Reviewerly/exHarmony huggingface dataset:
RFE-UNet: Remote Feature Exploration with Local Learning for Medical Image Segmentation

Although convolutional neural networks (CNNs) have produced great achievements in various fields, many scholars are still exploring better network models, since CNNs have an inherent limitation—that is, the remote modeling ability of convolutional kernels is limited. On the contrary, the transformer has been applied by many scholars to the field of vision, and although it has a strong global modeling capability, its close-range modeling capability is mediocre. While the foreground information to be segmented in medical images is usually clustered in a small interval in the image, the distance between different categories of foreground information is uncertain. Therefore, in order to obtain a perfect medical segmentation prediction graph, the network should not only have a strong learning ability for local details, but also have a certain distance modeling ability. To solve these problems, a remote feature exploration (RFE) module is proposed in this paper. The most important feature of this module is that remote elements can be used to assist in the generation of local features. In addition, in order to better verify the feasibility of the innovation in this paper, a new multi-organ segmentation dataset (MOD) was manually created. While both the MOD and Synapse datasets label eight categories of organs, there are some images in the Synapse dataset that label only a few categories of organs. The proposed method achieved 79.77% and 75.12% DSC on the Synapse and MOD datasets, respectively. Meanwhile, the HD95 (mm) scores were 21.75 on Synapse and 7.43 on the MOD dataset.
A New Meta-Analysis of the Proteus Effect: Studies in VR Find Stronger Effect Sizes

Abstract The present study examines why some studies of the Proteus effect—the phenomenon that people tend to conform behaviorally and attitudinally to their avatars’ identity characteristics—facilitate the phenomenon more effectively than others. A previous meta-analysis of the Proteus effect (Ratan et al., 2020) failed to examine potentially notable moderating factors of the phenomenon, so we examine such factors through a meta-analysis of the 56 quantitative experimental Proteus effect studies published at the time of this analysis. Studies that utilized virtual reality technology (e.g., head-mounted displays) elicited stronger effect sizes than those that utilized flat screens, as hypothesized. No support was found for the hypothesis that effect sizes differ by software type utilized (commercial or custom-built). We offer suggestions for future research into the Proteus effect, and how to best examine possible variables of the phenomenon.
A Simple Python Testbed for Federated Learning Algorithms

Nowadays many researchers are developing various distributed and decentralized frameworks for federated learning algorithms. However, development of such a framework targeting smart Internet of Things in edge systems is still an open challenge. In this paper, we present our solution to that challenge called Python Testbed for Federated Learning Algorithms. The solution is written in pure Python, and it supports both centralized and decentralized algorithms. The usage of the presented solution is both validated and illustrated by three simple algorithm examples.
Bringing Machine Learning Systems into Clinical Practice: A Design Science Approach to Explainable Machine Learning-Based Clinical Decision Support Systems

Clinical decision support systems (CDSSs) based on machine learning (ML) hold great promise for improving medical care. Technically, such CDSSs are already feasible but physicians have been skeptical about their application. In particular, their opacity is a major concern, as it may lead physicians to overlook erroneous outputs from ML-based CDSSs, potentially causing serious consequences for patients. Research on explainable AI (XAI) offers methods with the potential to increase the explainability of black-box ML systems. This could significantly accelerate the application of MLbased CDSSs in medicine. However, XAI research to date has mainly been technically driven and largely neglects the needs of end users. To better engage the users of ML-based CDSSs, we applied a design science approach to develop a design for explainable ML-based CDSSs that incorporates insights from XAI literature while simultaneously addressing physicians’ needs. This design comprises five design principles that designers of ML-based CDSSs can apply to implement user-centered explanations, which are instantiated in a prototype of an explainable ML-based CDSS for lung nodule classification. We rooted the design principles and the derived prototype in a body of justificatory knowledge consisting of XAI literature, the concept of usability, and an online survey study involving 57 physicians. We refined the design principles and their instantiation by conducting walk-throughs with six radiologists. A final experiment with 45 radiologists demonstrated that our design resulted in physicians perceiving the ML-based CDSS as more explainable and usable in terms of the required cognitive effort than a system without explanations.
XAVE: Cross-Platform Based Asymmetric Virtual Environment for Immersive Content

