Reviewerly/exHarmony huggingface dataset:
Interval Type-2 Fuzzy Neural Network Based on Active Semi-Supervised Learning for Non-Stationary Industrial Processes

Accurate models ensure the efficient and safe operations of industrial processes. However, modeling of industrial processes with non-stationary characteristic is challenging. In this study, an interval type-2 fuzzy neural network (IT2FNN) based on active semi-supervised learning (ASSL-IT2FNN) is proposed for such industrial processes. First, an IT2FNN with adaptive fuzzy membership function (FMF) and hierarchical learning method is utilized to achieve considerable modeling accuracy and efficiency. Second, to better tackle with the non-stationary characteristic, an unsupervised distribution detection method is proposed to identify the occurrence moments of concept drift actively from the perspective of probability and spatial projection. Then, by an active semi-supervised learning method, the concept drift samples with partial labels are used to build a subnetwork, guaranteeing the performance of the constructed IT2FNN models with incomplete data in non-stationary environments. Finally, the proposed ASSL-IT2FNN is verified by benchmark simulations and real industrial data, and the experimental results demonstrate its outperformance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Concept drift is an important indicator of a non-stationary environment. It manifests as changes in the distribution of samples over time, which may reduce the accuracy of models constructed based on historical samples. This study aims to alleviate the impact of concept drift on model performance in non-stationary industrial processes while reduce the cost of sample labeling. By using the adaptive FMF and hierarchical learning method, the designed IT2FNN can effectively deal with the inherent nonlinearity of complicated industrial processes. The samples representing concept drift are actively identified by the Kullback-Leibler (KL) divergence and maximum mean difference (MMD), enabling timely detections of concept drift moments in unsupervised situations. The active semi-supervised learning is used to adapt the constructed model to handle the concept drift, wherein a subnetwork is designed based on concept drift samples and similar historical samples.
Joint least-squares algorithm correcting phase-shift errors and detector nonlinearity simultaneously in phase-shifting interferometry

Phase-shifting interferometry may suffer from the errors caused by the miscalibration of the phase shifter and the nonlinearity of the detector simultaneously. These errors are not easy to eliminate because they are generally coupled with each other in interferograms. For solving this issue, we suggest a joint least-squares phase-shifting algorithm. It allows one to decouple these errors through an alternate least-squares fitting procedure, thus accurately estimating phases, phase shifts, and coefficients of the detector response simultaneously. The converging condition of this algorithm, associated with the uniqueness of the equation solution and anti-aliasing phase shifting, is discussed. Experimental results demonstrate that this proposed algorithm is helpful for improving phase-measuring accuracy in phase-shifting interferometry.
Breast Cancer Classification from Ultrasound Images using VGG16 Model based Transfer Learning

Ultrasound based breast screening is gaining attention recently especially for dense breast.The technological advancement, cancer awareness, and cost-safety-availability benefits lead rapid rise of breast ultrasound market.The irregular shape, intensity variation, and additional blood vessels of malignant cancer are distinguishable in ultrasound images from the benign phase.However, classification of breast cancer using ultrasound images is a difficult process owing to speckle noise and complex textures of breast.In this paper, a breast cancer classification method is presented using VGG16 model based transfer learning approach.We have used median filter to despeckle the images.The layers for convolution process of the pretrained VGG16 model along with the maxpooling layers have been used as feature extractor and a proposed fully connected two layers deep neural network has been designed as classifier.Adam optimizer is used with learning rate of 0.001 and binary cross-entropy is chosen as the loss function for model optimization.Dropout of hidden layers is used to avoid overfitting.Breast Ultrasound images from two databases (total 897 images) have been combined to train, validate and test the performance and generalization strength of the classifier.Experimental results showed the training accuracy as 98.2% and testing accuracy as 91% for blind testing data with a reduced of computational complexity.Gradient class activation mapping (Grad-CAM) technique has been used to visualize and check the targeted regions localization effort at the final convolutional layer and found as noteworthy.The outcomes of this work might be useful for the clinical applications of breast cancer diagnosis.
An Improved Lightweight Yolo-Fastest V2 for Engineering Vehicle Recognition Fusing Location Enhancement and Adaptive Label Assignment

