Reviewerly/exHarmony huggingface dataset:
Emergency Department Triage Hospitalization Prediction Based on Machine Learning and Rule Extraction

The objective of this work was to investigate the usefulness of explainable AI (XAI) functionality in the Emergency Department (ED) triage hospitalization prediction modelling based on machine learning and rule extraction. Prediction modelling was carried out using the MIMIC-IV-ED dataset that contains over 400,000 ED visits for predicting hospitalisation Yes vs No (discharged). The Gradient Boosting (GB) prediction modelling gave the best performance achieving an accuracy, sensitivity and specificity of 83%, 82% and 84% respectively. These results are comparable with previous studies published in the literature. Subsequently, interpretable rules were extracted with a high precision threshold, demonstrating the rules’ overall accuracy in replicating the model’s behaviour. Further work is needed to validate the rules extracted with the ED medical experts as well as to benchmark the findings with other models and studies.Clinical Relevance: The use of explainable AI (XAI) in predicting hospitalization outcomes in the Emergency Department (ED) using machine learning and rule extraction, has the potential to improve clinical decision-making.
Towards Lightweight Deep Reference Frame for Versatile Video Coding

Deep neural network (DNN)-based methods have demonstrated enormous potential for Versatile Video Coding (VVC) inter prediction enhancement. However, due to their typically high computational complexity, implementing them in practical applications can be challenging. In this paper, we propose a lightweight deep reference frame interpolation network to enhance bi-prediction with low complexity. Specifically, given a pair of bi-directional reconstructed frames, first, we down-sample input frames to reduce the complexity before feeding them into the optical flow estimation network. Then the optical flows are utilized to warp extracted features at three different levels. The warped features are fused to generate the output intermediate frame. The additional reference frame is inserted into the reference picture lists to provide an additional reliable reference candidate. In contrast to previous efforts, the proposed method aims at achieving the trade-off between performance and complexity while maintaining a complexity of about 64 kMACs/pix. Experimental results demonstrate that our method achieves 1.82%/2.43%/2.02% coding efficiency improvements for Y/U/V components under random access (RA) configuration compared to the latest NNVC standard software VTM-11.0_NNVC-5.0.
GeAE: GAE-Embedded Autoencoder Based Causal Representation for Robust Domain Adaptation

In this work, we study the unsupervised robust domain adaptation problem where only a single well labeled source domain data is available during the learning process. A new causal representation method based on a Graph autoen-coder embedded AutoEncoder, named GeAE, is introduced to learn invariant representations across domains for robust domain adaption. The proposed method can handle nonlinear causal relations included in the data by a causal structure learning process similar to a graph autoencoder. Moreover, the cross-entropy loss as well as the causal structure loss and the reconstruction loss are incorporated in the objective function designed in a united autoencoder to improve the quality of predictions using causal representations. Experimental results on one generated dataset and three real-world datasets demonstrate the effectiveness of GeAE in comparison with the state-of-the-art methods.
Leveraging Image Gradients for Robust GAN-Generated Image Detection in OSN context

Creating hyper-realistic synthetic images has become effortless with tremendous development in Generative Artificial Intelligence technologies. Generative Adversarial Networks (GAN) generated synthetic images, especially non-existent face images that are visually indistinguishable from real faces, pose a severe social threat by enabling misinformation dissemination, often over online social networks and through fake social profiles. In spite of successful solutions being reported in the recent literature for detecting GAN-generated synthetic images, the performance of such schemes degrades considerably with the launch of post-processing attacks. In this work, we employ gradient of an image as the key component to detect synthetic images. According to our results, gradient proves to be a considerably efficient image derivative for synthetic image detection as well as to achieve robustness against post-processing attacks. We explore two different gradient operators and design four unique deep learning-based detection networks utilizing different gradient-based feature sets. Our solution achieves state-of-the-art (SOTA) detection accuracy (above 99%) on the test set consisting of STYLEGAN2 images and outperforms SOTA solutions for detecting post-processed and compressed images.
Kingmaking in Press Diplomacy

Humans play games like Diplomacy for more reasons than just trying to win. For example, when they know they are losing, they may choose to aid an opponent rather than concede. This paper presents Lyre, an agent skilled at playing/communicating in Diplomacy and adept at working towards the benefit or harm of a given opponent - kingmaking. We parallel Lyre's kingmaking with real human Diplomacy games and analyze how Lyre can best play kingmaker. Results show statistically significant benefit in Lyre's boardplay, negotiation, and kingmaking.
Extension Removal in Abstract Argumentation: Necessary and Sufficient Conditions

