Reviewerly/exHarmony huggingface dataset:
NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images

The early diagnosis of lymph node metastasis in breast cancer is essential for enhancing treatment outcomes and overall prognosis. Unfortunately, pathologists often fail to identify small or subtle metastatic deposits, leading them to rely on cytokeratin stains for improved detection, although this approach is not without its flaws. To address the need for early detection, multiple-instance learning (MIL) has emerged as the preferred deep learning method for automatic tumor detection on whole slide images (WSIs). However, existing methods often fail to identify some small lesions due to insufficient attention to small regions. Attention-based multiple-instance learning (ABMIL)-based methods can be particularly problematic because they may focus too much on normal regions, leaving insufficient attention for small-tumor lesions. In this paper, we propose a new ABMIL-based model called normal representative keyset ABMIL (NRK-ABMIL), which addresseses this issue by adjusting the attention mechanism to give more attention to lesions. To accomplish this, the NRK-ABMIL creates an optimal keyset of normal patch embeddings called the normal representative keyset (NRK). The NRK roughly represents the underlying distribution of all normal patch embeddings and is used to modify the attention mechanism of the ABMIL. We evaluated NRK-ABMIL on the publicly available Camelyon16 and Camelyon17 datasets and found that it outperformed existing state-of-the-art methods in accurately identifying small tumor lesions that may spread over a few patches. Additionally, the NRK-ABMIL also performed exceptionally well in identifying medium/large tumor lesions.
Deep supervision feature refinement attention network for medical image segmentation

The improvement of medical technology is closely related to the development of computers. Deep learning methods have become an important means for medical image processing, and their accuracy in processing lesions will have a significant impact on the final diagnosis result. Currently, some traditional algorithms and classical deep learning methods are no longer able to meet the increasing demand for higher accuracy in medical image processing. In order to achieve better performance in handling the edge and detail features of lesions, we create the Deep Supervision Feature Refinement Attention Network (DSFRA-Net) through extensive experiments. In DSFRA-Net, the Depth Feature Attention Block is created to enhance the long-range dependency between pixels in deep neural networks. The Feature Refinement Block is developed to enhance the details in shallow features. The Adaptive Feature Extraction Block is created to strengthen the fusion of semantic information and detail information. A deep supervision mechanism is used to supervise each layer of the feature reconstruction process, feeding back the training in the form of a loss function to optimize the training. DSFRA-Net experiments on four datasets, all of which show better performance than the current mainstream networks. It shows superior capabilities in areas such as feature continuity and detailed feature processing.
Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective

This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called the Multi-Agent Exploration–Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm’s average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.
A blockchain-enabled sharing platform for personal health records

BackgroundLongitudinal personal health record (PHR) provides a foundation for managing patients' health care, but we do not have such a system in the U.S. except for the patients in the Department of Veterans Affairs. Such a gap exists mainly in the rest of the U.S. by the fact that patients' electronic health records are scattered across multiple health care facilities and often not shared due to privacy, security, and business interests concerns from both patients and health care organizations. In addition, patients have ethical concerns related to consent. To patients, data security, privacy, and consent are based on trustfulness, rather than patients’ engagement in ensuring only authorized people can view their PHRs with patient-managed granularity. Resolving these challenges is an important step in making longitudinal PHR useful for patient care.ObjectiveThis research aims to design and implement a blockchain-enabled sharing platform prototype for PHR with desired patient-controlled data security, privacy, and consent granularity.MethodsBuilt upon our prior work of a blockchain-enabled access control (BAC) model, we design a blockchain-enabled sharing platform for PHR with patient-controlled security, privacy, and consent granularity. We further implement the construct by building a prototypical platform among a patient and two typical health care organizations. Health organizations that hold the patient's electronic health records can join the platform with trust based on the validation from the patient. The mutual trust can be established through a rigorous validation process by both the patient and the built-in Hyperledger Fabric blockchain consensus mechanism.ResultsWe proposed a system trusted by patients and health care providers and constructed a Web-based PHR sharing platform with patient-controlled security, privacy, and consent granularity. We analyzed the system scalability in three aspects and showed millisecond range of performance when simultaneously changing access permissions on hundreds of PHRs. Consent, security and privacy of the model are ensured by the merits of the BAC model. We discovered the current blockchain model limits the system scalability due to using a non-graphical database. A new graphical database is suggested for future improvements.ConclusionsIn this research, we report a solution to electronically sharing and managing patients’ electronic health records originating from multiple organizations, focusing on privacy, security, and granularity control of consent in the U.S. Specifically, the system protects data security and privacy, and provides auditability, scalability, distributedness, patient consent autonomy, and zero-trust capabilities. The prototypical instantiation of the designed model suggested the feasibility of combining emerging blockchain technology with next generation access control model to tackle a longstanding longitudinal PHR problem.
A projection-based approach to extend digital volume correlation for 4D spacetime measurements

