Reviewerly/exHarmony huggingface dataset:
Design of low-power acceleration processor for convolutional neural networks based on RISC-V

A low-power RISC-V-based convolutional neural network acceleration processor is proposed to cope with the problem that the increasing resource requirements of convolutional neural networks in the direction of hardware convolutional acceleration are difficult to be met on embedded devices. The processor is designed with three instructions that can configure the parameters of each CNN layer to accommodate different input data, multiplex computational resources to reduce power consumption, and execute operations that repeat a large number of executions in parallel to speed up operation efficiency. Through comparison experiments, it can be found that this processor acceleration instruction set is 20.93 times, 7.67 times, and 8.97 times faster than the base RISC-V instruction set after verified with the same data on three operations, including convolution, activation, and pooling, respectively. The experimental results show that the total power consumption of the processor with this custom instruction set is only 0.221 W at 16 MHZ operating frequency, which is advantageous in terms of performance-to-power ratio compared to other RISC-V accelerated processors with less resource consumption and lower power consumption.
News recommendation model based on user implicit feedback noise processing

Most news recommendation methods believe that all news in the implicit feedback sequence is beneficial to the final result, but it includes the influence of irrelevant news (i.e. false user behavior). This is not ideal for mining user interest information, which usually contains large noise. In order to address this issue, a news recommendation model based on positive and negative implicit feedback feature fusion with sparse attention mechanism is proposed. Firstly, the method adopts the method based on deep learning. In the user interest representation module, the adaptive sparse transformation function is introduced to eliminate the influence of irrelevant news in the user's positive and negative implicit feedback sequence. Secondly, the user's interest features are better constructed by fusing the user's positive and negative implicit feedback features. Finally, the proposed model is examined and analyzed on the MIND dataset. Compared with other news recommendation methods, the recommendation evaluation index of this method has been effectively improved.
Multi-objective quintuple polynomial trajectory optimization for unmanned underwater vehicles based on waypoint navigation

The unmanned underwater vehicle (UUV) based on waypoint navigation is used as the research object to optimize the multi-objective trajectory for the smooth operation, low energy consumption and low smooth impact required in the patrol task. The spatial trajectory of the unmanned underwater vehicle is constructed by a quintuple polynomial, and the motion trajectory is optimally solved using a quadratic programming algorithm by combining the position, velocity and acceleration requirements of the unmanned underwater vehicle at the beginning and end moments as well as the continuity constraints among the waypoints. The results show that the multi-objective quintuple polynomial-based algorithm achieves an effective multi-objective optimization of the unmanned underwater vehicle trajectory.
Hardware and Software Cyber Security Tools

The issue of cybercrime is becoming crucial in society. This is partly a result of the widespread adoption of technology in both essential government infrastructure and our daily lives. Due to an overreliance on technology, hackers and other people with bad intentions now have more ways to exploit systems and access databases containing sensitive data, including records relating to people's personal, financial, educational, and medical records. The importance of cybersecurity can be understood through research. Cyber security follows real-time information on the latest IT data. So far, various methods have been proposed by researchers around the world to prevent cyber-attacks or reduce the damage caused by them.
Improvement of Hierarchical Byzantine Fault Tolerance Algorithm in RAFT Consensus Algorithm Election

Currently, blockchain technology is not only gaining momentum in the financial industry but also showing promising developments in other sectors. Due to its suitability for various societal needs, an increasing number of non-financial institutions are expanding the scope of blockchain services beyond finance to areas such as healthcare service management and enterprise governance. The core of a blockchain system lies in consensus, which plays a crucial role in its performance and security. The PBFT algorithm is a widely used fault-tolerant consensus algorithm. However, it suffers from issues like high consensus latency, low throughput, and poor performance. In recent years, the HBFT algorithm has addressed these concerns. This paper proposes an improved HBFT blockchain consensus algorithm tailored for large enterprises and non-financial institutions with multiple independent functions. The goal is to achieve more efficient data management and election strategies that are both more secure than PBFT and more efficient than HBFT. Leveraging the enhanced HBFT consensus algorithm ensures stability and efficiency in the consensus and verification processes between organizations. Additionally, it enables the efficient management of data and value creation in the application context.
Dual-input anomaly detection method based on deep reinforcement learning

