Reviewerly/exHarmony huggingface dataset:
Amalgamation of Blockchain and Smart Contracts in Copyright

Technology development is in full swing during the fourth industrial revolution. Numerous modifications to the service platform throughout the years have begun to be apparent. Technology and copyright exist on a vague wavelength. Furthermore, blockchain is a flexible technology with a wide range of potential uses. In particular, the article looks at how copyright might use blockchain technology to manage and distribute works that are copyrighted. The problems and challenges that can arise from such use are also examined. Blockchains have attracted a lot of interest globally. There are undoubtedly many obstacles to be solved but if they are and blockchain-based systems are successful in proving their viability in the copyright arena they have the potential to completely change the way copyright law is exercised. Certainly, there are few challenges that need to be overcome but if they are and blockchain based systems are successful in demonstrating their viability in the copyright space, they have the potential to transform copyright.
Blockchain Technology with Applications to Distributed Control and Cooperative Robotics

Various advantages of blockchain include decentralisation, persistence, anonymity, and auditability. Numerous researchers are now interested in distributed control systems that facilitate coordinated and cooperative autonomy as a result of the rising market for robotics. The fields of distributed control and cooperative robotics have constantly faced unsolvable issues with safety and distributed communication. Blockchain innovation has recently been recognized in this setting as a viable technique to solve several of the above problems, including the recognition of malicious nodes, malfunction in process automation, non-compliance with the established standards and privacy protections, safety strikes on robotic, and lack of clarity in measuring performance and audits. This research aims to offer a pathway for newcomers to enter this study area by providing a comprehensive image of blockchain technology’s operating principle and important components in the terminology of management and robotics. Blockchain innovation holds out hope for a comprehensive resolution to this issue and offers a comprehensive review of the system in the form of tutorials. The importance of blockchain in various robotics application cases is then examined. The approaches for future study that can lead to breakthroughs and successful blockchain incorporation in the robotics industry are then highlighted.
Data‐driven predictive modeling in risk assessment: Challenges and directions for proper uncertainty representation

Abstract Data‐driven predictive modeling is increasingly being used in risk assessments. While such modeling may provide improved consequence predictions and probability estimates, it also comes with challenges. One is that the modeling and its output does not measure and represent uncertainty due to lack of knowledge, that is, “epistemic uncertainty.” In this article, we demonstrate this point by conceptually linking the main elements and output of data‐driven predictive models with the main elements of a general risk description, thereby placing data‐driven predictive modeling on a risk science foundation. This allows for an evaluation of such modeling with reference to risk science recommendations for what constitutes a complete risk description. The evaluation leads us to conclude that, as a minimum, to cover all elements of a complete risk description a risk assessment using data‐driven predictive modeling needs to be supported by assessments of the uncertainty and risk related to the assumptions underlying the modeling. In response to this need, we discuss an approach for assessing assumptions in data‐driven predictive modeling.
Software architecture for quantum computing systems — A systematic review

Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower architects who can leverage architecture-centric processes, practices, description languages to model, develop, and evolve quantum computing software (quantum software for short) at higher abstraction levels. We conducted a Systematic Literature Review (SLR) to investigate (i) architectural process, (ii) modelling notations, (iii) architecture design patterns, (iv) tool support, and (iv) challenging factors for quantum software architecture. Results of the SLR indicate that quantum software represents a new genre of software-intensive systems; however, existing processes and notations can be tailored to derive the architecting activities and develop modelling languages for quantum software. Quantum bits (Qubits) mapped to Quantum gates (Qugates) can be represented as architectural components and connectors that implement quantum software. Tool-chains can incorporate reusable knowledge and human roles (e.g., quantum domain engineers, quantum code developers) to automate and customise the architectural process. Results of this SLR can facilitate researchers and practitioners to develop new hypotheses to be tested, derive reference architectures, and leverage architecture-centric principles and practices to engineer emerging and next generations of quantum software.
Magnetic skyrmions and domain walls for logical and neuromorphic computing

Abstract Topological solitons are exciting candidates for the physical implementation of next-generation computing systems. As these solitons are nanoscale and can be controlled with minimal energy consumption, they are ideal to fulfill emerging needs for computing in the era of big data processing and storage. Magnetic domain walls (DWs) and magnetic skyrmions are two types of topological solitons that are particularly exciting for next-generation computing systems in light of their non-volatility, scalability, rich physical interactions, and ability to exhibit non-linear behaviors. Here we summarize the development of computing systems based on magnetic topological solitons, highlighting logical and neuromorphic computing with magnetic DWs and skyrmions.
Anomaly detection based on multi-teacher knowledge distillation

