Reviewerly/exHarmony huggingface dataset:
Negotiation and Mediation

This chapter deals with the topic of mediation from the perspectives of both negotiation theory and game theory. It begins by exploring the conditions that can facilitate mediation and discusses the benefits to the parties and to the overall outcome from a game-theoretic perspective. The authors analyse typical patterns of conflict and examine possible obstacles that may arise during mediation or that are caused by the characteristics of the parties involved. Finally, different forms of mediation are presented that appear well suited for certain conflict constellations and that provide solution-oriented approaches for mediation settings.
Automl Systems for Medical Imaging

The integration of machine learning in medical image analysis can greatly enhance the quality of healthcare provided by physicians. The combination of human expertise and computerized systems can result in improved diagnostic accuracy. An automated machine learning approach simplifies the creation of custom image recognition models by utilizing neural architecture search and transfer learning techniques. Medical imaging techniques are used to non-invasively create images of internal organs and body parts for diagnostic and procedural purposes. This article aims to highlight the potential applications, strategies, and techniques of AutoML in medical imaging through theoretical and empirical evidence.
Introduction to Medical Imaging Informatics

Medical imaging informatics is a rapidly growing field that combines the principles of medical imaging and informatics to improve the acquisition, management, and interpretation of medical images. This chapter introduces the basic concepts of medical imaging informatics, including image processing, feature engineering, and machine learning. It also discusses the recent advancements in computer vision and deep learning technologies and how they are used to develop new quantitative image markers and prediction models for disease detection, diagnosis, and prognosis prediction. By covering the basic knowledge of medical imaging informatics, this chapter provides a foundation for understanding the role of informatics in medicine and its potential impact on patient care.
Speech File Cryptography Based on Chaotic Theory and Speech-blocking

In the pursuit of enhancing food safety protocols, this article explores a cutting-edge approach to quality control in the coconut oil industry. We present a multispectral image processing system designed specifically for the detection of reheated coconut oil, leveraging advancements in machine learning. Machine learning algorithms, fused with image classification techniques, provide a robust framework for accurately identifying reheated coconut oil. It is proposed to develop a spectral clustering-based classifier to determine the effect of reheating and reuse of coconut oil. Post-processing methods refine classification results, while validation ensures the system's adaptability to diverse datasets.
fraud and anomaly detection research: a bibliometric study

Due to the scientific status and the practical consquences of fraud and anomaly detection research, the aim of this study is to uncover the intellectual and conceptual structure of this research field in audit literature. 430 documents( include article, Conference paper, Review, Book chapter, Book, Review, Note, Letter) related to the period between 1979 and 2022 from the Scopus database were analyzed using two bibliometric analysis methods: co-word analysis and social network analysis to discover the intellectual and conceptual structure of the field. The findings of the research show that the main and most dominant topics include "fraud and anomalies", "audit and forensic accounting", "data mining, machine learning and data analytics", "risk assessment" and "internal controls and corporate governance". Also, the article "An empirical analysis of the relation between the board of director composition and financial statement fraud" by Beasley (1996) with 1977 citations, the most cited scientific document, and Peter Gottschalk with 6 articles, the United States with 150 documents, "Managerial Auditing" journal with the publication of 22 articles, and "Universiti Teknologi Mara" from Malaysia, with the publication of 13 documents, were recognized as the most prolific actors in this research field. The important finding of this study is to reveal the remarkable transition to technogical aspects in the field of fraud and anomaly detection, which has attracted the attention of researchers in recent years by using emerging technologies such as artificial intelligence and advanced data analytics methods. The findings of the present research, which illustrates the intellectual structure surrounding fraud and anomaly detection research, reveal the orientations of this field. This study can serve as the basis for developing research policies by scientific institutions to help optimize research activities in the field and enable them to play an effective and efficient role in combating fraud.
POST-EDITING DECISIONS IN MACHINE TRANSLATION OF LEGAL CONTRACTS

