Reviewerly/exHarmony huggingface dataset:
Federated Active Learning for Multicenter Collaborative Disease Diagnosis

Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts. Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated active learning methods for multicenter collaborative diagnosis of diseases: the Labeling Efficient Federated Active Learning (LEFAL) and the Training Efficient Federated Active Learning (TEFAL). The proposed LEFAL applies a task-agnostic hybrid sampling strategy considering data uncertainty and diversity simultaneously to improve data efficiency. The proposed TEFAL evaluates the client informativeness with a discriminator to improve client efficiency. On the Hyper-Kvasir dataset for gastrointestinal disease diagnosis, with only 65% of labeled data, the LEFAL achieves 95% performance on the segmentation task with whole labeled data. Moreover, on the CC-CCII dataset for COVID-19 diagnosis, with only 50 iterations, the accuracy and F1-score of TEFAL are 0.90 and 0.95, respectively on the classification task. Extensive experimental results demonstrate that the proposed federated active learning methods outperform state-of-the-art methods on segmentation and classification tasks for multicenter collaborative disease diagnosis.
Independent Relation Representation With Line Graph for Cross-Lingual Entity Alignment

Cross-lingual entity alignment, which is an important task in the field of graph mining, aims to find equivalent entity pairs from two knowledge graphs. Recent methods show that relation representation can be used to improve entity representation and entity alignment. However, relation representation is learned dependently on entity representation, and both representations are learned from node-centered knowledge graphs. This dependency of relation on entity results in poor relation representation and leads to limited enhancement of entity representation. Therefore, to address this challenge, we propose novel relation-aware line graph neural networks for cross-lingual entity alignment (RALG). More specifically, we first propose to learn relation representation with heterogeneous line graphs independently from entities. The constructed heterogeneous line graphs can capture the correlation of relations explicitly. Secondly, we design a new way of aggregation in the form of triples to strengthen the relevance between entities and their corresponding relations. Experiments conducted on real-world datasets show that independent learning of relation representation with line graphs can represent relations better, and our method achieves better performance than the state-of-the-art methods for entity alignment.
Towards a Foundation of Web3

Abstract The Web3 vision takes blockchain disintermediation to a next level by making it ubiquitous, encompassing not only payments and financial services but also digital identities, data and business models. Recently, Web3 has gained massive attention by major analysts such as Gartner, Forrester, Forbes Technology Council and the Harvard Business Review. Albeit the current enthusiasm about Web3, we are lost in a state of confusion about what Web3 actually is – or could be. In this paper, we take an engineering approach. We discuss a potential foundation of Web3 in terms of fundamental components, architectural principles and a Web3 design space. We conclude that, from an engineering viewpoint, the Web3 can be characterized as the integration of digital rights exchange into the (application layer) internet protocols. Finally, on the basis of these findings, we discuss the Alphabill platform as a Web3 enabling technology.
Realistic Real-Time Simulation of RGB and Depth Sensors for Dynamic Scenarios using Augmented Image Based Rendering

Simulation remains one of the key methods for testing and validation of robotic perception systems and it also becomes increasingly important for training visuomotor policies for autonomous driving or manipulation. Further, as perception pipelines tend to leverage increasing amounts of modalities, it appears vital to simulate additional cues such as depth maps aside from RGB images. To align simulation with real-world observations, it is key to achieve realistic renderings of these maps, which includes the capability of rendering other dynamic objects in the scene. In this work, we propose an approach to real-time simulation of photo-realistic RGB images and sensor-realistic depth maps, that can contain dynamic objects at user-defined locations. Our method employs a selection of static samples of a pre-recorded database and multimodal cues from CAD models that are fused and warped to synthesize new imagery for a target camera pose. We show the efficacy of our method on newly proposed datasets recorded in a variety of different setups.
Boosting Out-of-Distribution Image Detection With Epistemic Uncertainty

Modern deep neural networks are known to generate over-confident class predictions even for unseen samples. However, safety-critical applications are required to understand examples that differ from the training distribution. For example, an autonomous vehicle must return a instant refusal feedback when encountering an unexpected situation. The voice secretary should re-ask the user for a command that was not understood to prevent malfunction. In this paper, we propose an out-of-distribution sample detection algorithm using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Uncertainty-based Additive Fast Gradient Sign Method</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UA-FGSM</i> ), which uses Monte Carlo (MC) dropout during backpropagation. The proposed uncertainty-based method forces in-distribution sample predictions to be more over-confident and out-of-distribution sample predictions to be less over-confident in the pre-trained model. This boosts the discrimination between the in-distribution and out-of-distribution samples. In addition, we further boost this difference by continuously accumulating uncertainty-based gradients. Our method uses the inherent epistemic uncertainty of the pre-trained model. Therefore, the proposed algorithm does not require knowledge of the domain of the in-distribution dataset and works by simple pre-processing of the already trained model without any re-training. We demonstrate its effectiveness using diverse network architectures on various popular image datasets and noisy settings.
Characteristics of the information needs of hypertension patients on online healthcare service platforms