Anomaly detection on high-dimensional data is crucial for real-world industrial applications. Recent works adopt the Knowledge Distillation (KD) technique to improve the accuracy of anomaly detection Neural Networks (NN). Most KD-based solutions only adopt a single teacher NN and have not yet fully incorporated the distinct advantages of different NN structures. To fill this gap, this paper proposes a novel Multi-teacher Knowledge Distillation approach, which effectively integrates multiple teachers with importance weights to provide guidance for the accurate anomaly detection of students. However, the importance weights are hard to get when training only with normal data. To overcome this challenge, we use an autoencoder-based reconstruction process to update teacher importance weights. In the meantime, the student model parameters are optimized by giving a set of teacher importance weights. Anomalies are then detected based on the deviations between the outputs of teacher and student, as well as the reconstruction errors through the student network. Our proposed approach is evaluated on both CIFAR10 and MVTec datasets. The results show good performance on both high-level semantic anomaly detection and low-level pixel anomaly detection.
A random elite ensemble learning swarm optimizer for high-dimensional optimization

Abstract High-dimensional optimization problems are increasingly pervasive in real-world applications nowadays and become harder and harder to optimize due to increasingly interacting variables. To tackle such problems effectively, this paper designs a random elite ensemble learning swarm optimizer (REELSO) by taking inspiration from human observational learning theory. First, this optimizer partitions particles in the current swarm into two exclusive groups: the elite group consisting of the top best particles and the non-elite group containing the rest based on their fitness values. Next, it employs particles in the elite group to build random elite neighbors for each particle in the non-elite group to form a positive learning environment for the non-elite particle to observe. Subsequently, the non-elite particle is updated by cognitively learning from the best elite among the neighbors and collectively learning from all elites in the environment. For one thing, each non-elite particle is directed by superior ones, and thus the convergence of the swarm could be guaranteed. For another, the elite learning environment is randomly formed for each non-elite particle, and hence high swarm diversity could be maintained. Finally, this paper further devises a dynamic partition strategy to divide the swarm into the two groups dynamically during the evolution, so that the swarm gradually changes from exploring the immense solution space to exploiting the found optimal areas without serious diversity loss. With the above mechanisms, the devised REELSO is expected to explore the search space and exploit the found optimal areas properly. Abundant experiments on two popularly used high-dimensional benchmark sets prove that the devised optimizer performs competitively with or even significantly outperforms several state-of-the-art approaches designed for high-dimensional optimization.
Dual-graph regularized concept factorization for multi-view clustering

Matrix factorization is an important technology that obtains the latent representation of data by mining the potential structure of data. As two popular matrix factorization techniques, concept factorization (CF) and non-negative matrix factorization (NMF) have achieved excellent results in multi-view clustering tasks. Compared with multi-view NMF, multi-view CF not only removes the non-negative constraint but also utilizes the idea of the kernel to learn the latent representation of data. However, both of them ignore the local geometric structure in the nonlinear low-dimensional manifold. Furthermore, most of the existing CF-based methods are designed for single-view tasks, which cannot be directly applied to multi-view clustering tasks. To tackle the above shortcomings, we present a new multi-view clustering algorithm, called dual-graph regularized concept factorization for multi-view clustering (MVDGCF). Specifically, we first extend conventional single-view CF to a multi-view version, which can explore the complementary information of multi-view data more effectively. Then we develop a novel dual-graph regularization strategy, which can simultaneously capture the local structure information of the data space and feature space, respectively. Moreover, an adaptive weight vector is introduced to balance the importance of different views. Finally, extensive experiments are carried out on seven datasets. The results show that our method is superior to several popular multi-view clustering methods.
Early Lung Cancer Prediction using Correlation and Regression

In this study, we created a machine learning approach for symptom-based diagnosis of lung cancer. Lung cancer detection was accomplished using a number of machine learning regression strategies. By assessing the efficacy of many regression algorithms in predicting lung cancer, we can better understand the risk factors and symptoms associated with this illness. Lung cancer predictions and evaluations are made using regression methods such the linear algorithm, polynomial regression, logistic regression, logarithmic regression, and multiple regression. Compared to other regression approaches, multiple regression has a 96% higher accuracy in predicting lung cancer. The r-squared value may be calculated using a number of regression machine learning algorithms, making it possible to evaluate the association between the symptoms and lung cancer. Several algorithms calculate a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{r}$</tex> squared value based on key symptoms, such as long-term illness, to diagnose lung cancer.
An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm

Cancer that originates in the breast tissue then spreads to the chest wall is called breast cancer. Doctors routinely examine mammograms for signs of cancer; however, aberrant macrocalcifications and microcalcifications might appear on mammograms when the picture quality is subpar. Always get checked out if you see anything out of the ordinary, especially if it involves your breasts, such abnormal calcium deposits. For this mammographic deposit to be properly interpreted, top-notch picture quality is necessary. Many different breast cancer screening methods and the many breast cancer phases are still the subject of active study. In order to construct effective medical image processing systems, experts use methods including the Ant Colony Algorithm (ACA), the Improved Adaptive Fuzzy C-Means (IAFCM), and TNM (the size of the breast tumor (T), the lymph nodes around the tumor, and metastasized). Classes were determined using an MPIG, or a modified Poisson inverse gradient classifier. More than five hundred picture modalities are used across all methods. Medical professionals that rely on images to establish diagnoses or treatments might find the results of this research useful.
Machine Learning Based Approach to Predict the Position of Robot and its Application

Having a robot that can fine-tune its own settings is a terrific way to keep up with the latest developments in mechanical technology. The principle of the pendulum serves as the basis for this idea. A framework will go through a cycle of self-adjustment to bolster itself from the inside out. The goal of this study is to provide the robot with the means of self-adjustment and balance, making it possible for it to successfully address the difficulties associated with maintaining a shaky structure. By analyzing data from the accelerometer and gyrator, the robot's accurate three-dimensional location may be calculated and sent to a controller. The regulator's own code then legitimately directs the drive module's wheels to revolve, improving the robot's steadiness. When compared to robots with four wheels, this one's superiority lies in its ability to swiftly spin and avoid obstructions. Because of this, it has become an indispensable resource in several modern contexts.
Dissipative preparation and stabilization of many-body quantum states in a superconducting qutrit array

We present and analyze a protocol for driven-dissipatively preparing and stabilizing a manifold of quantum many-body entangled states with symmetry-protected topological order. Specifically, we consider the experimental platform consisting of superconducting transmon circuits and linear microwave resonators. We perform theoretical modeling of this platform via pulse-level simulations based on physical features of real devices. In our protocol, transmon qutrits are mapped onto spin-1 systems. The qutrits' sharing of nearest-neighbor dispersive coupling to a dissipative microwave resonator enables elimination of state population in the ${S}^{\mathrm{total}}=2$ subspace for each adjacent pair, and thus, the stabilization of the many-body system into the Affleck, Kennedy, Lieb, and Tasaki state up to the edge mode configuration. We also analyze the performance of our protocol as the system size scales up to four qutrits, in terms of its fidelity as well as the stabilization time. Our work shows the capacity of driven-dissipative superconducting cQED systems to host robust and self-corrected quantum many-body states that are topologically nontrivial.
Drone Elevation Control Based on Python-Unity Integrated Framework for Reinforcement Learning Applications

Reinforcement learning (RL) applications require a huge effort to become established in real-world environments, due to the injury and break down risks during interactions between the RL agent and the environment, in the online training process. In addition, the RL platform tools (e.g., Python OpenAI’s Gym, Unity ML-Agents, PyBullet, DART, MoJoCo, RaiSim, Isaac, and AirSim), that are required to reduce the real-world challenges, suffer from drawbacks (e.g., the limited number of examples and applications, and difficulties in implementation of the RL algorithms, due to difficulties with the programing language). This paper presents an integrated RL framework, based on Python–Unity interaction, to demonstrate the ability to create a new RL platform tool, based on making a stable user datagram protocol (UDP) communication between the RL agent algorithm (developed using the Python programing language as a server), and the simulation environment (created using the Unity simulation software as a client). This Python–Unity integration process, increases the advantage of the overall RL platform (i.e., flexibility, scalability, and robustness), with the ability to create different environment specifications. The challenge of RL algorithms’ implementation and development is also achieved. The proposed framework is validated by applying two popular deep RL algorithms (i.e., Vanilla Policy Gradient (VPG) and Actor-Critic (A2C)), on an elevation control challenge for a quadcopter drone. The validation results for these experimental tests, prove the innovation of the proposed framework, to be used in RL applications, because both implemented algorithms achieve high stability, by achieving convergence to the required performance through the semi-online training process.
Replay Attention and Data Augmentation Network for 3-D Face and Object Reconstruction

