Reviewerly/exHarmony huggingface dataset:
Synthetic augmentation for semantic segmentation of class imbalanced biomedical images: A data pair generative adversarial network approach

In recent years, deep learning (DL) has been recognized very useful in the semantic segmentation of biomedical images. Such an application, however, is significantly hindered by the lack of pixel-wise annotations. In this work, we propose a data pair generative adversarial network (DPGAN) for the purpose of synthesizing concurrently the diverse biomedical images and the segmentation labels from random latent vectors. First, a hierarchical structure is constructed consisting of three variational auto-encoder generative adversarial networks (VAEGANs) with an extra discriminator. Subsequently, to alleviate the influence from the imbalance between lesions and non-lesions areas in biomedical segmentation data sets, we divide the DPGAN into three stages, namely, background stage, mask stage and advanced stage, with each stage deploying a VAEGAN. In such a way, a large number of new segmentation data pairs are generated from random latent vectors and then used to augment the original data sets. Finally, to validate the effectiveness of the proposed DPGAN, experiments are carried out on a vestibular schwannoma data set, a kidney tumor data set and a skin cancer data set. The results indicate that, in comparison to other state-of-the-art GAN-based methods, the proposed DPGAN shows better performance in the generative quality, and meanwhile, gains an effective boost on semantic segmentation of class imbalanced biomedical images.
Magnetic Anomaly Detection Method Based on Feature Fusion and Isolation Forest Algorithm

Under the background of Gaussian colored environmental magnetic noise, for the problem that a supervised learning algorithm has high requirements on samples and poor practicability, a magnetic anomaly detection method based on feature fusion and isolation forest (IForest) algorithm is proposed.The method uses different feature algorithms to extract the statistical features, time-frequency features and fractal features of the signal, reduces the dimensionality of the features by principal component analysis (PCA) and generates feature fusion tensors.Finally the IForest algorithm is used to achieve target detection.The simulation and experimental results show that the method has a higher detection rate under different SNR of Gaussian color noise, which is approximately 5%-18% higher than that of the traditional feature detection algorithm.This method can train an effective detection model with only a small number of negative samples.Compared with the fully connected neural network (FCN) model trained with unbalanced samples, the detection rate increases by approximately 5%-12%, and it takes less time.
Effective data-driven precision medicine by cluster-applied deep reinforcement learning

The significance of machine-learning approaches in the healthcare domain has grown rapidly owing to the existence of enormous amounts of data and well-established simulation models and algorithms. The digitization of health-related data, as well as rapid technological advancements are accelerating the development and application of machine learning in healthcare, particularly in precision medicine. The ultimate goal of precision medicine is to provide personalized medicine, which requires tailoring medical decisions to each patient based on their projected disease response. In this study, we propose a cluster-applied deep reinforcement learning-based type 2 diabetes treatment recommendation model based on the electronic health records of South Koreans. The purpose of applying a clustering algorithm is to group patients who are in a similar state, to boost the performance of deep reinforcement learning, build a more realistic treatment recommendation model to support clinicians, and develop expert systems in the field of healthcare. The proposed model demonstrated significant performance by decreasing diabetes-related medical checkup measurements. Furthermore, the proposed model delivered high-quality performance when compared with existing reinforcement-learning methods. Finally, the recommendation outcomes of the proposed model were validated against real-life prescriptions to ensure the accuracy of the findings.
Initial-State Dependent Optimization of Controlled Gate Operations with Quantum Computer

