Reviewerly/exHarmony huggingface dataset:
A pruning feedforward small-world neural network by dynamic sparse regularization with smoothing l1/2 norm for nonlinear system modeling

Aiming to solve the problem of the relatively large architecture for the small-world neural network and improve its generalization ability, we propose a pruning feedforward small-world neural network based on a dynamic regularization method with the smoothing l1/2 norm (PFSWNN-DSRL1/2) and apply it to nonlinear system modeling. A feedforward small-world neural network is first constructed by the rewiring rule of Watts–Strogatz. By minimizing the modified error function added with a smoothing l1/2 norm, redundant weights are pruned to generate a sparse architecture. A dynamic adjusting strategy is further designed for the regularization strength to balance the tradeoff between the training accuracy and the sparsity. Several experiments are carried out to evaluate the performance of the proposed PFSWNN-DSRL1/2 on nonlinear system modeling. The results show that the PFSWNN-DSRL1/2 can achieve the satisfactory modeling accuracy with an average of 17% pruned weights. The comparative results demonstrate that the generalization performance of the proposed model is improved by 8.1% relative to the baseline method (FSWNN) but with a sparse structure, and the pruning does not degenerate its small-world property.
Adaptive vibration control method for double-crystal monochromator base on VMD and FxNLMS

Double-crystal monochromators (DCMs) are one of the most critical optical devices in beamlines at synchrotron sources, directly affecting the quality of the beam energy and position. As the performance of synchrotron light sources continues to improve, higher demands are placed on the stability of DCMs. This paper proposes a novel adaptive vibration control method combining variational modal decomposition (VMD) and filter-x normalized least mean squares (FxNLMS), ensuring DCM stability under random engineering disturbance. Firstly, the sample entropy of the vibration signal is selected as the fitness function, and the number of modal components k and the penalty factor α are optimized by a genetic algorithm. Subsequently, the vibration signal is decomposed into band frequencies that do not overlap with each other. Eventually, each band signal is individually governed by the FxNLMS controller. Numerical results have demonstrated that the proposed adaptive vibration control method has high convergence accuracy and excellent vibration suppression performance. Furthermore, the effectiveness of the vibration control method has been verified with actual measured vibration signals of the DCM.
The Impact of Prior Mammograms on the Diagnostic Performance of Radiologists in Early Breast Cancer Detection: A Focus on Breast Density, Lesion Features and Vendors Using Wholly Digital Screening Cases

This study aims to investigate the diagnostic efficacy of radiologists when reading screening mammograms in the absence of previous images, and with the presence of prior images from the same and different vendors.612 radiologists' readings across 9 test sets, consisting of 540 screening mammograms (361-normal and 179-cancer) with 245 cases having prior images obtained from same vendor as current images, 129 from a different vendor and 166 cases having no prior images, were retrospectively analysed. True positive (sensitivity), true negative (specificity) and area under ROC curve (AUC) values of radiologists were calculated for three groups of cases (without prior images (NP), with prior images from same vendor (SP), and with prior images from different vendor (DP)). Logistic regression was used to estimate the odds ratio (OR) of true positive, true negative and true cancer localization among case groups with different levels of breast density and lesion characteristics.Radiologists obtained 12.8% and 10.3% higher sensitivity in NP and DP than SP (0.803-and-0.785 vs. 0.712; p < 0.0001). Specificity in NP and DP cases were 4.8% and 2.0% lower than SP cases (0.749 and 0.771 vs. 0.787). The AUC values for NP and DP were significantly higher than SP cases across different levels of breast density (0.814-and-0.820 vs. 0.782; p < 0.0001). The odds ratio (OR) of true positive for NP relative to SP was 1.6 (p < 0.0001) and DP relative to SP was 1.5 (p < 0.0001). Radiologists were more like to detect architectural distortion in DP than SP cases (OR = 3.2; p < 0.0001), whilst the OR for abnormal calcifications was 2.85 (p < 0.0001).Cases without previous mammograms or with prior mammograms obtained from different vendors were more likely to benefit radiologists in cancer detection, whilst prior mammograms undertaken from the same vendor were more useful for radiologists in evaluating normal cases.
High-Speed Videogrammetry for Seismic Performance of the Spherical Reticulated Shell Structure on the Shaking Table

