Reviewerly/exHarmony huggingface dataset:
Nature-inspired query optimisation models for medical information retrieval with relevance feedback

Medical information retrieval (MedIR) involves retrieving relevant medical-related information from a set of medical documents for a particular user query. As the volume of medical records grows, the challenging problem is determining those documents which best suiting a given query by considering user satisfaction. Statistical term weighting and probabilistic approaches for this purpose have several limitations. The gap between information need and user query can be addressed through query optimisation and relevance feedback. In this paper, we propose a document retrieval framework that incorporates query optimisation using techniques like genetic algorithm, particle swarm optimisation (PSO), and global swarm optimisation (GSO). Further, we use relevance feedback methods to reformulate the user query. The proposed techniques are applied to datasets with predefined relevance judgments to perform quantitative validation. Experimental results using the relevance judgements available in the University of Glasgow's Medline collection underscored the significant improvement achieved using BM25 scores as the fitness function.
Research on user phase identification algorithm based on improved cloud model and adaptive segmented voltage

To solve the problem of inaccurate user phase identification, the paper proposes a new algorithm based on improved cloud model and adaptive segmented voltage algorithm. Firstly, the new algorithm uses improved cloud model to calculate the digital features of station area and users’ voltage sequences quickly. Secondly, the paper uses the adaptive segmentation voltage algorithm to divide the full voltage sequences into three parts automatically to add local features into phase identification. Finally, the paper calculates cosine similarity between each segmented voltage cloud model to identify users’ voltage phase. The analysis based on station data and field verification shows that the new algorithm has not only improved the calculation efficiency by 41% compared with traditional user phase identification algorithm, but also increased the difference in identification results between different phases by 1000 times. In the final result, the accuracy of the new algorithm is 95%. The new algorithm has more obvious differentiation and higher accuracy. The analysis results based on the actual engineering data also prove the feasibility and effectiveness of the new user phase identification algorithm.
Model Construction and Path Optimization of Cultivating Top Innovative Talents in Universities

With the increasingly fierce competition for talent both domestically and internationally, talent strategy has risen to national level. In this context, the construction of a harmonious society and the realization of the "Chinese Dream" require a large number of top-notch innovative talents as support. Therefore, the cultivation of top-notch innovative talents has gradually become an important measure for various universities to implement the fundamental task of "cultivating morality and cultivating talents". After thirty years of exploration and practice, Chinese universities have formed a preliminary mature model in cultivating top-notch innovative talents, and have achieved significant results in improving the innovation ability of college students, promoting the comprehensive optimization of the quality of talent cultivation in higher education. In this regard, various universities need to attach sufficient importance to the cultivation of top-notch innovative talents, improve the cultivation mode and path of top-notch innovative talents from the perspective of innovative development, achieve comprehensive improvement of college students' innovation ability, cultivate more and more outstanding top-notch innovative talents for the country and society, and assist in the realization of the great rejuvenation of the Chinese nation.
Software Application Profile: <i>dynamicLM</i>—a tool for performing dynamic risk prediction using a landmark supermodel for survival data under competing risks

Abstract Motivation Providing a dynamic assessment of prognosis is essential for improved personalized medicine. The landmark model for survival data provides a potentially powerful solution to the dynamic prediction of disease progression. However, a general framework and a flexible implementation of the model that incorporates various outcomes, such as competing events, have been lacking. We present an R package, dynamicLM, a user-friendly tool for the landmark model for the dynamic prediction of survival data under competing risks, which includes various functions for data preparation, model development, prediction and evaluation of predictive performance. Implementation dynamicLM as an R package. General features The package includes options for incorporating time-varying covariates, capturing time-dependent effects of predictors and fitting a cause-specific landmark model for time-to-event data with or without competing risks. Tools for evaluating the prediction performance include time-dependent area under the ROC curve, Brier Score and calibration. Availability Available on GitHub [https://github.com/thehanlab/dynamicLM].
Analysis of influence factors in Quantum Approximate Optimization Algorithm for Solving Max-cut Problem

In recent years, quantum algorithm research has developed rapidly, and it has become an important mode to use quantum computing cloud platform to carry out algorithm application exploration. Quantum approximate optimization algorithm can be used to solve the max-cut problem. In the process of algorithm execution, optimizer and various noise had obvious influence on the algorithm execution result. For the quantum computing simulator of HiQ platform, quantum approximate optimization algorithm was used for experimental verification, to study the types of optimizer, different noise scenarios, and the impact of noise error parameters on the maximum number of cutting edges. The prospect of quantum algorithm research was also discussed.
TED$^+$: Towards Discovering Top-k Edge-Diversified Patterns in a Graph Database

