Reviewerly/exHarmony huggingface dataset:
Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal

Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label noise on the performance of learning models by examining the effect of random and class-dependent label noise on a binary classification task: quality assessment for photoplethysmography (PPG). PPG signal is used to detect physiological changes and its quality can have a significant impact on the subsequent tasks, which makes PPG quality assessment a particularly good target for examining the impact of label noise in the field of biomedicine. Random and class-dependent label noise was introduced separately into the training set to emulate the errors associated with fatigue and bias in labeling data samples. We also tested different representations of the PPG, including features defined by domain experts, 1D raw signal and 2D image. Three different classifiers are tested on the noisy training data, including support vector machine (SVM), XGBoost, 1D Resnet and 2D Resnet, which handle three representations, respectively. The results showed that the two deep learning models were more robust than the two traditional machine learning models for both the random and class-dependent label noise. From the representation perspective, the 2D image shows better robustness compared to the 1D raw signal. The logits from three classifiers are also analyzed, the predicted probabilities intend to be more dispersed when more label noise is introduced. From this work, we investigated various factors related to label noise, including representations, label noise type, and data imbalance, which can be a good guidebook for designing more robust methods for label noise in future work.
Towards A Secure Joint Cloud With Confidential Computing

As data security in public clouds attracts more attention and concerns, researchers and practitioners have proposed techniques to secure cloud computing. Confidential computing (CC) is a compelling approach that guarantees both privacy and integrity of data and code in public clouds. In this paper, we first survey the status of CC in today’s commercialized public clouds, including the cloud CC abstractions, infrastructures, metrics, third-party service vendors, and real-world cloud use cases. We also discover the limitations such as re-programming efforts, extra cost, limited availability, etc. We further take a step forward to prospect CC in the joint cloud scenario. We finally showcase the challenges of realizing a secure joint cloud and propose possible solutions.
Code-Switching in Automatic Speech Recognition: The Issues and Future Directions

Code-switching (CS) in spoken language is where the speech has two or more languages within an utterance. It is an unsolved issue in automatic speech recognition (ASR) research as ASR needs to recognise speech in bilingual and multilingual settings, where the accuracy of ASR systems declines with CS due to pronunciation variation. There are very few reviews carried out on CS, with none conducted on bilingual and multilingual CS ASR systems. This study investigates the importance of CS in bilingual and multilingual speech recognition systems. To meet the objective of this study, two research questions were formulated, which cover both the current issues and the direction of the research. Our review focuses on databases, acoustic and language modelling, and evaluation metrics. Using selected keywords, this research has identified 274 papers and selected 42 experimental papers for review, of which 24 (representing 57%) have discussed CS, while the rest look at multilingual ASR research. The selected papers cover many well-resourced and under-resourced languages, and novel techniques to manage CS in ASR systems, which are mapping, combining and merging the phone sets of the languages experimented with in the research. Our review also examines the performance of those methods. This review found a significant variation in the performance of CS in terms of word error rates, indicating an inconsistency in the ability of ASRs to handle CS. In the conclusion, we suggest several future directions that address the issues identified in this review.
Living in a Historic Neighborhood in the Technology-Era: Understanding Residents’ Expectations from Domestic Spaces

Our use of everyday spaces continually transforms with the domestic technologies entering our homes. However, accommodating these technologies is especially challenging in historic neighborhoods as these spaces were built in eras suitable for different lifestyles. Understanding users’ expectations living in this context could provide valuable opportunities to support occupants’ everyday comfort and increase the built environments’ lifecycle. Therefore, we conducted in-depth semi-structured interviews with 12 early-adopter residents in two historic neighborhoods to explore expectations towards future domestic spaces. Findings reveal four themes that cover individual values and neighborhood identity in shaping residents’ expectations. We uncover three design directions for the field: (1) integration of location-specific multi-sensory experiences, (2) consideration of bodily interacted flexible and adaptable spaces, and (3) imagination of human-like interactions with domestic spaces. We further unveil the potential for design fiction studies and discuss for how Human-Building interaction researchers/practitioners, and architects could respond to these future directions.
A New Clustering Preserving Transformation for k-Means Algorithm Output