Spherical reticulated shell structure is an important structural form of large-span space buildings. It is of great significance to monitor three-dimensional (3D) dynamic responses of spherical reticulated shell structure to better understand its seismic performances, which will be helpful in the future to ensure the healthy condition of large-span space buildings during their lifespan. In this study, with the advantages of non-contact and high accuracy, a high-speed videogrammetric measurement method is proposed for monitoring the 3D dynamic responses of the seismically isolated, spherical, reticulated shell structural model. Two issues—the high-speed videogrammetric acquisition system and network configuration, as well as image sequence target tracking and positioning—are emphasized to achieve a cache of high-speed images and to improve the accuracy of tracking and positioning target points. The experimental results on the shaking table from the proposed method have been compared with those from traditional Optotrak Certus and accelerometers. The results prove that the proposed method is capable and useful for analyzing the seismic performance of spherical reticulated shell structures, as the dynamic responses monitoring accuracy of the method can reach the submillimeter level, with root mean square error values of 0.32 mm, 0.7 mm and 0.06 mm in the X, Y and Z directions, respectively.
SAM-HIT: A Simulated Annealing Multispectral to Hyperspectral Imagery Data Transformation

Space-borne hyperspectral imagery data are known for their high spectral resolution in a number of narrow wavelength intervals, which makes these data useful for mineral mapping. However, the available free-of-charge hyperspectral scenes cover only narrow and scattered geographic areas. In contrast, multispectral imagery scenes have a nearly complete spatial coverage and wider wavelength intervals. The low spectral resolution of the multispectral data, however, limits their efficiency in the mineral mapping of small geological massifs or hydrothermal alteration halos. The present contribution presents a new transformation tool (SAM-HIT) to simulate the hyperspectral sensor responses in unscanned areas based on partially overlapping hyperspectral and multispectral scenes. Simulation or prediction of the pseudohyperspectral data is here accomplished by using the simulated annealing linear optimization algorithm, which allows the lowest possible mismatch between the original and predicted data. The high visual and numerical correlation of the resultant data confirms the reliability of the newly adopted transformation. Further, the application of the SAM-HIT to a well-exposed part of the Egyptian basement complex with available hyperspectral data showed high concordance and nearly identical band signatures, opening a new outlook for mineral exploration in vast areas by a nearly automated cost-free means.
Improving Multiple Pedestrian Tracking in Crowded Scenes with Hierarchical Association

Recently, advances in detection and re-identification techniques have significantly boosted tracking-by-detection-based multi-pedestrian tracking (MPT) methods and made MPT a great success in most easy scenes. Several very recent works point out that the two-step scheme of first detection and then tracking is problematic and propose using the bounding box regression head of an object detector to realize data association. In this tracking-by-regression paradigm, the regressor directly predicts each pedestrian's location in the current frame according to its previous position. However, when the scene is crowded and pedestrians are close to each other, the small and partially occluded targets are easily missed. In this paper, we follow this pattern and design a hierarchical association strategy to obtain better performance in crowded scenes. To be specific, at the first association, the regressor is used to estimate the positions of obvious pedestrians. At the second association, we employ a history-aware mask to filter out the already occupied regions implicitly and look carefully at the remaining regions to find out the ignored pedestrians during the first association. We integrate the hierarchical association in a learning framework and directly infer the occluded and small pedestrians in an end-to-end way. We conduct extensive pedestrian tracking experiments on three public pedestrian tracking benchmarks from less crowded to crowded scenes, demonstrating the proposed strategy's effectiveness in crowded scenes.
Trace Elements in Pyrite and Its Crystallization Temperature: An Example of Gold Deposits in the Darasun Goldfield, Eastern Transbaikalia, Russia

The distributions of trace elements in pyrite were studied in samples of high-grade gold ores from the Talatui, Teremkyn, and Darasun deposits. The paper presents LA-ICP-MS data on concentrations of trace elements in pyrite in gold ores from mineral deposits of the Darasun goldfield, which were produced by a single fluid–magmatic system at various temperatures. The high-temperature pyrite was found to be enriched in Co, Se, and W, whereas the medium-temperature pyrite was enriched in Cu, Zn, Ag, Te, As, and In. The behaviors of some elements (Ni, Au, Bi, Sb, and Pb) seem to be independent of temperature. The identified trends and relations in the behaviors of elements can be used in studying the Au-Bi mineralization of the intrusion-related type.
Non-Identical Inverter Rings as an Entropy Source: NIST-90B-Verified TRNG Architecture on FPGAs for IoT Device Integrity

True random key generator (TRNG) architectures play a notable role in strengthening information security infrastructure. The development of new entropy sources based on reconfigurable hardware is always in demand, especially for the integrity of devices in IoT applications. TRNGs can be adopted for generating unique device IDs that form the data network in the IoT. A ring oscillator (RO) is an efficient entropy source which can be implemented on FPGAs or realised as ASIC hardware. This work proposes a non-identical RO array as an entropy source. The TRNG architecture, based on an increasing odd number of inverters per ring, was extensively studied. The various statistical and hardware analyses provided encouraging results for this reliable entropy unit. The suggested device-independent non-identical RO structure was implemented on five different types of FPGA hardware belonging to the Xilinx and Intel families, consuming 13 registers and nearly 15 combinational functions. This TRNG achieved a throughput of 3.5 Mbps. While the emergence of the Gaussian response evaluated true randomness, the NIST 800-90B and NIST 800-22 tests yielded good results in terms of the justification of randomness evolving from the proposed TRNG architecture.
Reconsidering Read and Spontaneous Speech: Causal Perspectives on the Generation of Training Data for Automatic Speech Recognition