With an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or medium-sized data graphs (eg chemical compounds). Although subgraph enumeration and subgraph mining have been proposed to bring insights into a graph database by a set of subgraph structures, they often end up with similar or homogenous topologies, which is undesirable in many graph applications. To address this limitation, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Top-k Edge-Diversified Patterns Discovery problem</i> to retrieve a set of subgraphs that cover the maximum number of edges in a database. To efficiently process such query, we present a generic and extensible framework called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\textsc {Ted}^+$</tex-math></inline-formula> which achieves a guaranteed approximation ratio to the optimal result. Three optimization strategies are further developed to improve the performance, and a lightweight version called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TedLite</small> is designed for even larger graph databases. Experimental studies on real-world datasets demonstrate the superiority of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\textsc {Ted}^+$</tex-math></inline-formula> to traditional techniques.
Resource Provisioning for CPU-FPGA Environments with Adaptive HLS-Versioning and DVFS

Cloud warehouses have been adopting CPU-FPGA environments to accelerate clients’ applications with scalability. On the CPU side, DVFS improves energy efficiency. On the FPGA side, High-Level Synthesis enables hardware optimizations that lead to designs with variant characteristics (e.g., latency and power). Although both techniques have been used, they have never been cooperatively exploited to improve execution efficiency. For that, we propose RAHD, a framework that bridges the gap between DVFS, HLS multiple design versions, and CPU-FPGA environments. RAHD offers automatic fine-tuning selection of design versions and DVFS to efficiently balance workload, achieving 32.86x energy improvements over a standard provisioning strategy.
UAV 3D Trajectory Planning and Optimization Using A* Algorithm Based on Free Space Method

Due to the complexity of three-dimensional (3D) map modeling, the A* algorithm is inefficient in planning 3D trajectories for unmanned aerial vehicles (UAVs). This paper designs a method for planning 3D trajectories using A* algorithm based on free space method to address this issue. The method translates the 3D trajectory planning problem into a two-dimensional (2D) trajectory planning problem through flight surface extraction, thereby enhancing the efficiency of trajectory planning. In addition, a trajectory optimization method considering safety parameters and offset costs is designed for the waypoints that fail to meet UAV restrictions. The optimization method consists of longitudinal trajectory altitude adjustment and horizontal trajectory smoothing to ensure the trajectory aligns with the UAV climb rate constraint, normal overload constraint and minimum turning radius constraint. Simulation results show that the designed method delivers higher efficiency in 3D trajectory planning and optimization than the conventional method, and the optimized trajectory adheres to the above constraints.
Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising

Affected by the hardware conditions and environment of imaging, images generally have serious noise. The presence of noise diminishes the image quality and compromises its effectiveness in real-world applications. Therefore, in real-world applications, reducing image noise and improving image quality are essential. Although current denoising algorithms can somewhat reduce noise, the process of noise removal may result in the loss of intricate details and adversely impact the overall image quality. Hence, to enhance the effectiveness of image denoising while preserving the intricate details of the image, this article presents a multi-scale feature learning convolutional neural network denoising algorithm (MSFLNet), which consists of three feature learning (FL) modules, a reconstruction generation module (RG), and a residual connection. The three FL modules help the algorithm learn the feature information of the image and improve the efficiency of denoising. The residual connection moves the shallow information that the model has learned to the deep layer, and RG helps the algorithm in image reconstruction and creation. Finally, our research indicates that our denoising method is effective.
An investigation on the structural vibrations of multi-rotor passenger drones

In order to solve the structural damage problem of the first generation of large multi-rotor manned drones, the present work has designed to study the structural vibration problems of multi-rotor drones. On a small multi-rotor drone, the laser vibration meter verified the reliability of acceleration sensor measurement of vibration and found that circular shape carbon fiber arms have strong damping abilities, with the strongest vibration in the Z-axis direction. To improve the design of the second generation of large multi-rotor manned drones, elliptical shape carbon fiber arms were employed instead of circular arms. Experiments showed that the main vibrations of the large multi-rotor manned drone’s arm are low-frequency vibrations below 200Hz, producing mainly torsional and bending modes, and the elliptical carbon fiber arms significantly reduce vibrations in the Z-axis direction. This study provides experimental data support for multi-rotor manned drones and further presents an improvement strategy for suppressing the vibrations of the multi-rotor manned drones.
Using a multi-criteria approach for a regional differentiation of the likelihood of future volcanic activity in Germany