Hypertension has become the most risk factor for cardiovascular and cerebrovascular diseases. The purpose of this study is to reveal the information needs and evolution of hypertensive patients on online healthcare service platforms. We use the methods of statistical analysis, word frequency analysis and social network analysis to explore the information needs of hypertensive patients. The study found that the middle-aged and elderly people have the most information needs about hypertension on the online healthcare service platform, while the patients with physical examination and exercise needs are more inclined to be younger, and young male hypertension patients have more information needs than female patients. The findings recommend that middle-aged and elderly people should have regular physical examinations and exercise, and a comprehensive face-to-face system should be established on online healthcare service platforms to improve the utilization of existing question-and-answer data.
Industrial Augmented Reality: Lessons learned from a long-term On-site Assessment of Augmented Reality Maintenance Worker Support Systems

Augmented Reality (AR) has ever since made big promises in terms of improving maintenance in industry. However, long-term on-site assessments of AR maintenance worker support systems do not exist. In cooperation with three industrial companies we developed two such systems and conducted a 14-month evaluation period. The lessons we learned from the pitfalls we ran into during this assessment period provide valuable insights for researchers and practitioners alike who want to deploy AR maintenance worker support systems to the shop floor.
Multi-Virtual-Agent Reinforcement Learning for a Stochastic Predator-Prey Grid Environment

Generalization problem of reinforcement learning is crucial especially for dynamic environments. Conventional reinforcement learning methods solve the problems with some ideal assumptions and are difficult to be applied in dynamic environments directly. In this paper, we propose a new multi-virtual- agent reinforcement learning (MVARL) approach for a predator-prey grid game. The designed method can find the optimal solution even when the predator moves. Specifically, we design virtual agents to interact with simulated changing environments in parallel instead of using actual agents. Moreover, a global agent learns information from these virtual agents and interacts with the actual environment at the same time. This method can not only effectively improve the generalization performance of reinforcement learning in dynamic environments, but also reduce the overall computational cost. Two simulation studies are considered in this paper to validate the effectiveness of the designed method. We also compare the results with the conventional reinforcement learning methods. The results indicate that our proposed method can improve the robustness of reinforcement learning method and contribute to the generalization to certain extent.
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach

Deep neural network quantization with adaptive bitwidths has gained increasing attention due to the ease of model deployment on various platforms with different resource budgets. In this paper, we propose a meta-learning approach to achieve this goal. Specifically, we propose MEBQAT, a simple yet effective way of bitwidth-adaptive quantization-aware training (QAT) where meta-learning is effectively combined with QAT by redefining meta-learning tasks to incorporate bitwidths. After being deployed on a platform, MEBQAT allows the (meta-)trained model to be quantized to any candidate bitwidth with minimal inference accuracy drop. Moreover, in a few-shot learning scenario, MEBQAT can also adapt a model to any bitwidth as well as any unseen target classes by adding conventional optimization or metric-based meta-learning. We design variants of MEBQAT to support both (1) a bitwidth-adaptive quantization scenario and (2) a new few-shot learning scenario where both quantization bitwidths and target classes are jointly adapted. Our experiments show that merging bitwidths into meta-learning tasks results in remarkable performance improvement: 98.7% less storage cost compared to bitwidth-dedicated QAT and 94.7% less back propagation compared to bitwidth-adaptive QAT in bitwidth-only adaptation scenarios, while improving classification accuracy by up to 63.6% compared to vanilla meta-learning in bitwidth-class joint adaptation scenarios.
Towards Better Understanding with Uniformity and Explicit Regularization of Embeddings in Embedding-based Neural Topic Models

