Reviewerly/exHarmony huggingface dataset:
RRCNet: Refinement residual convolutional network for breast ultrasound images segmentation

Breast ultrasound images segmentation is one of the key steps in clinical auxiliary diagnosis of breast cancer, which seriously threatens women’s health. Currently, deep learning methods have been successfully applied to breast tumors segmentation. However, blurred boundaries, heterostructure and other factors can cause serious missed detections and false detections in the segmentation results. In this paper, we developed a novel refinement residual convolutional network to segment breast tumors accurately from ultrasound images, which mainly composed of SegNet with deep supervision module, missed detection residual network and false detection residual network. In SegNet, we add six side-out deep supervision modules to guide the network to learn to predict precise segmentation masks scale-by-scale. In missed detection residual network, the receptive field provided by different dilation rates can provide more global information, which is easily lost in deep convolutional layer. The introduction of false detection and missed detection residual network can promotes the network to make more efforts on those hardly-predicted pixels to help us obtain more accurate segmentation results of the breast tumor. To evaluate the segmentation performance of the network, we compared with several state-of-the-art segmentation approaches using five quantitative metrics on two public breast datasets. Experimental results demonstrate that our method achieves the best segmentation results, which indicates that our method has better adaptability on breast tumors segmentation.
Controlling chaotic itinerancy in laser dynamics for reinforcement learning

Photonic artificial intelligence has attracted considerable interest in accelerating machine learning; however, the unique optical properties have not been fully used for achieving higher-order functionalities. Chaotic itinerancy, with its spontaneous transient dynamics among multiple quasi-attractors, can be used to realize brain-like functionalities. In this study, we numerically and experimentally investigate a method for controlling the chaotic itinerancy in a multimode semiconductor laser to solve a machine learning task, namely, the multiarmed bandit problem, which is fundamental to reinforcement learning. The proposed method uses chaotic itinerant motion in mode competition dynamics controlled via optical injection. We found that the exploration mechanism is completely different from a conventional searching algorithm and is highly scalable, outperforming the conventional approaches for large-scale bandit problems. This study paves the way to use chaotic itinerancy for effectively solving complex machine learning tasks as photonic hardware accelerators.
High-Fidelity Quantum Control by Polychromatic Pulse Trains

We introduce a quantum control technique using polychromatic pulse trains, consisting of pulses with different carrier frequencies, i.e., different detunings with respect to the qubit transition frequency. We derive numerous polychromatic pulse trains, which generate broadband, narrowband, and passband excitation profiles for different target transition probabilities. This makes it possible to create high-fidelity excitation profiles which are either (i) robust to deviations in the experimental parameters, which is attractive for quantum computing, or (ii) more sensitive to such variations, which is attractive for crosstalk elimination and quantum sensing. The method is demonstrated experimentally using one of IBM's superconducting quantum processors, in a very good agreement between theory and experiment. These results demonstrate both the excellent coherence properties of the IBM qubits and the accuracy, robustness, and flexibility of the proposed quantum control technique. They also show that the detuning is a control parameter which is as efficient as the pulse phase that is commonly used in composite pulses. Hence the method opens a variety of perspectives for quantum control in areas where phase manipulation is difficult or inaccurate.
Adaptive Multiframe Detection Algorithm With Range-Doppler-Azimuth Measurements

The multi-frame detection (MFD) with range-Doppler-azimuth measurements has been shown to be significant in detecting and tracking weak targets in active radar and sonar systems. Existing methods suffer from integrated energy loss for a long time noncoherent observation sequences due to approximate target evolution model or rough energy integration strategies. Moreover, it is hard to maintain consistency of multi-frame integration for targets in different near-field and far-field regions. By carefully mapping echo measurements to a discrete grid space without the introduction of new conversion errors, we proposed an efficient solution to build an accurate grid state model based on sensor parameters. Then, the combination of the grid state model and target evolution relationship among adjacent frames enables an adaptive MFD implementation. Each search path during multi-frame integration is adjusted adaptively with different grid state sizes. Finally, an improved strategy based on the predicted estimate is further presented to reduce the complexity of algorithms and improve the accuracy of possible search paths. The proposed methods can effectively integrate target energies among multi-frame range-Doppler-azimuth measurements, and maintain the robustness of the long time integration for targets in different near-field and far-field regions. Numerical results and tests with real radar data further show that the proposed method achieves high detection probability and tracking accuracy for weak targets with range-Doppler-azimuth measurements.
DisARM: Displacement Aware Relation Module for 3D Detection

