Reviewerly/exHarmony huggingface dataset:
Interactive Multi-Objective Reinforcement Learning for Continuous Robot Control

Many real-world continuous control problems inherently involve multiple conflicting objectives, especially in the case of robotic control problems. In recent years, with the development of deep multi-objective reinforcement learning (MORL), these problems have been preliminarily solved in many scenarios. However, the role of the decision-maker (DM) is often overlooked. The existing methods often find many tradeoff policies, from which the DM needs to manually select the ones they truly need, which is often very difficult in real-world problems. In this paper, we propose an interactive MORL algorithm called I-MORL. It includes a rank model, which learns the latent preferences of the DM through a small amount of interaction. Then, this rank model, together with a proposed prediction model, guides the algorithm to accurately find the non-dominated policies that align with the DM's preferences. We compare I-MORL with baseline algorithms on five benchmark robot control problems. The results demonstrate that our method outperforms the baseline algorithms.
A Survey on Content-aware Image Retargeting Techniques for Visually Impaired Assistance

Content-aware image retargeting is widely used in many applications. It is beneficial for visually-impaired patients who suffer from tunnel vision such as Retinitis Pigmentosa patients. This paper introduces the most popular methods of content-aware image retargeting as well as the most commonly used datasets. Assessment criteria for the resulting images are also mentioned and explained. This survey focuses on the techniques that serve scene optimization to help people with tunnel vision by compressing the scene into their visual field without losing important details.
Forecasting Hospital Mental-Health Admissions with a Novel Hybrid Deep Learning Architecture

Forecasting hospital admissions, especially for mental health patients, is crucial for efficient resource allocation planning. This study used multivariate time-series data to forecast hospital admissions for mental health patients at the Thunder Bay Regional Health Sciences Centre in Ontario, Canada. The main contribution of this work is proposing a novel deep learning architecture, the Stacked-dilated-causal Convolutional Neural Network and Bidirectional Long Short-Term Memory (SCNN-BiLSTM), which outperformed other statistical and machine learning models. The hybrid architecture provides full history-coverage of the input window and maintains the causal structure of the input time series, making it suitable for long-term forecasting. For univariate one-week forecasting, among the commonly used methods, CNN-BiLSTM achieves the minimum Mean Absolute Percentage Error (MAPE) score (13.8). However, for 4-week forecasting, the MAPE score increases to 14.78. Modelling multivariate data reduces this score to 13.33. The multivariate model trained with the proposed architecture performs the best by achieving a MAPE score of 12.96 in 4-weeks forecasts. Training multivariate models using the SCNN-BiLSTM architecture developed in this study may be potentially utilized in other health forecasting domains to achieve improved forecasting accuracy.
Data Centric Blockchain Based Evaluation Approach to Analyze E-Commerce Reviews Using Machine and Deep Learning Techniques

Machine and deep learning techniques are now essential for many different kinds of solutions in many areas, applications, and businesses. Analyzing e-commerce reviews using machine and deep learning techniques can motivate a company to make more wise decisions regarding the quality and services it offers. But issues can occur if there is insufficient high-quality data for the training, scalability, and upkeep of learning models. To address these important issues, we suggest a data-centric learning architecture that makes use of a blockchain and Interplanetary File System (IPFS)-based storage. Our architecture provides secure and cost-effective data storage and an incentive mechanism. Our experiment shows that as the dataset gets bigger, the Naïve Bayes (NB) and the Support Vector Machine (SVM) models get 2% more accurate, the Convolutional Neural Network (CNN) model gets 4% more accurate, and the Long Short-Term Memory (LSTM) model gets 3 % more accurate. The deep learning models exhibited higher accuracy rates than the machine learning models because hidden layers of deep learning possess the ability to extract intricate syntactic features from the review text.
STPR: Personalized Recommendation via Matrix Factorization with Social Ties

In the present situation, the massive rise of Internet platforms and the proliferation of various social network-based websites leads to information overload, users face difficulties in making choices as well as consuming users' time to filter out the products they want. In this context, recommender systems have been proposed to successfully address the information overload. It has been even used to solve the user's cold start problem by recommending products that have been tasted by the user's trusted people under the user's social network ties. In our paper, a personalized recommender system based on social ties-aware to solve the problems of data sparsity, user cold-start, and information overload is proposed. Experiments show that this model effectively reduces computational complexity and improves prediction accuracy compared to the model of the previous works of literature.
Transformer-Based Word Sense Disambiguation: Advancements, Impact, and Future Directions