A novel clustering preserving transformation of cluster sets obtained from k-means algorithm is introduced. This transformation may be used to generate new labeled datasets from existent ones. It is more flexible than Kleinberg axiom based consistency transformation because data points in a cluster can be moved away and datapoints between clusters may come closer together.
Performance of QR Code Detectors near Nyquist Limits

For the interacting with real world, augmented reality devices need lightweight yet reliable methods for recognition and identification of physical objects. In that regard, promising possibilities are offered by supporting computer vision with 2D barcode tags. These tags, as high contrast and visually well-defined objects, can be used for finding fiducial points in the space or to identify physical items. Currently, QR code readers have certain demands towards the size and visibility of the codes. However, the increase of resolution of built-in cameras makes it possible to identify smaller QR codes in the scene. On the other hand, growing resolutions cause the increase to the computational effort of tag location. Therefore, resolution reduction in decoders is a common trade-off between processing time and recognition capabilities. In this article, we propose the simulation method of QR codes scanning near limits that stem from Shannon’s theorem. We analyze the efficiency of three publicly available decoders versus different size-to-sampling ratios (scales) and MTF characteristics of the image capture subsystem. The MTF we used is based on the characteristics of real devices, and it was modeled using Gaussian low-pass filtering. We tested two tasks—decoding and locating-and-decoding. The findings of the work are several-fold. Among others, we identified that, for practical decoding, the QR-code module should be no smaller than 3–3.5 pixels, regardless of MTF characteristics. We confirmed the superiority of Zbar in practical tasks and the worst recognition capabilities of OpenCV. On the other hand, we identified that, for borderline cases, or even below Nyquist limit where the other decoders fail, OpenCV is still capable of decoding some information.
Patient co‐design of digital health storytelling tools for multimorbidity: A phenomenological study

Abstract Background Interest in both narrative medicine and electronic health records has increased over the past 30 years. However, electronic health records are mainly written by and for clinicians, and the patient narrative and voice are not incorporated. Recent studies within the United Kingdom have indicated that there is a need to incorporate patient stories into health records, to improve quality and continuity of care. This is particularly important when treating people with multiple long‐term health conditions (multimorbidity), whose health stories can be particularly complex. Objective To understand the goals and requirements of people with multimorbidity for digital health storytelling tools. Methods The methodology uses narrative within a phenomenological approach to inform a process of co‐design. Results The findings indicate that people living with multimorbidity would use health storytelling tools to understand and reflect on their journeys, convey their experiences to others and advocate for themselves against scepticism. Conclusion Outputs from the project give insight into the lived experience of multimorbidity, as well as understanding the goals of people living with multimorbidity for using health storytelling tools as part of treatment and self‐management. Future research could explore other areas such as collaborative health storytelling or the technical implementation of tools. Patient or Public Contribution Five adults with multiple long‐term conditions participated in the project, and research was carried out in three stages. First, semistructured interviews were used to understand each participant's health story. Second, each participant worked with the researcher to co‐design a visual representation of their story. Finally, digital prototypes based on their health story were reviewed with each of the participants.
Blockchain apply to the supply chain of essential medicines for the treatment of Covid-19 in Colombia

The outbreak of SARS-CoV-2, called COVID-19, in Colombia as well as in a great part of the world, has been declared as a public health emergency of international concern, affecting different aspects of the daily life, being one of the most important the access and distribution of necessary medicine, not only for the known deceases but also for the mitigation of COVID 19. the distribution of medicines should maintain stability in the supply chain of essential medicines, thus ensuring high quality medicines that could help in the treatment to fight the virus. through this work a detailed analysis of the variable and critical points in the supply chain of essential medicines to mitigate COVID-19 is developed. A new architecture based on the Blockchain Hyperledger is presented. It will help to solve those critical points found, through the secure trace of the medicine supply chain. In Colombia, by using this architecture all actors take responsibilities in the distribution chain, ensuring a high level of transparency in the distribution process. by verifying the proper functioning of the model, the main difficulty when using Hyperledger is the lack of suppliers with experience; besides, there are not enough standardization or regulations of Blockchain. It is proved that Blockchain is an optimal technology to ensure essential medicine supply chain for the COVID 19 treatment in Colombia.
GUBS: Graph-Based Unsupervised Brain Segmentation in MRI Images

