Reviewerly/exHarmony huggingface dataset:
An Effective Deep Learning based Recommender System with user and item embedding

The success of deep learning in a variety of fields has spurred a rush of desire to develop new novel recommender systems. As a result, our research introduces new RecDNNing, a ground-breaking technique that combines integrated people and products with a deep learning model. There are two phases to the suggested suggestion method. We begin by constructing a unique mathematical version for each human and item, which we refer to as user and item embedding. The embeddings of the goods and users then are aggregated, concatenated, and sent into the deep learning model. In the second phase, the spliced people and items implants are utilised as sources, and the ratings scores are forecasted using the forward propagation technique. The suggested new RecDNNing algorithm outperforms state-of-the-art techniques on MovieLens, according to the results.
Adaptive measurement filter: efficient strategy for optimal estimation of quantum Markov chains

Continuous-time measurements are instrumental for a multitude of tasks in quantum engineering and quantum control, including the estimation of dynamical parameters of open quantum systems monitored through the environment. However, such measurements do not extract the maximum amount of information available in the output state, so finding alternative optimal measurement strategies is a major open problem. In this paper we solve this problem in the setting of discrete-time input-output quantum Markov chains. We present an efficient algorithm for optimal estimation of one-dimensional dynamical parameters which consists of an iterative procedure for updating a `measurement filter&amp;apos; operator and determining successive measurement bases for the output units. A key ingredient of the scheme is the use of a coherent quantum absorber as a way to post-process the output after the interaction with the system. This is designed adaptively such that the joint system and absorber stationary state is pure at a reference parameter value. The scheme offers an exciting prospect for optimal continuous-time adaptive measurements, but more work is needed to find realistic practical implementations.
On-Demand-FL: A Dynamic and Efficient Multicriteria Federated Learning Client Deployment Scheme

In this article, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, FL, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the model converges, the server combines the updated weights obtained from each data set over a number of rounds. The majority of the literature suggested client selection techniques to accelerate convergence and boost accuracy. However, none of the existing proposals have focused on the flexibility to deploy and select clients as needed, wherever and whenever that may be. Due to the extremely dynamic surroundings, some devices are actually not available to serve as clients in FL, which affects the availability of data for learning and the applicability of the existing solution for client selection. In this article, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process. We make use of the containerization technology, such as Docker, to build efficient environments using Internet of Things and mobile devices serving as volunteers. Furthermore, Kubernetes is used for orchestration. A multiobjective optimization problem representing the client and model deployment is solved using the genetic algorithm (GA) due to its evolutionary strategy. The performed experiments using the mobile data challenge (MDC) data set and the Localfed framework illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed with less discarded rounds and more available data.
Wind dynamic and energy-efficiency path planning for unmanned aerial vehicles in the lower-level airspace and urban air mobility context

Unmanned aerial vehicles (UAVs) have been extensively used in urban environments for logistics, parcel delivery and surveillance, and the development of air taxi services. Given the dynamic nature of urban air mobility in terms of decision-making time limit, wind dynamics and other external factors, one should consider their safe and efficient operations in an urban context. Therefore, we propose an energy-efficient path-planning model for UAVs under large and complex urban environments and wind dynamics. The proposed method adopted Voronoi Diagram to decompose the complex urban environment into a simplified network model, given the presence of no-fly zones and restricted areas as obstacles. One could obtain the feasible initial path by solving the network model using the Dijkstra shortest path algorithm concerning the distance matrix. Given the nonlinearity of energy consumption along a path. We further model the UAV energy consumption and propose an efficient particle swarm optimisation (PSO) metaheuristic algorithm to achieve better solution quality. Compared to traditional PSO, the proposed algorithm achieved a 5% to 22% improvement under different wind scenarios by simulating real-life situations. In conclusion, the proposed method can achieve safe UAV operations with sufficient separation and less energy consumption.
Better Steady than Speedy: Full Break of SPEEDY-7-192