Aiming at the problem of low accuracy of unsupervised learning anomaly detection algorithm, a dual-input anomaly detection method based on deep reinforcement learning was proposed. The proposed model mainly consists of a feature extractor and anomaly detector. Based on the deep reinforcement learning framework, the feature extractor uses a dual-input deep neural network to form the current value network and the target value network, which are used to extract the low-dimensional feature vectors. Based on the 3 σ principle, the reward function of reinforcement learning is designed to reward and punish the output results of the model during training. The model was trained only with the normal data, and the extracted feature vector of the normal class was used as the input of the anomaly detector to complete the learning of the detector. During the test, the input anomaly detection was realized based on the dual-input convolutional neural network, and the anomaly detector was completed by learning. To illustrate the generality and generalization performance of the proposed method, four sets of image data and two sets of rolling bearing fault data in different fields were verified respectively. At the same time, the proposed method is applied to the fault detection of a real aero-engine rolling bearing.The results show that the proposed model has high anomaly detection accuracy, which is superior to the current optimal method.
Passive state preparation continuous variable quantum key distribution in a satellite-mediated link

Passive state preparation (PSP) continuous variable quantum key distribution (CVQKD) drops out optical amplitude and phase modulators, making this scheme suitable for application in space-confined situations. In this paper, we propose a satellite-mediated PSP-CVQKD scheme. To improve the performance of the scheme further, we introduce a phase-insensitive amplifier (PIA) at the receiver side. Based on the detailed noise model and channel parameter estimation analysis, we perform numerical simulations of the proposed scheme while the finite-size effect is considered. Simulation results show that the satellite-mediated PSP-CVQKD is feasible in both downlink and uplink cases based on typical system parameters. The performance can be improved at a relatively low satellite altitude when the PIA is used. Our analysis demonstrates that the proposed scheme may be a potentially cost-effective solution for future chip-scale CVQKD over satellite-mediated links.
The Implementation of the RISE Algorithm for the Captum Framework

This paper introduces an implementation of the RISE method, using the Captum library in PyTorch. RISE is an algorithm that generates explanations for the predictions of a deep learning model by randomly masking parts of the input image and observing the changes in the model’s output. Our focus is on the implementation and its performance, rather than on the RISE method itself. Through a series of experiments, we have obtained results that demonstrate the importance of the number of masks used in the RISE method. We found that the number of masks can have a significant impact on the interpretability of the outcome. Additionally, we conducted tests to determine the effect of mask size on the interpretability of the results. Our findings indicate that the size of the masks, whether larger or smaller, is also a decisive factor in achieving a good interpretable outcome. Furthermore, we demonstrate how tuning RISE parameters can yield in different results, making RISE effective for several tasks and neural network architectures. Overall, our implementation of RISE over Captum provides a powerful tool for interpreting deep learning models in PyTorch.
Constrained Optimization

To introduce the fundamental principle of constrained optimization we'll start with a simple physical situation, and increase the complexity by adding constraints.
Fuzzy control of obstacle avoidance for substation patrol robot based on laser radar

The current fuzzy control nodes for mobile obstacle avoidance of patrol robots are mostly set to the single-layer form, and the control speed is slow, leading to the increase of fuzzy control time consumption. Therefore, the design and verification analysis of the fuzzy control method for mobile obstacle avoidance of patrol robots in substations based on laser radar are proposed. According to the actual control requirements and standard changes, it is necessary to determine the basic control indicators of the patrol robot first, increase the control speed of the robot in a multi-level manner, deploy multi-level 3D fuzzy control nodes, establish a grid multi-level control matrix, and build a fuzzy control model for the moving obstacle avoidance of the laser radar patrol robot on this basis. Fuzzy control is realized by improved artificial potential field processing. The final test results show that: through the verification of four test areas in the substation, the final fuzzy control time of the patrol robot is well controlled below 1.5s, indicating that the practical application effect of this robot control method is better, the speed and range of control are further expanded, and the handling of robot obstacle avoidance and displacement tasks is more diversified and flexible. It has stronger pertinence and practical application value.
Mentoring in the context of the implementation of the dual approach in personnel training

