Reviewerly/exHarmony huggingface dataset:
Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools

Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.
Comparing and Contrasting Near-Field, Object Space, and a Novel Hybrid Interaction Technique for Distant Object Manipulation in VR

In this contribution, we propose a hybrid interaction technique that integrates near-field and object-space interaction techniques for manipulating objects at a distance in virtual reality (VR). The objective of the hybrid interaction technique was to seamlessly leverage the strengths of both the near-field and object-space manipulation techniques. We employed bimanual near-field metaphor with scaled replica (BMSR) as our near-field interaction technique, which enabled us to perform multilevel degrees-of-freedom (DoF) separation transformations, such as 1~3DoF translation, 1~3DoF uniform and anchored scaling, 1DoF and 3DoF rotation, and 6DoF simultaneous translation and rotation, with enhanced depth perception and fine motor control provided by near-field manipulation techniques. The object-space interaction technique we utilized was the classic Scaled HOMER, which is known to be effective and appropriate for coarse transformations in distant object manipulation. In a repeated measures within-subjects evaluation, we empirically evaluated the three interaction techniques for their accuracy, efficiency, and economy of movement in pick-and-place, docking, and tunneling tasks in VR. Our findings revealed that the near-field BMSR technique outperformed the object space Scaled HOMER technique in terms of accuracy and economy of movement, but the participants performed more slowly overall with BMSR. Additionally, our results revealed that the participants preferred to use the hybrid interaction technique, as it allowed them to switch and transition seamlessly between the constituent BMSR and Scaled HOMER interaction techniques, depending on the level of accuracy, precision and efficiency required.
A Comparative Study of Performance Between Federated Learning and Centralized Learning Using Pathological Image of Endometrial Cancer

Federated learning, an innovative artificial intelligence training method, offers a secure solution for institutions to collaboratively develop models without sharing raw data. This approach offers immense promise and is particularly advantageous for domains dealing with sensitive information, such as patient data. However, when confronted with a distributed data environment, challenges arise due to data paucity or inherent heterogeneity, potentially impacting the performance of federated learning models. Hence, scrutinizing the efficacy of this method in such intricate settings is indispensable. To address this, we harnessed pathological image datasets of endometrial cancer from four hospitals for training and evaluating the performance of a federated learning model and compared it with a centralized learning model. With optimal processing techniques (data augmentation, color normalization, and adaptive optimizer), federated learning exhibited lower precision but higher recall and Dice similarity coefficient (DSC) than centralized learning. Hence, considering the critical importance of recall in the context of medical image processing, federated learning is demonstrated as a viable and applicable approach in this field, offering advantages in terms of both performance and data security.
High-fidelity robust qubit control by phase-modulated pulses

We present a set of robust and high-fidelity pulses that realize paradigmatic operations such as the transfer of the ground state population into the excited state and arbitrary $X/Y$ rotations on the Bloch sphere. These pulses are based on the phase modulation of the control field. We provide an experimental proof-of-concept of these operations by using a transmon qubit, demonstrating resilience against deviations in the drive amplitude of more than $\ensuremath{\approx}20%$, and/or detuning from the qubit transition frequency in the order of $10\phantom{\rule{4pt}{0ex}}\mathrm{MHz}$. This modulation scheme is straightforward to implement in practice and can be deployed to any other qubit-based experimental platform.
Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems

In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm’s feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO’s wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving.
Algorithms of the Möbius function by random forests and neural networks

Abstract The Möbius function $$\mu (n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> is known for containing limited information on the prime factorization of n . Its known algorithms, however, are all based on factorization and hence are exponentially slow on $$\log n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> . Consequently, a faster algorithm of $$\mu (n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> could potentially lead to a fast algorithm of prime factorization which in turn would throw doubt upon the security of most public-key cryptosystems. This research introduces novel approaches to compute $$\mu (n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> using random forests and neural networks, harnessing the additive properties of $$\mu (n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . The machine learning models are trained on a substantial dataset with 317,284 observations (80%), comprising five feature variables, including values of n within the range of $$4\times 10^9$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>4</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>9</mml:mn> </mml:msup> </mml:mrow> </mml:math> . We implement the Random Forest with Random Inputs (RFRI) and Feedforward Neural Network (FNN) architectures. The RFRI model achieves a predictive accuracy of 0.9493, a recall of 0.5865, and a precision of 0.6626. On the other hand, the FNN model attains a predictive accuracy of 0.7871, a recall of 0.9477, and a precision of 0.2784. These results strongly support the effectiveness and validity of the proposed algorithms.
Recognition of REEs anomalies using an image Fusion fractal-wavelet model in Tarom metallogenic zone, NW Iran

