: Alberto Brunete
raw_affiliation_string: Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Politécnica de Madrid, 28012 Madrid, Spain
raw_author_name: Miguel Hernando
raw_affiliation_string: Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Politécnica de Madrid, 28012 Madrid, Spain
raw_author_name: David Álvarez
raw_affiliation_string: Department of Electrical, Electronic and Automation Engineering and Applied Physics, ETSIDI, Universidad Politécnica de Madrid, 28012 Madrid, Spain

Abstract: the use of 3D cameras for gait analysis has been highly questioned due to the low accuracy they have demonstrated in the past. The objective of the study presented in this paper is to improve the accuracy of the estimations made by robot-mounted 3D cameras in human gait analysis by applying a supervised learning stage.the 3D camera was mounted in a mobile robot to obtain a longer walking distance. This study shows an improvement in detection of kinematic gait signals and gait descriptors by post-processing the raw estimations of the camera using artificial neural networks trained with the data obtained from a certified Vicon system. To achieve this, 37 healthy participants were recruited and data of 207 gait sequences were collected using an Orbbec Astra 3D camera. There are two basic possible approaches for training and both have been studied in order to see which one achieves a better result. The artificial neural network can be trained either to obtain more accurate kinematic gait signals or to improve the gait descriptors obtained after initial processing. The former seeks to improve the waveforms of kinematic gait signals by reducing the error and increasing the correlation with respect to the Vicon system. The second is a more direct approach, focusing on training the artificial neural networks using gait descriptors directly.the accuracy of the 3D camera to objectify human gait was measured before and after training. In both training approaches, a considerable improvement was observed. Kinematic gait signals showed lower errors and higher correlations with respect to the ground truth. The accuracy of the system to detect gait descriptors also showed a substantial improvement, mostly for kinematic descriptors rather than spatio-temporal. When comparing both training approaches, it was not possible to define which was the absolute best.supervised learning improves the accuracy of 3D cameras but the selection of the training approach will depend on the purpose of the study to be conducted. This study reveals the great potential of 3D cameras and encourages the research community to continue exploring their use in gait analysis.
Design of a dynamic and robust recommender system based on item context, trust, rating matrix and rating time using social networks analysis

Authors:
raw_author_name: H. Hamidi
raw_affiliation_string: Department of Industrial Engineering, K. N. Toosi University of Technology
raw_author_name: R. Moradi
raw_affiliation_string: Department of Industrial Engineering, K. N. Toosi University of Technology

Abstract: Collaborative filtering recommender systems type have been increasingly used in e-commerce sites both to facilitate users' decision-making and increase sales. On the one hand, the open and interactive nature of recommender systems makes them vulnerable to shilling attacks, and on the other hand, given that most recommender systems are used in dynamic environments, and this issue generates incremental data over time. One of the main obstacles of this type of recommender systems is the inability to model the dynamic behavior of users and the incremental flow of data, as well as the vulnerability to shilling attacks. Therefore, classic models do not have the necessary ability to provide suitable recommendations to the user and also to detect shilling attacks dynamically. The objective of this study was to address this gap by designing a dynamic and robust recommender system model against shilling attacks. This model was based on item context, trust, users' rating and users' rating time, and it benefits from the social networks analysis of users and items to suggest @N top items to the target user, and had a dynamic and increasing property over time and robust against shilling attacks. Finally, to assess the performance and robustness of the recommender system to shilling attacks, 4850 different tests have been performed without shilling attacks and under three average, random, and bandwagon attacks. To validate the proposed model, the results of the tests have been compared with similar methods such as TRACCF, TOTAR and T&TRS using the evaluation criteria of Precision, Recall, F1, MAE and RMSE.The results depicted that the proposed method, due to finding users and similar items in communities created by social networks, causes a reduction in the number of predicted items that are not liked by the user (FP) and the number of unpredicted items that are liked by the user (FN) and finally the F1 criterion (which is a combination of Precision and Recall criteria) performs better than the comparison methods. Also, this method is robust against the shilling attacks by detecting fake profiles and ignoring them in the recommendation process, and the evaluation criteria before and after shilling attacks show this.
Automatic recognition of second language speech-in-noise