There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivity, and coherence times, a quantum circuit optimization is essential to make the best use of near-term quantum devices. We introduce a new circuit optimizer called AQCEL, which aims to remove redundant controlled operations from controlled gates, depending on initial states of the circuit. Especially, the AQCEL can remove unnecessary qubit controls from multi-controlled gates in polynomial computational resources, even when all the relevant qubits are entangled, by identifying zero-amplitude computational basis states using a quantum computer. As a benchmark, the AQCEL is deployed on a quantum algorithm designed to model final state radiation in high energy physics. For this benchmark, we have demonstrated that the AQCEL-optimized circuit can produce equivalent final states with much smaller number of gates. Moreover, when deploying AQCEL with a noisy intermediate scale quantum computer, it efficiently produces a quantum circuit that approximates the original circuit with high fidelity by truncating low-amplitude computational basis states below certain thresholds. Our technique is useful for a wide variety of quantum algorithms, opening up new possibilities to further simplify quantum circuits to be more effective for real devices.
A Bug Triage Technique Using Developer-Based Feature Selection and CNN-LSTM Algorithm

With an increase in the use of software, the incidence of bugs and resulting maintenance costs also increase. In open source projects, developer reassignment accounts for approximately 50%. Software maintenance costs can be reduced if appropriate developers are recommended to resolve bugs. In this study, features are extracted by applying feature selection for each developer. These features are entered into CNN-LSTM algorithm to learn the model and recommend appropriate developers. To compare the performance of the proposed model, open source projects (Google Chrome, Mozilla Core, and Mozilla Firefox) were used to compare the performance of the proposed method with a baseline for developer recommendation. In this paper, the performance showed 54% for F-measure and 52% for accuracy in open source projects. The proposed model has improved and showed about a 13% more effective performance improvement than with DeepTriage. It was discovered that the performance of the proposed model was better.
MG-CNN: A deep CNN to predict saddle points of matrix games

Finding the saddle point of a matrix game is a classical problem that arises in various fields, e.g., economics, computer science, and engineering. The standard problem-solving methods consist of formulating the problem as a linear program (LP). However, this approach seems less efficient when many instances need to be solved. In this paper, we propose a Convolutional Neural Network based approach, which is able to predict both the strategy profile (x,y) and the optimal value v of the game. We call this approach Matrix Game-Conventional Neural Network or MG-CNN for short. Thanks to a global pooling technique, MG-CNN can solve matrix games with different shapes. We propose a specialized algorithm to train MG-CNN, which includes both data generation and model training. Our numerical experiments show that MG-CNN outperforms standard LP solvers in terms of computational CPU time and provides a high-quality prediction.
Administration of Machine Learning Based Access Control

When the access control state of a system is complex, Machine learning (ML)-based access control decision engines have demonstrated advantages of accuracy and generalizability. This field is emerging with multiple efforts where an ML model is trained based on either existing access control state or historic access logs; the trained model then makes access control decisions. This paper explores ML-based access control’s administration problem, focusing on capturing changes in the access control state. We investigate this problem in a simulated system with Random Forest (RF) method from a symbolic ML class and the ResNet method from a non-symbolic one. Both classes have their respective advantages and disadvantages for issues such as insufficient learning of new changes and forgetting existing access information while updating the ML model. Our experimental results show that the non-symbolic approaches perform better than the symbolic ones while adjusting for continual (or incremental) changes in the access control state .
FREED: An Efficient Privacy-Preserving Solution for Person Re-IDentification

Person Re-IDentification (Re-ID) is a critical technology to identify a target person from captured person images by surveillance cameras. However, person Re-ID has triggered great concerns of personal image privacy. Although the law (e.g., GDPR) has stipulated person images are personal private data, there is no an efficient solution to tackle the image privacy concern for person Re-ID. To this end, we propose FREED, the first system solution for privacy-preserving person Re-ID, which supports the state-of-the-art person Re-ID operations on encrypted feature vectors of person images. To handle the encryption of feature vectors effectively and enable person Re-ID operations on encrypted feature vectors efficiently, FREED develops a suite of batch secure computing protocols based on a twin-server architecture and the threshold Paillier cryptosystem. We demonstrate our secure computing protocols are more efficient than existing protocols and FREED achieves a precision equal to the state-of-the-art plaintext method.
Automated metadata annotation: What is and is not possible with machine learning

