Reviewerly/exHarmony huggingface dataset:
A VGG attention vision transformer network for benign and malignant classification of breast ultrasound images

Breast cancer is the most commonly occurring cancer worldwide. The ultrasound reflectivity imaging technique can be used to obtain breast ultrasound (BUS) images, which can be used to classify benign and malignant tumors. However, the classification is subjective and dependent on the experience and skill of operators and doctors. The automatic classification method can assist doctors and improve the objectivity, but current convolution neural network (CNN) is not good at learning global features and vision transformer (ViT) is not good at extraction local features. In this study, we proposed a visual geometry group attention ViT (VGGA-ViT) network to overcome their disadvantages.In the proposed method, we used a CNN module to extract the local features and employed a ViT module to learn the global relationship among different regions and enhance the relevant local features. The CNN module was named the VGGA module. It was composed of a VGG backbone, a feature extraction fully connected layer, and a squeeze-and-excitation block. Both the VGG backbone and the ViT module were pretrained on the ImageNet dataset and retrained using BUS samples in this study. Two BUS datasets were employed for validation.Cross-validation was conducted on two BUS datasets. For the Dataset A, the proposed VGGA-ViT network achieved high accuracy (88.71 ±$\ \pm \ $ 1.55%), recall (90.73 ±$\ \pm \ $ 1.57%), specificity (85.58 ±$\ \pm \ $ 3.35%), precision (90.77 ±$\ \pm \ $ 1.98%), F1 score (90.73 ±$\ \pm \ $ 1.24%), and Matthews correlation coefficient (MCC) (76.34 ±7$\ \pm \ 7$ 3.29%), which were better than those of all compared previous networks in this study. The Dataset B was used as a separate test set, the test results showed that the VGGA-ViT had highest accuracy (81.72 ±$\ \pm \ $ 2.99%), recall (64.45 ±$\ \pm \ $ 2.96%), specificity (90.28 ±$\ \pm \ $ 3.51%), precision (77.08 ±$\ \pm \ $ 7.21%), F1 score (70.11 ±$\ \pm \ $ 4.25%), and MCC (57.64 ±$\ \pm \ $ 6.88%).In this study, we proposed the VGGA-ViT for the BUS classification, which was good at learning both local and global features. The proposed network achieved higher accuracy than the compared previous methods.
A smooth path planning method for mobile robot using a BES-incorporated modified QPSO algorithm

• The path planning and path smoothing are combined through the high-order continuous Bezier curve. • An improved QPSO that incorporates two search mechanisms from bald eagle search (BES) is proposed. • The concept of semi-randomization is infused in some of our modifications in BES-MQPSO. • Enhanced intensive exploitation is performed by incorporating BES search and swoop mechanisms. • It may be the first time to apply BES tricks to help to enhance QPSO and into the path planning field. The design of path planning algorithms to obtain a smooth path that satisfies constraints such as obstacle avoidance, minimized cost, and dynamic feasibility for mobile robots in the workspace is a hot issue in the field of intelligent robotics. The metaheuristic optimization algorithm, which aims to solve path planning problems, is more efficient compared to traditional algorithms. In the current metaheuristic approaches, avoiding local minimum traps is the core goal of the scheme. In this paper, with the benefit of a high-order continuous Bezier curve, we construct an optimization problem of smooth path planning based on the length and the requirement of collision-free safety as constraints of the robot's expected path. Then, the smooth path planning problem is transformed into an optimization problem that searches the locations of control nodes of the Bezier curve. We solve the constructed path planning problem with our modified quantum particle swarm optimization algorithm (QPSO). Specifically, to overcome the shortcomings of the current PSO and QPSO-based particle swarm algorithms like prematurity, unbalanced global search, local exploitation, and local minima, we implement an improved QPSO algorithm denoted as BES-MQPSO that incorporates the search and swoop mechanisms from bald eagle search (BES) strategies. The method consists of four improved modifications: an improved local attractor, improved feature length, BES-enhanced intensive exploitation Strategy, and random variation mechanism. Our proposed BES-MQPSO with these four modifications is tested with benchmark functions and then applied to the smooth path planning problem. Finally, the superiority of BES-MQPSO is confirmed, and the ideal smooth path of the mobile robot is successfully planned. The planned path length is reduced by 4.45% and 2.82% compared to e-QPSO and SDEQPSO respectively, while the standard deviation is reduced by 67.46% and 69.82%, respectively.
Ensemble grey wolf Optimizer and its application for image segmentation