Introduction. The article presents the experience of training qualified workers and employees in the process of mutually beneficial partnership in the dual interaction of an educational organization and an employer in the context of the implementation of the «Specialty» project and the reduction of training periods, when the success of professional training depends on the effectiveness of the interaction of the teacher and mentor and when the role of the mentor is largely actualized. Purpose setting. The purpose of this study is to consider the possibilities of a dual approach at the level of the closest interaction – between the teacher of the educational organization (engineering teacher) and the heads of the practice in production (mentors) in joint training for the needs of the enterprise, which allows to combine the personnel and material resources of interested parties and, in general, contributes to improving the quality of education. Solving this problem contributes to: 1) systematic professional development of the tandem «teacher» and «mentor», exchange of professional and pedagogical experience; 2) development of psychological and pedagogical knowledge and skills of teachers; 3) activation of educational and cognitive activity of students; 4) formation of the need for self-development of students, teachers and mentors. Methodology and methods of the study. The leading approaches to the research are personality-oriented, activity-oriented, systematic, contributes to improving the effectiveness of the interaction of the «teachers – mentor» tandem in the joint training of in-demand personnel corresponding to modern realities and contributing to the successful implementation of the project «Specialty». Results. The significance of the research results lies in the fact that didactic conditions have been identified and a model of dual-creative interaction has been developed, taking into account the interests of all parties in the training process, where the key figure is the personality of the student. The results also allow us to determine the prospects for further studies of this problem, which consist in the creation of scientifically based technologies for designing the content of education in conditions of effective social partnership, identifying more effective forms, means and methods of mentoring. Conclusion. The presented methodology of training workers on the basis of effective dual-creative interaction can be useful for teachers of practical training, masters of vocational training, teachers of technology and successfully applied in short-term courses of adult education, professional development, training and retraining of qualified workers and employees.
On the issue of organizing physical education and sports during the transition of universities to e-learning

Introduction. Currently, a search is underway for new forms and methods of organizing the educational process, among which the most promising is the organization of the educational process in the form of E-learning. Purpose setting. To develop a pedagogical technology for implementing the main provisions of E-learning and conduct a pedagogical experiment to assess its effectiveness in organizing physical education and sports at the university. Methodology and methods of the study. The «quantitative» and «qualitative» indicators of the student»s individual educational goal are determined. The standards of the All-Russian Physical Culture and Sports Complex «Ready for Labour and Defence» (VFSK TRP) are taken as «quantitative» indicators. Among the «qualitative» indicators, the following are highlighted: assessment of the level of formation of individual needs for a healthy lifestyle, the level of approval in the student»s social environment, the level of involvement in State programs in the field of demography and preservation of public health. An algorithm for formalizing the individual educational goals of students based on the selected «quantitative» and «qualitative» indicators has been developed. It is determined in the individual educational goal of the student and, accordingly, in the individual educational trajectory of its achievement. The results of the conducted pedagogical experiment and the analysis of these results are presented. The conclusion is made about the ways to improve the forms and methods of physical culture and sports in the organization of the educational process in the form of E-learning at the university. A pedagogical technology has been developed to ensure that a student achieves an individual educational goal in the educational space of the Federal State Budgetary Educational Institution of Higher Education «Voronezh State Pedagogical University». A pedagogical experiment was conducted and quantitative estimates of the effectiveness of the proposed pedagogical technology were obtained. Results. A comprehensive analysis of the results obtained during the pedagogical experiment is presented. Conclusion. The conclusion is made about the ways to improve the forms and methods of physical culture and sports in the organization of the educational process in the form of E-learning at the university.
Research on automatic path planning for obstacle avoidance of intelligent substation patrol robot

Most of the current automatic planning and execution codes for the obstacle avoidance path of the patrol robot in the substation are single structure, and the planning scope is limited, which leads to the increase of the optimal time consumption of the automatic planning. Therefore, the research on the automatic planning of the obstacle avoidance path of the patrol robot in the intelligent substation is proposed. According to the actual planning requirements and standards, the basic data collection of the robot is carried out, and the obstacle avoidance operator is set. In the form of multi-objective, expand the actual planning scope, design the robot multi-objective obstacle avoidance path code, and based on this, calculate the fitness function, design an adaptive three-dimensional obstacle avoidance path automatic planning model, and use depth grid modification to achieve automatic path planning processing. The final test results show that after three stages and five independent unit areas of test comparison, the TR20 robot selected in the G intelligent substation has relatively high completion rate for the test task, and better control the optimal time consumption of automatic obstacle avoidance path planning to less than 0.5s, indicating that this kind of planning scheme will be more targeted and stable. The patrol robot can also avoid the area where obstacles appear faster and more timely, and design the shortest path, which has practical application value.
Application of the bulge test combined with 3D-DIC for biaxial characterization of PLA films and defect detection