Brain segmentation in magnetic resonance imaging (MRI) images is the process of isolating the brain from non-brain tissues to simplify the further analysis, such as detecting pathology or calculating volumes. This paper proposes a Graph-based Unsupervised Brain Segmentation (GUBS) that processes 3D MRI images and segments them into brain, non-brain tissues, and backgrounds. GUBS first constructs an adjacency graph from a preprocessed MRI image, weights it by the difference between voxel intensities, and computes its minimum spanning tree (MST). It then uses domain knowledge about the different regions of MRIs to sample representative points from the brain, non-brain, and background regions of the MRI image. The adjacency graph nodes corresponding to sampled points in each region are identified and used as the terminal nodes for paths connecting the regions in the MST. GUBS then computes a subgraph of the MST by first removing the longest edge of the path connecting the terminal nodes in the brain and other regions, followed by removing the longest edge of the path connecting non-brain and background regions. This process results in three labeled, connected components, whose labels are used to segment the brain, non-brain tissues, and the background. GUBS was tested by segmenting 3D T1 weighted MRI images from three publicly available data sets. GUBS shows comparable results to the state-of-the-art methods in terms of performance. However, many competing methods rely on having labeled data available for training. Labeling is a time-intensive and costly process, and a big advantage of GUBS is that it does not require labels.
Comparison of Different Deployment Approaches of FPGA-Based Hardware Accelerator for 3D Object Detection Models

GPU servers have been responsible for the recent improvements in the accuracy and inference speed of the object detection models targeted to autonomous driving. However, its features, namely, power consumption and dimension, make its integration in autonomous vehicles impractical. Hybrid FPGA-CPU boards emerged as an alternative to server GPUs in the role of edge devices in autonomous vehicles. Despite their energy efficiency, such devices do not offer the same computational power as GPU servers and have fewer resources available. This paper investigates how to deploy deep learning models tailored to object detection in point clouds in edge devices for onboard real-time inference. Different approaches, requiring different levels of expertise in logic programming applied to FPGAs, are explored, resulting in three main solutions: utilization of software tools for model adaptation and compilation for a proprietary hardware IP; design and implementation of a hardware IP optimized for computing traditional convolutions operations; design and implementation of a hardware IP optimized for sparse convolutions operations. The performance of these solutions is compared in the KITTI dataset with computer performances. All the solutions resort to parallelism, quantization and optimized access control to memory to reduce the usage of logical FPGA resources, and improve processing time without significantly sacrificing accuracy. Solutions probed to be effective for real-time inference, power limited and space-constrained purposes.
Deep Learning Approaches for mice glomeruli segmentation

Deep learning (DL) is widely applied in biomedical image processing nowadays.In this paper, we propose the use of DL architectures for glomerulus segmentation in histopathological images of mouse kidneys.Indeed, in humans, the analysis of the glomeruli is fundamental to decide on the transplantability of the organ.However, no datasets with human samples are publicly available.Therefore, obtaining good segmentation performance on the kidneys of mice could be the first step for a transfer learning approach to humans.We compared the use of two well-known architectures for image segmentation, namely MobileNet and DeepLab V2.Both models showed very promising results.
Mixed‐Precision Continual Learning Based on Computational Resistance Random Access Memory

Continual Learning Artificial neural networks suffer from catastrophic forgetting when meeting sequential tasks. In article number 2200026, Zhongrui Wang, Dashan Shang, and co-workers propose a metaplasticity-inspired mixed-precision continual learning model to address this issue. By deploying it on a neuromorphic prototype system with the in-memory computing paradigm, outperforming energy efficiency and high accuracy are demonstrated, paving a promising way for autonomous edge systems.
Breast cancer detection in digital mammography using phase features and machine learning approach

For several years, Computer-Aided Detection (CAD) systems have been used by radiologists as a second interpreter for breast cancer detection in digital mammography. However, for every true-positive cancer detected by a CAD system, more false predictions must be revised by the expert to avoid an unnecessary biopsy. On the other hand, the research community has been exploring different approaches for the detection and classification of breast abnormalities. Machine learning, and particularly deep learning approaches, are being used to analyze digital mammography images. Nevertheless, most of the models proposed so far are trained on a single database and do not have high reliability. In this work, several deep learning models were compared for benign-malign mammography classification. A pre-processing stage is designed to remove noise and extract features using local image phase information. Then, a machine learning approach is utilized for digital mammography classification. Experimental results are presented using various digital mammography datasets and evaluated under different performance metrics.
A Deep Neural Network-Based Model for Quantitative Evaluation of the Effects of Swimming Training

