Reviewerly/exHarmony huggingface dataset:
Behind the Bait: Delving into PhishTank's hidden data

Phishing constitutes a form of social engineering that aims to deceive individuals through email communication. Extensive prior research has underscored phishing as one of the most commonly employed attack vectors for infiltrating organizational networks. A prevalent method involves misleading the target by employing phishing URLs concealed through hyperlink strategies. PhishTank, a website employing the concept of crowd-sourcing, aggregates phishing URLs and subsequently verifies their authenticity. In the course of this study, we leveraged a Python script to extract data from the PhishTank website, amassing a comprehensive dataset comprising over 190,0000 phishing URLs. This dataset is a valuable resource that can be harnessed by both researchers and practitioners for enhancing phish- ing filters, fortifying firewalls, security education, and refining training and testing models, among other applications.
Research on efficient detection network method for remote sensing images based on self attention mechanism

Remote sensing images are widely used in aerial drones, satellites, and other fields. However, traditional object detection methods face challenges of low efficiency and accuracy due to the diverse scales, numerous target types, and complex backgrounds of remote sensing images. To address these issues, this paper proposes a remote sensing image detection network based on self-attention, which adaptively learns the relative importance of pixels to achieve more precise and efficient object detection in remote sensing images, meeting the demands of large-scale remote sensing image processing. Firstly, through end-to-end training, the object detection network is optimized as a whole to directly output detection results from the input remote sensing images, eliminating the need for additional intermediate steps. This not only reduces the impact of information loss and inconsistency but also simplifies the entire detection process. Secondly, we integrate the Faster R-CNN architecture as the foundation, combining region extraction and object classification into a unified process. Lastly, we embed self-attention mechanisms at different levels of the Faster R-CNN to progressively extract multi-scale and multi-level feature information, enhancing the network's ability to learn the correlation of information from different positions in the image, automatically capturing the relationships between objects, and improving the accuracy of object detection. This significantly reduces redundant computation, making it more efficient for large-scale remote sensing image processing. Experimental verification demonstrates that this approach outperforms traditional methods in terms of detection accuracy and efficiency, better addressing the particularities of remote sensing images, and providing an efficient and precise solution for aerial drone and satellite image processing. Remote sensing image detection has become one of the research hotspots in the field of remote sensing, holding significant theoretical significance and practical application value.
Geochemical baselines of potentially toxic elements in Brunei-Muara soils, Brunei Darussalam: Towards environmental assessment and risk management

We present a comprehensive study on the presence of potentially toxic elements (PTEs) in the topsoil and subsoil of Brunei-Muara District in Brunei Darussalam. Descriptive statistics were employed to analyze the data, and the threshold values for PTEs in both soil layers were determined. Adopting the Tukey's inner fence method, we recommend baselines for PTEs. The study delved into the distribution patterns and potential sources of PTEs in Brunei-Muara, and we identified traffic and agricultural activities as significant contributors to the presence of As, Cr, Cu, and Pb. Industrial and agricultural areas exhibit spatial associations with Mn, Ni and Zn. Cadmium (Cd) was determined to be negligible in Brunei-Muara's soils. Furthermore, geochemical mapping unveiled highly acidic soil conditions in the region, characterized by variations in pH between the topsoil (2.13–8.00) and subsoil (1.89–6.76) layers that generally facilitate mobility of heavy metals. We employed Principal Component Analysis on the log10-transformed data to examine the behavior and mechanisms governing the mobility and retention of PTEs in the soil. This helped us to better characterize the element distribution within the baseline and the recognition of three additional processes enriching the topsoil and subsoil in PTEs. Overall, this study offers valuable insights into the distribution, sources, and behavior of PTEs in the soils of the Brunei-Muara District, which can greatly aid environmental monitoring and management endeavors.
Stable Minimum Solution Calibration Method Using 2-D Laser Radar and Camera