• The top three wolves update their positions on the basis of their knowledge. • The weight makes good use of knowledge from the top three wolves. • An Ensemble GWO (EGWO) is developed. • The experiment results have revealed the effectiveness of EGWO algorithm. Grey Wolf Optimizer (GWO) is a novel optimization algorithm. It mimics the hierarchy of wolves and has shown potential optimization ability. However, it may suffer from poor performance as it is based on a single optimization strategy. An Ensemble GWO (EGWO) is developed using two strategies to boost the performance of GWO. The positions of the top three wolves are updated according to the difference with the alpha. The rest wolves adjust their positions by extending the position-updated equation to improve the exploitation. Then, an elite-based random search is introduced to advance the exploration. The proposed EGWO algorithm is validated on the CEC 2019 and image segmentation in the real world. The obtained results show that the proposed EGWO algorithm is reliable and effective for benchmark functions and real-world applications.
Global Relation-Aware Contrast Learning for Unsupervised Person Re-Identification

The goal of unsupervised person re-identification (Re-ID) is to use unlabeled person images to learn discriminative features. In recent years, many approaches have adopted clustered pseudo labels to construct proxies for contrastive learning, and have thereby achieved great success. However, existing methods of this kind only utilize local structures within IDs to design their proxies while ignoring the relations between samples of different IDs, which limits the improvement for inter-ID discriminative ability. To resolve this issue, we propose a Global Relation-Aware Contrast Learning (GRACL) method for the task of unsupervised Re-ID. Our method first sets up two proxies for each cluster to capture the inter- and intra-ID relations respectively, which enables us to both effectively increase inter-ID variances and reduce the intra-ID discrepancies. Specifically, the samples that are most different from those in different clusters are selected as inter-ID relation-aware proxies, while those that are least similar to samples from the same clusters are employed as intra-ID relation-aware proxies. With the aid of these proxies, we design both inter- and intra-ID relation-aware contrastive learning modules to facilitate model learning. By pulling each sample close to the positive proxy, we can obtain identity-invariant discriminative features. Experiments on five widely-used Re-ID datasets prove that our GRACL model outperforms current state-of-the-art approaches to a remarkable extent.
Vision transformers for dense prediction: A survey

Transformers have demonstrated impressive expressiveness and transfer capability in computer vision fields. Dense prediction is a fundamental problem in computer vision that is more challenging to solve than general image-level prediction tasks. The inherent properties of transformers enable them to process feature representations with stable and relatively high resolution, which precisely satisfies the demands of dense prediction tasks for finer-grained and more globally coherent predictions. Furthermore, compared to convolutional networks, transformer methods require minimal inductive bias and permit long-range information interaction. These strengths have contributed to exciting advancements in dense prediction tasks that apply transformer networks. This survey aims to provide a comprehensive overview of transformer models with a specific focus on dense prediction. In this survey, we provide a well-rounded view of state-of-the-art transformer-based approaches, explicitly emphasizing pixel-level prediction tasks. We generally consider transformer variants from the network architecture perspective. We further propose a novel taxonomy to organize these models according to their constructions. Subsequently, we examine various specific optimization strategies to tackle certain bottleneck problems in dense prediction tasks. We explore the commonalities and differences among these works and provide multiple horizontal comparisons from the experimental point of view. Finally, we summarize several stubborn problems that continue to impact visual transformers and outline some possible development directions.
Accelerating generalized linear models with MLWeaving

Learning from the data stored in a database is an important function increasingly available in relational engines. Methods using lower precision input data are of special interest given their overall higher efficiency. However, in databases, these methods have a hidden cost: the quantization of the real value into a smaller number is an expensive step. To address this issue, we present ML-Weaving, a data structure and hardware acceleration technique intended to speed up learning of generalized linear models over low precision data. MLWeaving provides a compact in-memory representation that enables the retrieval of data at any level of precision. MLWeaving also provides a highly efficient implementation of stochastic gradient descent on FPGAs and enables the dynamic tuning of precision, instead of using a fixed precision level during learning. Experimental results show that MLWeaving converges up to 16 x faster than low-precision implementations of first-order methods on CPUs.
Discovering Chaos-Based Communications by Recurrence Quantification and Quantified Return Map Analyses