Engineering vehicle recognition based on video surveillance is one of the key technologies to assist illegal land use monitoring. At present, the engineering vehicle recognition mainly adopts the traditional deep learning model with a large number of floating-point operations. So, it cannot be achieved in edge devices with limited computing power and storage in real-time. In addition, some lightweight models have problems with inaccurate bounding box locating, low recognition rate, and unreasonable selection of positive training samples for the small object. To solve the problems, the paper proposes an improved lightweight Yolo-Fastest V2 for engineering vehicle recognition fusing location enhancement and adaptive label assignment. The location-enhanced Feature Pyramid Network (FPN) structure combines deep and shallow feature maps to accurately localize bounding boxes. The grouping k-means clustering strategy and adaptive label assignment algorithm select an appropriate anchor for each object based on its shape and Intersection over Union (IoU). The study was conducted on Raspberry Pi 4B 2018 using two datasets and different models. Experiments show that our method achieves the optimal combination in speed and accuracy. Specifically, the mAP50 is increased by 7.02 % with the speed of 11.24FPS under the engineering vehicle data obtained by video surveillance in a rural area of China.
Extended reality for biomedicine

Extended reality (XR) refers to an umbrella of methods that allows users to be immersed in a three-dimensional (3D) or a 4D (spatial + temporal) virtual environment to different extents, including virtual reality (VR), augmented reality (AR), and mixed reality (MR). While VR allows a user to be fully immersed in a virtual environment, AR and MR overlay virtual objects over the real physical world. The immersion and interaction of XR provide unparalleled opportunities to extend our world beyond conventional lifestyles. While XR has extensive applications in fields such as entertainment and education, its numerous applications in biomedicine create transformative opportunities in both fundamental research and healthcare. This Primer outlines XR technology from instrumentation to software computation methods, delineating the biomedical applications that have been advanced by state-of-the-art techniques. We further describe the technical advances overcoming current limitations in XR and its applications, providing an entry point for professionals and trainees to thrive in this emerging field.
Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges

A digital twin uses a virtual model of the physical system to fulfill the diverse requirements (e.g., latency, reliability, quality of physical experience) for emerging vehicular network applications. Although a twin-based implementation of vehicular networks can offer performance optimization, modeling a digital twin is a significantly challenging task. Federated learning (FL) is a better privacy-preserving, distributed learning scheme that can be used to model twin models. Although FL can offer performance enhancement, it requires careful design. Therefore, in this article, we present an overview of FL for a twinbased vehicular network. A general architecture for FL-enabled digital twins for a vehicular network is presented. Our proposed architecture consists of two spaces, such as twin space and the physical space. A physical space consists of all the physical entities (e.g., cars and edge servers) required for vehicular networks, whereas, the twin space refers to the logical space that is used for the deployment of twins. A twin space can be implemented either using edge servers and cloud servers. We also outline a few use cases of FL for the twin-based vehicular network. Finally, the paper is concluded and an outlook on open challenges is presented.
The optimisation of travelling salesman problem based on parallel ant colony algorithm

General search algorithms offer some solutions to solve and avoid the constraints of the finding shortest path problem. Ant Colony Optimisation (ACO) is a meta-heuristic search-based and probabilistic searching technique for an optimal path. However, ACO is computationally intensive and may not achieve the desired performance. Thus, a parallel implementation of the ACO for the travelling salesman problem is proposed and implemented on FPGA platform. Different optimisation techniques are adopted and applied to enhance the performance of the algorithm. The overall results have shown a 28-fold improvement in performance due to the applied optimisation techniques with different numbers of ants and iterations. The speedup is improved by increasing the number of iterations/ants which further validated the proposed method. The network on chip methodology was also adopted to connect the ants where a comprehensive analysis of FPGA chip traffic is modelled and emulated to get the best architecture.
Novel image processing method inspired by wavelet transform

I developed a novel image processing method inspired by wavelet transform that uses a concentric mother wavelet. This method can be used for noise filtering, feature extraction, image enhancement, and other applications and is effective for images with low spatial frequencies. When filtering small-sized images, setting masks for Fourier filtering is often difficult; however, the proposed method does not require mask setting, thus facilitating filtering. In practice, many concentric mother wavelets of different frequencies are prepared in advance, and the cross-correlation between the input image and prepared wavelets is calculated. Reconstruction or image processing can be easily performed by combining the obtained results for each frequency. I believe that the present method can aid image processing related to material analysis.
Memory Sampled-Data Control for T–S Fuzzy-Based Permanent Magnet Synchronous Generator via an Improved Looped Functional