This study proposes XAVE—an asymmetric virtual environment in which users on various devices and platforms (personal computer (PC), mobile devices, virtual reality (VR) and augmented reality (AR) users, and actor-based avatars using motion capture) participate together by expanding the range of users considered in the existing asymmetric virtual environment studies. XAVE aims to provide a novel experiential environment that includes non-immersive and immersive participation methods for immersive applications and contents. All users interact with virtual environments, objects, and users easily and conveniently through a common and popular interface that considers the characteristics of the devices and experiential methods. PC and mobile users interact through keyboard, mouse, and touchscreen inputs based on graphical user interfaces (GUIs). VR users interact using controllers while wearing head-mounted displays (HMDs), and AR users interact through the image target and GUI on mobile devices. Avatar users participating in virtual environments using motion capture were considered to construct asymmetric virtual environments that can be used in various metaverse and immersive content fields. Furthermore, an application where all users can participate and experience together was created and used to statistically analyze and examine user satisfaction with the interface and presence in the virtual environment through surveys.
Uncomputation in the Qrisp High-Level Quantum Programming Framework

Uncomputation is an essential part of reversible computing and plays a vital role in quantum computing. Using this technique, memory resources can be safely deallocated without performing a non-reversible deletion process. For the case of quantum computing, several algorithms depend on this as they require disentangled states in the course of their execution. Thus, uncomputation is not only about resource management, but is also required from an algorithmic point of view. However, synthesizing uncomputation circuits is tedious and can be automated. In this paper, we describe the interface for automated generation of uncomputation circuits in our Qrisp framework. Our algorithm for synthesizing uncomputation circuits in Qrisp is based on an improved version of “Unqomp”, a solution presented by Paradis et al. Our paper also presents some improvements to the original algorithm, in order to make it suitable for the needs of a high-level programming framework. Qrisp itself is a fully compilable, high-level programming language/framework for gate-based quantum computers, which abstracts from many of the underlying hardware details. Qrisp’s goal is to support a high-level programming paradigm as known from classical software development.
Mobile computing-enabled health physique evaluation in campus based on amplified hashing

Abstract With the increasingly wide spread of COVID-19 pandemic, people’s various behavior activities are influenced more or less all over the world. For example, students in campus have to learn at home or in dormitory so as to avoid the attacks of the virus as much as possible. However, such a location distribution structure of student places a heavy burden on the monitoring and evaluating the sport physique of students in an effective and efficient way. Fortunately, the wide adoption of various mobile computing terminals (e.g., smart watches, mobile phones, etc.) and wireless communication technology makes it possible to know about the daily physique of students in a remote way. However, students’ health physique data are accumulated with time, which raises a challenge of quick data processing and cost-effective data scalability. Moreover, since the students are geographically distributed, we need to integrate their respective health physique data into a central cloud platform for more comprehensive data analysis and mining. However, the above data integration operations often involve student privacy. Motivated by the above two challenges, a mobile computing-aided health physique evaluation solution is brought forth in this paper, which is mainly based on a kind of amplified hashing technique. To prove the evaluation performances of the proposal, extensive experiments are designed to test the algorithm performances in terms of various evaluation metrics.
Multi-class Classification of Imbalanced Intelligent Data using Deep Neural Network

In recent years, studies in the field of deep learning have made significant progress. These studies have focusedmore on datasets with balanced classification, and less research has been done on imbalanced datasets, whichare of great importance in the real world and present significant challenges for classification. This articlestudies the problem of classifying imbalanced data, introduces dynamic sampling for deep neural networks,investigates the imbalanced multiclass problem, and proposes a dynamic sampling method for deep learning.In our proposed method, all samples are fed to the current deep neural network for each training iteration,and the accuracy, precision, and mean error of the deep neural network are estimated. The proposed methoddynamically selects informative data for training the deep neural network. Comprehensive experiments wereconducted to evaluate and understand its strengths and weaknesses. The results of 13 imbalanced multiclassdatasets show that the proposed method outperforms other methods, such as initial sampling techniques,active learning, cost-sensitive learning, and reinforcement learning.
Cloud-Based Internet of Things in Healthcare Applications: A Systematic Literature Review

