Reviewerly/exHarmony huggingface dataset:
Regional perception and multi-scale feature fusion network for cardiac segmentation

Objective.Cardiovascular disease (CVD) is a group of diseases affecting cardiac and blood vessels, and short-axis cardiac magnetic resonance (CMR) images are considered the gold standard for the diagnosis and assessment of CVD. In CMR images, accurate segmentation of cardiac structures (e.g. left ventricle) assists in the parametric quantification of cardiac function. However, the dynamic beating of the heart renders the location of the heart with respect to other tissues difficult to resolve, and the myocardium and its surrounding tissues are similar in grayscale. This makes it challenging to accurately segment the cardiac images. Our goal is to develop a more accurate CMR image segmentation approach.Approach.In this study, we propose a regional perception and multi-scale feature fusion network (RMFNet) for CMR image segmentation. We design two regional perception modules, a window selection transformer (WST) module and a grid extraction transformer (GET) module. The WST module introduces a window selection block to adaptively select the window of interest to perceive information, and a windowed transformer block to enhance global information extraction within each feature window. The WST module enhances the network performance by improving the window of interest. The GET module grids the feature maps to decrease the redundant information in the feature maps and enhances the extraction of latent feature information of the network. The RMFNet further introduces a novel multi-scale feature extraction module to improve the ability to retain detailed information.Main results.The RMFNet is validated with experiments on three cardiac data sets. The results show that the RMFNet outperforms other advanced methods in overall performance. The RMFNet is further validated for generalizability on a multi-organ data set. The results also show that the RMFNet surpasses other comparison methods.Significance.Accurate medical image segmentation can reduce the stress of radiologists and play an important role in image-guided clinical procedures.
Mechanical Mathematicians

A new generation of automatic theorem provers eliminate bugs in software and mathematics.
Self-adaptive fuzzy learning ensemble systems with dimensionality compression from data streams

Ensemble learning is a widely used methodology to build powerful predictors from multiple individual weaker ones. However, the vast majority of ensemble learning models are designed for offline application scenarios, the use of evolving fuzzy systems in ensemble learning for online learning from data streams has not been sufficiently explored, yet. In this paper, a novel self-adaptive fuzzy learning ensemble system is introduced for data stream prediction. The proposed ensemble system employs the very sparse random projection technique to compress the consequent parts of the learned fuzzy rules by individual base models to a more compressed form, thereby reducing redundant information and improving computational efficiency. To improve the overall prediction performance, a dynamical base model pruning scheme is introduced to the proposed ensemble system together with a novel inferencing scheme, such that less accurate base models will be removed from the ensemble structure at each learning cycle automatically and only these more accurate ones will be involved in joint decision-making. Numerical examples based on a wide range of benchmark datasets demonstrate the stronger prediction performance of the proposed ensemble system over the state-of-the-art alternatives.
Level-set-based multiplicative intrinsic component optimization for brain tissue segmentation in T1-W and T2-W modality MRI

Brain tissue segmentation is fundamental to structure extraction and quantitative analysis, thereby plays a critical role in lesion detection and aid diagnosis. However, intensity inhomogeneity caused by bias field presents a considerable challenge in accurate segmentation. Multiplicative intrinsic component optimization is one of the most widely used models for brain tissue segmentation. Nevertheless, limited by clustering properties and no penalty term, the accuracy decreases rapidly with the increased number of tissues and the enhancement of noise. To seek better approaches to these issues, a level-set-based method with constraint term is proposed in this paper. First, the cerebrospinal fluid atlas representing pre-segmented tissue and the white matter atlas utilized in the constraint term are obtained from the difference image composed of T1- and T2-weighted magnetic resonance images. Then, the T1-weighted image is modeled as multiplicative components, namely bias field and true image. A novel membership function driven by level set method is presented for the true image. Finally, the contour evolution for tissue segmentation and the component optimization for bias field correction are simultaneously performed during an energy minimization. Experiments on the BrainWeb dataset and clinical brain images have validated the segmentation ability of the proposed method. Comparisons with some state-of-the-art approaches have demonstrated the superiority of our method in terms of accuracy and robustness.
Hierarchical memory-guided long-term tracking with meta transformer inquiry network

