Reviewerly/exHarmony huggingface dataset:
A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare

Abstract In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data.
Breaking universal limitations on quantum conference key agreement without quantum memory

Quantum conference key agreement is an important cryptographic primitive for future quantum network. Realizing this primitive requires high-brightness and robust multiphoton entanglement sources, which is challenging in experiment and unpractical in application because of limited transmission distance caused by channel loss. Here we report a measurement-device-independent quantum conference key agreement protocol with enhanced transmission efficiency over lossy channel. With spatial multiplexing nature and adaptive operation, our protocol can break key rate bounds on quantum communication over quantum network without quantum memory. Compared with previous work, our protocol shows superiority in key rate and transmission distance within the state-of-the-art technology. Furthermore, we analyse the security of our protocol in the composable framework and evaluate its performance in the finite-size regime to show practicality. Based on our results, we anticipate that our protocol will play an indispensable role in constructing multipartite quantum network.
Implementation of Internet of Things With Blockchain Using Machine Learning Algorithm

In recent days, all networks are connected by internet and all people around the world are able to control things in their remote locations. Even though these technologies have been used only by selected people, it is definite in the future that all people will use this technology and they will move towards building smart cities, homes, and industries. However, when advanced technologies are created, people always worry about security when they move towards smart environment. If the internet is connected to their home then much valuable information in their home can also be sent through smart devices. Therefore, for this IoT-based technology, a blockchain-based method can be introduced where more security for data transfer process can be provided. Also, these technologies have to work efficiently by integrating a new artificial intelligence-based machine learning algorithm. Therefore, for this, a deep learning model will be integrated, thus providing effective data transfer from transmitter to receiver.
Realization of Quantum Secure Direct Communication with Continuous Variable

With the progress of theoretical and applied technologies, the communication system based on the classical encryption is seriously threatened by quantum computing and distributed computing. A communication method that directly loads confidential information on the quantum state, quantum secure direct communication (QSDC), came into being for resisting security threats. Here, we report the first continuous-variable QSDC (CV-QSDC) experimental demonstration for verifying the feasibility and effectiveness of the CV-QSDC protocol based on Gaussian mapping and propose a parameter estimation for signal classification under the actual channels. In our experiment, we provided 4 × 10 2 blocks, where each block contains 10 5 data for direct information transmission. For the transmission distance of 5 km in our experiment, the excess noise is 0.0035 SNU, where SNU represents the unit of shot-noise units. The 4.08 × 10 5 bit per second experimental results firmly demonstrated the feasibility of CV-QSDC under the fiber channel. The proposed grading judgment method based on parameter estimation provides a practical and available message processing scheme for CV-QSDC in a practical fiber channel and lays the groundwork for the grading reconciliation.
Transformation-Equivariant 3D Object Detection for Autonomous Driving

3D object detection received increasing attention in autonomous driving recently. Objects in 3D scenes are distributed with diverse orientations. Ordinary detectors do not explicitly model the variations of rotation and reflection transformations. Consequently, large networks and extensive data augmentation are required for robust detection. Recent equivariant networks explicitly model the transformation variations by applying shared networks on multiple transformed point clouds, showing great potential in object geometry modeling. However, it is difficult to apply such networks to 3D object detection in autonomous driving due to its large computation cost and slow reasoning speed. In this work, we present TED, an efficient Transformation-Equivariant 3D Detector to overcome the computation cost and speed issues. TED first applies a sparse convolution backbone to extract multi-channel transformation-equivariant voxel features; and then aligns and aggregates these equivariant features into lightweight and compact representations for high-performance 3D object detection. On the highly competitive KITTI 3D car detection leaderboard, TED ranked 1st among all submissions with competitive efficiency. Code is available at https://github.com/hailanyi/TED.
Online dynamic ensemble deep random vector functional link neural network for forecasting