Introduction: The Internet of Things (IoT) and Cloud computing are two recent technological advances whose potential have not been realized in a range of industries. These technologies have been used in healthcare systems to improve their performance. In this regard, the IoT generates a large amount of data; also, cloud computing is a viable option for data storage and complex computing. The purpose of this study is to identify and categorize various aspects of CIoT-based healthcare in terms of main application domains, sensors, wireless communication technologies, messaging protocols, cloud platforms, and artificial intelligence (AI) algorithms.Material and Methods: We conducted a literature systematic review and reported according to the PRISMA guideline. PubMed, IEEE, Scopus, and Web of Science were searched using the related keywords and their synonyms. Two independent authors reviewed the papers' eligibility according to the defined inclusion and exclusion criteria.Results: Of the 2,118 papers retrieved, 61 were eventually selected in the study. Results of the present study revealed that the majority of CIoT research works were applied to patient monitoring systems and cardiovascular patient monitoring systems using Amazon and IBM dominating Cloud platforms. In addition, the most widely used communication technologies for CIoT in healthcare are cellular networks (3G and 4G), Wi-Fi, and Bluetooth. Cardiovascular, environmental, and position sensors are also the most common types of sensors used in healthcare CIoT applications. Among the Cloud platform providers, Amazon and IBM have the highest utility in healthcare systems. The majority of the included studies used Cloud-based AI algorithms to diagnose, classify, and predict diseases.Conclusion: The integration of the Cloud into the IoT can support healthcare systems in terms of processing power, storage capacity, security, privacy, performance, reliability, and scalability. We suggest researchers conduct experimental studies to evaluate the effectiveness of the CIoT approach in healthcare applications.
Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems

The reptile search algorithm is an effective optimization method based on the natural laws of the biological world. By restoring and simulating the hunting process of reptiles, good optimization results can be achieved. However, due to the limitations of natural laws, it is easy to fall into local optima during the exploration phase. Inspired by the different search fields of biological organisms with varying flight heights, this paper proposes a reptile search algorithm considering different flight heights. In the exploration phase, introducing the different flight altitude abilities of two animals, the northern goshawk and the African vulture, enables reptiles to have better search horizons, improve their global search ability, and reduce the probability of falling into local optima during the exploration phase. A novel dynamic factor (DF) is proposed in the exploitation phase to improve the algorithm’s convergence speed and optimization accuracy. To verify the effectiveness of the proposed algorithm, the test results were compared with ten state-of-the-art (SOTA) algorithms on thirty-three famous test functions. The experimental results show that the proposed algorithm has good performance. In addition, the proposed algorithm and ten SOTA algorithms were applied to three micromachine practical engineering problems, and the experimental results show that the proposed algorithm has good problem-solving ability.
Semantic Consistent Embedding for Domain Adaptive Zero-Shot Learning

Unsupervised domain adaptation has limitations when encountering label discrepancy between the source and target domains. While open-set domain adaptation approaches can address situations when the target domain has additional categories, these methods can only detect them but not further classify them. In this paper, we focus on a more challenging setting dubbed Domain Adaptive Zero-Shot Learning (DAZSL), which uses semantic embeddings of class tags as the bridge between seen and unseen classes to learn the classifier for recognizing all categories in the target domain when only the supervision of seen categories in the source domain is available. The main challenge of DAZSL is to perform knowledge transfer across categories and domain styles simultaneously. To this end, we propose a novel end-to-end learning mechanism dubbed Three-way Semantic Consistent Embedding (TSCE) to embed the source domain, target domain, and semantic space into a shared space. Specifically, TSCE learns domain-irrelevant categorical prototypes from the semantic embedding of class tags and uses them as the pivots of the shared space. The source domain features are aligned with the prototypes via their supervised information. On the other hand, the mutual information maximization mechanism is introduced to push the target domain features and prototypes towards each other. By this way, our approach can align domain differences between source and target images, as well as promote knowledge transfer towards unseen classes. Moreover, as there is no supervision in the target domain, the shared space may suffer from the catastrophic forgetting problem. Hence, we further propose a ranking-based embedding alignment mechanism to maintain the consistency between the semantic space and the shared space. Experimental results on both I2AwA and I2WebV clearly validate the effectiveness of our method.
SAC-Net: Enhancing Spatiotemporal Aggregation in Cervical Histological Image Classification via Label-Efficient Weakly Supervised Learning

Cervical cancer is the fourth most common cancer in women and its subtyping requires examining histopathological slides or digital images, such as whole slide images (WSIs). However, manually inspecting WSIs with gigapixel sizes can be laborious and prone to errors for pathologists. To address this issue, computer-aided approaches based on weakly-supervised learning techniques have been proposed. These methods can predict disease types directly from WSIs and highlight diagnosis-relevant regions, which can help pathologists achieve faster and more accurate diagnoses. WSIs are divided into overlapping patches using a sliding window approach, and these patches are subsequently screened in a sequential zig-zag pattern to identify spatiotemporal dependencies. These dependencies are further analyzed to generate predictions at the WSI level. Therefore, effective patch feature learning and spatiotemporal aggregation are two key issues in the weakly-supervised WSI classification (WSWC) task. In this paper, we present a label-efficient WSWC method called spatiotemporal aggregation for cervical WSIs (SAC-Net), which jointly performs online feature extraction and feature aggregation to infer the WSI-level prediction in an end-to-end manner. The online feature extractor helps to learn cervical-cancer-specific features and obtain more accurate patch representations. The feature aggregator uses an online instance clustering method to learn proper weight parameters for each cluster, which generates the WSI embedding with enhanced spatiotemporal aggregation. SAC-Net is developed and evaluated on a public cervical WSI dataset (TissueNet) containing 1015 WSIs, which are also externally tested on three independent cervical WSI datasets. Our results demonstrate that SAC-Net achieves state-of-the-art classification performance and is robust. SAC-Net has the potential to be a useful tool for clinical cervical cancer detection.
Quantum coherence of critical unstable two-level systems