Two-dimensional laser radar and cameras are widely used to perceive external environments in unmanned vehicle navigation, target recognition, mobile robot obstacle avoidance, and simultaneous localization and mapping. Herein, the two sensors must be calibrated first to fuse laser data and image. The minimum solution method for calibration is one of the most popular ones but has some shortcomings, such as poor numerical stability and accuracy. Therefore, we present a new stable minimum solution calibration method, which introduces the RP3P algorithm to solve the perspective three-point problem (P3P) and improve the mechanisms of optimal solution selection. First, according to the P3P problem constructed using three checkerboards, the enhanced RP3P algorithm is used to improve the stability of the solution. Second, an error measurement model based on region and boundary constraints is proposed to evaluate the deviation of the multiple solutions to select the optimal solution. Simulation and actual experimental results show that the proposed algorithm can significantly improve the percentage of valid solutions and calibration accuracy compared with the algorithms in the literature. In the simulation experiments, compared with Francisco’s, Hu’s, and Zhou’s methods, the percentage of valid solutions of our method is improved by 7%-33%, 6%-10%, and 5%-9% under different noise levels. Furthermore, the accuracy of the calibration result of our calibration method obtained significant performance improvements.
Double-Layer InSb Photonic Crystal Structure for Enhanced Nonreciprocal OTS and Its Potential in Extending the Measurement Range of Bivariate Sensors

In this paper, the dependence of optical Tamm state (OTS) to the refractive index of the interface medium is used to form nonreciprocal absorption peaks after introducing the double InSb layers on both sides of the structure. And compared to the introduction of a single layer, the energy coupling between the double InSb layers is stronger, which greatly improves the absorption performance. The enhancement of the incident angle can significantly regulate the polarization separation of the absorption peak in different directions, and the degree of nonreciprocity will also be affected by temperature and magnetic field. In the opposite direction, the relevant absorption peaks show a differentiated linear range for refractive indexes and incident angles, thereby increasing the measurement range (MR) of dual variable detection. For positive incidence, the MR, sensitivity (S) and detection limit (DL) of refractive index detection are 0.52 THz/RIU, 2.6-3.4 RIU, and 5.5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> RIU, respectively. The corresponding indicators of angle measurement are successively 0.0074 THz/degree, 15-40 degrees, and 0.039 degrees. For negative incidence, the performance indexes of refractive index measurement are 0.81 THz/RIU, 2.1-2.6 RIU, and 3.25×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> RIU in turn. The angle detection characteristics are 0.01 THz/degree, 40-76 degrees, and 0.047 degrees. By comparison, the MR of the refractive index sensor is extended by at least 62.5%, while that of the angle sensor rises by 69.4%. We hope that the proposed design is beneficial to the improvement of sensor performances.
A Low-Complexity Air-Digit-Writing Recognition Method Based on Adaptive Trajectory Learning Using MIMO Radar

Touchless hand gesture recognition is one of the hottest areas of the human-computer interaction. In this paper, we propose a low-complexity air-digit-writing recognition method based on adaptive trajectory learning using a frequency modulated continuous wave multiple-input-multiple-output (MIMO) radar. The core idea of the proposed method is the adaptive learning trajectory, which includes the rough trajectory estimation, the effective trajectory interception, and the fine trajectory processing. Firstly, the rough trajectory estimation is sequentially conducted by the clutter suppression, range object detection, direction of arrival estimation, angle object detection and centroid estimation. This could reduce the effective feature dimension and further the complexity of the following network and real-time system. Secondly, to remove the interference of redundant actions, the effective trajectory interception employs the proposed double adaptive thresholds gradient valley search approach to detect the start and end of the hand gestures. Thirdly, the fine trajectory processing is performed by the position-shift, normalization and resampling to eliminate the differences of hand gestures, including the position, size and speed, and improve the generalization ability and accuracy. Moreover, in order to further reduce the computational complexity, a single-layer long short-term memory network is constructed for identification. Finally, the method is deployed on a commonly used micro unit control to realize real-time system. The experimental results show that the proposed method can achieve an average accuracy of 99.6% in train and test spilt test, 98.24% in leave one subject out test, 97.4% in real-time test on trained people, and 97% in real-time test on untrained people.
Dual-stream pyramid and attention network for stereo matching