We introduce Displacement Aware Relation Module (DisARM), a novel neural network module for enhancing the performance of 3D object detection in point cloud scenes. The core idea is extracting the most principal contextual information is critical for detection while the target is incomplete or featureless. We find that relations between proposals provide a good representation to describe the context. However, adopting relations between all the object or patch proposals for detection is inefficient, and an imbalanced combination of local and global relations brings extra noise that could mislead the training. Rather than working with all relations, we find that training with relations only between the most representative ones, or an-chors, can significantly boost the detection performance. Good anchors should be semantic-aware with no ambiguity and able to describe the whole layout of a scene with no redundancy. To find the anchors, we first perform a preliminary relation anchor module with an objectness-aware sampling approach and then devise a displacement based module for weighing the relation importance for better utilization of contextual information. This lightweight relation module leads to significantly higher accuracy of object instance detection when being plugged into the state-of-the-art detectors. Evaluations on the public benchmarks of real-world scenes show that our method achieves the state-of-the-art performance on both SUN RGB-D and Scan-Net V2. The code and models are publicly available at https://github.com/YaraDuan/DisARM.
FaceFormer: Aggregating Global and Local Representation for Face Hallucination

Recently, face hallucination methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e.g., facial parsing maps and landmarks) to focus on global facial structure and constrain facial texture generation. However, the limited receptive fields of CNNs and inaccurate facial priors will reduce the naturalness and fidelity of restored face. In this paper, we propose a FaceFormer that aggregates global representation of Transformers and local representation of CNNs to maintain the consistency of facial structure while restoring local facial details. The reason for this design is that the Transformer can capture global facial information by exploiting the long-distance visual relation modeling, while the local modeling capability of CNNs can recover fine-grained facial details. Therefore, aggregating these two independent representations can help to maximize their merits and reconstruct high-quality and high-fidelity face images. Experimental results of face reconstruction and recognition verify that the proposed FaceFormer significantly outperforms current state-of-the-arts.
FlexIT: Towards Flexible Semantic Image Translation

Deep generative models, like GANs, have considerably improved the state of the art in image synthesis, and are able to generate near photo-realistic images in structured domains such as human faces. Based on this success, recent work on image editing proceeds by projecting images to the GAN latent space and manipulating the latent vector. However, these approaches are limited in that only images from a narrow domain can be transformed, and with only a limited number of editing operations. We propose FlexIT, a novel method which can take any input image and a user-defined text instruction for editing. Our method achieves flexible and natural editing, pushing the limits of semantic image translation. First, FlexIT combines the input image and text into a single target point in the CLIP multimodal embedding space. Via the latent space of an autoencoder, we iteratively transform the input image toward the target point, ensuring coherence and quality with a variety of novel regularization terms. We propose an evaluation protocol for semantic image translation, and thoroughly evaluate our method on ImageNet. Code will be available at https://github.com/facebookresearch/SemanticImageTranslation/.
Reversible Data Hiding using Pixel-Value-Ordering and Difference Expansion in Digital Images

Pixel value ordering (PVO) and difference expansion (DE) are promising methods for digital image steganography. PVO is a steganographic method of digital images that deals with pixel values sorting before the data embedding. In digital image steganography, DE is a method for data protection based on hiding secret data in differences computed between pixels. Even though the previous methods based on PVO and DE tried to improve the embedding capacity and the stego image quality, improvement is still needed to increase both simultaneously. This work proposes a new method that combines PVO and DE to improve the number of embeddable areas and the embedding capacity in the pixels of a digital image. The experimental results showed that the maximum number of the embeddable regions within used images with the existing methods was increased from 17582 pixels to 131009 pixels, yielding the embedding capacity improvement from 6.70% up to 49.97%.
Generative label fused network for image–text matching

Although there is a long line of research on bidirectional image–text matching, the problem remains a challenge due to the well-known semantic gap between visual and textual modalities. Popular solutions usually first detect the objects and then find the association between visual objects and the textual words to estimate the relevance; however, these methods only focus on the visual object features while ignoring the semantic attributions of the detected regions, which is an important clue in terms of bridging the semantic gap. To remedy this issue, we propose a generative multiattribution tag fusion method that further includes region attribution to alleviate the semantic gap. In particular, our method comprises three steps: the extraction of image features, the extraction of text features, and the matching of image and text by an attention mechanism. We first divide the image into blocks to obtain the region image features and region attribute labels. Then, we fuse them to reduce the semantic gap between the image features and text features. Second, BERT and bi-GRU are used to extract text features, and third, we use the attention mechanism to match each area in the image with each word in the text with the same meaning. The quantitative and qualitative results on the public datasets Flickr30K and MS-COCO demonstrate the effectiveness of our method. The source code is released on Github https://github.com/smileslabsh/Generative-Label-Fused-Network.
Towards Case-Optimized Hybrid Homomorphic Encryption