Abstract. Future volcanic activity, which might influence the long-term stability of radioactive waste repositories, must be evaluated critically. In Germany, according to the Repository Site Selection Act (StandAG), areas in which Quaternary volcanism is either present or future volcanic activity is expected within the next 1 Ma must be excluded from the site selection process for a high-level radioactive waste repository. However, while regions with Quaternary volcanism are reasonably well known in Germany, forecasting volcanic activity in intraplate fields is difficult, as many processes and their interaction control the spatial distribution of the volcanic centres. To develop an appropriate method to forecast possible regions of volcanic activity, a semi-quantitative, multi-criteria approach is used that combines a variety of indicators including, amongst others, seismic anomalies in the mantle, gravity data, tectonic activity, sutures, ground motion, earthquakes, mantle degassing centres and the geochronological data of volcanic rocks. The used indicators provide possible information about the source of melting in the mantle, the ascent of melt through the lithosphere and the eruption process at the surface. In total, 30 indicators were defined and ranked according to their presumed relevance for the long-term prediction of volcanism, following the results of two expert surveys. Quantifiable parameters were selected for the individual indicators, and threshold values were assigned and related to levels of significance. The parameters were uniformly applied for Germany in order to enable a transparent and equal risk assessment of future volcanic activity in all of the federal states. To consider the uncertainties regarding the distribution and manifestation of parameters in addition to their mostly unknown underlying large-scale processes, buffer zones were defined in which the values of relevance decrease with increasing distance from the initial parameter extent. By combining all parameters into an overall index, the presented results allow the regional differentiation of the likelihood of future volcanic eruptions in Germany. The robustness of the results is shown by varying the weights of the used parameters in the Germany-wide risk map. Different categories for the probability of future volcanic activity can be defined and used, depending on the needs and assessment of the volcanic hazard potential.
Implications of Distributed Energy Development in Blockchain-Based Institutional Environment

This research focuses on the high urgency of utilizing Artificial Intelligence (AI) and the Internet of Things (IoT) to enhance Smart Tourism Destinations (STDs). The integration of AI and IoT technologies offers unprecedented opportunities to revolutionize various aspects of tourism, from personalized recommendations to real-time data collection. The research aims to provide a comprehensive analysis of the current state, challenges, and future direction of STDs in the context of AI and IoT integration. It explores various AI techniques and IoT-enabled data collection mechanisms that can enrich the traveler experience and improve destination management. However, challenges such as privacy and data security issues need to be addressed. The research also provides foresight into future technologies like Augmented Reality (AR) and Virtual Reality (VR) that can further enhance STDs. The ultimate goal is to contribute to the development of smarter, visitor-oriented tourism destinations. The research highlights the significance of AI in shaping STDs and emphasizes the importance of addressing ethical considerations, data quality, interpretability, and human-AI collaboration to ensure responsible and effective use of AI in the tourism industry.
Classification of human activities by smart device measurements

The prevalence of activity detectors in users’ personal mobile devices has been incorporated into an increasing interest in research into physical function recognition (HAR - Human Activity Recognition). With this research interest, different enterprises developed HAR systems working with measurement devices and still work on this subject. Although many HAR systems have been developed, there are still concrete practical limits. This situation is improved with modern techniques such as machine learning. A properly trained machine learning model predicts human activity from measured data. The data was measured at certain time intervals by sensors on smartphones. These different machine learning architectures were trained on sensor data that detected human activities, and their accuracy was calculated. A HAR system that predicts human activity is constructed separately with five approaches. KNN, Random Forest, Decision Tree, MLP and Gaussian Naive Bayes algorithms were used, and KNN produced the most accurate results.
Dynamic Route Planning of Intelligent Robot Based on Immune Neural Network

At present, the development of robots has entered the third generation intelligent robot era, and the research on robots has also entered an increasingly mature stage. Among them, mobile robots with autonomous decision-making ability are a hotspot in robot research. The key problems of mobile robot research include: robot structure, architecture, path planning, feedback stabilization, etc. among them, path planning is particularly important in mobile robot research. In this article, a DRP (Dynamic Route Planning) optimization design scheme of intelligent robot based on immune neural network is proposed. The path selection design of intelligent robot in dynamic state is realized by combining immune arithmetic and neural network. Finally, the accuracy of the arithmetic is analyzed experimentally. The final experiment shows that the immune neural network can improve the DRP by 8.45% compared with the traditional arithmetic. The research on dynamic path design with intelligent robots as the core has important practical significance for guiding the research on how robots can be more suitable for the needs of modern manufacturing industry.
Compression Performance Analysis of Experimental Holographic Data Coding Systems