In order to accurately estimate the disparity of ill-posed regions, such as weak texture and occlusion regions, we propose DSPANet, a stereo matching network that incorporates a dual-stream pyramid module and a channel and spatial attention module. The dual-stream pyramid module captures numerous complementary features from different layers by utilizing multi-resolution inputs and feature extraction blocks. This approach enables the learning of local detailed features at various scales. These features at various scales are then combined to calculate the stereo matching cost. By incorporating channel and spatial attention module into the feature extraction process to obtain richer and more concise contextual information, the matching cost can be constructed more accurately, which provides powerful conditions for subsequent cost aggregation. In the cost aggregation stage, we utilize the stacked hourglass module for both encoding and decoding. Additionally, we incorporate 3D global attention upsampling during the decoding stage, which enables high-level features to provide guidance information to low-level features in a simple way. We evaluate our proposed method on the Scene Flow dataset, as well as the KITTI2012 and KITTI2015 datasets. The experimental results demonstrate that our DSPANet achieves superior performance and effectively enhances the matching results in ill-posed regions. Our code has been implemented using PyTorch and will be released after paper publication at https://github.com/Shi-LiQing/DSPANet.
MTT-27 Autonomous and Connected Systems: Report for 2021–2022 [MTT-S Society News]

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
A hierarchical probabilistic underwater image enhancement model with reinforcement tuning

Underwater Image Enhancement (UIE) is a challenging problem due to the complex underwater environment. Traditional UIE methods can hardly adapt to various underwater environments. Deep learning-based UIE methods are more powerful but often rely on a large deal of real-world underwater images with distortion-free reference images. This gives rise to two issues: First, the reference images are highly uncertain because the ground-truth images cannot be are captured directly in underwater environment. Second, learning-based methods may lack generalization ability for diverse underwater environments. To tackle these issues, we propose HPUIE-RL, a hierarchical probabilistic UIE model facilitated by reinforcement learning. This model integrates UNet with hierarchical probabilistic modules to produce various enhanced candidate images that reflect the uncertainty of the enhancement. Then, a reinforcement learning fine-tuning framework is designed to fine-tune the pretrained model in an unsupervised manner, which responds to the dynamic underwater environment. Experiments on real-world datasets from diverse underwater environments demonstrate that our HPUIE-RL model outperforms state-of-the-art UIE methods regarding visual and quantitative performance and generalizability.
A low-noise, high-SNR and large-dynamic-range balanced homodyne detector for broadband squeezed light measurement

Squeezing-based quantum metrology has been extensively applied in the field of precision measurement. Performance characteristics of squeezed light are obtained by a balanced homodyne detector. Here, we report a large dynamic range balanced homodyne detector with low-noise and high-signal-to-noise-ratio (SNR) for broadband squeezed light measurement. We perform a comprehensive theoretical analysis of the SNR in a wide bandwidth and experimentally analyze the important printed circuit board (PCB) layout and electronic components. Consequently, in the 100 MHz frequency band, the SNR and common mode rejection ratio (CMRR) rise over 12.6 dB and 55 dB, respectively, and the electronic noise is suppressed below −90 dB. The directly observed squeezing level at 1550 nm with the specified detector is 11.8 dB at 2 MHz and 3.1 dB at 100 MHz.
Efficient number theoretic transform accelerator for CRYSTALS-Kyber

&lt;p&gt;The national institute of standards and technology (NIST) has presented its draft of the module-lattice-based key-encapsulation mechanism standard (MLBKEMS), choosing cryptographic suite for algebraic lattices (CRYSTALS)- Kyber as the base encryption. Existing hardware implementations of modern cryptography will need to process the new standard efficiently. The primary process in CRYSTALS-Kyber key-encapsulation mechanism (KEM) is the number theoretic transform (NTT), which requires heavy computing power. This paper contributes an efficient hardware accelerator for NTT and inverse NTT (INTT) by CRYSTAL-Kyber parameters. The proposed design utilizes the K-RED algorithm for reducing polynomial multiplication. It also incorporates the BrentKung method for efficient modular addition and subtraction operation with an address generator to control the sequences of computation. On the Xilinx Artix 7 field programmable gate array (FPGA), our design achieves 262 MHz clock speed, utilizing only 1405 LUTs.&lt;/p&gt;
Navigation of mobile robots using neural networks and genetic algorithms