Superficially, read and spontaneous speech—the two main kinds of training data for automatic speech recognition—appear as complementary, but are equal: pairs of texts and acoustic signals. Yet, spontaneous speech is typically harder for recognition. This is usually explained by different kinds of variation and noise, but there is a more fundamental deviation at play: for read speech, the audio signal is produced by recitation of the given text, whereas in spontaneous speech, the text is transcribed from a given signal. In this review, we embrace this difference by presenting a first introduction of causal reasoning into automatic speech recognition, and describing causality as a tool to study speaking styles and training data. After breaking down the data generation processes of read and spontaneous speech and analysing the domain from a causal perspective, we highlight how data generation by annotation must affect the interpretation of inference and performance. Our work discusses how various results from the causality literature regarding the impact of the direction of data generation mechanisms on learning and prediction apply to speech data. Finally, we argue how a causal perspective can support the understanding of models in speech processing regarding their behaviour, capabilities, and limitations.
Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT

Resource management is a key issue that needs to be addressed in the future smart Internet of Things (IoT). This paper focuses on a Federated Learning (FL)-based resource management mechanism in IoT. It incorporates blockchain technology to guarantee the security of the FL model parameters exchange. We propose an IoT resource management framework incorporating blockchain and federated learning technologies; then, a specific FL-based resource management with a blockchain trust assurance algorithm is given. We use a Support Vector Machine (SVM) classifier to detect malicious nodes in order to avoid the impact on the performance of the FL-based algorithm. Finally, we perform simulation to verify the SVM classification effect and the proposed algorithm performance. The results show that the SVM-based malicious node identification accuracy can be acceptable. Moreover, the proposed algorithm obtains better performance when malicious nodes are excluded from the FL selected participant.
Impulsive Noise Suppression Methods Based on Time Adaptive Self-Organizing Map

Removal of noise and restoration of images has been one of the most interesting topics in the field of image processing in the past few years. Existing filter-based methods can remove image noise; however, they cannot preserve image quality and information such as lines and edges. In this article, various classifiers and spatial filters are combined to achieve desirable image restoration. Meanwhile, the time adaptive self-organizing map (TASOM) classifier is more emphasized in our feature extraction and dimensionality reduction approaches to preserve the details during the process, and restore the images from noise. The TASOM was compared with the self-organizing map (SOM) network, and a suitable noise reduction method for images was attempted. As a result, we achieved an optimum method to reduce impulsive noise. In addition, by using this neural network, better noise suppression was achieved. Experimental results show that the proposed method effectively removes impulse noise and maintains color information as well as image details.
AF-OSD: An Anchor-Free Oriented Ship Detector Based on Multi-Scale Dense-Point Rotation Gaussian Heatmap

Due to the complexity of airborne remote sensing scenes, strong background and noise interference, positive and negative sample imbalance, and multiple ship scales, ship detection is a critical and challenging task in remote sensing. This work proposes an end-to-end anchor-free oriented ship detector (AF-OSD) framework based on a multi-scale dense-point rotation Gaussian heatmap (MDP-RGH) to tackle these aforementioned challenges. First, to solve the sample imbalance problem and suppress the interference of negative samples such as background and noise, the oriented ship is modeled via the proposed MDP-RGH according to its shape and direction to generate ship labels with more accurate information, while the imbalance between positive and negative samples is adaptively learned for the ships with different scales. Then, the AF-OSD based on MDP-RGH is further devised to detect the multi-scale oriented ship, which is the accurate identification and information extraction for multi-scale vessels. Finally, a multi-task object size adaptive loss function is designed to guide the training process, improving its detection quality and performance for multi-scale oriented ships. Simulation results show that extensive experiments on HRSC2016 and DOTA ship datasets reveal that the proposed method achieves significantly outperforms the compared state-of-the-art methods.
Noise2Clean: Cross-Device Side-Channel Traces Denoising with Unsupervised Deep Learning