Word Sense Disambiguation (WSD) is a challenging field of research in natural language processing. Enhanced WSD techniques have the potential to significantly enhance the performance of various Natural Language Processing tasks, such as text translation, question answering, sentiment analysis, and text generation, among others. Recently, the advent of novel deep learning techniques, particularly transformers, has yielded noteworthy advancements in the field of Natural Language Processing and its associated sub-tasks. Today, AI-driven tools pave a new era of technology by harnessing the transformative architecture of transformers as a fundamental component. Transformers have presumed a pivotal role in numerous NLP tasks, notably in WSD, and their significance continues to grow as they progress. This paper presents a comprehensive study that explores the advancements achieved in addressing the problem of word sense disambiguation, with a specific focus on the utilization of transformer models.
Edge Computing and Distributed Ledger Technology for The Industrial IoT

The tremendous increase in implementing IoT devices for critical infrastructures yells for higher performance and robust protection. Traditional cloud based IoT architecture is unsuitable for delay-sensitive or real-time applications and lacks the robust security needed by industrial systems. Thus, new technologies are introduced to endorse the industry's digital transformation. Edge computing greatly satisfies required performance while Distributed Ledger Technology (DLT) mitigates potential data compromises. This work proposes an integrated four layers architecture based on Edge Computing and DLT that provides efficient computing, security, and scalability. Edge computing handles transmission delay and scalability issues, while DLT handles data validation and trust. A lightweight DLT model is introduced to enable faster data commitment and better networking. The model incorporates two main features: a new grouping technique that enables network segmentation reducing network complexity. The second technique dynamically optimizes resource-utilization by adjusting DLT requirements according to the hardware capabilities of each participating node.
Emotion Analysis in the Movie Script for Frozen Characters

To analyze the personality and emotions of each character in the movie ‘Frozen’, we analyzed the dialogues of each character in the movie script and constructed a feature vector for the characters by using BERT embedding technique. We recognized the friendliness between characters by similarity heatmap and also we attempted to analyze the emotional types of the characters through the movie script analysis.
Improving Accessibility and Readability of Survey Reports in Digital Health Platforms using Conversational AI

This study proposes the integration of conversational AI in community-based survey platforms to generate personalized narratives for vulnerable patients such as the elderly, those with palliative care needs, or from diverse language communities. The study emphasizes the capabilities of AI-driven analysis and proposes the use of this technology to analyze health survey data and produce personalized narratives that address the specific needs of each individual. The proposed approach aims to enhance patient engagement by providing compassionate assistance and insightful guidance derived directly from the survey responses.
Multi-modal Learning with Missing Modality in Predicting Axillary Lymph Node Metastasis

Multi-modal Learning has attracted widespread attention in medical image analysis. Using multi-modal data, whole slide images (WSIs) and clinical information, can improve the performance of deep learning models in the diagnosis of axillary lymph node metastasis. However, clinical information is not easy to collect in clinical practice due to privacy concerns, limited resources, lack of interoperability, etc. Although patient selection can ensure the training set to have multi-modal data for model development, missing modality of clinical information can appear during test. This normally leads to performance degradation, which limits the use of multi-modal models in the clinic. To alleviate this problem, we propose a bidirectional distillation framework consisting of a multi-modal branch and a single-modal branch. The single-modal branch acquires the complete multi-modal knowledge from the multi-modal branch, while the multi-modal learns the robust features of WSI from the single-modal. We conduct experiments on a public dataset of Lymph Node Metastasis in Early Breast Cancer to validate the method. Our approach not only achieves state-of-the-art performance with an AUC of 0.861 on the test set without missing data, but also yields an AUC of 0.842 when the rate of missing modality is 80%. This shows the effectiveness of the approach in dealing with multi-modal data and missing modality. Such a model has the potential to improve treatment decision-making for early breast cancer patients who have axillary lymph node metastatic status.
CARE-30: A Causally Driven Multi-Modal Model for Enhanced 30-Day ICU Readmission Predictions

Accurate prediction of unplanned readmissions allows healthcare systems to adopt preventive measures, reducing these occurrences. Creating a model that accurately predicts readmissions while simultaneously providing insights that are clinically interpretable is a complex and demanding task. This interpretability is vital, as it informs clinicians about the underlying reasons behind a model’s decisions, thus aiding in more informed clinical decision-making. In light of this, we introduce CARE-30 (Causal Analysis based Readmission Estimator within a 30-day time frame), a novel causal inference-based model, designed with clear cause-and-effect relationships, enhancing its potential acceptance and understanding among clinicians.In our approach, we generate a directed acyclic graph (DAG) to elucidate the latent causal relationships among pivotal clinical variables. Leveraging this causal graph, we can effectively apply the front-door criterion to construct the model and eliminate bias. By utilizing transformers, we integrate clinical texts, time-series data and categorical data into patient representation data as a robust input to the CARE-30 model. Our work signifies the effective application of knowledge from the field of causal inference in the domain of clinical medicine, and it also reflects the originality and impact of our research. CARE-30 unites causality and clinical prediction, unlocking new vistas in healthcare forecasting. Experiments demonstrated that our model outperforms all other methods in Accuracy and F1 scores.
Pretrain Once and Finetune Many Times: How Pretraining Benefits Brain MRI Segmentation