The navigation of robots has been a subject of widespread interest over the last few decades. In the previous years, traditional methods based on mathematical equations were used, and there has been an evolution towards the use of methods based on artificial intelligence. Two of which have been used in this work: neural networks and genetic algorithms. Neural networks are used as a machine learning model to teach the robot to move from any starting point to a goal, avoiding obstacles along the way. However, this model needs an algorithm to learn how to carry out this activity, which is what the genetic algorithm will be used for. Furthermore, this method of navigation will be compared with the traditional method based on potential fields, where it can be observed how this new method based on artificial intelligence improves and solves some typical problems of the old methods, such as the tendency to get stuck in local minima.
Table 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

&lt;p&gt;Patient characteristics: MGUS.&lt;/p&gt;
Figure 3 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

&lt;p&gt;Performance evaluation of MoSaicNet and AwareNet deep learning models: &lt;b&gt;A&lt;/b&gt;, The ROC curves and AUC values of the MoSaicNet superpixel classifier. The values in brackets indicate the 95% CI. &lt;b&gt;B,&lt;/b&gt; Two-dimensional mapping of superpixels using MoSaicNet learned 200-dimensional features after dimensionality reduction by UMAP. &lt;b&gt;C,&lt;/b&gt; The ROC curves and AUC values of single-cell classifier model on separately held test data. The values in brackets indicate the 95% CI. &lt;b&gt;D,&lt;/b&gt; UMAP features visualization of deep learned features by AwareNet single-cell classifier CNN. &lt;b&gt;E&lt;/b&gt; and &lt;b&gt;F,&lt;/b&gt; Validation of AwareNet model using correlation of density of CD8&lt;sup&gt;+&lt;/sup&gt; (&lt;b&gt;E&lt;/b&gt;) and CD4&lt;sup&gt;+&lt;/sup&gt; (&lt;b&gt;F&lt;/b&gt;) cells in panel 1 and panel 2.&lt;/p&gt;
Table 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

&lt;p&gt;Patient characteristics: paired diagnostic and posttreatment samples.&lt;/p&gt;
Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

&lt;div&gt;Abstract&lt;p&gt;Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect the diverse cellular communities and mosaic tissue habitats, we developed a superpixel-inspired deep learning method (MoSaicNet) that adapts to complex tissue architectures and a cell imbalance aware deep learning pipeline (AwareNet) to enable accurate detection and classification of rare cell types in multiplex immunohistochemistry images. MoSaicNet and AwareNet achieved an AUC of &gt;0.98 for tissue and cellular classification on separate test datasets. Application of MoSaicNet and AwareNet enabled investigation of bone heterogeneity and thickness as well as spatial histology analysis of bone marrow trephine samples from monoclonal gammopathies of undetermined significance (MGUS) and from paired newly diagnosed and posttreatment multiple myeloma. The most significant difference between MGUS and newly diagnosed multiple myeloma (NDMM) samples was not related to cell density but to spatial heterogeneity, with reduced spatial proximity of BLIMP1&lt;sup&gt;+&lt;/sup&gt; tumor cells to CD8&lt;sup&gt;+&lt;/sup&gt; cells in MGUS compared with NDMM samples. Following treatment of patients with multiple myeloma, there was a reduction in the density of BLIMP1&lt;sup&gt;+&lt;/sup&gt; tumor cells, effector CD8&lt;sup&gt;+&lt;/sup&gt; T cells, and regulatory T cells, indicative of an altered immune microenvironment. Finally, bone heterogeneity decreased following treatment of patients with multiple myeloma. In summary, deep learning–based spatial mapping of bone marrow trephine biopsies can provide insights into the cellular topography of the myeloma marrow microenvironment and complement aspirate-based techniques.&lt;/p&gt;Significance:&lt;p&gt;Spatial analysis of bone marrow trephine biopsies using histology, deep learning, and tailored algorithms reveals the bone marrow architectural heterogeneity and evolution during myeloma progression and treatment.&lt;/p&gt;&lt;/div&gt;
Figure 4 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