Long-term tracking is a critical and rapidly developing field in visual object tracking research. The tracking target's appearance can change over time due to its motion or environmental factors, resulting in various appearance patterns. Those factors, such as dense distractors, confusing backgrounds, and motion blurs, make it difficult to track objects. Existing tracking algorithms typically rely on online learning to adapt to such long-term variations. However, obtaining reliable training samples and implementing effective updating schemes can be difficult, particularly when the target object frequently disappears and reappears. Therefore, in this paper, we propose a Hierarchical Memory-guided Long-term Tracker (HMLT) with a Meta Transformer Inquiry Network (MTIN) that refines online learning. Our method introduces a hierarchical memory strategy that considers simple and trustworthy updating of long-term tracking components using multiple target memories. We also devise MTIN to identify available memory based on the long-term variation pattern of the target, so as to avoid the risk of updating from incorrect samples. In addition, we devise a memory attention network to perform robust redetection based on long-term memory. Based on the hierarchical memory strategy, we construct a complete and learnable long-term tracking framework that uses a validator learned from memory to reconcile local and global searches. Our experimental results on several benchmarks, including LaSOT, VOT-LT2018, VOT-LT2019, TLP, OTB-2015, and UAV123, demonstrate that our proposed method achieves comparable performance to the state-of-the-art long-term tracking algorithms.
An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem

Metaheuristics, providing high level guidelines for heuristic optimisation, have successfully been applied to many complex problems over the past decades. However, their performances often vary depending on the choice of the initial settings for their parameters and operators along with the characteristics of the given problem instance handled. Hence, there is a growing interest into designing adaptive search methods that automate the selection of efficient operators and setting of their parameters during the search process. In this study, an adaptive binary parallel evolutionary algorithm, referred to as ABPEA, is introduced for solving the uncapacitated facility location problem which is proven to be an NP-hard optimisation problem. The approach uses a unary and two other binary operators. A reinforcement learning mechanism is used for assigning credits to operators considering their recent impact on generating improved solutions to the problem instance in hand. An operator is selected adaptively with a greedy policy for perturbing a solution. The performance of the proposed approach is evaluated on a set of well-known benchmark instances using ORLib and M*, and its scaling capacity by running it with different starting points on an increasing number of threads. Parameters are adjusted to derive the best configuration of three different rewarding schemes, which are instant, average and extreme. A performance comparison to the other state-of-the-art algorithms illustrates the superiority of ABPEA. Moreover, ABPEA provides up to a factor of 3.9 times acceleration when compared to the sequential algorithm based on a single-operator.
A privacy awareness framework for NFT avatars in the metaverse

Metaverse is a platform that offers unique user experiences. Users join the virtual worlds by using a virtual representation called an avatar. There is increasing use of NonFungible Token (NFT) avatars in metaverse applications that enable users to interact with each other in virtual worlds. These avatars contain not only personal information but also the behavioural footprints of users. Therefore, privacy preservation is critical for establishing a safe environment and enhancing the level of confidence in the metaverse. This paper proposes a novel privacy awareness framework that leverages machine learning (ML) algorithms to identify malicious patterns and alert users. This enables user awareness and the ability to adopt a required level of risk mitigation strategies. To test the framework, we have designed and implemented a test-bed where we can deploy various attack scenarios and collect data. We also present a case study on NFT cloning with the associated data collection.
A Performance Efficient Approach of Global Training in Federated Learning

Federated learning is a novel approach of training the global model on the server by utilizing the personal data of the end users while data privacy is preserved. The users called clients are required to perform the local training using their local datasets and forward those trained local models to the server, in which the local models are aggregated to update the global model. This process of global training is carried out for several rounds until the convergence. Practically, the clients' data is non-independent and identically distributed (Non-IID). Hence, the updated local model of each client may vary from every other client due to heterogeneity among them. Hence, the process of aggregating the diversified local models of clients has a huge impact on the performance of global training. This article proposes a performance efficient aggregation approach for federated learning, which considers the data heterogeneity among clients before aggregating the received local models. The proposed approach is compared with the conventional federated learning methods, and it achieves improved performance.
AFFIRM: Privacy-by-Design Blockchain for Mobility Data in Web3 using Information Centric Fog Networks with Collaborative Learning

Micromobility IoT devices and Connected Vehicles generate massive mobility data, crucial for time-critical safety-related data analytics. It is challenging to study and understand such data without compromising user privacy. We propose AFFIRM, a secure privacy-preserving blockchain framework for efficient, scalable and lightweight mobility data generation, validation, storage and retrieval in future Web3 applications. AFFIRM enables nearby devices to self-organize as a fog network and collaboratively train machine learning algorithms locally to securely generate, validate, store and retrieve mobility data via consensus leveraging Information Centric Networking as the underlying architecture. The proposed collaborative learning enables nodes to learn and adapt with respect to parameters related to scalability, timeliness, security, privacy, and resource consumption. We evaluate AFFIRM using mobility data from New York city and results shows it to scalably store mobility data from up to 700 devices with lower delays and overhead.
Hybrid Quantum Machine learning using Quantum Integrated Cloud Architecture (QICA)