This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple randomized layers to enhance the single-layer RVFL’s representation ability. Each hidden layer’s representation is utilized for training an output layer, and the ensemble of all output layers forms the edRVFL’s output. However, the original edRVFL is not designed for online learning, and the randomized nature of the features is harmful to extracting meaningful temporal features. In order to address the limitations and extend the edRVFL to an online learning mode, this paper proposes a dynamic edRVFL consisting of three online components, the online decomposition, the online training, and the online dynamic ensemble. First, an online decomposition is utilized as a feature engineering block for the edRVFL. Then, an online learning algorithm is designed to learn the edRVFL. Finally, an online dynamic ensemble method, which can measure the change in the distribution, is proposed for aggregating all layers’ outputs. This paper evaluates and compares the proposed model with state-of-the-art methods on sixteen time series.
Leveraging AI Technologies in Libraries through Experimentation-Driven Frameworks

AbstractThis column aims to explore the frameworks to help libraries foster digital innovation by leveraging AI technologies through continuous experimentation to innovate their services for their patrons. Additionally, the column seeks to highlight the benefits and interplay between the frameworks, providing insights for librarians interested in implementing AI solutions and driving technological advancements in library settings. The column reports two frameworks - The Need-Based Experimentation (NBE) Framework and the Curiosity-Based Experimentation (CBE) Framework based on the author’s professional experiences and empirical observations of 10 university libraries’ experimentation-driven AI technology adoption practices. The NBE framework focuses on experimenting with AI technologies that have the functional capability to address the library’s current business needs. In contrast, the CBE framework explores AI technologies out of curiosity, aiming to gain practical experiences and uncover potential future applications, aligned with the librarian’s interests. These frameworks guide librarians to effectively experiment with AI technology based on their motivations and goals. To the best of the authors’ knowledge, there is no experimentation-driven framework for adopting AI technologies to assist libraries do so strategically. The adoption of AI should be influenced by carefully planned, ongoing experiments, the results of which should be deployed in real to inform adoption decisions.Keywords: Artificial intelligencecuriosity-based experimentation (CBE) frameworkexperimentationlibrariesneed-based experimentation (NBE) frameworktechnology adoption AcknowledgmentsThe research’s authors would like to acknowledge the library staff members who took part in the study.Disclosure statementThe authors declare no conflict of interest.Institutional review board statementThe conducted study was approved by the Institutional Review Board of Gisma University of Applied Sciences, Potsdam, Germany under protocol number 02/2023.Notes1 https://www.iso.org/obp/ui/#iso:std:iso-iec:2382:-28:ed-1:v1:en2 https://hbr.org/2023/05/ai-can-help-you-ask-better-questions-and-solve-bigger-problemsAdditional informationFundingThis research is funded by the European Union’s Horizon research and innovation programme (Project ID: 101061516, Project Acronym: LibrarIN). This publication solely reflects the views of the authors, and the Agency cannot be held responsible for any use made of the information contained herein. The paper was also co-funded by Winning Scientific Management, Portugal (Project ID: WINBUSMOD001).
GrabCut: Interactive Foreground Extraction Using Iterated Graph Cuts

The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for ''border matting'' has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.
Evaluating progress in automatic chest X-ray radiology report generation

Artificial intelligence (AI) models for automatic generation of narrative radiology reports from images have the potential to enhance efficiency and reduce the workload of radiologists. However, evaluating the correctness of these reports requires metrics that can capture clinically pertinent differences. In this study, we investigate the alignment between automated metrics and radiologists' scoring of errors in report generation. We address the limitations of existing metrics by proposing new metrics, RadGraph F1 and RadCliQ, which demonstrate stronger correlation with radiologists' evaluations. In addition, we analyze the failure modes of the metrics to understand their limitations and provide guidance for metric selection and interpretation. This study establishes RadGraph F1 and RadCliQ as meaningful metrics for guiding future research in radiology report generation.
Directional Connectivity-based Segmentation of Medical Images