Brain MRI segmentation plays an important role in analyzing brain anatomical structures and understanding brain images. In this paper, we consider building a uniform 3D brain MRI segmentation framework using the pre-training and fine- tuning style to fully leverage existing public brain images and segmentation masks. Based on existing Transformer-based 3D image segmentation models, UNETR and Swin UNETR, we study the necessity and benefit of using pre-training, through pretraining on a big collection of over 6,000 brain scans from OASIS, ADNI, and CC359, and fine-tuning with limited segmentation masks to perform three downstream tasks, i.e., skull stripping, 4-structure segmentation, and 33-structure segmentation. Experimental results demonstrate that in most cases the pre-training can help reduce 90% of segmentation masks and half the time. Also, our method outperforms the recent method SynthSeg by a good margin. Our pre-trained model and source code are available online at https://github.com/AllanIverson/medical-segmentation.
A U-Shaped Convolution Recurrent Network for Spatiotemporal Feature Fusion in 4D-CT Image Registration

In lung 4DCT registration, pairwise methods do not fully utilize the temporal component of serial images. Recently some studies have tried to address this issue. However, interpretability and high-dimensional feature modeling in a temporally explicit manner still need to be further explored. In this paper, a U-shaped network based on Conv-LSTM was proposed that defines image registration as a spatiotemporal sequence prediction problem. The backbone structure consists of stacked Conv-LSTM layers, and a time channel is introduced to fuse the spatio-temporal features of image sequences. We train model in one-shot style on DIRLAB dataset. The experimental results demonstrate that the proposed method achieves better registration performance in spatiotemporal sequence image registration tasks compared with current works.
Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps

Over decades, neuroscience has accumulated a wealth of research results in the text modality that can be used to explore cognitive processes. Meta-analysis is a typical method that successfully establishes a link from text queries to brain activation maps using these research results, but it still relies on an ideal query environment. In practical applications, text queries used for meta-analyses may encounter issues such as semantic redundancy and ambiguity, resulting in an inaccurate mapping to brain images. On the other hand, large language models (LLMs) like ChatGPT have shown great potential in tasks such as context understanding and reasoning, displaying a high degree of consistency with human natural language. Hence, LLMs could improve the connection between text modality and neuroscience, resolving existing challenges of meta-analyses. In this study, we propose a method called Chat2Brain that combines LLMs to basic text-2-image model, known as Text2Brain, to map open-ended semantic queries to brain activation maps in data-scarce and complex query environments. By utilizing the understanding and reasoning capabilities of LLMs, the performance of the mapping model is optimized by transferring text queries to semantic queries. We demonstrate that Chat2Brain can synthesize anatomically plausible neural activation patterns for more complex tasks of text queries.
Enhancing Clinical Outcome Predictions through Auxiliary Loss and Sentence-Level Self-Attention

Predicting clinical outcomes such as mortality and length of hospital stay during the initial admission to the intensive care unit (ICU) is vital for enhancing patient survival rates and optimizing hospital resource allocation. Accurate diagnostic information is deeply correlated with mortality and hospitalization duration; however, initial admission data often lack sufficient diagnostic details and may even have discrepancies with discharge diagnoses. Previous studies have leveraged Electronic Health Record (EHR) data, encompassing clinical notes and vital sign monitoring data, to boost the performance of clinical outcomes prediction. Nevertheless, they have often overlooked the relevance between sentence information, resorting to simple averages, and have missed the importance of diagnostic information. To overcome this limitation, we propose the Diagnosis-Enhanced Sentence Attention Model for Clinical Prediction (DESAM-cp), a multimodal neural network. We employed an auxiliary loss to ensure that embeddings of clinical note were informative of the patient's discharge diagnosis information and used sentence-level self-attention to account for the relevance between sentences. Ultimately, clinical note embeddings containing diagnostic information were fused with monitoring data embeddings to predict clinical outcomes. Our experiments demonstrate that the methodology of DESAM-cp outperforms previous research methods. Additionally, through ablation tests, visualization analysis, and attention analysis, we have substantiated that our individual modules function effectively. Our analysis also suggests that our model focuses on medically relevant sentences in relation to diagnosis and clinical outcome predictions and can provide supporting evidence to assist clinicians in making informed decisions regarding diagnoses and treatments. The code for DESAM-cp used in this work is available at https://github.com/a11525/DESAM-cp
DDFormer: Dual-domain and Dual-aggregation Transformer for Multi-contrast MRI Super-Resolution