In the research on dynamics of abstract argumentation frameworks, Baumann et al. proposed the extension removal problem for the first time in 2019 and provided an axiomatization of removal operators. In contrast, we are interested in how to solve the extension removal problem in practice. Our consideration includes designing a special removal operator for preventing undesired extensions from being accepted whatsoever in the modified AF; and limiting AF modification to normal expansion so as to honor the original arguments and their attacks. The contribution of this paper consists of four jointly necessary and sufficient conditions identified for solving the extension removal problem under preferred, stable, semi-stable, grounded, ideal and complete semantics, as well as an impossibility conclusion under naive semantics. Such conditions are both minimum and absolute which can be used to determine when the removal problem is (un)solvable.
Training Knowledge Inheritance Through Deep Q-Net

When training neural networks, the weights of the model are updated at each optimization step, and the older weights are discarded. In this paper, we propose a method called, Training Knowledge Inheritance (TKI), to use the knowledge about the progression of weight and loss data in reducing overfitting and improving the generalization in the later stages of training. We reformulate the traditional gradient optimization problem as a reinforcement learning task. In particular, by treating the trainable weight space as an environment, the learning rate as action, and the validation accuracies as the rewards, we train a Q-network (controller) to learn the discounted future validation accuracy and guide the later training of another network (worker). We conduct the experiments on the MNIST, CIFAR-10, and CIFAR-100 datasets with naive dense neural networks and ResNet-56. Our results show that TKI could rediscover learning rate schedule rules similar to previous works, including increasing, decaying, and cyclical repeating.
Accuracy Improvement of Depth Map Estimation from Multi-View Images Using NeRF

In this paper, we propose a method to improve the accuracy of depth map estimation from multi-view images using Neural Radiance Fields (NeRF). A depth map can be estimated from multi-view images using Multi-View Stereo (MVS), which can estimates the depths inside objects with high accuracy, while NeRF can estimate the depths at object boundaries with high accuracy. We consider using both advantages to improve the accuracy of depth map estimation from multi-view images and making NeRF as a refinement module. Through a set of experiments using a public MVS dataset, we demonstrate the effectiveness of the proposed method compared to conventional depth map estimation methods.
Algorithm for Hierarchical Evaluation of the Entity of Direct Opinion in the Task of Sentiment Analysis of Complicated Text Content From Social Media

Internet messengers and chat platforms, including those utilized by transport companies, taxi services, energy providers, and similar entities, have long been established as customary channels for customers to interact with service providers and provide feedback.In this context, the main task is the analysis of the semantic and sentiment components of the textual content produced in them. A feature of chat messages is a relatively small number of words, the use of jargon and specific symbols, emojis, etc., which creates a serious problem for extracting the semantic component for both humans and artificial intelligence. The authors call such a text complicated.The authors propose an algorithm for evaluating the entity, which is the subject of discussion in a chat message, and their features, based on the use of dependency parsing. Its quality is assessed on real data, where both humans and artificial intelligence based on a generative model act as experts.Implementing such an approach, specifically through the utilization of AI-powered chatbots or managers, will enhance the quality of service experienced by customers of power generating companies and similar services.
Enhancing Human Action Recognition with Asymmetric Generalized Gaussian Mixture Model-Based Hidden Markov Models and Bounded Support

Human action recognition (HAR) is a crucial research field that necessitates the implementation of advanced mathematical concepts to recognize human activities from sequences of observations. This paper presents a novel framework employing a mixture-based Hidden Markov Model (HMM) that capitalizes on the advantages of asymmetric modeling, bounded support, and robustness in sensor-based HAR. To accommodate variations in observations within each human activity class, we propose an asymmetric generalized Gaussian mixture model (AGGM) to model the emission probabilities. Subsequently, we propose incorporating the bounded asymmetric generalized Gaussian mixture model (BAGGM) to address the constraints inherent in real-life data. The parameters of the corresponding HMM are estimated using the Baum-Welch algorithm, and the most probable sequence of hidden states is inferred using the Viterbi algorithm. We validate our proposed framework using four datasets for human activities. Experimental results demonstrate that our proposed model outperforms all state-of-the-art HAR models that are HMM-based, thereby emphasizing the superiority of our proposed frameworks in HAR systems to understand behaviours, predict potential actions, and facilitate sports applications for the general population and independent living applications for vulnerable populations.
Effect of Smile on Facial Landmark based Face Recognition