Many studies show the possibility of transmitting messages in a protected and covert manner using a noise-like chaotic waveform as a carrier. Among popular chaotic communication system (CCS) types, one may distinguish chaotic shift keying (CSK) and parameter modulation (PM) which are based on the manipulation of the transmitting chaotic oscillators. With the development of direct digital synthesis (DDS), it became possible to modulate chaotic signals by varying the properties of the numerical method used in digital chaos generators. The symmetry coefficient modulation (SCM) is such an approach potentially able to provide higher secrecy. However, the actual security of chaos-based communications is still a questionable and controversial feature. To quantitatively evaluate the CCS security level, a certain numerical metric reflecting the difficulty of breaking a communication channel is needed. Return maps are commonly used to attack chaotic communication systems, but the standard algorithm does not involve any kind of quantification. In this study, we propose a new method for estimating the differences between two return maps based on a two-dimensional (2D) histogram. Then, we investigate the resistance of chaotic shift keying, parameter modulation, and SCM communication schemes against three types of attacks: the proposed quantified return map analysis (QRMA), recurrence quantification analysis (RQA), which had not been previously reported for attacking chaos-based communications, and the classical approach based on spectral analysis. In our experiments we managed to recover the plain binary message from the waveform in the channel when transmitted using all three chaos-based messaging techniques; among them, SCM appeared to be the most secure communication scheme. The proposed QRMA turned out to be the most efficient technique for message recovery: the sensitivity of the QRMA appeared to be 2–5 times higher than that in the case of spectral analysis. The proposed QRMA method can be efficiently used for evaluating the difficulty of hacking chaos-based communication systems. Moreover, it is suitable for the evaluation of any other secure data transmission channel.
Federated Learning in Dentistry: Chances and Challenges

Building performant and robust artificial intelligence (AI)-based applications for dentistry requires large and high-quality data sets, which usually reside in distributed data silos from multiple sources (e.g., different clinical institutes). Collaborative efforts are limited as privacy constraints forbid direct sharing across the borders of these data silos. Federated learning is a scalable and privacy-preserving framework for collaborative training of AI models without data sharing, where instead the knowledge is exchanged in form of wisdom learned from the data. This article aims at introducing the established concept of federated learning together with chances and challenges to foster collaboration on AI-based applications within the dental research community.
A multi-objective quantum-inspired genetic algorithm for workflow healthcare application scheduling with hard and soft deadline constraints in hybrid clouds

Recently, the use of quantum cloud computing for different applications has been increasing. For instance, weather forecasting, financial modeling, healthcare, and automation are geographically distributed in practice. These applications are workflows and consist of compute-intensive dependent tasks with precedence constraints. However, workflow processing on quantum-based cloud services still faces issues in the literature regarding makespan and energy consumption. This study presents the Multi-objective Quantum-inspired Genetic Algorithm (MQGA) to address the problems of workflow scheduling in the hybrid cloud, attempting to reduce makespan and energy consumption simultaneously. The proposed algorithm relies on the concept and principle of quantum mechanics, which explores the computational power of quantum computing. It adopted a qubit to represent the individual chromosome for better population diversity. It also uses a quantum rotation gate to lead the schedule to better convergence and avoids classical genetic operators. The simulation results show that the proposed algorithm can effectively reduce energy consumption by 23.36% and makespan 20% on average.
A chaos-based block cipher based on an enhanced logistic map and simultaneous confusion-diffusion operations

Over the years, there has been considerable interest in the area of chaos-based encryption due to the fact that cryptographic algorithms and chaotic maps share a wide-range of similar characteristics. The majority of chaos-based ciphers were designed for encrypting digital images and vast amounts of data. This paper proposes a new chaos-based block cipher algorithm (CBCA) based on an improved logistic chaotic map. To strengthen the performance of the classical logistic chaotic map, a new chaotification method based on a multiplicative inverse function is used which leads to improved properties such as ergodicity and entropy, both of which are desirable for cryptographic applications. The secret key is used to perturb the chaotic variables, and these perturbed variables are used to produce data sequences for the block cipher’s diffusion and confusion structures. The permutation (diffusion) structure is controlled by an ergodic chaotic map, while the substitution (confusion) structure is controlled by the chaotic points themselves. This new ergodicity-based diffusion approach provides higher security and efficiency. The proposed algorithm not only possess good statistical properties provided by the enhanced chaotic map, but it is also key sensitive and uniformly distributed. Statistical evaluation of the proposed algorithm was performed using text and images as inputs to depict its capability to secure various types of media. The performance of the proposed cipher is then compared to other recently proposed algorithms in literature. The performance comparison indicates that CBCA is efficient, secure and that it can be used to encrypt both small and large amounts of data.
Free‐Space Quantum Key Distribution with Single Photons from Defects in Hexagonal Boron Nitride

We present a proof-of-concept demonstration of free-space quantum key distribution (QKD) with single photons generated from an isolated defect in hexagonal boron nitride (hBN). The source, operating at room temperature with a 10% brightness, is integrated into a B92 protocol and a secure key rate (SKR) of 238 bps and a quantum bit error rate (QBER) of 8.95% are achieved with 1 MHz clock rate. The effect of temporal filtering of detected photons on the performance of QKD parameters is also studied. We believe that our results will accelerate the work on improving the performance of optically active defects in hBN and their use in high-performance practical QKD systems.
Vulnerability Analysis of Smart Contract for Blockchain-Based IoT Applications: A Machine Learning Approach