Legal translation requires special language use. It has therefore been many a time credited to be one of the most difficult genres to translate. Post-editing of machine translation is becoming more and widely employed in the translation industry. Even in languages that cannot boast huge amounts of resources, translating texts of certain genres is rarely conducted without resorting to machine translation, as the resulting quality seems to be approaching parity with human translation. This study attempts to analyze and compare post-editing decisions in machine translated legal contracts from English to Lithuanian and from English to Ukrainian. Legal contracts are part of the legal discourse with language characterized as being archaic, complex, culturally bound and typically stocked with formulaic phrases, specialized terms, and Latinisms. Being a technical type of discourse, legal translation may benefit from machine translation. The conclusions of the study are based on the findings of assessment of machine translation post-editing decisions made by professional translators. The findings demonstrate and support the results of previous research with other languages that the main trends are as follows: changes in noun and verb phrases in both languages, co- reference and reordering changes. Further study of the stated problem will allow expanding (targeting and detailing) the typology of post-editing actions, considering the specifics of Baltic (Lithuanian) and Slavic (Ukrainian) languages.&#x0D;
Radial search for accelerating block matching to detect copy-move forgery in digital photographs

One of the exciting areas of image processing research right now is image forensics. The most common kind of image forgery is copy-move (CM) forgery. In this study, a method for quickly detecting CM counterfeiting is proposed. The suggested approach speeds up the block matching process by employing the proposed radial search method. DCT features are extracted from each block and only the most "R' significant features are considered to create the feature vector which in turn shortens the processing time for feature matching. Then radix sorting is used to sort the blocks based on the DCT coefficients, and the proximity of blocks is used to indicate how similar they are. Each pair of adjacent blocks has their difference measured, and if the difference between two blocks is less than a predetermined threshold, the blocks are deemed comparable. Once a match is found, the nearby eight blocks are located using the radial search strategy, which eliminates the need for the traditional sequential block comparison. As opposed to exhaustive block matching, the radial search approach is experimented with which gives commendable improvement in copy move forgery detection.
Generative Adversarial Networks for Data Augmentation

One way to expand the available dataset for training AI models in the medical field is through the use of Generative Adversarial Networks (GANs) for data augmentation. GANs work by employing a generator network to create new data samples that are then assessed by a discriminator network to determine their similarity to real samples. The discriminator network is taught to differentiate between actual and synthetic samples, while the generator system is trained to generate data that closely resemble real ones. The process is repeated until the generator network can produce synthetic data that is indistinguishable from genuine data. GANs have been utilized in medical image analysis for various tasks, including data augmentation, image creation, and domain adaptation. They can generate synthetic samples that can be used to increase the available dataset, especially in cases where obtaining large amounts of genuine data is difficult or unethical. However, it is essential to note that the use of GANs in medical imaging is still an active area of research to ensure that the produced images are of high quality and suitable for use in clinical settings.
2-Point Markov Evolutions

We study the Markov evolutions associated to the expected Markov processes.
BreastRegNet: A Deep Learning Framework for Registration of Breast Faxitron and Histopathology Images

A standard treatment protocol for breast cancer entails administering neoadjuvant therapy followed by surgical removal of the tumor and surrounding tissue. Pathologists typically rely on cabinet X-ray radiographs, known as Faxitron, to examine the excised breast tissue and diagnose the extent of residual disease. However, accurately determining the location, size, and focality of residual cancer can be challenging, and incorrect assessments can lead to clinical consequences. The utilization of automated methods can improve the histopathology process, allowing pathologists to choose regions for sampling more effectively and precisely. Despite the recognized necessity, there are currently no such methods available. Training such automated detection models require accurate ground truth labels on ex-vivo radiology images, which can be acquired through registering Faxitron and histopathology images and mapping the extent of cancer from histopathology to x-ray images. This study introduces a deep learning-based image registration approach trained on mono-modal synthetic image pairs. The models were trained using data from 50 women who received neoadjuvant chemotherapy and underwent surgery. The results demonstrate that our method is faster and yields significantly lower average landmark error ( $$2.1\pm 1.96$$ mm) over the state-of-the-art iterative ( $$4.43\pm 4.1$$ mm) and deep learning ( $$4.02\pm 3.15$$ mm) approaches. Improved performance of our approach in integrating radiology and pathology information facilitates generating large datasets, which allows training models for more accurate breast cancer detection.
A Hierarchical Descriptor Framework for On-the-Fly Anatomical Location Matching Between Longitudinal Studies