Smile reveals distinctive facial features that makes a face more recognizable. However, little is known about the impact of smile on face recognition accuracy, especially in the context of automated face recognition systems using traditional machine learning algorithms. This is an important topic to study because understanding the influence of smile on face recognition could contribute to improve the performance of various applications that use face recognition underneath. In this paper, we propose a handful of novel features to capture the effect of smile on faces. With the new feature set, we then apply several machine learning algorithms on the data set that includes a comprehensive set of both smiling and neutral facial images of people with different gender, ethnicity and age groups. Our experimental result suggests that smiling faces produce high discriminating power that can be used effectively by the machine-learning algorithms to improve their accuracy and fairness.
Guided Reward Design in Continuous Reinforcement Learning for Quantum Control

Reinforcement learning has been intensively applied to tackle complex quantum control problems owing to its adaptability in dynamic environments. However, commonly used reinforcement learning algorithms are restricted to selecting actions from a discrete action space, which may result in inaccurate control. In order to surpass this constraint, we propose an improved continuous reinforcement learning algorithm that can generate control policies in a continuous action space, enabling precise control of quantum systems. Moreover, a guided reward function design method is proposed to guide the learning process toward higher fidelity. Numerical results demonstrate that the proposed continuous reinforcement learning algorithm with a guided reward function can capably prepare states on one-qubit and two-qubit systems.
IFF-Net: I-Frame Fusion Network for Compressed Video Action Recognition

Compressed video action recognition has received significant attention due to its potential for reducing storage and computational costs. However, the current methods typically only capture a few RGBs and compressed motion cues (e.g., motion vectors and residuals), which are insufficient for modeling actions at their full temporal extent. To address this issue, we propose a Time Domain Fusion (TDF) Module that can extract both low-frequency and high-frequency components from the video and integrate them seamlessly, resulting in the effective integration of abundant motion information into a single frame. More importantly, by using the TDF module, we introduced a new network called I-Frame Fusion Network (IFF-Net). The IFF -Net interacts with the original network (I-frame, motion vector, and residual) in two ways: explicit and implicit. Explicit interaction involves extracting the new representation and the original compressed representation information separately and then performing a later fusion. In contrast, implicit interaction uses the distillation approach, with the IFF-Net acting as the teacher to guide the I-frame network to learn full temporal expressions. Our approach performs better than state-of-the-art methods on the UCF-101 and HMDB-51 datasets for compressed video action recognition.
More task-balanced Class-Incremental Learning

Although deep learning has achieved high performance on many tasks, neural networks suffer from catastrophic forgetting in incremental learning scenarios. Class-Incremental Learning focuses on the scenario where new category data continuously arrives. PASS (Prototype Augmentation and Self-Supervised) is a classic method that does not require storing old exemplars. Based on PASS, we propose three improvement measures from the perspective of making the new and old tasks more balanced. Firstly, we propose a balanced prototype replay (Balanced PR) strategy to make the image number of new and old categories that the model head encounters more balanced. Secondly, at the feature knowledge distillation (FKD) stage, we assign different weights to the incoming data based on the confidence of the old model, which is named Focal FKD. The goal is to minimize interference with learning new task data while preserving the representation capability of the model backbone on old data. Thirdly, we combine FKD with weight regularization on the model backbone. In the feature space, FKD is beneficial for preserving the positions of the old categories while weight regularization helps to separate the new and old categories well. Experiments on the benchmark indicate that there has been obvious improvement in the performance of PASS after our modifications.
Diagnosis-oriented Medical Image Compression with Efficient Transfer Learning

Remote medical diagnosis has emerged as a critical and indispensable technique in practical medical systems, where medical data are required to be efficiently compressed and transmitted for diagnosis by either professional doctors or intelligent diagnosis devices. In this process, a large amount of redundant content irrelevant to the diagnosis is subjected to high-fidelity coding, leading to unnecessary transmission costs. To mitigate this, we propose diagnosis-oriented medical image compression, a special semantic compression task designed for medical scenarios, targeting to reduce the compression cost without compromising the diagnosis accuracy. However, collecting sufficient medical data to optimize such a compression system is significantly expensive and challenging due to privacy issues and the lack of professional annotation. In this study, we propose DMIC, the first efficient transfer learning-based codec, for diagnosis-oriented medical image compression, which can be effectively optimized with only few-shot annotated medical examples, by reusing the knowledge in the existing reinforcement learning-based task-driven semantic coding framework, i.e., HRLVSC [1]. Concretely, we focus on tuning only the partial parameters of the policy network for bit allocation within HRLVSC, which enables it to adapt to the medical images. In this work, we validate our DMIC with the typical medical task, Coronary Artery Segmentation. Extensive experiments have demonstrated that our DMIC can achieve 47.594%BD-Rate savings compared to the HEVC anchor, by tuning only the A2C module (2.7% parameters) of the policy network with only 1 medical sample.
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI<sup>*</sup>