In-situ (tomography) experiments are generally based on scans reconstructed from a large number of projections acquired under constant deformation of samples.Standard digital volume correlation (DVC) methods are based on a limited number of scans due to acquisition duration.They thus prevent analyses of time-dependent phenomena.In this paper, a modal procedure is proposed that allows time-dependent occurrences to be analyzed.It estimates spacetime displacement fields during the whole loading history.The spatial modes are based on standard DVC, which is subsequently enriched using projection-based digital volume correlation (P-DVC) to measure the temporal amplitudes.The method is applied to two cases, namely, a virtual experiment mimicking wedge splitting and an actual shear test on a pantographic metamaterial inducing large motions.With the proposed method, the temporal amplitude in the real test was measured for each projection leading to a temporal resolution of one tenth of a second and the analysis of 16,400 time steps.For the proposed algorithm, the sensitivity to the acquisition angle of the sample was investigated and measurement uncertainties were assessed.
Securing Federated Learning Enabled NWDAF Architecture With Partial Homomorphic Encryption

Network data analytics function (NWDAF), introduced to provision data analytics and machine learning model training in the 5G core network, is expected to be an essential functional entity and play a significant role in the emerging AI-native 6G wireless network. However, refining the NWDAF architecture to support machine learning (ML) model sharing among multiple NWDAFs with distributed data sources and different privacy constraints remains a major challenge. To address this challenge, we propose a federated learning enabled NWDAF architecture with Partial Homomorphic Encryption to secure ML model sharing with privacy preserving. Simulation results demonstrate the feasibility of our proposed architecture.
Flexible and Optimal Dependency Management via Max-SMT

Package managers such as NPM have become essential for software development. The NPM repository hosts over 2 million packages and serves over 43 billion downloads every week. Unfortunately, the NPM dependency solver has several shortcomings. 1) NPM is greedy and often fails to install the newest versions of dependencies; 2) NPM's algorithm leads to duplicated dependencies and bloated code, which is particularly bad for web applications that need to minimize code size; 3) NPM's vulnerability fixing algorithm is also greedy, and can even introduce new vulnerabilities; and 4) NPM's ability to duplicate dependencies can break stateful frameworks and requires a lot of care to workaround. Although existing tools try to address these problems they are either brittle, rely on post hoc changes to the dependency tree, do not guarantee optimality, or are not composable. We present Pacsolve, a unifying framework and implementation for dependency solving which allows for customizable constraints and optimization goals. We use Pacsolve to build Maxnpm, a complete, drop-in replacement for NPM, which empowers developers to combine multiple objectives when installing dependencies. We evaluate Maxnpm with a large sample of packages from the NPM ecosystem and show that it can: 1) reduce more vulnerabilities in dependencies than NPM's auditing tool in 33% of cases; 2) chooses newer dependencies than NPM in 14% of cases; and 3) chooses fewer dependencies than NPM in 21% of cases. All our code and data is open and available.
Closed-Loop Analysis of Vision-Based Autonomous Systems: A Case Study