Differential attacks are among the most important families of cryptanalysis against symmetric primitives. Since their introduction in 1990, several improvements to the basic technique as well as many dedicated attacks against symmetric primitives have been proposed. Most of the proposed improvements concern the key-recovery part. However, when designing a new primitive, the security analysis regarding differential attacks is often limited to finding the best trails over a limited number of rounds with branch and bound techniques, and a poor heuristic is then applied to deduce the total number of rounds a differential attack could reach. In this work we analyze the security of the SPEEDY family of block ciphers against differential cryptanalysis and show how to optimize many of the steps of the key-recovery procedure for this type of attacks. For this, we implemented a search for finding optimal trails for this cipher and their associated multiple probabilities under some constraints and applied non-trivial techniques to obtain optimal data and key-sieving. This permitted us to fully break SPEEDY-7-192, the 7-round variant of SPEEDY supposed to provide 192-bit security. Our work demonstrates among others the need to better understand the subtleties of differential cryptanalysis in order to get meaningful estimates on the security offered by a cipher against these attacks.
Run Your 3D Object Detector on NVIDIA Jetson Platforms:A Benchmark Analysis

This paper presents a benchmark analysis of NVIDIA Jetson platforms when operating deep learning-based 3D object detection frameworks. Three-dimensional (3D) object detection could be highly beneficial for the autonomous navigation of robotic platforms, such as autonomous vehicles, robots, and drones. Since the function provides one-shot inference that extracts 3D positions with depth information and the heading direction of neighboring objects, robots can generate a reliable path to navigate without collision. To enable the smooth functioning of 3D object detection, several approaches have been developed to build detectors using deep learning for fast and accurate inference. In this paper, we investigate 3D object detectors and analyze their performance on the NVIDIA Jetson series that contain an onboard graphical processing unit (GPU) for deep learning computation. Since robotic platforms often require real-time control to avoid dynamic obstacles, onboard processing with a built-in computer is an emerging trend. The Jetson series satisfies such requirements with a compact board size and suitable computational performance for autonomous navigation. However, a proper benchmark that analyzes the Jetson for a computationally expensive task, such as point cloud processing, has not yet been extensively studied. In order to examine the Jetson series for such expensive tasks, we tested the performance of all commercially available boards (i.e., Nano, TX2, NX, and AGX) with state-of-the-art 3D object detectors. We also evaluated the effect of the TensorRT library to optimize a deep learning model for faster inference and lower resource utilization on the Jetson platforms. We present benchmark results in terms of three metrics, including detection accuracy, frame per second (FPS), and resource usage with power consumption. From the experiments, we observe that all Jetson boards, on average, consume over 80% of GPU resources. Moreover, TensorRT could remarkably increase inference speed (i.e., four times faster) and reduce the central processing unit (CPU) and memory consumption in half. By analyzing such metrics in detail, we establish research foundations on edge device-based 3D object detection for the efficient operation of various robotic applications.
An improved UAV target detection algorithm based on ASFF-YOLOv5s

&lt;abstract&gt;&lt;p&gt;Object detection in drone-captured scenarios is a recent popular task. Due to the high flight altitude of unmanned aerial vehicle (UAV), the large variation of target scales, and the existence of dense occlusion of targets, in addition to the high requirements for real-time detection. To solve the above problems, we propose a real-time UAV small target detection algorithm based on improved ASFF-YOLOv5s. Based on the original YOLOv5s algorithm, the new shallow feature map is passed into the feature fusion network through multi-scale feature fusion to improve the extraction capability for small target features, and the Adaptively Spatial Feature Fusion (ASFF) is improved to improve the multi-scale information fusion capability. To obtain anchor frames for the VisDrone2021 dataset, we improve the K-means algorithm to obtain four different scales of anchor frames on each prediction layer. The Convolutional Block Attention Module (CBAM) is added in front of the backbone network and each prediction network layer to improve the capture capability of important features and suppress redundant features. Finally, to address the shortcomings of the original GIoU loss function, the SIoU loss function is used to accelerate the convergence of the model and improve accuracy. Extensive experiments conducted on the dataset VisDrone2021 show that the proposed model can detect a wide range of small targets in various challenging environments. At a detection rate of 70.4 FPS, the proposed model obtained a precision value of 32.55%, F1-score of 39.62%, and a mAP value of 38.03%, which improved 2.77, 3.98, and 5.1%, respectively, compared with the original algorithm, for the detection performance of small targets and to meet the task of real-time detection of UAV aerial images. The current work provides an effective method for real-time detection of small targets in UAV aerial photography in complex scenes, and can be extended to detect pedestrians, cars, etc. in urban security surveillance.&lt;/p&gt;&lt;/abstract&gt;
Multi-task learning for few-shot biomedical relation extraction