Gate-based and Annealing Quantum Computing backend systems purposely built and deployed based on Quantum Integrated Cloud Architecture (QICA) design patterns, provide enhanced quantum computational capabilities that consist of a suite of hybrid quantum-classical cloud-based software and specialized hybrid quantum-classical machine learning algorithms, tools, solvers, and simulators using physics-inspired models and High-Performance Computing circuits simulators. By harnessing the unique properties of quantum mechanics and building on decades of computer science and quantum physics research, our motivation in this paper consists of two folds: (1) to provide an overview of our PATENT-PENDING Quantum Integrated Cloud Architecture (QICA) implemented in support of aerospace programs, fielded and deployed to accelerate the pace of research and commercial enterprise adoption of low-bandwidth satellite networks using nanosatellites in a low orbit. (2) Demonstrate QICA-based computational advantages of Hybrid Quantum-Classical machine learning architecture using IBM Quantum, exploring the base generic quantum neural network (QNN) interfaces provided in Qiskit Machine Learning based on which many agnostic variations of both hybrid classical-quantum and quantum neural networks (QNN) are based on hybrid quantum machine learning.
A Hybrid Delay-aware Approach Towards UAV Flight Data Anomaly Detection

With the rapid development of unmanned aerial vehicle (UAV) technologies, UAVs are now increasingly leveraged to perform military and civilian tasks today. Meanwhile, as a complex cyber-physical system, UAVs are also facing security and reliability concerns raised by internal systems errors and external cyber-attacks from multiple aspects. Recent research has spent efforts on leveraging AI and machine learning techniques to predict the flying status of UAVs using their flight data for anomaly detection. However, these methods often ignore the prediction delay existing in status-changing periods during the UAV’s operation, which inevitably causes false alarms and opens a window for malicious adversaries if they are not appropriately addressed. In this paper, we propose a new approach to enable effective anomaly detection and recovery for UAV flight data. Our approach adopts a hybrid design to eliminate false alarms during the status-changing periods while maintaining the high reliability of anomaly detection. We evaluate the proposed approach on flight data collected from multiple UAV flight paths. Our evaluation results validate the effectiveness of our hybrid design, which achieves both high anomaly detection accuracy and reliable recovery.
Question Answering Chatbots for Biomedical Research Using Transformers

Professionals as well as the general public need effective help to access, understand and consume complex biomedical concepts. The existence of an interaction environment capable of automatically processing such information - thus replacing human intervention - such as chatbots, is however challenging. In this paper we propose a method of utilizing chatbots in the domain of biomedicine. In the implementation we choose to incorporate the BERT algorithm, so as to adopt a modern technique for natural language processing tasks. We use several pre-trained models (RoBERTa, XLM-R, BERT Large, and BioBert) in order to evaluate their ability to back the chatbot infrastructure. The data is retrieved from the PubMed repository, with the final set being formed into full sentences or potential chatbot responses, thus preserving their conceptual meaning. Response selection is performed using similarity metrics and F-score. The results create a ranking of the models placing related ones closely, recognizing the ability to always answer each question and highlighting the importance of the training previously applied to them. These are compared to the Count Vectorizer technique, which appears to perform better, but with several weaknesses, as many questions could not be answered.
Study on High Availability and Fault Tolerance

Availability is one of the most important requirements for modern computing systems. In cloud computing, it is common to use it as a key factor in adopting a cloud service. This paper studies the breakdown in calculating the availability and proposes a conceptual model as middleware in order to speed up the time for detection during a system crash. It defines the maximum acceptable outage time for any given period. Through simulations tests, we verified that the proposed model is able to detect the system crash in sub-seconds and improve the overall availability of the system compared to currently used industry solutions.
Captioning Remote Sensing Images Using Transformer Architecture

Image Captioning aspires to achieve a description of images with machines as a combination of Computer Vision (CV) and Natural Language Processing (NLP) fields. The current state of the art for image captioning use the Attention-based Encoder-Decoder model. The Attention-based model uses an ‘Attention mechanism’ that focuses on a particular section of the image to generate its corresponding caption word. The NLP side of this model uses Long Short-Term Memory (LSTM) for word generation. Attention-based models did not emphasize the relative arrangement of words in a caption thereby, ignoring the context of the sentence. Inspired by the versatility of Transformers in NLP, this work tries to utilise its architecture features for the Image Captioning use case. This work also makes use of a pretrained Bidirectional Encoder Representation of Transformer (BERT) which generates a contextually rich embedding of a caption. The Multi-Head Attention of the Transformer establishes a strong correlation between the image and contextually aware caption. This experiment is performed on the Remote Sensing Image Captioning Dataset. The results of the model are evaluated using NLP evaluation metrics such as Bilingual Evaluation Understudy 1–4 (BLEU), Metric for Evaluation of Translation with Explicit ORdering (METEOR) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The proposed model shows better results for a few of the metrics.
22.9 A 12nm 18.1TFLOPs/W Sparse Transformer Processor with Entropy-Based Early Exit, Mixed-Precision Predication and Fine-Grained Power Management