Hybrid Homomorphic Encryption (HHE) reduces the amount of computation client-side and bandwidth usage in a Fully Homomorphic Encryption (FHE) framework. HHE requires the usage of specific symmetric schemes that can be evaluated homomorphically efficiently. In this paper, we introduce the paradigm of Group Filter Permutator (GFP) as a generalization of the Improved Filter Permutator paradigm introduced by Méaux et al.. From this paradigm, we specify Elisabeth , a family of stream cipher and give an instance: Elisabeth-4 . After asserting the security of this scheme, we provide a Rust implementation of it and ensure its performance is comparable to state-of-the-art HHE. The true strength of Elisabeth lies in the available operations server-side: while the best HHE applications were limited to a few multiplications server-side, we used data sent through Elisabeth-4 to homomorphically evaluate a neural network inference. Finally, we discuss the improvement and loss between the HHE and the FHE framework and give ideas to build more efficient schemes from the Elisabeth family.
Cybersecurity awareness factors among adolescents in Jordan: Mediation effect of cyber scale and personal factors

Cybersecurity for Jordanians’ adolescents/teens has become a complicated phenomenon representing complex socio-technical concerns for the personalities of adolescents in Jordanian society. Recent cyberattacks have had a significant impact on teens in Jordan, both personally and in terms of their quality of life. As such, it is important to develop strategies to increase cybersecurity awareness among teens in Jordan. This research was conducted in both planned and random areas in Jordan, with the aim of understanding the differences in risk-aware cultures and teens’ opinions and activities after introducing cybersecurity awareness in cyberspace. The study was based on 400 responses, and numerous statistical analyses were applied to the responses from each area, including the validity and reliability test, feasibility test of a variable, correlation test, and carried out using SPSS. Moreover, multiple coefficients of determination, hypothesis testing and partial statistical tests were carried out. The research hypotheses indicate that there is a mediation effect of the cyber scale and personal factors (planned/random) with a 76% acceptance rate. This suggests that understanding the significant association between the cyber scale and the quality-of-life scale is important, and that effective cyber-risk management is critical to realizing the importance of cybersecurity awareness. As such, it is recommended that national cybersecurity programs be launched in all sectors in Jordan. Overall, this research highlights the importance of increasing cybersecurity awareness among teens in Jordan. It is essential that teens are educated on the risks associated with cyberattacks, and that effective strategies are developed to protect them from such attacks. By doing so, teens in Jordan can be better equipped to protect themselves and their society from the dangers of cyberattacks.
A critical review on the state-of-the-art and future prospects of machine learning for Earth observation operations

The continuing Machine Learning (ML) revolution indubitably has had a significant positive impact on the analysis of downlinked satellite data. Other aspects of the Earth Observation industry, despite being less susceptible to widespread application of Machine Learning, are also following this trend. These applications, actual use cases, possible prospects and difficulties, as well as anticipated research gaps, are the focus of this review of Machine Learning applied to Earth Observation Operations. A wide range of topics are covered, including mission planning, fault diagnosis, fault prognosis and fault repair, optimization of telecommunications, enhanced GNC, on-board image processing, and the use of Machine Learning models on platforms with constrained compute and power capabilities, as well as recommendations in the respective areas of research. The review tackles all on-board and off-board applications of machine learning to Earth Observation with one notable exception: it omits all post-processing of payload data on the ground, a topic that has been studied extensively by past authors. In addition, this review article discusses the standardization of Machine Learning (i.e., Guidelines and Roadmaps), as well as the challenges and recommendations in Earth Observation operations for the purpose of building better space missions.
Parallel and heralded multiqubit entanglement generation for quantum networks

Quantum networks involving multiqubit entanglement allow exciting applications in quantum communication, quantum sensing, and distributed quantum computation. The efficiency of nonlocal entanglement generation through optical channels drops exponentially with the distance between network nodes. We present a parallel and heralded protocol for generating distributed multiqubit entanglement across multiple nodes. This is achieved by using a high-dimensional single photon that works as a common-data bus connecting all stationary qubits (i.e., silicon-vacancy electron spins), each of which is coupled to a single-sided optical cavity. Parallel multiqubit entangled states are heralded by the detection of the single photon after it interacts with each stationary qubit and passes through each photonic modulation circuit. This parallel protocol can significantly improve the efficiency of distributed entanglement generation and provides a viable route towards distributed multinode quantum networks.
Nonlocal Temporal Interferometry for Highly Resilient Free-Space Quantum Communication