ABSTRACT Automated metadata annotation is only as good as training dataset, or rules that are available for the domain. It's important to learn what type of data content a pre-trained machine learning algorithm has been trained on to understand its limitations and potential biases. Consider what type of content is readily available to train an algorithm—what's popular and what's available. However, scholarly and historical content is often not available in consumable, homogenized, and interoperable formats at the large volume that is required for machine learning. There are exceptions such as science and medicine, where large, well documented collections are available. This paper presents the current state of automated metadata annotation in cultural heritage and research data, discusses challenges identified from use cases, and proposes solutions.
How to Induce a Physical and Virtual Rubber Hand Illusion

Understanding body ownership is essential when creating virtual reality (VR) applications using avatars. One of the most widely-used paradigm to investigate body ownership is the rubber hand illusion (RHI). When a real hand and a rubber hand are stroked synchronously, participants can experience the rubber hand as their own hand. Although the knowledge from RHI experiments in the real world is applied to when users embody avatars in VR, it is still unclear whether the illusory ownership of a virtual and physical body produce the same effects. In addition, conducting RHI studies in VR would allow gaining a range of novel experiments that are not possible in the real world. With this demonstration, we therefore present a system and approach to investigate the RHI in the real world and in VR.
Visualizations for the evolution of Variant-Rich Systems: A systematic mapping study

Variant-Rich Systems (VRSs), such as Software Product Lines or variants created through clone & own, aim at reusing existing assets. The long lifespan of families of variants, and the scale of both the code base and the workforce make VRS maintenance and evolution a challenge. Visualization tools are a needed companion. We aim at mapping the current state of visualization interventions in the area of VRS evolution. We tackle evolution in both functionality and architecture. Three research questions are posed: What sort of analysis is being conducted to assess VRS evolution? (Analysis perspective); What sort of visualizations are displayed? (Visualization perspective); What is the research maturity of the reported interventions? (Maturity perspective). We performed a systematic mapping study including automated search in digital libraries, expert knowledge, and snowballing. The study reports on 41 visualization approaches to cope with VRS evolution. Analysis wise, feature identification and location is the most popular scenario, followed by variant integration towards a Software Product Line. As for visualization, nodelink diagram visualization is predominant while researchers have come up with a wealth of ingenious visualization approaches. Finally, maturity wise, almost half of the studies are solution proposals. Most of the studies provide proof-of-concept, some of them also include publicly available tools, yet very few face proof-of-value. This study introduces a comparison framework where to frame future studies. It also points out distinct research gaps worth investigating as well as shortcomings in the evidence about relevance and contextual considerations (e.g., scalability).
Efficient Anchor Learning-based Multi-view Clustering -- A Late Fusion Method

Anchor enhanced multi-view late fusion clustering has attracted numerous researchers' attention for its high clustering accuracy and promising efficiency. However, in the existing methods, the anchor points are usually generated through sampling or linearly combining the samples within the datasets, which could result in enormous time consumption and limited representation capability. To solve the problem, in our method, we learn the view-specific anchor points by learning them directly. Specifically, in our method, we first reconstruct the partition matrix of each view through multiplying a view-specific anchor matrix by a consensus reconstruction matrix. Then, by maximizing the weighted alignment between the base partition matrix and its estimated version in each view, we learn the optimal anchor points for each view. In particular, unlike previous late fusion algorithms, which define anchor points as linear combinations of existing samples, we define anchor points as a series of orthogonal vectors that are directly learned through optimization, which expands the learning space of the anchor points. Moreover, based on the above design, the resultant algorithm has only linear complexity and no hyper-parameter. Experiments on $12$ benchmark kernel datasets and 5 large-scale datasets illustrate that the proposed Efficient Anchor Learning-based Multi-view Clustering (AL-MVC) algorithm achieves the state-of-the-art performance in both clustering performance and efficiency.
A study on the effects of normalized TSP features for automated algorithm selection

Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) nearest neighbor relationships of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-nearest neighbor graphs (NNG) of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. A proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.
Waiting for a better possibility: delay of gratification in corvids and its relationship to other cognitive capacities

Self-control, the ability to resist temptation and wait for better but delayed possibilities, is an important cognitive skill that underpins decision-making and planning. The capacity to exert self-control has been linked to intelligence in humans, chimpanzees and most recently cuttlefish. Here, we presented 10 Eurasian jays, Garrulus glandarius, with a delayed maintenance task, which measured the ability to choose a preferred outcome as well as the ability to sustain the delay prior to that outcome. Jays were able to wait for better possibilities, but maximum wait times varied across the subjects. We also presented them with five cognitive tasks that assessed spatial memory, spatial relationships and learning capacity. These tasks are commonly used as measures of general intelligence within an ecological context. Individual performance was correlated across the cognitive tasks, which suggests that there was a general intelligence factor underlying their performance. Performance in these tasks was correlated significantly with the jays' capacity to wait for better possibilities. This study demonstrates that self-control and intelligence are correlated in jays. The fact that this correlation exists in diverse species suggests that self-control is a fundamental feature of cognition. Our results are discussed in the context of convergent evolution. This article is part of the theme issue 'Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny'.
Python tools for ESA’s Swarm mission: VirES for Swarm and surrounding ecosystem

ESA’s Swarm mission is a constellation probing both Earth’s interior and geospace, delivering magnetic and plasma measurements which are used to generate many derived data products. From empirical magnetic field models of the core, crust, ionosphere, and magnetosphere, to multi-point estimates of ionospheric currents and in-situ plasma properties, these are challenging to navigate, process, and visualize. The VirES for Swarm platform ( https://vires.services ) has been built to tackle this problem, providing tools to increase usability of Swarm data products. The VirES (Virtual environments for Earth Scientists) platform provides both a graphical web interface and an API to access and visualise Swarm data and models. This is extended with a cloud-hosted development environment powered by JupyterHub (the “Virtual Research Environment/VRE”). VirES provides two API’s: the full VirES API for which a dedicated Python client is provided, viresclient , and the more interoperable Heliophysics API (HAPI). The VRE is furnished with a bespoke Python environment containing thematic libraries supporting science with Swarm. This service aims to ease the pathway for scientists writing computer code to analyze Swarm data products, increase opportunities for collaboration, and leverage cloud technologies. Beyond simply providing data and model access to Python users, it is extremely helpful to provide higher-level analysis and visualization tools, and ready-to-use code recipes that people can explore and extend. Critically for space physics, this involves crossover with many other datasets and so it is highly valuable to embed such tools within the wider data and software ecosystems. Through Swarm DISC (Data, Innovation, and Science Cluster), we are tackling this through cookbooks and Python libraries. Cookbooks are built and presented using Jupyter technologies, and tested to work within the VRE. A new library we are building is SwarmPAL , which includes tools for time-frequency analysis and inversion of magnetic field measurements for electric current systems, among others, while relying on the VirES server to provide data portability and other utilities. This paper reviews the current state of these tools and services for Swarm, particularly in the context of the Python in Heliophysics Community, and the wider heliophysics and geospace data environment.
Low-Light Image Enhancement Method by Using a Modified Gamma Transform for Convex Combination Coefficients

Recently, a lot of low-light image enhancement methods have been proposed. However, these methods have some problems such as causing over enhancement in bright regions and/or high computational cost. In this study, we propose a new low-light image enhancement method to improve these problems. In the proposed method, the input pixels are represented by a convex combination of white, black, and pure color. Then, an equhue plane in RGB color space is represented as the triangle whose vertices correspond to white, black, and pure color. The visibility of low-light image is improved by applying a modified gamma transform to the combination coefficients on an equhue plane in RGB color space. The contrast of image is enhanced by smoothing the histogram of black coefficient with a gamma filter. In the experiments, the effectiveness of the proposed method is verified by the comparison with the state-of-the-art low-light image enhancement methods.