Abstract Artificial intelligence (AI) has advanced rapidly, but it has limited impact on biomedical text understanding due to a lack of annotated datasets (a.k.a. few-shot learning). Multi-task learning, which uses data from multiple datasets and tasks with related syntax and semantics, has potential to address this issue. However, the effectiveness of this approach heavily relies on the quality of the available data and its transferability between tasks. In this paper, we propose a framework, built upon a state-of-the-art multi-task method (i.e. MT-DNN), that leverages different publicly available biomedical datasets to enhance relation extraction performance. Our model employs a transformer-based architecture with shared encoding layers across multiple tasks, and task-specific classification layers to generate task-specific representations. To further improve performance, we utilize a knowledge distillation technique. In our experiments, we assess the impact of incorporating biomedical datasets in a multi-task learning setting and demonstrate that it consistently outperforms state-of-the-art few-shot learning methods in cases of limited data. This results in significant improvement across most datasets and few-shot scenarios, particularly in terms of recall scores.
Quantum feature maps for graph machine learning on a neutral atom quantum processor

Using a Rydberg quantum processor with up to 32 qubits, the authors implement machine learning tasks on data structured into graphs and show that their platform can distinguish two different graph connectivities. To illustrate the potential of such a method, they show that it can classify the toxicity of a given molecule based on a real-world biochemistry dataset.
Adaptive feature selection for image steganalysis based on classification metrics

Feature selection can remove redundant and useless features, which is an essential way to improve steganalysis efficiency. However, with the diversity of steganalysis features, feature selection has run into bottlenecks of high time cost, poor universality, and experience depending on parameter setting. To this end, an adaptive steganalytic feature selection based classification metrics is proposed. First, the categories to which the features belong are redefined and classified. Secondly, three metrics are proposed for three different categories of features, to make the metric more precise. Then, to reduce the computational cost and optimize parameters, two adaptive threshold models are designed, which achieve the purpose of fast and effective feature selection without relying on the time-consuming classification results. Experimental results on 11 typical steganalytic features demonstrate that compared with classic and state-of-the-art feature selection methods, the proposed method achieves competitive performance on detection accuracy, calculation cost, storage cost, and universality.
Ultimate Quantum Sensitivity in the Estimation of the Delay between two Interfering Photons through Frequency-Resolving Sampling

We demonstrate the ultimate sensitivity allowed by quantum physics in the estimation of the time delay between two photons by measuring their interference at a beam splitter through frequency-resolving sampling measurements. This sensitivity can be increased quadratically by decreasing the photonic temporal bandwidth even at values smaller than the time delay when standard two-photon interferometers become inoperable and without adapting the path of the reference photon, nor the need of time-resolving detectors with an unfeasible high resolution. Applications can range from the more feasible imaging of nanostructures, including biological samples, and nanomaterial surfaces to quantum enhanced estimation based on frequency-resolved boson sampling in optical networks.
Optimal Trajectory Generation for Transitioning Quadrotor Biplane Tailsitter using Differential Flatness

This paper presents the development of an optimization-based trajectory planner for the autonomous transition of a quadrotor biplane tailsitter (QRBP) between the flight modes of hover to forward flight and forward flight to hover. The trajectory planner is formulated as an optimization problem with an embedded dynamic model of the QRBP, vehicle design constraints (e.g. power), physical constraints (e.g. stall) and initial/terminal states for the transition. A differentially flat reformulation is employed to reduce the computational cost of the trajectory planner for on-board mission planning. The solution of this problem yields time-optimal state and input trajectories for transition. Using this trajectory planner, we generate trajectories for various transition flight missions (from hover to forward flight and vice versa) under various constraints. Further, we demonstrate how the proposed algorithm can also be used to assess the agility of a vehicle in terms of minimum space required to perform a specific maneuver or transition, given physical design constraints (such as maximum power). Finally, we demonstrate trajectory tracking on a high fidelity simulation of a QRBP with a cascaded dynamic-inversion based controller with a control blending strategy between the quadcopter and forward-flight control modes, for hover to forward flight.
Artificial Intelligence in e-Healthcare Supply Chain Management System: Challenges and Future Trends

Many healthcare organizations have developed the practice of acquiring essential medical supplies and equipment, storing vital medications, meeting medical needs, and scheduling patient treatments without coordinating and synchronizing those actions. Professionals in the supply chain are finding it difficult to keep up with the vast amounts of data that are needed to create an integrated, efficient, effective, and agile supply chain in today’s complex, connected world. As a result, there is now a high demand for systems that can swiftly and intelligently analyze diverse large amounts of supply chain data. Artificial Intelligence (AI) is one of the tools used to resolve the emerging industrial challenges. This study intends to explore how AI is used to manage e-Healthcare supply chains while also focusing on numerous security and privacy issues that occur in e-healthcare environment.
Pixel-wise rational model for a structured light system