3D face reconstruction from single-view images plays an important role in the field of biometrics, which is a long-standing challenging problem in the wild. Traditional 3DMM-based methods directly regressed parameters, which probably caused that the network learned the discriminative informative features insufficiently. In this paper, we propose a replay attention and data augmentation network (RADAN) for 3D dense alignment and face reconstruction. Unlike the traditional attention mechanism, our replay attention module aims to increase the sensitivity of the network to informative features by adaptively recalibrating the weight response in the attention, which typically reinforces the distinguishability of the learned feature representation. In this way, the network can further improve the accuracy of face reconstruction and dense alignment in unconstrained environments. Moreover, to improve the generalization performance of the model and the ability of the network to capture local details, we present a data augmentation strategy to preprocess the sample data, which generates the images that contain more local details and occluded face in cropping and pasting manner. Furthermore, we also apply the replay attention to 3D object reconstruction task to verify the commonality of this mechanism. Extensive experimental results on widely-evaluated datasets demonstrate that our approach achieves competitive performance compared to state-of-the-art methods. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhouzhiyuan1/RADANet</uri> .
Performance Evaluation of Fast DCP Algorithm for Single Image Dehazing

Poor weather circumstances including fog, dust, and atmospheric dispersion of other particles can degrade the clarity of photos taken in outdoor settings. High-level computer vision tasks like picture segmentation and object detection may become more difficult as a result of this issue. However, earlier research on image dehazing, such as Dark Channel Prior (DCP), suffers from a significant computing burden and degradation of the original image, such as oversaturation and halos. In this work, an improved image dehazing approach (Fast DCP) based on down sampling has been proposed. The proposed algorithm executes the dehazing of a haze image with a smaller amount computational time without degrading the quality of the image. The performance measures of proposed algorithm have been analysed in terms of PSNR, MMSE and SSIM and compared with existing DCP algorithm. The simulation results proven that, the computational time for proposed algorithm has been reduced by 70% when compared to DCP without degrading image quality.
3D vision-based bolt loosening assessment using photogrammetry, deep neural networks, and 3D point-cloud processing

Structural bolts are essential structural elements. Detection of structural bolt loosening is of great importance to provide earlier warnings of structural damages and prevent catastrophic system-level collapse. Most existing studies about bolt loosening assessment were built in 2D computer vision, where the assessment may be restricted based on the camera views. In this paper, a novel 3D vision-based methodology is proposed for autonomous bolt loosening assessment. First, a 3D point cloud of bolted connection is created using readily available 2D images. Second, a new convolutional neural network (CNN)-based method is developed to localize structural bolts in the 3D point cloud. Further, a 3D point cloud processing algorithm is developed to recognize and quantify bolt loosening. Parameter studies were conducted to investigate the effectiveness of the proposed pipeline. Finally, a real-world implementation has been conducted to quantify bolt loosening on a steel column base connection with bolts. The results indicate that the proposed bolt loosening assessment methodology can effectively localize and quantify bolt loosening at high accuracy and low cost.
DoubleU-NetPlus: a novel attention and context-guided dual U-Net with multi-scale residual feature fusion network for semantic segmentation of medical images

Accurate segmentation of the region of interest in medical images can provide an essential pathway for devising effective treatment plans for life-threatening diseases. It is still challenging for U-Net, and its modern state-of-the-art variants to effectively model the higher-level output feature maps of the convolutional units of the network mostly due to the presence of various scales of the region of interest, the intricacy of context environments, ambiguous boundaries, and multiformity of textures in medical images. In this paper, we exploit multi-contextual features and several attention strategies to increase networks' ability to model discriminative feature representation for more accurate medical image segmentation, and we present a novel dual-stacked U-Net-based architecture named DoubleU-NetPlus. The DoubleU-NetPlus incorporates several architectural modifications. In particular, we integrate EfficientNetB7 as the feature encoder module, a newly designed multi-kernel residual convolution module, and an adaptive feature re-calibrating attention-based atrous spatial pyramid pooling module to progressively and precisely accumulate discriminative multi-scale high-level contextual feature maps and emphasize the salient regions. In addition, we introduce a novel triple attention gate module and a hybrid triple attention module to encourage selective modeling of relevant medical image features. Moreover, to mitigate the gradient vanishing issue while incorporating high-resolution features with deeper spatial details, the standard convolution operation is replaced with the attention-guided residual convolution operations, which enables the model to achieve effective and relevant feature maps from a significantly increased network depth. Empirical results confirm that the proposed model accomplishes superior semantic segmentation performance compared to other state-of-the-art approaches on six publicly available benchmark datasets of diverse modalities. The proposed network achieves a Dice score of 85.17%, 99.34%, 94.30%, 96.40%, 95.76%, and 97.10% on DRIVE, LUNA, BUSI, CVCclinicDB, 2018 DSB, and ISBI 2012 datasets.