We propose a method to match anatomical locations between pairs of medical images in longitudinal comparisons. The matching is made possible by computing a descriptor of the query point in a source image based on a hierarchical sparse sampling of image intensities that encode the location information. Then, a hierarchical search operation finds the corresponding point with the most similar descriptor in the target image. This simple yet powerful strategy reduces the computational time of mapping points to a millisecond scale on a single CPU. Thus, radiologists can compare similar anatomical locations in near real-time without requiring extra architectural costs for precomputing or storing deformation fields from registrations. Our algorithm does not require prior training, resampling, segmentation, or affine transformation steps. We have tested our algorithm on the recently published Deep Lesion Tracking dataset annotations. We observed more accurate matching compared to Deep Lesion Tracker while being 24 times faster than the most precise algorithm reported therein. We also investigated the matching accuracy on CT and MR modalities and compared the proposed algorithm’s accuracy against ground truth consolidated from multiple radiologists.
MAD: Modality Agnostic Distance Measure for Image Registration

Multi-modal image registration is a crucial pre-processing step in many medical applications. However, it is a challenging task due to the complex intensity relationships between different imaging modalities, which can result in large discrepancy in image appearance. The success of multi-modal image registration, whether it is conventional or learning based, is predicated upon the choice of an appropriate distance (or similarity) measure. Particularly, deep learning registration algorithms lack in accuracy or even fail completely when attempting to register data from an “unseen” modality. In this work, we present Modality Agnostic Distance (MAD), a deep image distance measure that utilises random convolutions to learn the inherent geometry of the images while being robust to large appearance changes. Random convolutions are geometry-preserving modules which we use to simulate an infinite number of synthetic modalities alleviating the need for aligned paired data during training. We can therefore train MAD on a mono-modal dataset and successfully apply it to a multi-modal dataset. We demonstrate that not only can MAD affinely register multi-modal images successfully, but it has also a larger capture range than traditional measures such as Mutual Information and Normalised Gradient Fields. Our code is available at: https://github.com/ModalityAgnosticDistance/MAD .
PLMVQA: Applying Pseudo Labels for Medical Visual Question Answering with Limited Data

Different from Visual Question Answering (VQA) in the general domain, Medical VQA is more challenging due to the lack of large-scale labeled datasets. In addition, Medical VQA requires high interpretability when making decisions to answer clinical questions. Thus, it should be clear which visual elements within the medical image such as organs or abnormalities are essential for answering clinical questions. To overcome these challenges, we propose a novel method based on Vision Transformer (ViT), which reformulates Medical VQA as a multi-task learning task. We first construct soft pseudo labels of logits for essential selected visual elements from limited annotation data of the existing Medical VQA dataset. Then, we apply these pseudo labels in our proposed Medical VQA model by predicting the answer and pseudo labels at the same time, which not only improves the performance of the proposed model but also presents better interpretability. Extensive experiments on two Medical VQA datasets demonstrate the effectiveness of our proposed method.
Query Re-Training for Modality-Gnostic Incomplete Multi-modal Brain Tumor Segmentation

Although Magnetic Resonance Imaging (MRI) is crucial for segmenting brain tumors, it frequently lacks specific modalities in clinical practice, which limits prediction performance. In current methods, training involves multiple stages, and encoders are different for each modality, which means hybrid modules must be manually designed to incorporate multiple modalities’ features, lacking interaction across modalities. To ameliorate this problem, we propose a transformer-based end-to-end model with just one auto-encoder to provide interactive computations in any modality missing condition. Considering that it is challenging for a single model to perceive several missing states, we introduce learnable modality combination queries to assist the transformer decoder in adjusting to the incomplete multi-modal segmentation. Furthermore, to address the suboptimization issue of the Transformer under small datasets, we adopt a re-training mechanism to facilitate convergence to a better local minimum. The extensive experiments on the BraTS2018 and BraTS2020 datasets demonstrate that our method outperforms the current state-of-the-art methods for incomplete multi-modal brain tumor segmentation on average.
Groupwise Image Registration with Atlas of Multiple Resolutions Refined at Test Phase

Groupwise image registration (GIR) is a fundamental task that facilitates the simultaneous deformation of a group of subjects towards a specified or implicit center. Existing works mainly focus on either optimization-based methods that provide superb results but consume substantial time, or learning-based methods that are efficient but lack the flexibility to generalize across different domains and scales. To leverage the advantages of both methodologies, we present a robust method, Test-time Atlas adaptation for Groupwise registration (TAG), which generates a high-quality, group-specific atlas for groups of varying resolutions. Our method allows training at the test phase on target groups based on a learning-based GIR framework that bridges the gap between diverse groups. Besides the refinement of atlases at the original resolution, we propose additional modules to extend the scheme to groups of higher or lower resolutions at little cost. The method is evaluated on 3D brain MRI datasets to demonstrate its effectiveness. Evaluations of the registration accuracy and unbiasedness of atlases illustrate that TAG outperforms state-of-the-art benchmarks and maintains flexibility and robustness under a variety of scenarios.
Operating Critical Machine Learning Models in Resource Constrained Regimes