It is challenging to find a proper way to compress computer-generated holography (CGH) data owing to their huge data requirements and characteristics. This study proposes CGH data coding systems with high-efficiency video coding (HEVC), three-dimensional extensions of HEVC (3D-HEVC), and video-based point cloud compression (V-PCC) codecs. In the proposed system, we implemented a procedure for codec usage and format conversion and evaluated the objective and subjective results to analyze the performance of the three coding systems. We discuss the relative advantages and disadvantages of the three coding systems with respect to their coding efficiency and reconstruction results. Our analysis concluded that 3D-HEVC and V-PCC are potential solutions for compressing red, green, blue, and depth (RGBD)-sourced CGH data.
Shape optimization of 3D hydrofoil based on proxy model

In order to address the issue of numerical calculation difficulty in optimizing the hydrodynamic performance of 3D hydrofoil with flaps, this paper proposes an approach based on a proxy model. The proxy model adopts the Kriging model and introduces a combined adding method based on the accuracy of the optimal solution and the maximum uncertainty. The input parameters are the structural parameters of the NCAC0015 hydrofoil, with lift, lift coefficient, and drag coefficient as output. The optimization objective is to maximize the lift-to-drag ratio of the hydrofoil. To achieve this objective, a multi-island genetic algorithm combined with the improved sequential quadratic programming algorithm is used to optimize the lift-to-drag ratio coefficient. This enables us to overcome the limitations of a single algorithm. After optimization, the lift-to-drag ratio of the 3D hydrofoil increases by 18.5%
Design of upper limb posture detection and trajectory matching based on motion capture

As biomechanics research evolves, the significance of motion capture technology in domains like film and television, gaming, and motion training is progressively gaining prominence, with an expanding repertoire of applications. Optical motion capture, employing optical dynamic capture equipment founded on a 3D imaging analysis system within a specified setting, stands as the most mature technique. However, its high cost and constraints pertaining to venue and environmental factors limit its usage. Conversely, inertial motion capture devices, functional under non-optical conditions, can cater to the needs of the broader public. To further enhance affordability, portability, and simplification of the device, this study proposes an embedded system design grounded on a nine-axis inertial sensor. It captures the posture of each joint in the human upper limb and wirelessly transmits the data to the Unreal Engine 3Darmmodel for action replication and motion trajectory delineation. Thus, it generates feedback results facilitating remote motion control.
Research on parallel parking path planning based on the G-PSOA

This paper addresses the problems of discontinuous curvature and complex path tracking of parallel parking paths for autonomous driving, establishes its vehicle kinematic model based on Ackermann steering theory; determines the collision constraints of the parking process, and uses B-spline curve theory to plan the parking path. On this basis, a multi-objective optimization method of Genetic-Particle Swarm Optimization Algorithm(G-PSOA) is proposed to optimize the design of the spline curve control points with the vehicle travel trajectory length, wheel angle, and curvature of the path curve as the optimization objectives of Path Planning (PP). The simulation results show that the proposed optimization algorithm can quickly search for the optimal path compared with the traditional Particle Swarm Optimization (PSO), shorten the PP length by 4.2%, and improve the smoothness and continuity of the parking process, thus reducing the complexity of the path tracking control.
Innovative measures of student management by counselors in higher education institutions under a big data environment

Abstract Along with the rapid development of big mobile data and the popularization and deepening of education informatization, big data is an irresistible and inescapable new environment and new opportunity, and the way of student management by counselors in higher education institutions is constantly developing and changing. Based on this, the use of big data to promote the innovation of counselor student management in higher education institutions is an inevitable issue to deepen the comprehensive reform in the field of education and a real issue in front of the majority of educators. In this study, the performance of the K-Means algorithm and PageRank are tested from the perspective of big data to find the most effective ways and methods to improve the counselors’ student management work, and the performance and hit rate of each strategy is tested in the application of K-Means. This means application, the LRFU strategy ( step = 0) also achieved relatively good performance, using HiBench to run PageRank algorithm test cases. The results showed that the LRU strategy in the PageRank test case also improved the hit rate by 25% compared with other strategies and the hit rate by 13% compared with the LRFU strategy ( step = 0). Student management efficiency. Higher education institutions can reform counselors’ student management methods by establishing a large student database management center, strengthening big data analysis, improving the ability of student data mining, processing, and management work, and striving to maximize the efficiency of student management data and information.