Authors:
raw_author_name: Seung-Eun Kim
raw_affiliation_string: Department of Linguistics, Northwestern University 1 , Evanston, Illinois 60208, USA
raw_author_name: Bronya R. Chernyak
raw_affiliation_string: Faculty of Electrical & Computer Engineering, Technion—Israel Institute of Technology 2 , Haifa 3200003, Israel seungeun.kim@northwestern.edu , chernroni@gmail.com , olga.s@technion.ac.il , jkeshet@technion.ac.il , matt-goldrick@northwestern.edu , abradlow@northwestern.edu
raw_author_name: Olga Seleznova
raw_affiliation_string: Faculty of Electrical & Computer Engineering, Technion—Israel Institute of Technology 2 , Haifa 3200003, Israel seungeun.kim@northwestern.edu , chernroni@gmail.com , olga.s@technion.ac.il , jkeshet@technion.ac.il , matt-goldrick@northwestern.edu , abradlow@northwestern.edu
raw_author_name: Joseph Keshet
raw_affiliation_string: Faculty of Electrical & Computer Engineering, Technion—Israel Institute of Technology 2 , Haifa 3200003, Israel seungeun.kim@northwestern.edu , chernroni@gmail.com , olga.s@technion.ac.il , jkeshet@technion.ac.il , matt-goldrick@northwestern.edu , abradlow@northwestern.edu
raw_author_name: Matthew Goldrick
raw_affiliation_string: Department of Linguistics, Northwestern University 1 , Evanston, Illinois 60208, USA
raw_author_name: Ann R. Bradlow
raw_affiliation_string: Department of Linguistics, Northwestern University 1 , Evanston, Illinois 60208, USA

Abstract: Measuring how well human listeners recognize speech under varying environmental conditions (speech intelligibility) is a challenge for theoretical, technological, and clinical approaches to speech communication. The current gold standard-human transcription-is time- and resource-intensive. Recent advances in automatic speech recognition (ASR) systems raise the possibility of automating intelligibility measurement. This study tested 4 state-of-the-art ASR systems with second language speech-in-noise and found that one, whisper, performed at or above human listener accuracy. However, the content of whisper's responses diverged substantially from human responses, especially at lower signal-to-noise ratios, suggesting both opportunities and limitations for ASR--based speech intelligibility modeling.
BERT-Based Arabic Diacritization: A state-of-the-art approach for improving text accuracy and pronunciation

Authors:
raw_author_name: Ruba Kharsa
raw_affiliation_string: Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates
raw_author_name: Ashraf Elnagar
raw_affiliation_string: Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates
raw_author_name: Sane Yagi
raw_affiliation_string: Department of Foreign Languages, University of Sharjah, Sharjah, United Arab Emirates

Abstract: In order to accurately represent the meaning and pronunciation of Arabic words and sentences, the presence of diacritics plays a crucial role. Over the years, researchers have dedicated significant efforts to enhancing automated diacritization systems. This paper introduces a novel approach for Arabic diacritization utilizing Bidirectional Encoder representations from Transformers (BERT) models. To evaluate the effectiveness of the proposed approach, two publicly available datasets, namely the Arabic Diacritization (AD) dataset and the Tashkeela Processed (TP) dataset, were employed. The performance of the models was assessed using various error metrics, including Diacritic Error Rate (DER) and Word Error Rate (WER). The findings demonstrate the superior performance of BERT in the diacritization process, surpassing all models employed in other diacritization systems. On the AD dataset, the proposed system achieved state-of-the-art (SOTA) syntactic DER and WER of 1.14% and 3.34%, respectively. For morphological diacritization, the best results yielded a DER of 0.92% and a WER of 1.91%. These outcomes reflect a remarkable relative error reduction of over 30% compared to previous research. Additionally, on the TP dataset, the BERT models exhibited a substantial decrease in DER, reducing the benchmark from 4.0% to 1.11%. Furthermore, this study introduces a real-time diacritization system called SUKOUN, which offers diacritized text through a user-friendly website. A comparison with existing automatic diacritization tools, using six example texts, reveals the superior prediction accuracy and preservation of input format provided by SUKOUN.
GAP: A novel Generative context-Aware Prompt-tuning method for relation extraction