This study presents a memory sampled-data control design for the Takagi–Sugeno (T–S) fuzzy-based permanent magnet synchronous generator (PMSG) via an improved looped functional (ILF). To do this, first, the nonlinear PMSG is modeled as a T–S fuzzy system. Then, to derive the sufficient criteria, a novel ILF is proposed, which includes the available information about the sampling pattern characteristics from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {x}(t_{k})$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {x}(t)$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {x}(t)$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {x}(t_{k+1})$ </tex-math></inline-formula> together with the signal transmission delay. In addition, the ILF introduces a fractional parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0 &lt; \hat {\beta } &lt; 1$ </tex-math></inline-formula> that gives more information of splited sampling intervals. Also, the external disturbances of the proposed system are attenuated by using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance. Furthermore, the stabilization conditions expressed as linear matrix inequalities (LMIs), the proposed closed-loop system’s asymptotic stability is ensured under the designed controller. Finally, comparison and simulation results demonstrate the effectiveness and feasibility of the proposed method and the designed control scheme.
Photon scattering errors during stimulated Raman transitions in trapped-ion qubits

We study photon scattering errors in stimulated Raman driven quantum logic gates. For certain parameter regimes, we find that previous simplified models of the process significantly overestimate the gate error rate due to photon scattering. This overestimate is shown to be due to previous models neglecting the detuning dependence of the scattered photon frequency and Lamb-Dicke parameter, a second scattering process, interference effects on scattering rates to metastable manifolds, and the counterrotating contribution to the Raman transition rate. The resulting improved model shows that there is no fundamental limit on gate error due to photon scattering for electronic ground-state qubits in commonly used trapped-ion species when the Raman laser beams are red detuned from the main optical transition. Additionally, photon scattering errors are studied for qubits encoded in the metastable ${D}_{5/2}$ manifold, showing that gate errors below ${10}^{\ensuremath{-}4}$ are achievable for all commonly used trapped ions.
Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering

To date, graph collaborative filtering (CF) strategies have been shown to outperform pure CF models in generating accurate recommendations. Nevertheless, recent works have raised concerns about fairness and potential biases in the recommendation landscape since unfair recommendations may harm the interests of Consumers and Producers (CP). Acknowledging that the literature lacks a careful evaluation of graph CF on CP-aware fairness measures, we initially evaluated the effects on CP-aware fairness measures of eight state-of-the-art graph models with four pure CF recommenders. Unexpectedly, the observed trends show that graph CF solutions do not ensure a large item exposure and user fairness. To disentangle this performance puzzle, we formalize a taxonomy for graph CF based on the mathematical foundations of the different approaches. The proposed taxonomy shows differences in node representation and neighbourhood exploration as dimensions characterizing graph CF. Under this lens, the experimental outcomes become clear and open the doors to a multi-objective CP-fairness analysis (Codes are available at: https://github.com/sisinflab/ECIR2023-Graph-CF .).
Synchronization of Hyperchaotic Systems Based on Intermittent Control and its Application in Secure Communication

The synchronization of master-slave hyperchaotic systems is investigated by intermittent control and proved by the Lyapunov stable theory. Meanwhile, a new secure communication scheme is designed for the continuous and digital information. The encrypted information is transmitted to receiver through the intermittent controller, which reduces the disturbance to the synchronization of master-slave systems and improves the security and reliability of secure communication. Before transmitting to the receiver, the continuous signal is firstly modulated and masked by the chaotic signals. Furthermore, an encryption algorithm for the digital information of color image is proposed by the pseudo-random sequences of Chen hyperchaotic system, and then the encrypted image is modulated and masked by the variables of the master system. The original image can be decrypted successfully at the receiving end after the slave system is synchronized with the master system. Finally, the feasibility and effectiveness of this scheme are verified by simulation experiments. In addition, the security analysis of the image encryption algorithm is also discussed, such as key sensitivity, correlation coefficient, NIST test, and return map.
Quantum speed limit of Jaynes-Cummings model with detuning for arbitrary initial states

The quantum speed limit (QSL) of the Jaynes-Cummings model with detuning for arbitrary initial states is investigated. We mainly focus on the influences of the detuning, width of Lorentzian spectral density, and coherence of the initial state on the non-Markovian speedup evolution in an open system. It is found that even in the Markovian regime, increasing the detuning parameter leads to quantum speedup. Moreover, we reveal that the QSL has an inverse relation with the population of the initial excited state. Notably, we show that the QSL depends on the quantum coherence of the system's initial state such that the maximal coherent state can saturate its bound.
Clothing Image Classification with DenseNet201 Network and Optimized Regularized Random Vector Functional Link

