Reviewerly/exHarmony huggingface dataset:
Joint iterative state and parameter estimation for bilinear systems with autoregressive noises via the data filtering

Authors:
raw_author_name: Feng Ding
raw_affiliation_string: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China; School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
raw_author_name: Ahmed Alsaedi
raw_affiliation_string: Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia
raw_author_name: Tasawar Hayat
raw_affiliation_string: Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Abstract: This paper proposes a novel iterative algorithm for the joint state and parameter estimation of bilinear state-space systems disturbed by colored noise. Estimating the states and parameters of such systems is challenging due to their nonlinearity and greater number of parameters compared to linear systems. Our method is to modify the Kalman filtering appropriately to estimate the unknown states of bilinear systems. Once the unknown states are estimated, we develop the Kalman filtering-based multi-innovation gradient-based iterative (KF-MIGI) algorithm for parameter estimation. To further improve estimation accuracy and cope with colored noises, we introduce a data filtering-based KF-MIGI algorithm that uses an adaptive filter to filter input-output data. Additionally, we compare the gradient-based iterative algorithm and the stochastic gradient algorithm. The effectiveness of the proposed algorithm is demonstrated through numerical examples.
Survey of transformers and towards ensemble learning using transformers for natural language processing

Authors:
raw_author_name: Hongzhi Zhang
raw_affiliation_string: School of Information Technology, Carleton University, Ottawa, ON, Canada
raw_author_name: M. Omair Shafiq
raw_affiliation_string: School of Information Technology, Carleton University, Ottawa, ON, Canada

Abstract: Abstract The transformer model is a famous natural language processing model proposed by Google in 2017. Now, with the extensive development of deep learning, many natural language processing tasks can be solved by deep learning methods. After the BERT model was proposed, many pre-trained models such as the XLNet model, the RoBERTa model, and the ALBERT model were also proposed in the research community. These models perform very well in various natural language processing tasks. In this paper, we describe and compare these well-known models. In addition, we also apply several types of existing and well-known models which are the BERT model, the XLNet model, the RoBERTa model, the GPT2 model, and the ALBERT model to different existing and well-known natural language processing tasks, and analyze each model based on their performance. There are a few papers that comprehensively compare various transformer models. In our paper, we use six types of well-known tasks, such as sentiment analysis, question answering, text generation, text summarization, name entity recognition, and topic modeling tasks to compare the performance of various transformer models. In addition, using the existing models, we also propose ensemble learning models for the different natural language processing tasks. The results show that our ensemble learning models perform better than a single classifier on specific tasks. Graphical Abstract
Enhancing Image Captioning and Auto-Tagging Through a FCLN with Faster R-CNN Integration

Authors:
raw_author_name: Shalaka Prasad Deore

Abstract: In the realm of automated image captioning, which entails generating descriptive text for images, the fusion of Natural Language Processing (NLP) and computer vision techniques is paramount. This study introduces the Fully Convolutional Localization Network (FCLN), a novel approach that concurrently addresses localization and description tasks within a singular forward pass. It maintains spatial information and avoids detail loss, streamlining the training process with consistent optimization. The foundation of FCLN is laid by a Convolutional Neural Network (CNN), adept at extracting salient image features. Central to this architecture is a Localization Layer, pivotal in precise object detection and caption generation. The FCLN architecture amalgamates a region detection network, reminiscent of Faster Region-CNN (R-CNN), with a captioning network. This synergy enables the production of contextually meaningful image captions. The incorporation of the Faster R-CNN framework facilitates region-based object detection, offering precise contextual understanding and inter-object relationships. Concurrently, a Long Short-Term Memory (LSTM) network is employed for generating captions. This integration yields superior performance in caption accuracy, particularly in complex scenes. Evaluations conducted on the Microsoft Common Objects in Context (MS COCO) test server affirm the model's superiority over existing benchmarks, underscoring its efficacy in generating precise and context-rich image captions.
Assessment of differentially private synthetic data for utility and fairness in end-to-end machine learning pipelines for tabular data