Abstract Deep neural networks (DNNs) are increasingly used in safety-critical autonomous systems as perception components processing high-dimensional image data. Formal analysis of these systems is particularly challenging due to the complexity of the perception DNNs, the sensors (cameras), and the environment conditions. We present a case study applying formal probabilistic analysis techniques to an experimental autonomous system that guides airplanes on taxiways using a perception DNN. We address the above challenges by replacing the camera and the network with a compact abstraction whose transition probabilities are computed from the confusion matrices measuring the performance of the DNN on a representative image data set. As the probabilities are estimated based on empirical data, and thus are subject to error, we also compute confidence intervals in addition to point estimates for these probabilities and thereby strengthen the soundness of the analysis. We also show how to leverage local, DNN-specific analyses as run-time guards to filter out mis-behaving inputs and increase the safety of the overall system. Our findings are applicable to other autonomous systems that use complex DNNs for perception.
A Black-Box Adversarial Attack Method via Nesterov Accelerated Gradient and Rewiring Towards Attacking Graph Neural Networks

Recent studies have shown that Graph Neural Networks (GNNs) are vulnerable to well-designed and imperceptible adversarial attack. Attacks utilizing gradient information are widely used in the field of attack due to their simplicity and efficiency. However, several challenges are faced by gradient-based attacks: 1) Generate perturbations use white-box attacks (i.e., requiring access to the full knowledge of the model), which is not practical in the real world; 2) It is easy to drop into local optima; and 3) The perturbation budget is not limited and might be detected even if the number of modified edges is small. Faced with the above challenges, this article proposes a black-box adversarial attack method, named NAG-R, which consists of two modules known as <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> esterov <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ccelerated <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> radient attack module and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> ewiring optimization module. Specifically, inspired by adversarial attacks on images, the first module generates perturbations by introducing Nesterov Accelerated Gradient (NAG) to avoid falling into local optima. The second module keeps the fundamental properties of the graph (e.g., the total degree of the graph) unchanged through a rewiring operation, thus ensuring that perturbations are imperceptible. Intensive experiments show that our method has significant attack success and transferability over existing state-of-the-art gradient-based attack methods.
A multi-strategy enhanced northern goshawk optimization algorithm for global optimization and engineering design problems

Metaheuristic algorithms are widely utilized in various fields owing to their ability to produce a variety of solutions. The Northern Goshawk Optimization (NGO) is an effective optimization algorithm, however, its convergence rate is slow and it tends to fall into local optima in some cases. Therefore, this paper proposes a Multi-strategy Enhanced Northern Goshawk Optimization (MENGO) algorithm, which introduces a novel exploration strategy based on Levy flights to mitigate the risk of getting trapped in local optima. To balance exploration and exploitation, a new nonlinear reduction strategy based on the sine function is proposed. Additionally, a novel exploitation strategy is employed to accelerate the convergence speed while ensuring accuracy. The effectiveness of MENGO is demonstrated by comparing it with 13 advanced algorithms using 23 classical benchmark and 12 CEC2022 test functions in different dimensions. To evaluate the feasibility of the proposed approach in real-world applications, it is studied for nine constrained engineering problems, and the performance is compared with other contender algorithms extracted from the literature. The all experimental results show that MENGO outperforms other state-of-the-art algorithms in terms of solution quality and stability, making it a more competitive option.
A heterogeneous graph neural network model for list recommendation

List recommendation, an approach for recommending a set of items closely related to user preferences, plays a significant role in improving the user experience and has received growing attention in recent years. However, most existing models ignore list-level attributes and the heterogeneity of the interactions between the users and lists when predicting user preferences. In this paper, we propose a heterogeneous graph neural network list recommendation (HGLR) model to bridge these gaps. First, a representation learning module based on pre-training techniques is developed to learn the embeddings of the lists, by encoding textual information and aggregating the features of the constituent items. We then transform the user–list interaction into a bipartite graph and incorporate the list attributes into a heterogeneous information network (HIN). Propagation-based embedding learning is subsequently performed to generate user and list representation based on the message-passing mechanism of the graph neural network. Finally, an attentive fusion layer is designed to obtain the global representations for the recommendation. We also develop a new large-scale dataset containing multiple types of side information to evaluate the list recommendation model. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art list recommendation methods and achieves performance gains over 8.96% and 14.59% in Recall and NDCG, respectively, against the strongest baselines.
Virtually Connected in a Multiverse of Madness?—Perceptions of Gaming, Animation, and Metaverse