Embedding-based neural topic models could explicitly represent words and topics by embedding them to a homogeneous feature space, which shows higher interpretability. However, there are no explicit constraints for the training of embeddings, leading to a larger optimization space. Also, a clear description of the changes in embeddings and the impact on model performance is still lacking. In this paper, we propose an embedding regularized neural topic model, which applies the specially designed training constraints on word embedding and topic embedding to reduce the optimization space of parameters. To reveal the changes and roles of embeddings, we introduce uniformity into the embedding-based neural topic model as the evaluation metric of embedding space. On this basis, we describe how embeddings tend to change during training via the changes in the uniformity of embeddings. Furthermore, we demonstrate the impact of changes in embeddings in embedding-based neural topic models through ablation studies. The results of experiments on two mainstream datasets indicate that our model significantly outperforms baseline models in terms of the harmony between topic quality and document modeling. This work is the first attempt to exploit uniformity to explore changes in embeddings of embedding-based neural topic models and their impact on model performance to the best of our knowledge.
A Kalman Variational Autoencoder Model Assisted by Odometric Clustering for Video Frame Prediction and Anomaly Detection

The combination of different sensory information to predict upcoming situations is an innate capability of intelligent beings. Consequently, various studies in the Artificial Intelligence field are currently being conducted to transfer this ability to artificial systems. Autonomous vehicles can particularly benefit from the combination of multi-modal information from the different sensors of the agent. This paper proposes a method for video-frame prediction that leverages odometric data. It can then serve as a basis for anomaly detection. A Dynamic Bayesian Network framework is adopted, combined with the use of Deep Learning methods to learn an appropriate latent space. First, a Markov Jump Particle Filter is built over the odometric data. This odometry model comprises a set of clusters. As a second step, the video model is learned. It is composed of a Kalman Variational Autoencoder modified to leverage the odometry clusters for focusing its learning attention on features related to the dynamic tasks that the vehicle is performing. We call the obtained overall model Cluster-Guided Kalman Variational Autoencoder. Evaluation is conducted using data from a car moving in a closed environment [1] and leveraging a part of the University of Alcalá DriveSet dataset [2], where several drivers move in a normal and drowsy way along a secondary road.
An Empirical Study on Performance Bugs in Deep Learning Frameworks

Machine Learning (ML) and Deep Learning (DL) applications are becoming more popular due to the availability of DL frameworks such as TensorFlow and PyTorch. Therefore, the quality of DL frameworks is essential to ensure DL/ML application quality. Given the computationally expensive nature of DL tasks (e.g., training), performance is a critical aspect of DL frameworks. However, optimizing DL frameworks may have its own unique challenges due to the peculiarities of DL (e.g., hardware integration and the nature of the computation). In this paper, we conduct an empirical study on the performance bugs in DL frameworks. We conduct our study on TensorFlow and PyTorch by identifying the performance and non-performance bugs by mining the GitHub repositories. We find that 1) the proportion of newly reported performance bugs increases faster than fixed performance bugs, and the ratio of performance bugs among all bugs increases over time; 2) performance bugs take more time to fix, have larger fix sizes, and more community engagement (e.g., discussion) compared to non-performance bugs; and 3) we manually derived a taxonomy of 12 categories and 19 sub-categories of the root causes of performance bugs by studying all performance bug fixes. Finally, we present some actionable implications for researchers and developers.
Research Trend of Machine Learning for Big Data Application on Cloud

Recently, big data produces a lot of recognition. Agencies have large amounts of data and extract useful information from databases in which desired patterns and interrelationships can be discovered that cannot be understood by the human brain as the data volume range crosses our process. Big data is also generated from divergent structured, unstructured, and semi-structured resources. This paper is concerned with a small review relationship between cloud and big data, as the two are important to each other. The most important emphasis of the paper is the description of the outline of big data, reduce map environment and various tools related to big data, which plays a very important role in handling huge data volumes. Machine learning was also explored, stating a touch from the virtualization concept and its associated algorithm. Cloud computing offers flexibility, core competitiveness, applications, related infrastructure technology and big data tools. Here in this paper, we have related to big data computations. Has also discussed various issues that need to be explored more and find the best solution using the best techniques.
Virtualand: An Immersive Virtual Experience for Cultural and Natural Heritage Sites