Authors:
raw_author_name: Mayana Pereira
raw_affiliation_string: AI for Good Research Lab, Microsoft, Redmond, Washington, United States of America; Department of Electrical Engineering, University of Brasilia, Brasilia, Brazil
raw_author_name: Meghana Kshirsagar
raw_affiliation_string: AI for Good Research Lab, Microsoft, Redmond, Washington, United States of America
raw_author_name: Sumit Mukherjee
raw_affiliation_string: INSITRO, San Francisco, CA, United States of America
raw_author_name: Rahul Dodhia
raw_affiliation_string: AI for Good Research Lab, Microsoft, Redmond, Washington, United States of America
raw_author_name: Juan Lavista Ferres
raw_affiliation_string: AI for Good Research Lab, Microsoft, Redmond, Washington, United States of America

Abstract: Differentially private (DP) synthetic datasets are a solution for sharing data while preserving the privacy of individual data providers. Understanding the effects of utilizing DP synthetic data in end-to-end machine learning pipelines impacts areas such as health care and humanitarian action, where data is scarce and regulated by restrictive privacy laws. In this work, we investigate the extent to which synthetic data can replace real, tabular data in machine learning pipelines and identify the most effective synthetic data generation techniques for training and evaluating machine learning models. We systematically investigate the impacts of differentially private synthetic data on downstream classification tasks from the point of view of utility as well as fairness. Our analysis is comprehensive and includes representatives of the two main types of synthetic data generation algorithms: marginal-based and GAN-based. To the best of our knowledge, our work is the first that: (i) proposes a training and evaluation framework that does not assume that real data is available for testing the utility and fairness of machine learning models trained on synthetic data; (ii) presents the most extensive analysis of synthetic dataset generation algorithms in terms of utility and fairness when used for training machine learning models; and (iii) encompasses several different definitions of fairness. Our findings demonstrate that marginal-based synthetic data generators surpass GAN-based ones regarding model training utility for tabular data. Indeed, we show that models trained using data generated by marginal-based algorithms can exhibit similar utility to models trained using real data. Our analysis also reveals that the marginal-based synthetic data generated using AIM and MWEM PGM algorithms can train models that simultaneously achieve utility and fairness characteristics close to those obtained by models trained with real data.
An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning

Authors:
raw_author_name: Tanjim Mahmud
raw_affiliation_string: Department of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati 4500, Bangladesh
raw_author_name: Koushick Barua
raw_affiliation_string: Department of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati 4500, Bangladesh
raw_author_name: Sultana Umme Habiba
raw_affiliation_string: Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
raw_author_name: Mohammad Shahadat Hossain
raw_affiliation_string: Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh
raw_author_name: Karl Andersson
raw_affiliation_string: Pervasive and Mobile Computing Laboratory, Luleå University of Technology, 97187 Luleå, Sweden

Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early and accurate diagnosis of AD is crucial for effective intervention and disease management. In recent years, deep learning techniques have shown promising results in medical image analysis, including AD diagnosis from neuroimaging data. However, the lack of interpretability in deep learning models hinders their adoption in clinical settings, where explainability is essential for gaining trust and acceptance from healthcare professionals. In this study, we propose an explainable AI (XAI)-based approach for the diagnosis of Alzheimer's disease, leveraging the power of deep transfer learning and ensemble modeling. The proposed framework aims to enhance the interpretability of deep learning models by incorporating XAI techniques, allowing clinicians to understand the decision-making process and providing valuable insights into disease diagnosis. By leveraging popular pre-trained convolutional neural networks (CNNs) such as VGG16, VGG19, DenseNet169, and DenseNet201, we conducted extensive experiments to evaluate their individual performances on a comprehensive dataset. The proposed ensembles, Ensemble-1 (VGG16 and VGG19) and Ensemble-2 (DenseNet169 and DenseNet201), demonstrated superior accuracy, precision, recall, and F1 scores compared to individual models, reaching up to 95%. In order to enhance interpretability and transparency in Alzheimer's diagnosis, we introduced a novel model achieving an impressive accuracy of 96%. This model incorporates explainable AI techniques, including saliency maps and grad-CAM (gradient-weighted class activation mapping). The integration of these techniques not only contributes to the model's exceptional accuracy but also provides clinicians and researchers with visual insights into the neural regions influencing the diagnosis. Our findings showcase the potential of combining deep transfer learning with explainable AI in the realm of Alzheimer's disease diagnosis, paving the way for more interpretable and clinically relevant AI models in healthcare.
Simple but powerful interactive data analysis in R with R/LinekdCharts