Anatomical consistency in biomarker segmentation is crucial for many medical image analysis tasks. A promising paradigm for achieving anatomically consistent segmentation via deep networks is incorporating pixel connectivity, a basic concept in digital topology, to model inter-pixel relationships. However, previous works on connectivity modeling have ignored the rich channel-wise directional information in the latent space. In this work, we demonstrate that effective disentanglement of directional sub-space from the shared latent space can significantly enhance the feature representation in the connectivity- based network. To this end, we propose a directional connectivity modeling scheme for segmentation that decouples, tracks, and utilizes the directional information across the network. Experiments on various public medical image segmentation benchmarks show the effectiveness of our model as compared to the state-of-the-art methods. Code is available at https://github.com/Zyun-Y/DconnNet.
Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems

Abstract The primary objective of this study is to delve into the application and validation of the Resistance Capacitance Optimization Algorithm (RCOA)—a new, physics-inspired metaheuristic optimization algorithm. The RCOA, intriguingly inspired by the time response of a resistance–capacitance circuit to a sudden voltage fluctuation, has been earmarked for solving complex numerical and engineering design optimization problems. Uniquely, the RCOA operates without any control/tunable parameters. In the first phase of this study, we evaluated the RCOA's credibility and functionality by deploying it on a set of 23 benchmark test functions. This was followed by thoroughly examining its application in eight distinct constrained engineering design optimization scenarios. This methodical approach was undertaken to dissect and understand the algorithm's exploration and exploitation phases, leveraging standard benchmark functions as the yardstick. The principal findings underline the significant effectiveness of the RCOA, especially when contrasted against various state-of-the-art algorithms in the field. Beyond its apparent superiority, the RCOA was put through rigorous statistical non-parametric testing, further endorsing its reliability as an innovative tool for handling complex engineering design problems. The conclusion of this research underscores the RCOA's strong performance in terms of reliability and precision, particularly in tackling constrained engineering design optimization challenges. This statement, derived from the systematic study, strengthens RCOA's position as a potentially transformative tool in the mathematical optimization landscape. It also paves the way for further exploration and adaptation of physics-inspired algorithms in the broader realm of optimization problems.
High Quality Facial Surface and Texture Synthesis via Generative Adversarial Networks

In the past several decades, many attempts have been made to model synthetic realistic geometric data. The goal of such models is to generate plausible 3D geometries and textures. Perhaps the best known of its kind is the linear 3D morphable model (3DMM) for faces. Such models can be found at the core of many computer vision applications such as face reconstruction, recognition and authentication to name just a few. Generative adversarial networks (GANs) have shown great promise in imitating high dimensional data distributions. State of the art GANs are capable of performing tasks such as image to image translation as well as auditory and image signal synthesis, producing novel plausible samples from the data distribution at hand. Geometric data is generally more difficult to process due to the inherent lack of an intrinsic parametrization. By bringing geometric data into an aligned space, we are able to map the data onto a 2D plane using a universal parametrization. This alignment process allows for efficient processing of digitally scanned geometric data via image processing tools. Using this methodology, we propose a novel face synthesis model for generation of realistic facial textures together with their corresponding geometry. A GAN is employed in order to imitate the space of parametrized human textures, while corresponding facial geometries are generated by learning the best 3DMM coefficients for each texture. The generated textures are mapped back onto the corresponding geometries to obtain new generated high resolution 3D faces.
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Let F2m be a finite field of cardinality 2m and s a positive integer. Using properties for Kronecker product of matrices and calculation for linear equations over F2m, an efficient method for the construction of all distinct self-dual cyclic codes with length 2s over the finite chain ring F2m+uF2m (u2=0) is provided. On that basis, an explicit representation for every self-dual cyclic code of length 2s over F2m+uF2m and an exact formula to count the number of all these self-dual cyclic codes are given.
Selection of Robust and Relevant Features for 3-D Steganalysis