Multi-contrast high-resolution (HR) magnetic resonance (MR) images enrich available information for diagnosis and analysis. However, the resolution of MR images is typically low due to the limitations of hardware conditions and scanning time. Although convolutional neural network (CNN)-based and Transformer-based super-resolution (SR) methods are effective in improving the resolution and sharpness of images, most SR methods for multi-contrast magnetic resonance imaging (MRI) still have the following shortcomings. Firstly, most CNN-based methods are deficient in capturing global information, which is essential for regions with complicated anatomical structures. Secondly, although numerous Transformer-based methods capture long-range dependencies in the spatial dimension, they neglect the self-attention in the channel dimension, which is also important for low-level vision tasks. To address the above issues, we propose a novel dual-domain and dual-aggregation Transformer (DDFormer) for multi-contrast MRI SR. The dual-domain detail-enhanced Transformer (DDT) generates global features of the target domain; the dual-aggregation Transformer (DAT) effectively captures long-range dependencies in both spatial and channel dimensions. Specifically, we design DDT to model long-range dependencies in both reference and target images, so that the proposed DDFormer restores sharp structures and natural textures. Moreover, we propose a Channel-wise-Spatial Locally-enhanced Self-Attention layer to construct DAT to capture local features within a patch as well as global contextual features between patches in a single-channel feature and deeply aggregate semantic information of multiple protocols for MR images. Extensive experiments verify the effectiveness of our DDFormer, which outperforms state-of-the-art methods on benchmarks quantitatively and visually.
Online Unsupervised Arm Posture Adaptation for sEMG-based Gesture Recognition on a Parallel Ultra-Low-Power Microcontroller

Surface electromyography (sEMG) is a State-of-the-Art (SoA) data source for natural and dexterous control in human-machine interaction for industrial, commercial, and rehabilitation use cases. Despite non-invasiveness and versatility, a major challenge for sEMG-based control is the inherent presence of many signal variability factors, which hamper the generalization of automated learning models. In this work, we propose an unsupervised adaptation technique for sEMG classification and apply it to arm posture variability. The approach relies on aligning the Principal Components (PCs) of new data with the PCs of the training set. No classifier retraining is required, and the PCs are estimated online, consuming one sample at a time without storing any data. We validate our method on the UniBo-INAIL dataset, showing that it recovers 37% to 51% of the inter-posture accuracy drop. We deploy our solution on GAP9, a parallel ultra-low-power microcontroller, obtaining a latency within 3.57 ms and an energy consumption within 0.125 mJ per update step. These values satisfy the constraints for real-time operation on embedded devices. Our solution is unsupervised and thus suitable for real incremental learning conditions where ground truth is not available.
Wavelet-aware Transformer Network for Multi-contrast Knee MRI Super-resolution

In this paper, we propose a wavelet-aware transformer network (WATNet) for multi-contrast knee MRI super-resolution. Unlike conventional image domain-based super-resolution methods that can not explicitly model the lost high-frequency information, our WATNet endeavors to adaptively fuse the complementary frequency information of the multi-contrast image in the wavelet domain and further refine it in the image domain. The proposed WATNet consists of the multi-scale wavelet transformation (MSWT) module, wavelet-aware transformer (WAT) module, and reconstruction (Rec) module. Specifically, the MSWT module learns to transform the MR image to multi-scale wavelet domain features by the wavelet transformation. The WAT module can adaptively search and transfer similar wavelet domain reference information to the low-resolution one. The Rec module can restore high-quality images in the image domain. To further capture more high-frequency details, we also design the wavelet-based high-frequency loss. The qualitative and quantitative experimental results indicate that our proposed WATNet outperforms most state-of-the-art methods.
Deep-in-Biopsies: AI-supported biopsy image processing for accurate pathology diagnosis and staging

The Deep-in-Biopsies (DiB) platform supports diagnosing and staging diseases using patient biopsy imaging. This flexible service is designed for medical units to handle multiple organs and diseases. Specialists can train the system on demand using their selected images and annotations, which can be generated using an embedded annotation tool. The system proposes pathology findings to the expert for validation, and validated findings can be used for further training.
A Two-Stage Classification Model on Detecting Cache-Based Side-Channel Attacks

Cache-based side-channel attacks in cloud environments have attracted widespread attention. Many studies have successfully proved that sensitive data can be stolen from co-resident virtual machines by launching these attacks. Relatively various detection and defense methods are also proposed. To address such problems, this paper proposes a novel two-stage classification model that combines random forest and XGBoost, which can classify the samples collected from one process into attack samples, noise samples and normal samples. In the experiments, the accuracy of attack samples and normal samples produced results of 98.89% and 99.83%, respectively, demonstrating that our model is not inferior compared to the current methods in terms of classification performance and outperforms them in terms of implementation difficulty and classification speed.