With the emergence of Blockchain-based Internet of Things (BIoT) applications, smart contracts have become one of the most appealing aspects because they reduce the cost and complexity of distributed administration. However, the immaturity of smart contracts may result in significant financial losses or the leakage of sensitive information. This article first investigates the taxonomy of security issues associated with smart contracts considering BIoT scenarios. To address these security concerns and overcome the limitations of existing methods, a tree-based machine learning vulnerability detection (TMLVD) method is proposed to perform the vulnerability analysis of smart contracts. TMLVD feeds the intermediate representations of smart contracts derived from abstract syntax trees (AST) into a tree-based training network for building the prediction model. Multidimensional features are captured by this model to identify smart contracts as vulnerable. The detection phase can be implemented quickly with limited computing resources and the accuracy of the detection results is guaranteed. The experimental evaluation demonstrated the effectiveness and efficiency of TMLVD on a data set comprised of Ethereum smart contracts.
Optimizing frequency allocation for fixed-frequency superconducting quantum processors

Fixed-frequency superconducting quantum processors are one of the most mature quantum computing architectures with high-coherence qubits and simple controls. However, high-fidelity multiqubit gates pose tight requirements on individual qubit frequencies in these processors, and these constraints are difficult to satisfy when constructing larger processors due to the large dispersion in the fabrication of Josephson junctions. In this paper, we propose a mixed-integer-programming-based optimization approach that determines qubit frequencies to maximize the fabrication yield of quantum processors. We study traditional qubit and qutrit (three-level) architectures with cross-resonance interaction processors. We compare these architectures to a differential ac-Stark shift based on entanglement gates and show that our approach greatly improves the fabrication yield and also increases the scalability of these devices. Our approach is general and can be adapted to problems where one must avoid specific frequency collisions.
Animal Detection and Classification from Camera Trap Images Using Different Mainstream Object Detection Architectures

Camera traps are widely used in wildlife surveys and biodiversity monitoring. Depending on its triggering mechanism, a large number of images or videos are sometimes accumulated. Some literature has proposed the application of deep learning techniques to automatically identify wildlife in camera trap imagery, which can significantly reduce manual work and speed up analysis processes. However, there are few studies validating and comparing the applicability of different models for object detection in real field monitoring scenarios. In this study, we firstly constructed a wildlife image dataset of the Northeast Tiger and Leopard National Park (NTLNP dataset). Furthermore, we evaluated the recognition performance of three currently mainstream object detection architectures and compared the performance of training models on day and night data separately versus together. In this experiment, we selected YOLOv5 series models (anchor-based one-stage), Cascade R-CNN under feature extractor HRNet32 (anchor-based two-stage), and FCOS under feature extractors ResNet50 and ResNet101 (anchor-free one-stage). The experimental results showed that performance of the object detection models of the day-night joint training is satisfying. Specifically, the average result of our models was 0.98 mAP (mean average precision) in the animal image detection and 88% accuracy in the animal video classification. One-stage YOLOv5m achieved the best recognition accuracy. With the help of AI technology, ecologists can extract information from masses of imagery potentially quickly and efficiently, saving much time.
Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement

Osteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients' MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmentation can solve these problems to a certain extent. However, existing studies usually fail to balance segmentation accuracy and efficiency. They are either sensitive to noise with low accuracy or time-consuming. So we propose an auxiliary segmentation method based on denoising and local enhancement. The method first optimizes the osteosarcoma images, including removing noise using the Edge Enhancement based Transformer for Medical Image Denoising (Eformer) and using a non-parameter method to localize and enhance the tumor region in MRI images. Osteosarcoma was then segmented by Deep Feature Aggregation for Real-Time Semantic Segmentation (DFANet). Our method achieves impressive segmentation accuracy. Moreover, it is efficient in both time and space. It can provide information about the location and extent of the osteosarcoma as a basis for further diagnosis.
Advanced security model for multimedia data sharing in Internet of Things

Abstract Sharing a file that contains multimedia data among the different peers of wireless Internet of Things (IoT) networks has several challenges. One of the main challenges is their centralized system, which leads to high‐security risk and low user reachability. One solution could be to simply change the system to a decentralized network by using the blockchain network to store these files. However, it may solve the low user reachability and security problem at the cost of low latency, longer response time, scalability and privacy issues. Therefore, this article uses the advanced blockchain scheme and distributes InterPlanetary File System. We also presented the system framework and its working. Finally, we do the security analysis of our proposed system and found that it has strong potential to solve most of the security challenges that traditional system faces. Moreover, our proposed approach can be applied to any file‐changing wireless IoT network that needs to exchange multimedia data such as healthcare data, IoT data in wearable devices, traffic data in smart cities, etc.