The cross-border area of Greece and Albania is characterized by a rich and diverse cultural heritage in a unique natural beauty setting that could form the basis for tourism development. Cultural heritage has been preserved in various printed and audiovisual material (aka Cultural Heritage Objects - CHOs for short) that are stored in libraries across this area. However, many of such CHOs that are part of the history, tradition and culture of the two nations, are being stored behind glass showcases or in the basement of the libraries with no access to visitors due to their sensitive condition or significance. The overall objective of this work is: (i) to identify, protect and preserve cultural heritage of the area through highlighting content included in rare, old, significant assets in the libraries. (ii) to act as a novel touristic concept that will result to an innovative attraction pole for all tourists visiting the area. The central feature is to provide tourists the opportunity to live an in-depth experience with tangible and intangible assets and better understand the traditions, history and culture of the CB area. To this end, VirtuaLand delivers a Virtual Reality (VR) interactive platform, where selected CHOs are converted to cultural and touristic scenarios. Users not only browse the assets of interest, but experience life into another era, become acquainted with the living conditions of that period and interact with characters of the era. VirtuaLand has produced a library of 3D objects and an open VR platform, that will be used by librarians and tourism professionals with limited programming skills to create their own VR stories.
Multi-granularity Pruning for Model Acceleration on Mobile Devices

For practical deep neural network design on mobile devices, it is essential to consider the constraints incurred by the computational resources and the inference latency in various applications. Among deep network acceleration approaches, pruning is a widely adopted practice to balance the computational resource consumption and the accuracy, where unimportant connections can be removed either channel-wisely or randomly with a minimal impact on model accuracy. The coarse-grained channel pruning instantly results in a significant latency reduction, while the fine-grained weight pruning is more flexible to retain accuracy. In this paper, we present a unified framework for the Joint Channel pruning and Weight pruning, named JCW, which achieves a better pruning proportion between channel and weight pruning. To fully optimize the trade-off between latency and accuracy, we further develop a tailored multi-objective evolutionary algorithm in the JCW framework, which enables one single round search to obtain the accurate candidate architectures for various deployment requirements. Extensive experiments demonstrate that the JCW achieves a better trade-off between the latency and accuracy against previous state-of-the-art pruning methods on the ImageNet classification dataset.
Learning and Teaching Fluid Dynamics using Augmented and Mixed Reality

We have been developing an interactive and multimodal platform to facilitate learning fluid dynamics with the rationale of using an immersive environment as a visualisation medium. Before the pan-demic, we used our in-house virtual reality app to teach fluid dynamics (FD), significantly enhancing student engagement. Since the COVID-19 pandemic struck, we have explored AR and MR applications for scaling our remote online and hybrid teaching efforts. The work presented in this paper has two objectives. (i) Provide an AR learning medium for remotely located students. (ii) Provide a student-paced instructional learning medium using MR for the hy-brid or onsite students. To achieve this, we describe a methodology in four parts. (i) A computational fluid dynamics data processing and distribution pipeline for generating 3D models for AR and MR. (ii) A platform-independent FD learning platform that uses WebXR for rendering models in AR. (iii) Hololens-based instructional medium in MR for learning FD.(iv) A pedagogy design. We discuss the results of a feasibility study on 18 hybrid learning students to assess the effectiveness of the pedagogy design using MR. We conclude that by using our platform, students can interactively visualise our in-house fluid dynamics models aligned with the course work and acquire knowledge naturally and intuitively.
Comparison of Reinforcement Learning Based Control Algorithms for One Autonomous Driving Problem

Autonomous driving systems include modules of several levels. Thanks to deep learning architectures at the moment technologies in most of the levels have high accuracy. It is important to notice that currently in autonomous driving systems for many tasks classical methods of supervised learning are no longer applicable. In this paper we are interested in a specific problem, that is to control a car to move along a given reference trajectory using reinforcement learning algorithms. In control theory, this problem is called an optimal control problem for moving along the reference trajectory. Airsim environment is used to simulate a moving car for a fixed period of time without obstacles. The purpose of our research is to determine the best reinforcement learning algorithm for a formulated problem among state-of-the-art algorithms such as DDPG, PPO, SAC, DQN and others. As a result of the conducted training and testing, it was revealed that the best algorithm for this problem is A2C.
Data Stream Mining for Dynamic Student Modeling

Data Stream Mining has become a research topic of greater interest in recent years. Its goal is to extract knowledge and hidden patterns from continuous data streams. In e-learning systems, a large amount of data is generated from the interaction of the student with the system in the form of data streams, which nature or distribution changes over time. Data Stream Mining techniques are able to incrementally analyze this kind of data, by creating models that adapt their structure on the fly. In this context, an interesting feature to model is the academic performance of students. In this paper, a methodology for dynamic student modeling using Data Stream Mining classifiers is proposed. For this, the Students’ Academic Performance database, available on Kaggle, was used. Six Data Stream Mining classifiers were implemented in MOA. For the evaluation, the prequential error method, the Kappa statistic, and the execution time were used. The results obtained show that the Naive Bayes classifier generated the best results in terms of accuracy and execution times.