Deep learning (DL)-based side-channel analysis (SCA) has posed a severe challenge to the security and privacy of embedded devices. During its execution, an attacker exploits physical SCA leakages collected from profiling devices to create a DL model for recovering secret information from victim devices. Despite this success, recent works have demonstrated that certain countermeasures, such as random delay interrupts or clock jitters, would make these attacks more complex and less practical in real-world scenarios. To address this challenge, we present a novel denoising scheme that exploits the U-Net model to pre-process SCA traces for “noises” (i.e., countermeasures) removal. Specifically, we first pre-train the U-Net model on the paired noisy-clean profiling traces to obtain suitable parameters. This model is then fine-tuned on the noisy-only traces collected from the attacking device. The well-trained model will be finally deployed on the attacking device to remove the noises (i.e., countermeasures) from the measured power traces. In particular, a new inductive transfer learning method is also utilized in our scheme to transfer knowledge learned from the source domain (i.e., profiling device) to the target domain (i.e., attacking device) to improve the model’s generalization ability. During our experimental evaluations, we conduct a detailed analysis of various countermeasures separately or combined and show that the proposed denoising model outperforms current state-of-the-art work by a large margin, e.g., a reduction of at least 30% in computation costs and 5× in guessing entropy.
A large-scale continuous optimization benchmark suite with versatile coupled heterogeneous modules

The complexity of the engineering design optimizations mainly sources from the large-scale nature of the problems, and the ever-increasing large-scale global optimization (LSGO) problems place high demands on the performance of evolutionary algorithms (EAs). To compare and analyze large-scale optimization algorithms, benchmarks that simulate the features from real-world large-scale optimization are desired. In this paper, we propose a novel large-scale continuous optimization benchmark suite, which includes 15 test functions with the modular structure. The proposed benchmark suite introduces two new features abstracted from the engineering design problems: (1) Heterogeneous design, i.e., the modules are significantly different in terms of difficulty and explicit expressions. (2) Versatile coupling, i.e., different coupling degrees and random coupling topologies of the benchmark are emphasized. Two typical cooperative coevolution frameworks and several state-of-the-art large-scale optimization algorithms are used for comparative studies on the proposed benchmark, and experimental results demonstrate that the proposed benchmark suite is very challenging.
A bidirectional semantic segmentation method for remote sensing image based on super-resolution and domain adaptation

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An Improved Medical Image Compression Method Based on Wavelet Difference Reduction

Advanced microscopic techniques such as high-throughput, high-content, multispectral, and 3D imaging could include many images per experiment requiring hundreds of gigabytes (GBs) of memory. Efficient lossy image-compression methods such as joint photographic experts group (JPEG) and JPEG 2000 are crucial to managing these large amounts of data. However, these methods can get visual quality with high compression ratios but do not necessarily maintain the medical data and information integrity. This paper proposes a novel and improved medical image compression method based on color wavelet difference reduction. Specifically, the proposed method is an extension of the standard wavelet difference reduction (WDR) method using mean co-located pixel difference to select the optimum quantity of color images that present the highest similarity in the spatial and temporal domain. The images with large spatiotemporal coherence are encoded as one volume and evaluated regarding the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed method is evaluated in the challenging histopathological microscopy image analysis field using 31 slides of colorectal cancer. It is found that the perceptual quality of the medical image is remarkably high. The results indicate that the PSNR improvement over existing schemes may reach up to 22.65 dB compared to JPEG 2000. Also, it can reach up to 10.33dB compared to a method utilizing discrete wavelet transform (DWT), leading us to implement a mobile and web platform that can be used for compressing and transmitting microscopic medical images in real time.
Hierarchical Coupled Discriminative Dictionary Learning for Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize images of novel classes, but does not use any images belonging to the novel classes during model training, which is realized by exploiting the auxiliary semantic information. Recently, most ZSL methods focus on learning visual-semantic embeddings to transfer knowledge from the seen classes to the novel classes. Visual-semantic embedding is usually established based on the visual features of images and the semantic information of classes, i.e., class attributes. However, image features are extracted at the individual level, while class attributes are obtained at the group level, so the granularity of these features is different, which makes it difficult to match the two kinds of features. To tackle such problem, we propose hierarchical coupled discriminative dictionary learning (HCDDL) method to hierarchically establish visual-semantic embedding at class-level and image-level with a coarse-to-fine way. Firstly, a class-level coupled dictionary is trained to build basic and coarse-grained connection between visual space and semantic space. Using the class-level coupled dictionary, image attributes are generated. Based on the fine-grained image attributes and images features, an image-level coupled dictionary is learned. In addition, during the learning of hierarchical coupled dictionaries, the discriminative losses are adopted to ensure dictionaries learn more accurate representation, which is beneficial to the recognition task. Recognition of unseen images is performed through searching the class nearest to the unseen image in multiple spaces. Experiments on four widely used benchmark datasets show the effectiveness of the proposed method, and sufficient ablation experiments demonstrate that the coarse-to-fine way leads to good performances.