Large language models have substantially advanced nuance and context understanding in natural language processing (NLP), further fueling the growth of intelligent conversational interfaces and virtual assistants. However, their hefty computational and memory demands make them potentially expensive to deploy on cloudless edge platforms with strict latency and energy requirements. For example, an inference pass using the state-of-the-art BERT-base model must serially traverse through 12 computationally intensive transformer layers, each layer containing 12 parallel attention heads whose outputs concatenate to drive a large feed-forward network [1]. To reduce computation latency, several algorithmic optimizations have been proposed, e.g., a recent algorithm dynamically matches linguistic complexity with model sizes via entropy-based early exit [2]. Deploying such transformer models on edge platforms requires careful co-design and optimizations from algorithms to circuits, where energy consumption is a key design consideration.
Cloud services cost comparison: a clustering analysis framework

Abstract As cloud computing has grown in popularity, several different models and deployment strategies have emerged, fulfilling specific needs of different users. Thus cloud service selection is a major task that combines business and technical aspects. Several cloud providers categorize their bundles to help users find the best fit. However, the policy behind this categorization is unclear, making it difficult to be consistent among providers. Therefore, this work explores the complication of selecting the optimal cloud service among numerous and comparable solutions and introduces a selection framework for cloud services based on clustering analysis, providing an overall size categorization of cloud services derived from the cloud market and making the categorization policy explicit and homogeneous across all providers. Finally, a real-word case study is presented, highlighting the application of the proposed framework.
Institutionalisation of social risks of digitalisation of the educational space of universities in the provincial cities of Russia

The article analyses the problem of institutionalisation of social risks of digitalisation of the educational space of universities in the Russian provinces. The risk- generating nature of digitalisation is emphasised, the conclusion about risk as digitalisation systemic element, that includes not only threats, but also significant opportunities for development, is substantiated. The task of managing risk genesis is formulated, the neces sary condition of which is the institutionalisation of risks. The institutionalisation of social risks of digitalisation of the educational space of universities in the provincial cit ies of Russia is interpreted as a system of actions of actors in the educational space taken to prevent undesirable consequences of the risks of digitisation and digitalisation and to use their creative potential, including the establishment of corporate norms and behavioural standards, the definition of structures that ensure their implementation in risk situations. Three main tasks of institutionalisation are considered: definition of norms and rules of risk management; formation of institutions implementing it; development and implementation of corporate standards of behaviour in risk situations.
Design and implementation of a VoIP PBX integrated Vietnamese virtual assistant: a case study

As digitization is integrated into daily life, media are increasingly transferred over the Internet. Voice-over-Internet Protocol (VoIP), the most popular media transfer technology, is attracting many researchers and investments. The application of Artificial Intelligence (AI) technology into the Private Branch Exchange (PBX) has played a pivotal role in enhancing the customer experience and is able to unite employees in any company. One technology application used to optimize customer experience in a call centre is the use of an automatic PBX integrated with a Virtual Assistant (VA), which interacts directly with the PBX through voice and in multiple languages without any keystrokes. The Interactive Voice Response (IVR) module forwards the customer’s call to an operator or supports automatic processing. This solution can help businesses to handle thousands of calls per day with optimal performance, thus creating a customer care campaign that quickly reaches many users. A PBX integrated with Vietnamese Virtual Assistants (VVA) on an AI technology platform will also help businesses to cut down on operator costs with automated calls. Through comparison with a traditional PBX, this article analyzes, evaluates and optimizes an automatic PBX system with integrated VVA, thereby offering efficient solutions for interest companies.
Angle-robust two-qubit gates in a linear ion crystal

In trapped-ion quantum computers, two-qubit entangling gates are generated by applying spin-dependent force which uses phonons to mediate interaction between the internal states of the ions. To maintain high-fidelity two-qubit gates under fluctuating experimental parameters, robust pulse-design methods are applied to remove the residual spin-motion entanglement in the presence of motional mode frequency drifts. Here we propose an improved pulse-design method that also guarantees the robustness of the two-qubit rotation angle against uniform mode frequency drifts by combining pulses with opposite sensitivity of the angle to mode frequency drifts. We experimentally measure the performance of the designed gates and see an improvement on both gate fidelity and gate performance under uniform mode frequency offsets.