Authors:
raw_author_name: Zhenbin Chen
raw_affiliation_string: Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin 541004, China
raw_author_name: Zhixin Li
raw_affiliation_string: Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin 541004, China
raw_author_name: Yufei Zeng
raw_affiliation_string: Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin 541004, China
raw_author_name: Canlong Zhang
raw_affiliation_string: Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin 541004, China
raw_author_name: Huifang Ma
raw_affiliation_string: College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China

Abstract: Prompt-tuning was proposed to bridge the gap between pretraining and downstream tasks, and it has achieved promising results in Relation Extraction (RE) tasks in recent years. Although the existing prompt-based RE methods have outperformed the methods based on the fine-tuning paradigm, these methods require domain experts to design prompt templates, making them hard to generalize. In this paper, we proposed a Generative context-Aware Prompt-tuning method (GAP) to address these limitations. Our method consists of three crucial modules: (1) a pretrained prompt generator module that extracts or generates the relation triggers from the context and embeds them into the prompt tokens, (2) an in-domain adaptive pretraining module that further trains the Pretrained Language Models (PLMs) to promote the adaptability of the model, and (3) a joint contrastive loss that prevents PLMs from generating results unrelated to the relation labels while optimizing our model more effectively. We observed that the context-enhanced prompt tokens generated by GAP can better guide PLMs to make accurate relationship predictions. And the in-domain pretraining can effectively inject domain knowledge to enhance the robustness of the model. We conduct experiments on four public RE datasets under the supervised and few-shot settings. The experimental results have demonstrated the superiority of GAP over existing benchmark methods and GAP shows remarkable improvements in few-shot settings, with average F1 score enhancements of 3.5%, 2.7%, and 3.4% on the TACRED, TACREV, and Re-TACRED datasets, respectively. Furthermore, GAP still achieved state-of-the-art (SOTA) performance in supervised settings.
MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model

Authors:
raw_author_name: W.E.I. Lian-suo
raw_affiliation_string: School of information engineering, Suqian University, SuQian, jiangsu 223800, China.

Abstract: Underwater light attenuation leads to decreased image contrast. This reduction in contrast subsequently decreases target visibility. Additionally, marine target detection is challenging due to multi-scale problems from varying target-to-device distances, complex target clustering, and noise from waterborne particulates.To address these issues, we propose MTD-YOLOv5.Initially, we enhance image contrast with grayscale equalization and mitigate color shift issues through color space transformation.We then introduce a novel feature extraction module, PCBR, combining max pooling and convolution layers for more effective target feature extraction from the background.Furthermore, we present the Multi-Scale Perceptual Hybrid Pooling (MHP) module.This module integrates horizontal and vertical receptive fields to establish long-range dependencies, thereby capturing hidden target information in deep network feature maps. In the Labeled Fishes in the Wild test datasets, MTD-YOLOv5 achieves a precision of 88.1% and a mean Average Precision (mAP[0.5:.95]) of 49.6%.These results represent improvements of 2.6% in precision and 0.4% in mAP over the original YOLOv5.
An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images

Authors:
raw_author_name: Riaz Ullah Khan
raw_affiliation_string: Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China.
raw_author_name: Sultan Almakdi
raw_affiliation_string: Department of Computer Science, College of Computer Science and Information systems, Najran University, Najran 55461, Saudi Arabia.
raw_author_name: Mohammed Alshehri
raw_affiliation_string: Department of Computer Science, College of Computer Science and Information systems, Najran University, Najran 55461, Saudi Arabia.
raw_author_name: Amin Ul Haq
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
raw_author_name: Aman Ullah
raw_affiliation_string: Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China.
raw_author_name: Rajesh Kumar
raw_affiliation_string: Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China.