To ameliorate the precision of clothing image classification, we proposed a clothing image classification method via the DenseNet201 network based on transfer learning and the optimized regularized random vector functional link (RVFL). First, the formula extracts weight’s parameters about DenseNet201 that is pre-trained on the ImageNet dataset for transfer learning, thereby obtaining an incipient network，after that trim this model parameters. The modified network is utilized to pick up the clothing image features output by the DenseNet201’s global average pooling layer. Second, regularization coefficient is introduced to control RVFL’s model complexity and solve the problem of over-fitting. Then, the generated solution vector of aquila optimizer (AO) is produced by marine predators algorithm (MPA). The input weights, biases of hidden layer and renormalization modulus of regularized RVFL are optimized using the improved AO algorithm. Finally, we use the optimized RVFL to assort abstracted fashion graphics traits. We use Accuracy, Macro-F1, Macro-R and Macro-P to assess the algorithm’s ability and compare this algorithm with ResNet50 network, ResNet101 network, DenseNet201 network, InceptionV3 network and different classifiers, which use DenseNet201 as the feature extractor to get the input. From the experimental results, this algorithm proposed has excellent classification power and generalization ability.
Decentralized Vision-Based Byzantine Agent Detection in Multi-robot Systems with IOTA Smart Contracts

Multiple opportunities lie at the intersection of multi-robot systems and distributed ledger technologies (DLTs). In this work, we investigate the potential of new DLT solutions such as IOTA, for detecting anomalies and byzantine agents in multi-robot systems in a decentralized manner. Traditional blockchain approaches are not applicable to real-world networked and decentralized robotic systems where connectivity conditions are not ideal. To address this, we leverage recent advances in partition-tolerant and byzantine-tolerant collaborative decision-making processes with IOTA smart contracts. We show how our work in vision-based anomaly and change detection can be applied to detecting byzantine agents within multiple robots operating in the same environment. We show that IOTA smart contracts add a low computational overhead while allowing to build trust within the multi-robot system. The proposed approach effectively enables byzantine robot detection based on the comparison of images submitted by the different robots and detection of anomalies and changes between them.
Self-supervised anomaly detection, staging and segmentation for retinal images

Unsupervised anomaly detection (UAD) is to detect anomalies through learning the distribution of normal data without labels and therefore has a wide application in medical images by alleviating the burden of collecting annotated medical data. Current UAD methods mostly learn the normal data by the reconstruction of the original input, but often lack the consideration of any prior information that has semantic meanings. In this paper, we first propose a universal unsupervised anomaly detection framework SSL-AnoVAE, which utilizes a self-supervised learning (SSL) module for providing more fine-grained semantics depending on the to-be detected anomalies in the retinal images. We also explore the relationship between the data transformation adopted in the SSL module and the quality of anomaly detection for retinal images. Moreover, to take full advantage of the proposed SSL-AnoVAE and apply towards clinical usages for computer-aided diagnosis of retinal-related diseases, we further propose to stage and segment the anomalies in retinal images detected by SSL-AnoVAE in an unsupervised manner. Experimental results demonstrate the effectiveness of our proposed method for unsupervised anomaly detection, staging and segmentation on both retinal optical coherence tomography images and color fundus photograph images.
High-resistivity niobium nitride films for saturated-efficiency SMSPDs at telecom wavelengths and beyond

Incorporating a micrometer scale strip as the sensitive element in superconducting single-photon detectors can lead to significant improvements in their speed, footprint, and fabrication yield. However, the current application of microstrips has resulted in a decline in the detectors' intrinsic detection efficiency. We address this issue through the utilization of niobium nitride films with high values of resistance per square. Notably, the films used in our study possess an important characteristic of retaining high critical temperature values, which enables the devices to operate in conventional closed-cycle cryostats.
E-TPE: <u>E</u> fficient <u>T</u> humbnail- <u>P</u> reserving <u>E</u> ncryption for Privacy Protection in Visual Sensor Networks

When visual sensor networks (VSNs) enter daily life in society, they not only bring great convenience but also cause people to worry about privacy. Traditional image encryption often displays the effect of the enter snowflake in encrypted images playing a part in privacy protection, but the usability of directly browsing images taken by nodes in VSNs is sacrificed. Recently, thumbnail-preserving encryption (TPE) has been proposed to balance image usability and privacy. However, the Markov chain’s limited connectivity and high time cost, which result in low efficiency, may preclude its application in VSNs. Motivated by this, we propose a novel TPE scheme to protect the image collected in VSNs privacy efficiently. We first deeply study the bijective relationship between the three-pixel group and its rank, and then propose a new number method for both, based on which we design the efficient rank mapping. Subsequently, an efficient thumbnail-preserving image encryption via three-pixel rank mapping (E-TPE) is designed, achieving NR security and enhancing the Markov chain’s connectivity. The experiments demonstrate the scheme’s efficiency, that is, it is the currently quickest ideal-TPE scheme with NR security, and that a good balance is achieved between usability and privacy.