The bulge test is a versatile mechanical testing technique widely applied in materials science, engineering, and biomedical research to understand the mechanical behavior of materials and structures. In this study, PLA blown films were characterized through the bulge test, and the corresponding properties were compared to those obtained from uniaxial t!esting. To achieve more accurate and precise deformation measurements, 3D digital image correlation (3D-DIC) was used, offering superior capabilities in terms of accuracy, precision, and non-destructive testing. This technique can detect defects such as cracks, voids, and delamination in materials and structures. During the bulge testing, 3D-DIC was utilized to analyze the strain field evolution within the test sample and identify regions of reduced film thickness. Notably, the results indicated zones with strain concentration aligned parallel to the film machine direction, suggesting localized thickness reduction in those areas.
Revised DBSCAN clustering algorithm based on quadratic grid segmentation

This paper researches a grid-optimized DBSCAN algorithm called Grid-DBSCAN. Different from the traditional DBSCAN algorithm, we introduce a square grid array with side length ε. The whole study area is divided twice into several grids to study the computation. In this algorithm, any data point only needs to make a distance operation with the data points in the range of four different 3*3 grids that it may be in. This significantly reduces the computational complexity and required runtime of the algorithm. In addition, the two different center positions of the grid partition ensure the transitivity of the clustering algorithm and reduce the possibility of data points being misclassified. The simulation results indicate that the Grid-DBSCAN algorithm can effectively handle various clustering conditions. Both the computational time and clustering performance are superior to those of traditional DBSCAN and K-means algorithms.
MADNet: based on multiple attention and dilated convolution for CSI feedback

The massive multiple-input and multiple-output (MIMO) system based on channel state information (CSI) is the core technology of next-generation communication. As the complexity of the CSI matrix gradually increases, CSI feedback becomes more challenging. CSI feedback technology based on deep learning (DL) has been successful in frequency-division duplex (FDD) MIMO systems. In this paper, we propose a complex-valued lightweight neural network MADNet for CSI feedback. The network is based on an encoder-decoder structure, which enriches the information extraction of the CSI matrix by adopting channel information extraction modules and coordinates information extraction modules, which reduces the computational complexity of the network by using lightweight convolution. Experimental results show that our network outperforms most CNN-based network architectures at multiple compression rates and performs significantly better in both indoor and outdoor scenarios.
Mixed strategy improves dwarf mongoose optimization algorithm

Aiming at the problems of dwarf mongoose optimization algorithm with weak optimization ability and slow convergence speed, an improved dwarf mongoose optimization algorithm is proposed. Firstly, the dwarf mongoose optimization algorithm performs secondary reverse learning solution on elite individuals, and selects the best individuals to form a new population to improve the initial population diversity. Then, combined with the sparrow position update strategy in the Sparrow Search Algorithm, the dwarf mongoose location update method is improved to improve the local development ability of the algorithm. Then, the Gaussian mutation strategy is used to improve the algorithm's optimization ability and convergence speed. Finally, eight benchmark functions are compared with four different types of algorithms and three DMO algorithms with individual strategies, and the results show that the improved dwarf mongoose optimization algorithm has higher optimization accuracy, convergence speed and stability.
Kinodynamic Control Systems and Discontinuities in Clearance

We investigate the structure of discontinuities in clearance (or minimum time) functions for nonlinear control systems with general, closed obstacles (or targets). We establish general results regarding interactions between admissible trajectories and clearance discontinuities, e.g., instantaneous increases in clearance when passing through a discontinuity, and propagation of discontinuity along optimal trajectories. Then, investigating sufficient conditions for discontinuities, we explore a common directionality condition for velocities at a point, characterized by strict positivity of the minimal Hamiltonian. Elementary consequences of this common directionality assumption are explored before demonstrating how, in concert with corresponding obstacle configurations, it gives rise to clearance discontinuities both on the surface of the obstacle and propagating out into free space. Minimal assumptions are made on the topological structure of obstacle sets.
Optimization of Implementation Costs at Margatiga Dam with Inundation Area Parameters Case Study: Margatiga Dam Project, Lampung

Margatiga Dam is located at Lampung Province. The potential of high land acquisition cost may affect the total construction cost of the dam. A simulation needs to be conducted to calculate the volume of the dam reservoir with optimum reliability and benefit value. Calculation of the dam reservoir uses the storage capacity curve based on topographic map to derive the size area and reservoir volume on each elevation, a simulation is done afterwards to obtain the exact elevation of the optimum volume of reservoir. Simulation is conducted with three elevation alternatives; the spillway crest’s initial elevation of +22m, the raised elevation of +22,5m, and the lowered elevation of +21m. It can be concluded from the evaluation that spillway crest’s elevation with most benefit value is at an elevation of +22m with the reservoir reliability of 99,64%, BCR of 1,63, and the IRR of 13,34% with the total cost of Rp1.929.918.493.283.