Few studies analyze what are the common representations of the metaverse. Regarding what has been said about this concept, our research aims to verify how adults perceive and represent the metaverse. We carried out a study with focus groups, having as participants Portuguese adults all considered habitual gamers (or users of digital games). The objectives for this study were seven: verify how the metaverse is being represented and characterized; identify which technologies stimulate the immersion experience; identify the main dimensions that influence the acceptance of the metaverse concept; understand the perceptions of the metaverse and virtual reality regarding socialization and wellbeing; verify the perceptions of a gamer’s daily life regarding the metaverse, virtual reality, and gaming concepts; understand the impact of social representations on the gaming concept; and to understand the perceived role of animation regarding the metaverse, virtual reality, and gaming concepts. Our results reveal a common understanding of the metaverse, despite some confusion about this concept. We also verified the high importance of wellbeing and social dimensions in metaverse immersive experiences provided by technology or gaming characteristics. This exploratory study gave us essential findings about the perceptions of the metaverse and a deep understanding of the relations between the metaverse, virtual reality, animation, and gaming.
Semi-supervised Learning with Network Embedding on Ambient RF Signals for Geofencing Services

In applications such as elderly care, dementia anti-wandering and pandemic control, it is important to ensure that people are within a predefined area for their safety and well-being. We propose GEM, a practical, semi-supervised Geofencing system with network EMbedding, which is based only on ambient radio frequency (RF) signals. GEM models measured RF signal records as a weighted bipartite graph. With access points on one side and signal records on the other, it is able to precisely capture the relationships between signal records. GEM then learns node embeddings from the graph via a novel bipartite network embedding algorithm called BiSAGE, based on a Bipartite graph neural network with a novel bi-level SAmple and aggreGatE mechanism and non-uniform neighborhood sampling. Using the learned embeddings, GEM finally builds a one-class classification model via an enhanced histogram-based algorithm for in-out detection, i.e., to detect whether the user is inside the area or not. This model also keeps on improving with newly collected signal records. We demonstrate through extensive experiments in diverse environments that GEM shows state-of-the-art performance with up to 34% improvement in F-score. BiSAGE in GEM leads to a 54% improvement in F-score, as compared to the one without BiSAGE.
Chatbot Integration for Metaverse - A University Platform Prototype

The integration of chatbots in a university platform prototype for a metaverse has the potential to significantly enhance the user experience by providing instant, interactive, and personalized support. Chatbots can assist both students and faculty with a variety of tasks, including course navigation, assignment submissions, and answering frequently asked questions. By utilizing natural language processing (NLP) and machine learning algorithms, the chatbot is able to understand and respond to user inquiries with a high degree of accuracy. This integration has the added benefit of increasing the efficiency of information dissemination, reducing the workload of administrative staff. The use of a metaverse in the university platform creates an immersive and interactive virtual environment, making education more engaging and accessible. In this paper, we will highlight the representative applications for chatbot integration for metaverse on a university platform. To achieve this, we propose a three-layer metaverse architecture from a macro perspective, which includes a chatbot platform, a university platform, and a metaverse platform. We will illustrate our implemented metaverse prototype of a university campus and discuss the prototype design and insights for educational purposes. Overall, the integration of chatbots in a university platform prototype for a metaverse provides a promising avenue for enhancing the user experience and facilitating education in the digital age.
Pyramid-VAE-GAN: Transferring hierarchical latent variables for image inpainting

Abstract Significant progress has been made in image inpainting methods in recent years. However, they are incapable of producing inpainting results with reasonable structures, rich detail, and sharpness at the same time. In this paper, we propose the Pyramid-VAE-GAN network for image inpainting to address this limitation. Our network is built on a variational autoencoder (VAE) backbone that encodes high-level latent variables to represent complicated high-dimensional prior distributions of images. The prior assists in reconstructing reasonable structures when inpainting. We also adopt a pyramid structure in our model to maintain rich detail in low-level latent variables. To avoid the usual incompatibility of requiring both reasonable structures and rich detail, we propose a novel cross-layer latent variable transfer module. This transfers information about long-range structures contained in high-level latent variables to low-level latent variables representing more detailed information. We further use adversarial training to select the most reasonable results and to improve the sharpness of the images. Extensive experimental results on multiple datasets demonstrate the superiority of our method. Our code is available at https://github.com/thy960112/Pyramid-VAE-GAN .