While 3-D steganography and digital watermarking represent methods for embedding information into 3-D objects, 3-D steganalysis aims to find the hidden information. Previous research studies have shown that by estimating the parameters modeling the statistics of 3-D features and feeding them into a classifier we can identify whether a 3-D object carries secret information. For training the steganalyzer, such features are extracted from cover and stego pairs, representing the original 3-D objects and those carrying hidden information. However, in practical applications, the steganalyzer would have to distinguish stego-objects from cover-objects, which most likely have not been used during the training. This represents a significant challenge for existing steganalyzers, raising a challenge known as the cover source mismatch (CSM) problem, which is due to the significant limitation of their generalization ability. This paper proposes a novel feature selection algorithm taking into account both feature robustness and relevance in order to mitigate the CSM problem in 3-D steganalysis. In the context of the proposed methodology, new shapes are generated by distorting those used in the training. Then a subset of features is selected from a larger given set, by assessing their effectiveness in separating cover-objects from stego-objects among the generated sets of objects. Two different measures are used for selecting the appropriate features: 1) the Pearson correlation coefficient and 2) the mutual information criterion.
Polarization-entangled photon pairs from a single molecule

Photonic entanglement is one of the key resources in modern quantum optics. It opens the door to schemes such as quantum communication, quantum teleportation, and quantum-enhanced precision sensing. Sources based on parametric down-conversion or cascaded decays in atomic and atom-like emitters are limited because of their weak interaction with stationary qubits. This is due to their commonly broadband emission. Furthermore, these sources are commonly in the near-infrared such that quantum emitters in the blue spectral region, such as ions or many defect centers, cannot be addressed. Here, we present a sodium-resonant (589.0 nm) and narrow-band (14 MHz) degenerate entanglement source based on a single molecule. A beam-splitter renders two independently emitted photons into a polarization-entangled state. The quality of the entangled photon pairs is verified by the violation of Bell’s inequality. We measure a Bell parameter of S=2.26±0.05. This attests that the detected photon pairs exceed the classical limit; it is reconfirmed by quantum-state tomography and an analysis of the raw detector counts, which result in a value of S=2.24±0.12. The tomography shows fidelity of 82% to a maximally entangled Bell state. This work opens the route to background-free solid-state entanglement sources which surpass the probabilistic nature of the commonly used sources and are free from unwanted multi-photon events. The source is ideal for combination with stationary qubits such as atoms, ions, quantum dots, or defect centers.
Noise Robust Projection Rule for Hyperbolic Hopfield Neural Networks

A complex-valued Hopfield neural network (CHNN) is a multistate Hopfield model. Low noise tolerance is the main disadvantage of CHNNs. The hyperbolic Hopfield neural network (HHNN) is a noise robust multistate Hopfield model. In HHNNs employing the projection rule, noise tolerance rapidly worsened as the number of training patterns increased. This result was caused by the self-loops. The projection rule for CHNNs improves noise tolerance by removing the self-loops, however, that for HHNNs cannot remove them. In this brief, we extended the stability condition for the self-loops of HHNNs and modified the projection rule. Thus, the HHNNs had improved noise tolerance.
BE-CALF: Bit-Depth Enhancement by Concatenating All Level Features of DNN

There is a growing demand for monitors to provide high-quality visualization with more bits representing each rendered pixel. However, since most existing images and videos are of low bit-depth (LBD), transforming LBD images to visually pleasant high bit-depth (HBD) versions is of significant value. Most existing bit-depth enhancement methods generate unsatisfactory HBD images with annoying false contour artifacts or blurry details, and some algorithms are also time-consuming. To overcome these drawbacks, we propose a bit-depth enhancement framework via concatenating all level features of deep neural networks (DNNs). A novel deep learning network is proposed based on the deep convolutional variational auto-encoders (VAEs), and skip connections that concatenate every two layers are applied to pass low-level and high-level features to consequent layers, easing the gradient vanishing problem. Meanwhile, the proposed network is optimized to generate the residual between original images and its quantized ones, which performs better than recovering HBD images directly. The experimental results show that the proposed algorithm can eliminate false contour artifacts of the recovered HBD images with low time consumption, and can achieve dramatic restoration performance gains compared with state-of-the-art methods both subjectively and objectively.