Federated Learning (FL) enables collaborative model training among medical centers without sharing private data. However, traditional FL risks on server failures and suboptimal performance on local data due to the nature of centralized model aggregation. To address these issues, we present Gossip Mutual Learning (GML), a decentralized framework that uses Gossip Protocol for direct peer-to-peer communication. In addition, GML encourages each site to optimize its local model through mutual learning to account for data variations among different sites. For the task of tumor segmentation using 146 cases from four clinical sites in BraTS 2021 dataset, we demonstrated GML outperformed local models and achieved similar performance as FedAvg with only 25% communication overhead.Clinical Relevance: Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis, treatment planning and assessment. We proposed a novel learning schedule to build an effective AI model by learning from data across different sources without sacrificing patient privacy, which can be generalized to a broader clinical research and applications.
Towards a Retrieval Augmented Generation System for Information on Suicide Prevention<sup>*</sup>

Suicide is a leading cause of mortality worldwide with more than 700 000 per year. Hence, it is instrumental that the general public can access to reliable information that help in suicide prevention. In this work, we have tackled this problem by developing a question-answering system based on retrieval augmented generation — this approach allows the system to generate answers based on a corpus of documents curated by psychologists and psychiatrists. Several alternatives have been tested for the two main components of the system: an embedding model to retrieve relevant contexts from the corpus of documents to answer a given question (the best option was a BERT-based model), and a language model to generate a response from the extracted contexts (the best alternative was the Bertin model). The developed system is a first step towards helping in one of the greatest global public health concerns.Clinical Relevance: The system suggested in this work will offer a reliable and accessible resource that could aid in providing timely information and support to individuals at risk and their families, thereby potentially enhancing clinical interventions and patient outcomes in the realm of mental health.
Decoding Semantic Content of Visual Stimuli from BOLD fMRI Data

In vision, the brain is a feature extractor that works from images. We hypothesize that fMRI can mimic the latent space of a classifier, and employ deep diffusion models with BOLD data from the occipital cortex to generate images which are plausible and semantically close to the visual stimuli administered during fMRI. To this end, we mapped BOLD signals onto the latent space of a pretrained classifier and used its gradients to condition a generative model to reconstruct images. The semantic fidelity of our BOLD response to visual stimulus reconstruction model is superior to the state of the art.
Modeling the Social Acceptability of Technologies Using Twitter Data

Our society is built on the benefits of technological development, but this development also entails risks. When the convenience of a new technology outweighs the sense of anxiety about its risks, we can consider that this technology is socially accepted. In this study, we use the social media to analyze the general public's anxiety feelings toward various technologies and model the social acceptability of the new technologies based on this analysis. In this study, 6,452,730 tweets about three technologies: “automated driving,” “electronic currency,” and “drones” were collected, and the social acceptability of the technologies was examined using emotion classification, text categorization, and topic analysis techniques. As a result, we concluded that the anxiety about unemployment can be one perspective for analyzing the social acceptability of a technology.
MotionMLP: End-to-End Action Recognition with Motion Aware Vision MLP

Action recognition aims to interpret complex spatiotemporal patterns in the video. Current methods utilize CNN or Transformer structures, requiring extensive pre-training methods and optical flow to capture motion information. Such approaches are computationally expensive, necessitate significant storage, cannot be trained end-to-end, and typically neglect joint learning of temporal and spatial streams. In this paper, we propose MotionMLP, a novel MLP architecture that extracts motion information from videos, then dynamically adjusts the connection between tokens and static weights within the MLP structure. The MotionMLP solely relies on video frames as input and is independent of any pre-training method or optical flow computation. The experimental results indicate that MotionMLP outperforms the previous SOTA real-time end-to-end methods on UCF101 and HMDB51, and relies on one-tenth of the parameters compared with typical two-stream CNN approaches, while operating ten times faster.
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation

Cross-resolution face recognition has become a challenging problem for modern deep face recognition systems. It aims at matching a low-resolution probe image with high-resolution gallery images registered in a database. Existing methods mainly leverage prior information from high-resolution images by either reconstructing facial details with super-resolution techniques or learning a unified feature space. To address this challenge, this paper proposes a new approach that enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution image. A cross-resolution knowledge distillation paradigm is first employed as the learning framework. Then, an identity-preserving network, WaveResNet, and a wavelet similarity loss are designed to capture low-frequency details and boost performance. Finally, an image degradation model is conceived to simulate more realistic low-resolution training data. Consequently, extensive experimental results show that the proposed method consistently outperforms the baseline model and other state-of-the-art methods across a variety of image resolutions.