&lt;p&gt;Studying bone physiology using MoSaicNet. &lt;b&gt;A&lt;/b&gt;, Proportion of different compartments of BM trephine digital images. &lt;b&gt;B–E,&lt;/b&gt; One stacked bar represents a sample. Box plots showing the difference in %bone between samples from NDMM and posttreatment (&lt;b&gt;B&lt;/b&gt;), MGUS and NDMM (&lt;b&gt;C&lt;/b&gt;), different age groups (median age = 58.0 years; &lt;b&gt;D&lt;/b&gt;), and gender groups (&lt;b&gt;E&lt;/b&gt;). &lt;b&gt;F,&lt;/b&gt; Scatter plot showing the number of bone superpixels in 17 clusters from MGUS, NDMM, and posttreatment samples. The size of the dots represents the percentage of superpixels. The color represents the number of slides in each cluster. &lt;b&gt;G,&lt;/b&gt; Correlation of percentage of superpixels in each cluster between different patient groups. A point represents a cluster. &lt;b&gt;H,&lt;/b&gt; Scatter plot of slide-level heterogeneity of bone features measured by features variance (Supplementary Materials and Methods). A point represents a patient/slide. &lt;b&gt;I&lt;/b&gt; and &lt;b&gt;J,&lt;/b&gt; Box plots showing differences in bone density heterogeneity between NDMM and posttreatment (&lt;b&gt;I&lt;/b&gt;), and between MGUS and NDMM (&lt;b&gt;J&lt;/b&gt;). &lt;b&gt;K–N,&lt;/b&gt; Box plots showing the difference in bone thickness between samples from NDMM and posttreatment (&lt;b&gt;K&lt;/b&gt;), MGUS and NDMM (&lt;b&gt;L&lt;/b&gt;), and different age groups (median age = 58.0 years; &lt;b&gt;M&lt;/b&gt;) and gender (&lt;b&gt;N&lt;/b&gt;).&lt;/p&gt;
Supplementary Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

&lt;p&gt;Supplementary methods, tables and figures.&lt;/p&gt;
Figure 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

&lt;p&gt;Overview of computational deep learning and image processing pipelines for BM MIHC images: &lt;b&gt;A,&lt;/b&gt; MoSaicNet pipeline. The polygons (black) indicate superpixels. MoSaicNet dissects a tissue section into bone, blood, fat, and cellular tissue regions (Supplementary Materials and Methods). &lt;b&gt;B,&lt;/b&gt; AwareNet for attention-based cell detection and classification (Supplementary Materials and Methods). The attention image pixel values were generated from the abundance of cell types. An attention image was applied to the objective function during model parameter optimization to regularize the algorithm by assigning high attention to rare cell types. The cell detection algorithm generates a cell probability map. A postprocessing algorithm was developed to find the cell nucleus center, (&lt;i&gt;x&lt;/i&gt;, &lt;i&gt;y&lt;/i&gt;) location, from the probability map (Supplementary Materials and Methods). A patch centered on each cell was extracted and fed to deep learning (DL)-based classifier to infer its class. &lt;b&gt;C,&lt;/b&gt; Spatial and morphologic analysis of BM trephine samples. Bone texture and structural heterogeneity were investigated using an autoencoder-based machine learning method (Supplementary Materials and Methods). We used spatial proximity analysis to study the spatial relations of cells. &lt;i&gt;r&lt;/i&gt;, radius. Cell density refers to the number of cells per unit of tissue area.&lt;/p&gt;