Entanglement distribution via photons over long distances enables many applications, including quantum key distribution (QKD), which provides unprecedented privacy. The inevitable degradation of entanglement through noise accumulated over long distances remains one of the key challenges in this area. Exploiting the potential of higher-dimensional entangled photons promises to address this challenge, but poses extreme demands on the experimental implementation. Here, we present an interstate free-space quantum link, distributing hyper-entanglement over $10.2\,$km with flexible dimensionality of encoding by deploying a phase-stable non-local Franson interferometer. With this distribution of multidimensional energy-time entangled photons, we analyse the achievable key rate in a dimensionally-adaptive QKD protocol that can be optimized with respect to any environmental noise conditions. Our approach enables and emphasises the power of high-dimensional entanglement for quantum communication, yielding a positive asymptotic key rate well into the dawn of the day.
Scenarios Engineering: Enabling Trustworthy and Effective AI for Autonomous Vehicles

The first journal impact factor (IF) of IEEE TIV is 5.009 with a CiteScore = 10.9 in 2022, signifying a promising start for our periodical. By April 2023, our real-time IF stands at 7.03 according to Web of Science, and CiteScore from Elsevier is 11.6. These numbers indicate that IEEE TIV is among the top-tier publications in the related fields.
A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems

Many researchers have been interested in healthcare cybersecurity for a long time since it can improve the security of patient and health record data. As a result, a lot of research is done in the field of cybersecurity that focuses on the safe exchange of health data between patients and the medical setting. It still has issues with high computational complexity, increased time consumption, and cost complexity, all of which have an impact on the effectiveness and performance of the complete security system. Hence this work proposes a technique called Consultative Transaction Key Generation and Management (CTKGM) to enable secure data sharing in healthcare systems. It generates a unique key pair based on random values with multiplicative operations and time stamps. The patient data is then safely stored in discrete blocks of hash values using the blockchain methodology. The Quantum Trust Reconciliation Agreement Model (QTRAM), which calculates the trust score based on the feedback data, ensures reliable and secure data transfer. By allowing safe communication between patients and the healthcare system based on feedback analysis and trust value, the proposed framework makes a novel contribution to the field. Additionally, during communication, the Tuna Swarm Optimization (TSO) method is employed to validate nonce verification messages. Nonce message verification is a part of QTRAM that helps verify the users during transmission. The effectiveness of the suggested scheme has been demonstrated by comparing the obtained findings with other current state-of-the-art models after a variety of evaluation metrics have been analyzed to test the performance of this security model.
Quantum Computing for Molecular Biology**

Molecular biology and biochemistry interpret microscopic processes in the living world in terms of molecular structures and their interactions, which are quantum mechanical by their very nature. Whereas the theoretical foundations of these interactions are well established, the computational solution of the relevant quantum mechanical equations is very hard. However, much of molecular function in biology can be understood in terms of classical mechanics, where the interactions of electrons and nuclei have been mapped onto effective classical surrogate potentials that model the interaction of atoms or even larger entities. The simple mathematical structure of these potentials offers huge computational advantages; however, this comes at the cost that all quantum correlations and the rigorous many-particle nature of the interactions are omitted. In this work, we discuss how quantum computation may advance the practical usefulness of the quantum foundations of molecular biology by offering computational advantages for simulations of biomolecules. We not only discuss typical quantum mechanical problems of the electronic structure of biomolecules in this context, but also consider the dominating classical problems (such as protein folding and drug design) as well as data-driven approaches of bioinformatics and the degree to which they might become amenable to quantum simulation and quantum computation.
Design and Performance Analysis of Discrete Memristive Hyperchaotic Systems With Stuffed Cube Attractors and Ultraboosting Behaviors

Memristors are widely used for studying chaotic oscillations due to their special nonlinearity and plasticity. This article presents a framework for constructing the ultraboosting memristive hyperchaotic map. The complexity of the original map can be significantly enhanced by it. It can also effectively resolve the discontinuous chaotic range and low Lyapunov exponent problems of common hyperchaotic systems. There are four hyperchaotic maps that are derived from this framework with ultraboosting behaviors, and they generate cube attractors. Hardware devices based on microcontrollers are built to implement these maps, and the experimental results are highly consistent with the numerical simulations. A pseudorandom number generator is designed using these hyperchaotic maps, and the National Institute of Standards and Technology test results show that the generated pseudorandom numbers have extremely high randomness. Finally, we apply them in developing the image encryption algorithm.