We study in detail the dynamics of unstable two-level quantum systems by adopting the Bloch-sphere formalism of qubits. By employing the Bloch-vector representation for such unstable qubit systems, we identify a novel class of critical scenarios in which the so-called energy-level and decay-width vectors, $\mathbf{E}$ and $\mathbf{\ensuremath{\Gamma}}$, are orthogonal to one another, and the parameter $r=|\mathbf{\ensuremath{\Gamma}}|/(2|\mathbf{E}|)$ is less than 1. Most remarkably, we find that critical unstable qubit systems exhibit atypical behaviors like coherence-decoherence oscillations when analyzed in an appropriately defined codecaying frame of the system. In the same frame, a unit Bloch vector $\mathbf{b}$ describing a pure critical qubit will sweep out unequal areas during equal intervals of time, while rotating about the vector $\mathbf{E}$. These phenomena emerge beyond the usual oscillatory pattern due to the energy-level difference of the two-level quantum system. Interestingly enough, we observe that these new features will persist even for quasicritical scenarios, in which the vectors $\mathbf{E}$ and $\mathbf{\ensuremath{\Gamma}}$ are not perfectly orthogonal to each other. Applications of our results to quantum information and to unstable meson-antimeson and other systems are discussed.
The Impact, Advancements and Applications of Generative AI

Generative AI is a subfield of artificial intelligence ecosystem based on developing systems that can generate creative outputs, such as music, images, text content, and more.By harnessing the power of deep learning techniques, specifically generative models, these systems have the ability to create content that matches human-generated creations autonomously.The key to generative AI lies in its ability to learn from datasets and patterns and generate new content that exhibits similar traits.Generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), form the basis of generative AI.GANs consist of two components : (1) a generator and (2) a discriminator network engaged in a competitive & consistent process of generating and evaluating content.VAEs, employ an encoder-decoder architecture to learn and generate new output.This is research on Generative AI and its impact cutting across all verticals.
Perspectives on incorporating expert feedback into model updates

<h2>Summary</h2> Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficient consideration of how practitioners should translate domain expertise into ML updates. In this review, we consider how to capture interactions between practitioners and experts systematically. We devise a taxonomy to match expert feedback types with practitioner updates. A practitioner may receive feedback from an expert at the observation or domain level and then convert this feedback into updates to the dataset, loss function, or parameter space. We review existing work from ML and human-computer interaction to describe this feedback-update taxonomy and highlight the insufficient consideration given to incorporating feedback from non-technical experts. We end with a set of open questions that naturally arise from our proposed taxonomy and subsequent survey.
Keeping Pace with Ever-Increasing Data: Towards Continual Learning of Code Intelligence Models

Previous research on code intelligence usually trains a deep learning model on a fixed dataset in an offline manner. However, in real-world scenarios, new code repositories emerge incessantly, and the carried new knowledge is beneficial for providing up-to-date code intelligence services to developers. In this paper, we aim at the following problem: How to enable code intelligence models to continually learn from ever-increasing data? One major challenge here is catastrophic forgetting, meaning that the model can easily forget knowledge learned from previous datasets when learning from the new dataset. To tackle this challenge, we propose REPEAT, a novel method for continual learning of code intelligence models. Specifically, REPEAT addresses the catastrophic forgetting problem with representative exemplars replay and adaptive parameter regularization. The representative exemplars replay component selects informative and diverse exemplars in each dataset and uses them to re-train model periodically. The adaptive parameter regularization component recognizes important parameters in the model and adaptively penalizes their changes to preserve the knowledge learned before. We evaluate the proposed approach on three code intelligence tasks including code summarization, software vulnerability detection, and code clone detection. Extensive experiments demonstrate that REPEAT consistently outperforms baseline methods on all tasks. For example, REPEAT improves the conventional fine-tuning method by 1.22, 5.61, and 1.72 on code summarization, vulnerability detection and clone detection, respectively.