Authors:
raw_author_name: Svetlana Ovchinnikova
raw_affiliation_string: Center for Molecular Biology and BioQuant Center of the University of Heidelberg, Heidelberg, Germany
raw_author_name: Simon Anders
raw_affiliation_string: Center for Molecular Biology and BioQuant Center of the University of Heidelberg, Heidelberg, Germany

Abstract: Abstract In research involving data-rich assays, exploratory data analysis is a crucial step. Typically, this involves jumping back and forth between visualizations that provide overview of the whole data and others that dive into details. For example, it might be helpful to have one chart showing a summary statistic for all samples, while a second chart provides details for points selected in the first chart. We present R/LinkedCharts, a framework that renders this task radically simple, requiring very few lines of code to obtain complex and general visualization, which later can be polished to provide interactive data access of publication quality.
Differential Privacy Preservation for Continuous Release of Real-Time Location Data

Authors:
raw_author_name: Zhengquan Xu
raw_affiliation_string: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Abstract: Continuous real-time location data is very important in the big data era, but the privacy issues involved is also a considerable topic. It is not only necessary to protect the location privacy at each release moment, but also have to consider the impact of data correlation. Correlated Laplace Mechanism (CLM) is a sophisticated method to implement differential privacy on correlated time series. This paper aims to solve the key problems of applying CLM in continuous location release. Based on the finding that the location increment is approximately stationary in many scenarios, a location correlation estimation method based on the location increment is proposed to solve the problem of nonstationary location data correlation estimation; an adaptive adjustment model for the CLM filter based on parameter quantization idea (QCLM) as well as its effective implementation named QCLM-Lowpass utilizing the lowpass spectral characteristics of location data series is proposed to solve the problem of output deviations due to the undesired transient response of the CLM filter in time-varying environments. Extensive simulations and real data experiments validate the effectiveness of the proposed approach and show that the privacy scheme based on QCLM-Lowpass can offer a better balance between the ability to resist correlation-based attacks and data availability.
Emoji use in social media posts: relationships with personality traits and word usage

Authors:
raw_author_name: Shelia M. Kennison
raw_affiliation_string: Department of Psychology, Oklahoma State University, Stillwater, OK, United States
raw_author_name: Eric Chan-Tin
raw_affiliation_string: Department of Computer Science, Loyola University Chicago, Chicago, IL, United States

Abstract: Prior research has demonstrated relationships between personality traits of social media users and the language used in their posts. Few studies have examined whether there are relationships between personality traits of users and how they use emojis in their social media posts. Emojis are digital pictographs used to express ideas and emotions. There are thousands of emojis, which depict faces with expressions, objects, animals, and activities. We conducted a study with two samples ( n = 76 and n = 245) in which we examined how emoji use on X (formerly Twitter) related to users’ personality traits and language use in posts. Personality traits were assessed from participants in an online survey. With participants’ consent, we analyzed word usage in posts. Word frequencies were calculated using the Linguistic Inquiry Word Count (LIWC). In both samples, the results showed that those who used the most emojis had the lowest levels of openness to experience. Emoji use was unrelated to the other personality traits. In sample 1, emoji use was also related to use of words related to family, positive emotion, and sadness and less frequent use of articles and words related to insight. In sample 2, more frequent use of emojis in posts was related to more frequent use of you pronouns, I pronouns, and more frequent use of negative function words and words related to time. The results support the view that social media users’ characteristics may be gleaned from the content of their social media posts.
Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression

Authors:
raw_author_name: Anitha S
raw_affiliation_string: Govindammal Aditanar College for Women
raw_author_name: Kavitha J
raw_affiliation_string: Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

Abstract: The main aim of this research work is to compress grayscale images efficiently using prediction and intensity-based image compression algorithms. Image compression is useful for removing the duplication in an image to store and transmit the data in an efficient form. This research work analyzes four new schemes for gray scale lossy image compression. Among the four schemes considered, two compressive approaches are designed for Prediction Based Image Compression (PBIC) level implementation. Third approach is designed for Intensity Based Image Compression (IBIC). Finally, the previously designed PBIC and IBIC schemes lead to an Integrated Encoder. All the considered method performances are analyzed using the performance metrics. These results are compared with JPEG 2000 which is a extensively used benchmark compression encoder. The outcome of all the proposed methods is also compared with modern encoders.
A systematic analysis of deep learning in genomics and histopathology for precision oncology

Authors:
raw_author_name: Jakob Nikolas Kather
raw_affiliation_string: Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany

Abstract: Abstract Background Digitized histopathological tissue slides and genomics profiling data are available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has been broadly used to extract clinically actionable information and biological knowledge from pathology slides and genomic data in cancer. In addition, a number of recent studies have introduced multimodal DL models designed to simultaneously process both images from pathology slides and genomic data as inputs. By comparing patterns from one data modality with those in another, multimodal DL models are capable of achieving higher performance compared to their unimodal counterparts. However, the application of these methodologies across various tumor entities and clinical scenarios lacks consistency. Methods Here, we present a systematic survey of the academic literature from 2010 to November 2023, aiming to quantify the application of DL for pathology, genomics, and the combined use of both data types. After filtering 3048 publications, our search identified 534 relevant articles which then were evaluated by basic (diagnosis, grading, subtyping) and advanced (mutation, drug response and survival prediction) application types, publication year and addressed cancer tissue. Results Our analysis reveals a predominant application of DL in pathology compared to genomics. However, there is a notable surge in DL incorporation within both domains. Furthermore, while DL applied to pathology primarily targets the identification of histology-specific patterns in individual tissues, DL in genomics is more commonly used in a pan-cancer context. Multimodal DL, on the contrary, remains a niche topic, evidenced by a limited number of publications, primarily focusing on prognosis predictions. Conclusion In summary, our quantitative analysis indicates that DL not only has a well-established role in histopathology but is also being successfully integrated into both genomic and multimodal applications. In addition, there is considerable potential in multimodal DL for harnessing further advanced tasks, such as predicting drug response. Nevertheless, this review also underlines the need for further research to bridge the existing gaps in these fields.
Remote Diagnosis on Upper Respiratory Tract Infections Based on a Neural Network with Few Symptom Words—A Feasibility Study

Authors:
raw_author_name: Kuan-Hung Liu
raw_affiliation_string: School of Medicine, China Medical University, Taichung 404, Taiwan
raw_author_name: Da-Chuan Cheng
raw_affiliation_string: Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 404, Taiwan

Abstract: This study aims explore the feasibility of using neural network (NNs) and deep learning to diagnose three common respiratory diseases with few symptom words. These three diseases are nasopharyngitis, upper respiratory infection, and bronchitis/bronchiolitis. Through natural language processing, the symptom word vectors are encoded by GPT-2 and classified by the last linear layer of the NN. The experimental results are promising, showing that this model achieves a high performance in predicting all three diseases. They revealed 90% accuracy, which suggests the implications of the developed model, highlighting its potential use in assisting patients’ understanding of their conditions via a remote diagnosis. Unlike previous studies that have focused on extracting various categories of information from medical records, this study directly extracts sequential features from unstructured text data, reducing the effort required for data pre-processing.
An ensemble novel architecture for Bangla Mathematical Entity Recognition (MER) using transformer based learning

Authors:
raw_author_name: Tanjim Taharat Aurpa
raw_affiliation_string: Department of Data Science, Bangabandhu Sheikh Mujibur Rahman Digital University, Bangladesh
raw_author_name: Md Shoaib Ahmed
raw_affiliation_string: Department of Computer Science, Boise State University, Boise, ID, USA; Jahangirnagar University, Dhaka, Bangladesh

Abstract: <h2>Abstract</h2> Mathematical entity recognition is indispensable for machines to accurately explain and depict mathematical content and to enable adequate mathematical operations and reasoning. It expedites automated theorem proving, speeds up the analysis and retrieval of mathematical knowledge from documents, and improves e-learning and educational platforms. It also simplifies translation, scientific research, data analysis, interpretation, and the practical application of mathematical information. Mathematical entity recognition in the Bangla language is novel; to our best knowledge, no other similar works have been done. Here, we identify the mathematical operator, operands as numbers, and popular mathematical terms (complex numbers, real numbers, prime numbers, etc.). In this work, we recognize Bangla Mathematical Entity Recognition (MER) utilizing the ensemble architecture of deep neural networks known as Bidirectional Encoder Representations from Transformers (BERT). We prepare a novel dataset comprising 13,717 observations, each containing a mathematical statement, mathematical entity, and mathematical type. In our recognition process, we consider our proposed architectures using accuracy, precision, recall and f1-score as the performance metrics. The results have shown a satisfactory accuracy percentage of 97.98 with BERT and 99.76% with ensemble BERT.