The accelerated development of machine learning methods, primarily deep learning, are causal to the recent breakthroughs in medical image analysis and computer aided intervention. The resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive. These large resource costs can be barriers in deploying these models in clinics, globally. To address this, there are cogent efforts within the machine learning community to introduce notions of resource efficiency. For instance, using quantisation to alleviate memory consumption. While most of these methods are shown to reduce the resource utilisation, they could come at a cost in performance. In this work, we probe into the trade-off between resource consumption and performance, specifically, when dealing with models that are used in critical settings such as in clinics.
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI

Unsupervised anomaly segmentation aims to detect patterns that are distinct from any patterns processed during training, commonly called abnormal or out-of-distribution patterns, without providing any associated manual segmentations. Since anomalies during deployment can lead to model failure, detecting the anomaly can enhance the reliability of models, which is valuable in high-risk domains like medical imaging. This paper introduces Masked Modality Cycles with Conditional Diffusion (MMCCD), a method that enables segmentation of anomalies across diverse patterns in multimodal MRI. The method is based on two fundamental ideas. First, we propose the use of cyclic modality translation as a mechanism for enabling abnormality detection. Image-translation models learn tissue-specific modality mappings, which are characteristic of tissue physiology. Thus, these learned mappings fail to translate tissues or image patterns that have never been encountered during training, and the error enables their segmentation. Furthermore, we combine image translation with a masked conditional diffusion model, which attempts to ‘imagine’ what tissue exists under a masked area, further exposing unknown patterns as the generative model fails to recreate them. We evaluate our method on a proxy task by training on healthy-looking slices of BraTS2021 multi-modality MRIs and testing on slices with tumors. We show that our method compares favorably to previous unsupervised approaches based on image reconstruction and denoising with autoencoders and diffusion models. Code is available at: https://github.com/ZiyunLiang/MMCCD .
MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction

Hospital readmission prediction is considered an essential approach to decreasing readmission rates, which is a key factor in assessing the quality and efficacy of a healthcare system. Previous studies have extensively utilized three primary modalities, namely electronic health records (EHR), medical images, and clinical notes, to predict hospital readmissions. However, the majority of these studies did not integrate information from all three modalities or utilize the spatiotemporal relationships present in the dataset. This study introduces a novel model called the Multimodal Spatiotemporal Graph-Transformer (MuST) for predicting hospital readmissions. By employing Graph Convolution Networks and temporal transformers, we can effectively capture spatial and temporal dependencies in EHR and chest radiographs. We then propose a fusion transformer to combine the spatiotemporal features from the two modalities mentioned above with the features from clinical notes extracted by a pre-trained, domain-specific transformer. We assess the effectiveness of our methods using the latest publicly available dataset, MIMIC-IV. The experimental results indicate that the inclusion of multimodal features in MuST improves its performance in comparison to unimodal methods. Furthermore, our proposed pipeline outperforms the current leading methods in the prediction of hospital readmissions.
FusionNet: A Frame Interpolation Network for 4D Heart Models

Cardiac magnetic resonance (CMR) imaging is widely used to visualise cardiac motion and diagnose heart disease. However, standard CMR imaging requires patients to lie still in a confined space inside a loud machine for 40–60 min, which increases patient discomfort. In addition, shorter scan times decrease either or both the temporal and spatial resolutions of cardiac motion, and thus, the diagnostic accuracy of the procedure. Of these, we focus on reduced temporal resolution and propose a neural network called FusionNet to obtain four-dimensional (4D) cardiac motion with high temporal resolution from CMR images captured in a short period of time. The model estimates intermediate 3D heart shapes based on adjacent shapes. The results of an experimental evaluation of the proposed FusionNet model showed that it achieved a performance of over 0.897 in terms of the Dice coefficient, confirming that it can recover shapes more precisely than existing methods. This code is available at: https://github.com/smiyauchi199/FusionNet.git .