This study aims to detect REE geochemical anomalies in relationship to Iron-apatite ores utilizing an image Fusion based on Deep Learning (FDL). The geochemical data was modeled for elements related to Iron-apatite mineralization using multi b Spline B. The results were fusioned in applying the Deep learning method based on pre-trained networks. Wavelet-Number (WN) fractal model classified the best results based on the combination of a two-dimensional Discrete Wavelet Transformation (DWT) signal analysis and a Concentration-Area (C-A) fractal modeling. Sym8 carried the DWT as a selected wavelet pattern for REE based on Stream sediment samples collected from the Tarom region (NW Iran). In addition, the DWT was decomposed by wavelet coefficients at five levels. Furthermore, the DWT data were classified using a fractal-wavelet model to delineate REE anomalies from background levels in this region. Overlayed with the catchment basins model and weighted using the upstream and downstream parts. As a result, the prominent REE source anomalies are located in the southern parts of the study area. The results obtained by the proposed fractal-wavelet modeling are in connection with field check anomaly samples and the rock samples collected from the Iron-Apatite ore deposits.
Scheduling optimization of electric energy meter distribution vehicles for intelligent batch rotation

<h2>Abstract</h2> As industrial technology continues to advance through integration, society's demand for electricity is rapidly increasing. To meet the requirements of refined grid management and address the elevated challenges arising from the increased electrical load, this paper delves into the investigation of distribution vehicle scheduling for the practical scenario of batch rotation of smart meters. Initially, based on the practical distribution task requirements of a provincial metrology verification center, a multi-level optimization model is constructed for the batch rotation and distribution vehicle scheduling of smart meters. The primary objective is to maximize the enhancement of smart meter distribution efficiency while minimizing the overall distribution cost. Moreover, this paper introduces a refined Grey Wolf Optimization algorithm (OLC-GWO) based on Opposition-based Learning, Levy flight strategy, and Cauchy mutation to solve the model. By generating an opposite population to improve the quality of initial feasible solutions and further harnessing the global search capabilities of Levy flight and Cauchy mutation operators, the algorithm's effectiveness is enhanced. The algorithm is subjected to testing using multiple benchmark functions and its performance is compared with variants of GWO, as well as several cutting-edge intelligent optimization algorithms including Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Honey Bee Algorithm (HBA). The results indicate that OLC-GWO exhibits excellent performance in terms of convergence speed and optimization capability. Finally, the improved algorithm is subjected to simulation experiments by incorporating order data from the practical distribution operations of a provincial metrology verification center. The outcomes verify the efficiency of the proposed algorithm, reinforcing the practical significance of the established model in addressing the real-world challenge of batch rotation and distribution vehicle scheduling for smart meters.
The 2<sup>3</sup> Factorial Design in R

Abstract The paper describes the factorial design of the experiment with three input factors that change on two levels. For given values of the input parameters, it is shown how to obtain a variance analysis table and which factors and interactions between factors are significant. The example was done in the software intended for the design of the experiment and in the software R. It is shown how to use the software R to arrive at the final solution of the given example.
A zero-trust journey through the threat landscape

Zero trust is being widely adopted as a fundamental concept in cyber security architectures, and its implementation has accelerated due to so many organisations introducing new working practices. But creating a zero-trust environment is not simple, and requires a shift in mindset away from thinking about security in terms of fixed lines of defence. And it's not just a change in technology – it requires you to look deeply at your processes and corporate culture, too. So what are the best ways of embarking on the zero-trust journey?
SRSegNet: Super-resolution-assisted small targets polyp segmentation network with combined high and low resolution

Accurate and reliable segmentation of polyp targets is crucial in the treatment of colorectal cancer. However, in clinical practice, polyp structures represent only a small portion of the image, which is essential for the accurate diagnosis and treatment of colorectal cancer. The small size of lesion areas leads to a reduction in feature representation, thereby affecting the performance of traditional segmentation methods. To address this issue, To address this issue, recent advancements in segmentation methods for small medical objects meticulously extract feature information from these objects using attention mechanisms. However, these methods mainly focus on general small medical objects and perform poorly in the segmentation of small polyps.Therefore, this study fully exploits the advantages of joint learning and multi-task learning. By guiding the high-resolution features to guide the low-resolution encoder through joint learning and fusing effective features from the super-resolution task to assist the segmentation task, our method enhances the model’s ability to extract texture details. We also design a high-resolution guidance module and an information complementation module, effectively integrating learning between different resolutions and tasks.Evaluation experiments on two settings on the Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, CVC-300, and ETIS datasets demonstrate that our method achieves significant advantages in the segmentation of small polyps, outperforming various state-of-the-art methods.
Robust multi-view learning via M-estimator joint sparse representation

Recently, multi-view learning has achieved extraordinary success in many research areas such as pattern recognition and data mining. Most existing multi-view methods mainly focus on exploring the correlation information between different views and their performance may severely degrade in the presence of heavy noises and outliers. In this paper, we put forward a robust multi-view joint sparse representation (RMJSR) method for multi-view learning. Firstly, we design a novel multi-view Cauchy estimator based loss function originating from robust statistics to address complex noises and outliers in reality. Based on this, we leverage the ℓ1,q norm to enhance our model by encouraging the learned representation of multiple views to share the same sparsity pattern. Secondly, to explore the optimal solution for the RMJSR model, we devise an effective optimization algorithm based on the half-quadratic (HQ) theory and the alternating direction method of multipliers (ADMM) framework. Thirdly, we provide the theoretical guarantee for revealing the theoretical condition for the success of the proposed method. Further, we have also provided extensive analysis of the proposed method, including the optimality condition, convergence analysis, and complexity analysis. Extensive experimental results validate the effectiveness and robustness of the proposed method in comparison with state-of-the-art competitors. The source code is available at https://github.com/Huyutao7/RMJSRC.
DAST-Net: Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection

This paper introduces an innovative end-to-end trainable framework named Dense Attention-aware Spatio-Temporal Network (DAST-Net) for video anomaly detection. The framework adeptly leverages both spatial and temporal data in an unsupervised manner, eliminating the need for manually crafted features. To enhance spatial feature representation, DAST-Net incorporates visual attention-aware residual connections within the denser residual network (DenserResNet), deviating from traditional identity skip connections. The rationale behind this connection choice is to augment the contextual understanding of features across various scales. For capturing temporal patterns, the framework employs a Convolutional LSTM Autoencoder (ConvLSTM-AE) module, enabling effective learning and representation of temporal dependencies in video data. Consequently, discriminating features from attention modules are combined with the features extracted by the ConvLSTM-AE module, enhancing visual recognition capabilities for both spatial and temporal aspects. Our proposed architecture outperforms state-of-the-art methods on four benchmark datasets, showcasing AUC scores of 85.4% on Ped1, 97.9% on Ped2, 89.8% on Avenue, and 73.7% on the ShanghaiTech dataset. The results demonstrate the performance of our method in identifying unusual events in video data.
GAN-based adaptive cost learning for enhanced image steganography security

A well-defined cost function is a key issue for image steganography to minimize the embedding distortion. In recent years, deep learning has been introduced into image steganography to automatically learn embedding costs and improve steganographic security. For most existing generative adversarial network (GAN) based cost learning works, the generator usually adopts an encoder–decoder architecture. However, due to repeated encoding and decoding operations, this architecture is prone to information loss, making the generator difficult to well capture fine-grained image features. In this work, we propose a novel GAN-based image steganography work that improves the cost function by learning better embedding probability maps. Specifically, we design an attention mechanism to be integrated into the U-Net architecture, which enables the generator to concentrate on texture-rich regions of input images. Moreover, an extra input stream, namely the enhanced image, is introduced into the generator, improving the generator’s ability to learn structural features from input images. Different skip connections are used for different input streams to facilitate information flow between different layers. Extensive experimental results demonstrate that the proposed approach effectively learns the embedding probability maps and achieves superior security against various steganalysis attacks.
Explicit knowledge transfer of graph-based correlation distillation and diversity data hallucination for few-shot object detection

The performance of few-shot object detection has seen marked improvement through fine-tuning paradigms. However, existing methods often depend on shared parameters to implicitly transfer knowledge without explicit induction. This results in novel-class representations that are easily confused with similar base classes and poorly suited to diverse patterns of variation in the truth distribution. In view of this, the present paper focuses on mining transferable base-class knowledge, which is further subdivided into inter-class correlation and intra-class diversity. First, we design a graph to dynamically capture the relationship between base and novel class representations, and then introduce distillation techniques to tackle the shortage of correlation knowledge in few-shot labels. Furthermore, an efficient diversity knowledge transfer module based on the data hallucination is proposed, which can adaptively disentangle class-independent variation patterns from base-class features and generate additional trainable hallucinated instances for novel classes. Experiments on VOC and COCO datasets confirmed that our proposed method effectively reduces the reliance on novel-class samples and demonstrates superior performance compared to other state-of-the-art baseline methods.
A deep learning-based and adaptive region proposal algorithm for semantic segmentation

This paper presents an adaptive and new region proposal algorithm for generating high-quality regions. The main aim of this algorithm is to investigate different features in the proposal generation process. This algorithm is based on bottom-up image segmentation and deep learning techniques. Each primary region is represented by features derived from a convolutional neural network (CNN). The adjacent and similar regions are merged based on a new proposed searching algorithm and a distance function in a hierarchical way. Additionally, various hand-crafted texture features are examined for representing each region. These texture features are not being utilized previously in region proposal generation. This method also applied both texture-based and deep learning-based features, contemporaneously. Furthermore, the proposed region proposal algorithm was evaluated on two significant challenging datasets, including VOC2012 and COCO2017. The resulting proposals show more high-quality regions in high overlaps in comparison to previous region proposal algorithms. More importantly, the results verified that the deep learning-based features together with handcrafted texture features are complement and this fusion can overcome the shortcoming of other approaches. Additionally, the proposed region proposal algorithm is employed in weakly supervised semantic segmentation. The new generated proposals are used to create some new labeled masks. These masks are very useful and effective in the training phase of deep learning. This approach is evaluated on VOC2012 and compared with previous region-based and free region-based methods. The results shows the efficacy of the proposed method.