This paper analyzes the quantitative assessment model of the swimming training effect based on the deep neural network by constructing a deep neural network model and designing a quantitative assessment model of the swimming training effect. This paper addresses the problem of not considering the influence of the uncertainties existing in the virtual environment when evaluating swimming training and adds the power of the delays in the actual training operation environment, which is used to improve the objectivity and usability of swimming training evaluation results. To better measure the degree of influence of uncertainties, a training evaluation software module is developed to validate the usability of the simulated training evaluation method using simulated case data and compare it with the data after training evaluation using the unimproved evaluation method to verify the correctness and objectivity of the evaluation method in this paper. In the experiments, the feature extractor is a deep neural network, and the classifier is a gradient-boosting decision tree with integrated learning advantages. In the experimental comparison, we can achieve more than 60% accuracy and no more than a 1.00% decrease in recognition rate on DBPNN + GBDT, 78.5% parameter reduction, and 54.5% floating-point reduction on DPBNN. We can effectively reduce 32.1% of video memory occupation. It can be concluded from the experiments that deep neural network models are more effective and easier to obtain relatively accurate experimental results than shallow learning when facing high-dimensional sparse features. At the same time, deep neural networks can also improve the prediction results of external learning models. Therefore, the experimental results of this model are most intuitively accurate when combining deep neural networks with gradient boosting decision trees.
THE PRESENCE OF COMMERCIAL BANKS IN METAVERSE’S FINANCIAL ECOSYSTEM: OPPORTUNITIES AND RISKS

The metaverse has been widely discussed as it is believed will affect almost every sector, one of those is the financial sector. The financial sector developed in the metaverse will likely be based on decentralized finance and blockchain technology, the latter of which is the primary source of the development of cryptocurrency and stable coins. Other than cryptocurrency and stable coins, CBDC is also predicted to emerge as one of the payment instrument options used in the metaverse. The presence of these technologies has formed the unique financial ecosystem in metaverse, differentiating it from the existing ecosystem which is characterized as centralized and traditionalfinance. The recently developed Meta Fi has created the question of whether commercial banks, as one of the most important financial institutions in the current financial ecosystem, will be able to penetrate and keep its relevance in the Meta Fi. This research will attempt to answer this question by explaining Meta Fi, its characteristics, and how commercial banks could co-exist alongside blockchain-based service in Metaverse.
Neuroadaptive Tracking Control of Affine Nonlinear Systems Using Echo State Networks Embedded With Multiclustered Structure and Intrinsic Plasticity

In this article, we present an echo state network (ESN)-based tracking control approach for a class of affine nonlinear systems. Different from the most existing neural-network (NN)-based control methods that are focused on the feedforward NN, the proposed method adopts a bioinspired recurrent NN fusing with multiple cluster and intrinsic plasticity (IP) to deal with modeling uncertainties and coupling nonlinearities in the systems. The key features of this work can be summarized as follows: 1) the proposed control is built upon the ESN embedded with multiclustered reservoir inspired from the hierarchically clustered organizations of cortical connections in mammalian brains; 2) the developed neuroadaptive control scheme utilizes unsupervised learning rules inspired from the neural plasticity mechanism of the individual neuron in nervous systems, called IP; 3) a multiclustered reservoir with IP is integrated into the algorithm to enhance the approximation performance of NN; and 4) the multiclustered reservoir is constructed offline and is task-independent, rendering the proposed method less expensive in computation. The effectiveness of the method is also confirmed by comparison with the existing neuroadaptive methods via numerical simulations, demonstrating that better tracking precision is achieved by the proposed method.
Limit theorems and localization of three-state quantum walks on a line defined by generalized Grover coins

In this article, we undertake a detailed study of the limiting behavior of a three-state discrete-time quantum walk on a one-dimensional lattice with generalized Grover coins. Two limit theorems are proved and consequently we show that the quantum walk exhibits localization at its initial position for a wide range of coin parameters. Finally, we discuss the effect of the coin parameters on the peak velocities of probability distributions of the underlying quantum walks.