Abstract: Biomedical image analysis plays a crucial role in enabling high-performing imaging and various clinical applications. For the proper diagnosis of blood diseases related to red blood cells, red blood cells must be accurately identified and categorized. Manual analysis is time-consuming and prone to mistakes. Analyzing multi-label samples, which contain clusters of cells, is challenging due to difficulties in separating individual cells, such as touching or overlapping cells. High-performance biomedical imaging and several medical applications are made possible by advanced biosensors. We develop an intelligent neural network model that can automatically identify and categorize red blood cells from microscopic medical images using region-based convolutional neural networks (RCNN) and cutting-edge biosensors. Our model successfully navigates obstacles like touching or overlapping cells and accurately recognizes various blood structures. Additionally, we utilized data augmentation as a pre-processing method on microscopic images to enhance the model's computational efficiency and expand the sample size. To refine the data and eliminate noise from the dataset, we utilized the Radial Gradient Index filtering algorithm for imaging data equalization. We exhibit improved detection accuracy and a reduced model loss rate when using medical imagery datasets to apply our proposed model in comparison to existing ResNet and GoogleNet models. Our model precisely detected red blood cells in a collection of medical images with 99% training accuracy and 91.21% testing accuracy. Our proposed model outperformed earlier models like ResNet-50 and GoogleNet by 10-15%. Our results demonstrated that Artificial intelligence (AI)-assisted automated red blood cell detection has the potential to revolutionize and speed up blood cell analysis, minimizing human error and enabling early illness diagnosis.
Identifying Brexit voting patterns in the British house of commons: an analysis based on Bayesian mixture models with flexible concomitant covariate effects

Authors:
raw_author_name: Marco Berrettini
raw_affiliation_string: Department of Statistical Sciences, University of Bologna , Bologna , Italy
raw_author_name: Giuliano Galimberti
raw_affiliation_string: Department of Statistical Sciences, University of Bologna , Bologna , Italy
raw_author_name: Saverio Ranciati
raw_affiliation_string: Department of Statistical Sciences, University of Bologna , Bologna , Italy
raw_author_name: Thomas Brendan Murphy
raw_affiliation_string: School of Mathematics and Statistics, University College Dublin , Dublin , Ireland

Abstract: Brexit and its implications are an ongoing topic of interest since the Brexit referendum in 2016. In 2019 the House of commons held a number of "indicative" and "meaningful" votes as part of the Brexit approval process. The voting behaviour of members of the parliament in these votes is investigated to gain insight into the Brexit approval process. In particular, a mixture model with concomitant covariates is developed to identify groups of members of parliament who share similar voting behaviour while also considering characteristics of the members of parliament. The novelty of the method lies in the flexible structure used to model the effect of concomitant covariates on the component weights of the mixture, with the (potentially nonlinear) terms represented as a smooth function of the covariates. Results show this approach allows to quantify the effect of the age of members of parliament, as well as preferences and competitiveness in the constituencies they represent, on their position towards Brexit. This helps grouping the aforementioned politicians into homogeous clusters, whose composition departs sensibly from that of the parties.
Bitcoin forks: What drives the branches?

Authors:
raw_author_name: Thomas Conlon
raw_affiliation_string: Smurfit Graduate School of Business, University College Dublin, Ireland
raw_author_name: Shaen Corbet
raw_affiliation_string: DCU Business School, Dublin City University, Dublin 9, Ireland; School of Accounting, Finance and Economics, University of Waikato, New Zealand
raw_author_name: Yang (Greg) Hou
raw_affiliation_string: School of Accounting, Finance and Economics, University of Waikato, New Zealand
raw_author_name: Yang Hu
raw_affiliation_string: School of Accounting, Finance and Economics, University of Waikato, New Zealand
raw_author_name: Les Oxley
raw_affiliation_string: School of Accounting, Finance and Economics, University of Waikato, New Zealand

Abstract: Despite frequent Blockchain splits stemming from Bitcoin, few studies have examined the determinants of Bitcoin fork returns. In this paper, we investigate the relationships between the returns of Bitcoin forks and a range of common risk factors, including Bitcoin, currency, network and equity-based factors. From a statistical perspective, we find consistent and significant associations between fork returns, their Bitcoin counterparts, and equity markets. Other common factors, such as the equity small-minus-big factor and changes in the Japanese Yen, are found to have occasional links with fork returns. From an economic perspective, Bitcoin returns are the predominant driver of fork returns, accounting for essentially all of the explained variation. These findings are confirmed using orthogonalised common factors and with an alternative methodology, quantile regression. This research broadens our understanding of Bitcoin forks, indicating that a change in blockchain protocol is insufficient to sever links with the Bitcoin parent.