This Letter presents a novel structured light system model that effectively considers local lens distortion by pixel-wise rational functions. We leverage the stereo method for initial calibration and then estimate the rational model for each pixel. Our proposed model can achieve high measurement accuracy within and outside the calibration volume, demonstrating its robustness and accuracy.
Web3.0 Data Infrastructure: Challenges and Opportunities

The emergence of Web 3.0 marks a historic turning point. It ushers in a new era in the history of the web, one defined by a radical transfer of access, ownership, and governance from today's crop of Internet giants to the Internet's every user. The idea behind Web 3.0 is to create an internet that accurately interprets your input, understands what you convey, and allows complete control over what type of content you want to consume.
Fast and Secure Image Encryption Algorithm with Simultaneous Shuffling and Diffusion Based on a Time-Delayed Combinatorial Hyperchaos Map

Adding time delay to nonlinear systems can significantly improve their performance, making it possible to construct image-encryption algorithms with higher security. In this paper, we propose a time-delayed nonlinear combinatorial hyperchaotic map (TD-NCHM) with a wide hyperchaotic interval. Based on TD-NCHM, we develop a fast and secure image-encryption algorithm that includes a plaintext-sensitive key-generation method and a simultaneous row-column shuffling-diffusion encryption process. Plenty of experiments and simulations demonstrate the superiority of the algorithm in terms of efficiency, security, and practical value in secure communications.
Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data

Abstract Objectives As the real-world electronic health record (EHR) data continue to grow exponentially, novel methodologies involving artificial intelligence (AI) are becoming increasingly applied to enable efficient data-driven learning and, ultimately, to advance healthcare. Our objective is to provide readers with an understanding of evolving computational methods and help in deciding on methods to pursue. Target Audience The sheer diversity of existing methods presents a challenge for health scientists who are beginning to apply computational methods to their research. Therefore, this tutorial is aimed at scientists working with EHR data who are early entrants into the field of applying AI methodologies. Scope This manuscript describes the diverse and growing AI research approaches in healthcare data science and categorizes them into 2 distinct paradigms, the bottom-up and top-down paradigms to provide health scientists venturing into artificial intelligent research with an understanding of the evolving computational methods and help in deciding on methods to pursue through the lens of real-world healthcare data.
Optimization Experiment of Production Processes Using a Dynamic Decision Support Method: A Solution to Complex Problems in Industrial Manufacturing for Small and Medium-Sized Enterprises

In the industrial sector, production processes are continuously evolving, but issues and delays in production are still commonplace. Complex problems often require input from production managers or experts even though Industry 4.0 provides advanced technological solutions. Small and medium-sized enterprises (SMEs) normally rely more on expert opinion since they face difficulties implementing the newest and most advanced Industry 4.0 technologies. This reliance on human expertise can cause delays in the production processes, ultimately, impacting the efficiency and profitability of the enterprise. As SMEs are mostly niche markets and produce small batches, dynamics in production operations and the need for quick responses cannot be avoided. To address these issues, a decision support method for dynamic production planning (DSM DPP) was developed to optimize the production processes. This method involves the use of algorithms and programming in Matlab to create a decision support module that provides solutions to complex problems in real-time. The aim of this method is to combine not only technical but also human factors to efficiently optimize dynamic production planning. It is hardly noticeable in other methods the involvement of human factors such as skills of operations, speed of working, or salary size. The method itself is based on real-time data so examples of the required I 4.0 technologies for production sites are described in this article-Industrial Internet of Things, blockchains, sensors, etc. Each technology is presented with examples of usage and the requirement for it. Moreover, to confirm the effectiveness of this method, tests were made with real data that were acquired from a metal processing company in Lithuania. The method was tested with existing production orders, and found to be universal, making it adaptable to different production settings. This study presents a practical solution to complex problems in industrial settings and demonstrates the potential for DSM DPP to improve production processes while checking the latest data from production sites that are conducted through cloud systems, sensors, IoT, etc. The implementation of this method in SMEs could result in significant improvements in production efficiency, ultimately, leading to increased profitability.